Hyperspectral Close-Range and Remote Sensing of Soils and Related Plant Associations Spectroscopic Applications in the Boreal Environment

Hyperspectral close-range and remote sensing of soils and related plant associations Spectroscopic applications in the boreal environment

Maarit Middleton

mallikuva; kuva-alan koko voi vaihdella

Academic Dissertation

GEOLOGICAL SURVEY OF FINLAND Espoo 2014 HYPERSPECTRAL CLOSE-RANGE AND REMOTE SENSING OF SOILS AND RELATED PLANT ASSOCIATIONS Spectroscopic applications in the boreal environment

Maarit Middleton Geological Survey of Finland P.O. Box 77 FI-96101 Rovaniemi, Finland

ACADEMIC DISSERTATION

Doctoral dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the School of Engineering for public examination and debate in Auditorium M1 at the Aalto University School of Engineering (Otakaari 1, Espoo, Finland) on the 26th of September 2014 at 12 noon.

Unless otherwise indicated, the figures have been prepared by the author of the publication.

Geological Survey of Finland Espoo 2014 Instructor: Doc. Raimo Sutinen Geological Survey of Finland Rovaniemi, Finland Department of Geosciences University of Oulu Oulu, Finland

Supervisor: Prof. Henrik Haggrén Department of Real Estate, Planning and Geoinformatics Aalto University Espoo, Finland

Reviewers: Dr. Angela Harris School of Environment, Education and Development University of Manchester Manchester, United Kingdom

Prof. Alexander McBratney Department of Environmental Sciences University of Sydney Sydney, Australia

Opponents: Assoc. Prof. John Carranza School of Earth and Environmental Sciences James Cook University Townsville, Australia

Prof. Nicholas Coops Department of Forest Resources Management University of British Columbia Vancouver, Canada

Front cover: Peatland site type classification at southern boreal study site in Keminmaa, Finland (paper IV). Mapping was based on fuzzy support vector machine classification of airborne HyMap data. Peatland class-specific probabilities are presented with RGB composition. Class ‘bog’ is represented as red, ‘sedge fen’ as green, and ‘eutrophic fen’ as blue. Prob- ability values are scaled with histogrammic equalization to emphasize spatial details. Pure RGB colors depict high probabilities. Normalized differential vegetation index (NDVI) was calculated for background. High NDVI values are illustrated as light shades of grey. Middleton, M. 2014. Hyperspectral close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment. Geological Survey of Finland. 68 pages, 8 figures, 5 tables, with original articles (I−IV).

Maarit Middleton Geological Survey of Finland, P.O. Box 77, FI-96101 Rovaniemi, Finland

E-mail: maarit.middleton@gtk.fi

ISBN 978-952-217-281-5 (paperback) ISBN 978-952-217-282-2 (PDF version without articles)

Layout: Elvi Turtiainen Oy Printing house: Juvenes Print – Suomen Yliopistopaino Oy

– One of the primary resources upon which man directly depends for his existence is soil moisture. [...] This resource, among others, man now seeks to monitor from space, and towards this end he is applying some of the most sophisticated technology and management expertise. – Idso et al. (1975)

To Kent, Sofia and Benjamin Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi

Abstract

Author Maarit Middleton Author Maarit Middleton Name of the doctoral dissertation Hyperspectal close-range and remote sensing of soils and relatedName ofplant the associations. doctoral dissertation Spectroscopic Hyperspectal applications close-range in the boreal and environment remote sensing of soils and Publisherrelated plant Geological associations. Survey Spectroscopic of Finland applications in the boreal environment UnitPublisher Department Geological of Real Survey Estate, of FinlandPlanning and Geoinformatics SUniteries Department Geological of Survey Real Estate, of Finland, Planning Academic and Geoinformatics Dissertation FieldSeries of Geologicalresearch SurveyRemote of sensing Finland, Academic Dissertation ManuscriptField of research submitted Remote 3 December sensing 2013 Date of the defence 26 September 2014 PermissionManuscript to submitted publish granted 3 December 29 January 2013 2014 Date of the defenceLanguage 26 September English 2014 ArticlePermission dissertatio to publishn (summary granted + original 29 January articles) 2014 .. .. Language English.. Article dissertation (summary + original articles) . Abstract AbstractHyperspectral close-range and remote sensing techniques have been available to the research communityHyperspectral since close-range the 1980's and but remote applications sensing techniqueshave focused have on been forestry available and landto the use. research The objective commu- ofnity the since study the was 1980’s to explore but applications relevant haveapplications focused ofon visible forestry and and short land wavelength use. The objective infrared of spectro the studys- copywas to (350 explore−2500 relevant nm) for applications detection ofof physicalvisible and and short chemical wavelength properties infrared of glacial spectroscopy till soils (350−2500and plant speciesnm) for communitiesdetection of physical related andto the chemical soil properties properties in ofthe glacial boreal till environment soils and plant of northernspecies communities Finland. relatedEmpirical to the single soil properties and multivariate in the boreal regression environment techniques of northern (MVR) Finland.were applied for predicting glacial tillEmpirical soil dielectric single permittivity and multivariate (Ɛ, i.e. regression soil moisture) techniques and till(MVR) element wereal applied concentrations for predicting from glacialclose- rangetill soil spectrometry. dielectric permittivity Predictive (ε, kerneli.e. soil andmoisture) neural and network till elemental based fuzzyconcentrations classification from approachesclose-range TM werespectrometry. applied forPredictive classification kernel andof data neural acquired network with based AISA fuzzy and classification HyMap airborne approaches imaging were appliedspec- trometers. Ordination techniques were used for revealing plant community structures and optimiz- for classification of data acquired with AISA and HyMapTM airborne imaging spectrometers. Ordina- ing the thematic class hierarchal level. tion techniques were used for revealing plant community structures and optimizing the thematic class The till soil Ɛ could be well predicted from VSWIR spectra with the exponential single-spectral variatehierarchal but level. also with MVR techniques. The most accurate results were gained with relevance vector machines.The till soilPrediction ε was well of predictedtill soil chemical from VSWIR element spectra concentrations with the exponential of Al, Ba, single-spectral Co, Cr, Cu, Fe, variate Mg, Mn, but Ni,also V, with and MVR Zn was techniques. also statistically The most valid. accurate Soil results moisture were based gained site with suitability relevance for vector Scots machines.pine (Pinus Pre- sylvestrisdiction of )till from soil imagingchemical spectroscopicelement concentrations data was ofmoderately Al, Ba, Co, successfulCr, Cu, Fe, asMg, the Mn, highest Ni, V, areaand Znunder was thealso receiver statistically operating valid. Soil characteristics moisture based curve site (AUC) suitability value for was Scots 0.741. pine Site (Pinus type sylvestrismapping) offrom aapa imaging peat- landsspectroscopic with support data was vector moderately machines successful was highly as the succe highestssful area with under AUC the values receiver 0.946 operating−0.999 character for bog,- sedgeistics curve fen, and (AUC) eutrophic value wasfen. 0.741. Site type mapping of aapa peatlands with support vector machines wasUnderstanding highly successful the withƐ-reflectance AUC values relationship 0.946−0.999 would for bog, be sedgeevident fen, when and eutrophic artificial fen. regeneration to ScotsUnderstanding pine, intolerant the ε-reflectance of wet soils, wasrelationship considered would on clearbe evident-cuts withwhen high artificial soil moisture regeneration variability. to Scots Thepine, site intolerant suitability of wet on soils, site isprepared considered forest on clear-cuts compartments with high could soil be moisture predicted variability. using exposedThe site soilsuit- pixels in high spatial resolution imagery but also with indirect imaging of soil moisture through ability on site prepared forest compartments could be predicted using exposed soil pixels in high spatial understory species patterns. The high success of the peatland site type mapping was attributed to optimizationresolution imagery of class but hierarchalalso with indirect levels withimaging a constrained of soil moisture ordination through based understory approach species which patterns. was usedThe tohigh test success the spectral of the andpeatland ecological site type class mappingseparabilit wasy priorattributed to classification. to optimization These of novelclass hierarchalapplica- tionslevels ofwith imaging a constrained spectroscopic ordination data based can readilyapproach be which applied was in used practice to test once the spectralcost-effective and ecological satellite basedclass separability data is available. prior to Furtherclassification. research These is required novel applications to make the of imagingclose-range spectroscopic spectroscopy data canoper reada-- tionalily be appliedfor quantification in practice onceof element cost-effective concentrations satellite tobased serve data forest is available. soil research Further and research mineral is requiredpoten- tialto make mapping. the close-range spectroscopy operational for quantification of element concentrations to serve forest soil research and mineral potential mapping. Keywords (GeoRef Thesaurus, AGI) remote sensing, airborne methods, hyperspectral analysis, spectroscopy,Keywords (GeoRef dielectric Thesaurus, properties, AGI):chemical remote elements, sensing, multivariate airborne methods, analysis, hyperspectral till, biotopes, analysis, vegetspectroscopy,ation, forest dielectric soils, peatlands,properties, classification,chemical elements, Finland multivariate analysis, till, biotopes, vegetation, ISBNforest (soils,printed peatlands,) 978-952 classification,-217-281-5 FinlandISBN (pdf) 978-952-217-282-2 ISSNISBN XXX(printed) 978-952-217-281-5 ISBN (pdf) 978-952-217-282-2 .. Location of of publisher publisher Espoo Espoo. . LocationLocation of printing of printing Suomen TampereYliopistopaino Oy YearYear 20142014 Pages 68 url http:// tupa.gtk.fi/julkaisu/erikoisjulkaisu/ej_088.pdf – Juvenes Print. Pages 68 url http://urn http://urn.fi/URN:ISBN:978-952-217-282-2tupa.gtk.fi/julkaisu/erikoisjulkaisu/ej_088.pdf

Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi

Tiivistelmä

Maarit Middleton Tekijä Maarit Middleton Väitöskirjan nimi Maaperän ja siihen liittyvien kasviyhdyskuntien hyperspektrinen etätunnistus Väitöskirjan nimi Maaperän ja siihen liittyvien kasviyhdyskuntien hyperspektrinen etätunnistus ja ja kaukokartoitus. Spektroskopian sovelluksia boreaalisessa ympäristössä kaukokartoitus. Spektroskopian sovelluksia boreaalisessa ympäristössä Yksikkö Maankäyttötieteiden laitos JulkaisijaYksikkö Maankäyttötieteiden Geologian tutkimuskeskus laitos SJulkaisijaarja Geologian Geologian tutkimuskeskus tutkimuskeskus, väitöskirja TutkimusalaSarja Geologian Kaukokartoitus tutkimuskeskus, väitöskirja KäsikirjoituksenTutkimusala Kaukokartoitus pvm 3.12.2013 Väitöspäivä 26.9.2014 JulkaisuluvanKäsikirjoituksen myöntämispäivä pvm 3.12.2013 29.1.2014 VäitöspäiväKieli 26.9.2014englanti YhdistelmäväitöskirjaJulkaisuluvan myöntämispäivä (yhteenveto 29.1.2014-osa + erillisartikkelit) .. ..Kieli englanti.. Yhdistelmäväitöskirja (yhteenveto-osa + erillisartikkelit) ... Tiivistelmä TiivistelmäHyperspektiset etä- ja kaukokartoitusmenetelmät ovat olleet tiedeyhteisön aktiivisessa käytössä 1980Hyperspektiset-luvulta lähtien, etä- ja kaukokartoitusmenetelmätmutta sovellukset ovat suurimmaksi ovat olleet tiedeyhteisön osaksi keskittyneet aktiivisessa metsätalouden käytössä 1980-lu ja- maankäytönvulta lähtien, tarpeisiin.mutta sovellukset Tässä väitöskirjassa ovat suurimmaksi tutkittiin osaksi boreaalisessa keskittyneet ympäristössämetsätalouden merkityksellisiä ja maankäytön näkyväntarpeisiin. valon Tässä ja väitöskirjassa lähi-infrapunaspektroskopian tutkittiin boreaalisessa (350 −ympäristössä2 500 nm) sovelluksia, merkityksellisiä joilla näkyvänpyrittiin valontunni s-ja tamaanlähi-infrapunaspektroskopian moreenimaiden fysikaalis (350−2-kemiallis 500 nm) iasovelluksia, ominaisuuksi joillaa pyrittiinja niiden tunnistamaan muokkaami moreenimaidena kasviyhdys- kuntifysikaalis-kemiallisiaa Pohjois-Suomessa. ominaisuuksia ja niiden muokkaamia kasviyhdyskuntia Pohjois-Suomessa. EmpiiristenEmpiiristen yksi-yksi- jaja monimuuttujaregressiomenetelmien monimuuttujaregressiomenetelmien avulla avulla laboratoriossa laboratoriossa spektroradiometrillä spektroradiome mit-- rillätatuista mitatuista spektreistä spektreistä mallinnettiin mallinnettiin moreenin dielektrisyyttämoreenin dielektrisyyttä (ε, vesipitoisuutta) (Ɛ, vesipi ja alkuaineidentoisuutta) ja pitoisuuksia. alkuainei- denTukivektorikoneella pitoisuuksia. Tukivektorikoneella (SVM) ja neuroverkkomenetelmillä (SVM) ja neuroverkkomenetelmillä tehtiin sumean logiikan tehtiin luokitteluja sumean hyperspekt logiikan- TM luokittelujarisistä AISA- hyperspektrisistäja HyMapTM-lentoaineistoista. AISA- ja HyMap Ordinaatiomenetelmillä-lentoaineistoista. optimoitiin Ordinaatiomenetelmillä sopiva hierarkinen opt tasoi- moitiinsuoluokille. sopiva hierarkinen taso suoluokille. Moreenien Ɛ ennustettiin luotettavasti heijastusspektreistä yhden muuttujan eksponenttimallilla Moreenien ε ennustettiin luotettavasti heijastusspektreistä yhden muuttujan eksponenttimallilla ja useil- ja useilla monimuuttujaregressiomalleilla, mutta relevanssivektorikoneilla (RVM) saatiin tarkim- la monimuuttujaregressiomalleilla, mutta relevanssivektorikoneilla (RVM) saatiin tarkimmat ennusteet. mat ennusteet. Moreenin alkuainepitoisuuksia mallinnettiin tilastollisesti luotettavasti useille alkuaineille:Moreenin alkuainepitoisuuksia Al, Ba, Co, Cr, Cu, mallinnettiin Fe, Mg, Mn, tilastollisesti Ni, V ja Zn. luotettavasti Maaperän useille kosteuteen alkuaineille: perustuva Al, Ba, männyn Co, Cr, (Cu,Pinus Fe, sylvestrisMg, Mn, Ni,) uudistusanalyysi V ja Zn. Maaperän onnistui kosteuteen kohtuullisen perustuva luotettavasti männyn (Pinus, koska sylvestris mallin) uudistusanalyy oikeellisuutta- kuvaaviensi onnistui toimintaominaisuuskäyrienkohtuullisen luotettavasti, koska (ROC mallin-käyrä) oikeellisuutta alapuolelle kuvaavien jäävä alue toimintaominaisuuskäyrien (AUC) oli männylle soveltuvalle(ROC-käyrä) luokalle alapuolelle 0,741. jäävä Hyperspektrinen alue (AUC) oli kaukokartoitusaineistomännylle soveltuvalle luokalle soveltui 0,741. hyvin Hyperspektrinen myös aapasoiden kau- suotyyppiluokitteluun,kokartoitusaineisto soveltui koska hyvin AUC myös-arvot aapasoiden vaihtelivat suotyyppiluokitteluun, 0,946 ja 0,999:n koskavälillä AUC-arvotluokille rahkaneva, vaihtelivat neva0,946 jaja letto.0,999:n välillä luokille rahkaneva, neva ja letto. YmmärrysYmmärrys Ɛε:n:n jaja heijastuskertoimen heijastuskertoimen suhteesta suhteesta tulee tulee ilmeiseksi ilmeiseksi silloin, silloin kun arvioidaan, kun arvioi männyndaan männyn uudista- uudistamissoveltuvuuttamissoveltuvuutta maankosteudeltaan maankosteudeltaan vaihtelevilla vaihtel avohakkuualueilla.evilla avohakkuualueilla. Mänty tulisi Mänty uudistaa tulisi vain uudistaa kuiville vainosa-alueille, kuiville jotka osa- alueille,voidaan jotkaosoittaa voidaan luokittelemalla osoittaa luokittelemallamuokkauksessa muokkauksessapaljastettuja maapikseleitä. paljastettuja Uudista maa-- pikseleitä.missoveltuvuutta Uudistamissoveltuvuutta voidaan kartoittaa myös voidaan epäsuorasti kartoittaa luokittemalla myös epäsuorasti kasviyhdyskuntia luokittemalla kaukokartoitusai kasviyhdys-- kuntianeistolta. kaukokartoitusaineistolta. Suotyyppiluokittelu onnistui Suotyyppiluokittelu hyvin, kun ekologisesti onnistui ja spektrisesti hyvin, kun eroavat ekologisesti temaattiset ja spektr luokati- sestioptimoitiin eroavat rajoitettuun temaattiset ordinaatioon luokat optimoitiin perustuvalla rajoitettuun lähestymisellä ordinaatioon ennen spatiaalistaperustuvalla luokittelua. lähestymisellä Tässä ennen spatiaalista luokittelua. Tässä tutkimuksessa osoitettiin uusia hyperspektristen aineistojen tutkimuksessa osoitettiin uusia hyperspektristen aineistojen sovelluksia, jotka voidaan ottaa käyttöön ku- sovelluksia, jotka voidaan ottaa käyttöön kuvaavien spektrometrien siirtyessä satelliittialustoille. Moreenienvaavien spektrometrien alkuainepitoisuuksien siirtyessä satelliittialustoille. spektriseen ennustamiseen Moreenien alkuainepitoisuuksien liittyvää tutkimusta spektriseen tulee jatkaa, ennus en-- nentamiseen kuin liittyvääsitä voidaan tutkimusta käyttää tulee metsämaa jatkaa, -ennen ja mineraalipotentiaalitutkimuksissa. kuin sitä voidaan käyttää metsämaa- ja mineraalipotentiaalitutkimuksissa. Avainsanat (Geosanasto, GTK) kaukokartoitus, lentomittaukset, hyperspektrinen analyysi, spektroskopia,Asiasanat (Geosanasto, dielektriset ominaisuudet, GTK): kaukokartoitus, alkuaineet, lentomittaukset, monimuuttuja hyperspektrinen-analyysi, moreeni, analyysi, biotoopit, spekt - kasvillisuus,roskopia, dielektriset metsämaat, ominaisuudet, turvemaat alkuaineet,, luokitus, monimuuttuja-analyysi,Suomi moreeni, biotoopit, kasvillisuus, ISBNmetsämaat, (painettu turvemaat,) 978- 952luokitus,-217- Suomi281-5 ISBN (pdf) 978-952-217-282-2 ISSNISBN (painettu)XXX 978-952-217-281-5 ISBN (pdf) 978-952-217-282-2 .. Julkaisupaikka Espoo Espoo. PainopaikkaPainopaikka Suomen Tampere Yliopistopaino Oy VuosiVuosi 2014 2014 Sivumäärä 68 url http://–tupa.gtk.fi/julkaisu/erikoisjulkaisu/ej_088.pdf Juvenes Print. Sivumäärä 68 ururnl http http://urn.fi/URN:ISBN:978-952-217-282-2://tupa.gtk.fi/julkaisu/erikoisjulkaisu/ej_088.pdf

Geological Survey of Finland Maarit Middleton

Contents

List of original publications...... 8 Author’s contribution...... 8 List of abbreviations...... 9 List of symbols...... 9

1 .Introduction...... 10 1.1 Background and research environment...... 10 1.2 Objectives and scope...... 12 1.3 Research realization and dissertation structure...... 13

2 Theoretical foundation...... 14 2.1 Spectra of soil...... 14 2.2 Spectra of plant species in shrub, herb and bryophyte layers...... 17 2.3 Spectroradiometry...... 18 2.4 Prediction of soil water content from vswir spectra...... 19 2.5 Prediction of till soil chemical elements from vswir spectra...... 20 2.6 Mapping of site suitability for artificial regeneration of Scots pine from hyperspectral imaging of soil water based species patterns...... 21 2.7 Optimization of peatland class hierarchal level from hyperspectral remote sensing data..... 22 2.8 Chapter summary...... 23

3 Materials and methods...... 23 3.1 Study sites...... 24 3.2 Sampling and application of numerical techniques on research data...... 25 3.3 Hyperspectral instrumentation and data...... 28 3.3.1 Close-range spectroscopy...... 28 3.3.2 Airborne hyperspectral imaging spectrometry...... 28 3.4 Reference field data...... 29 3.4.1 Geophysical measurements...... 29 3.4.2 Geochemical, particle size and mineralogical analysis...... 29 3.4.3 Vegetation analysis...... 29 3.5 Ordination techniques...... 30 3.5.1 Non-metric multidimensional scaling ...... 30 3.5.2 Canonical correspondence analysis...... 30 3.5.3 Partitioning around medoids...... 30 3.6 Classification and hyperspectral feature extraction...... 31 3.6.1 Feature extraction...... 31 3.6.2 Supervised non-parametric classification...... 31 3.7 Chemometric techniques...... 31 3.7.1 Spectral transformations ...... 31 3.7.2 Partial least squares regression...... 32

6 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

3.7.3 Bagging regression trees...... 32 3.7.4 Multivariate adaptive regression splines...... 32 3.7.5 Random forests...... 33 3.7.6 Relevance vector machines...... 33 3.7.7 Mixed-effects modeling...... 33 3.8 Model validation...... 33

4 .Results...... 34 4.1 Predicting till soil dielectric permittivity from reflectance spectra...... 34 4.2 Predicting till soil element concentrations from reflectance spectra...... 38 4.3 Mapping site suitability for artificial regeneration to Scots pine based on hyperspectral imaging data...... 38 4.4 Peatland site type mapping from hyperspectral imaging data...... 42 4.5 Chapter summary...... 44

5 Discussion and conclusions...... 44 5.1 Theoretical implications...... 45 5.1.1 Close-range detection of till soil water content and chemical element concentrations...... 45 5.1.2 Remote sensing of boreal ground cover plant associations...... 46 5.2 Practical implications...... 49 5.2.1 Close-range detection of till soil water content and chemical element concentrations...... 49 5.2.2 Remote sensing of boreal ground cover plant associations...... 51 5.3 Reliability and validity...... 52 5.4 Recommendations for future research...... 55 5.5 Chapter summary...... 56 Acknowledgements...... 57 References...... 58 Original publications

7 Geological Survey of Finland Maarit Middleton

LIST OF ORIGINAL PUBLICATIONS

This thesis consists of an overview and of the fol- ability assessment for artificial regeneration to lowing publications, which are referred to by Ro- Scots pine. ISPRS Journal of Photogrammetry and man numerals in the text. Remote Sensing 66 (3), 287−297.

I Middleton, M., Teirilä, A. & Sutinen, R. 2004. IV Middleton, M., Närhi, P., Arkimaa, H., Hy- Response of reflectance to dielectric properties of vönen, E., Kuosmanen, V., Treitz, P. & Sutinen, R. bare tills. International Journal of Remote Sensing 2012. Ordination and hyperspectral remote sens- 25 (3), 627−641. ing approach to classify peatland biotopes along soil moisture and fertility gradients. Remote Sens- II Middleton, M., Närhi, P., Kuosmanen, V. & Su- ing of Environment 124, 596−609. tinen, R. 2011. Quantification of glacial till chemical composition by reflectance spectroscopy. Ap- The papers are PEER reviewed journal articles. plied geochemistry 26 (12), 2215−2225. Copyright of the published articles belongs to Elsevier and Taylor & Francis. III Middleton, M., Närhi, P. & Sutinen, R. 2011. Imaging spectroscopy in soil-water based site suit-

AUTHOR’S CONTRIBUTION

Maarit Middleton wrote the papers, and was the II The research topic and sampling were designed corresponding author of the papers being respon- by R. Sutinen. M. Middleton was solely responsible for all contents and errors. The coauthors sible for spectroscopic measurement design and have contributed to the text of the manuscripts by conducting the measurements in the laboratory. correcting the contents and the language. In ad- She also made Figures 1, 2, and 3 and Table 2. P. dition to the original papers, the dissertation in- Närhi made Figures 4, 5, and 6. Tables 1 and 3 were cludes previously published data which are further made by M. Middleton and P. Närhi together. M. processed by a plan made by M. Middleton. The Middleton and P. Närhi planned the statistical and plan was executed by Paavo Närhi who conducted chemometric analysis and P. Närhi executed them. the analyses, made Figures 3 and 4, and calculated M. Middleton and Viljo Kuosmanen interpreted the statistics for Table 5 presented in this synopsis. the results and M. Middleton discussed them.

I Raimo Sutinen and M. Middleton chose the re- III M. Middleton and R. Sutinen formulated the search topic and made the sampling plan. M. Mid- research scope. R. Sutinen designed the hyper- dleton conducted the measurements of dielectric spectral data collection and M. Middleton and permittivity and spectral reflectance in the labora- P. Närhi the field data sampling design and field tory, and made Figures 1 and 3 and the tables. M. data collection. They also acquired the dielectric Middleton and Ari Teirilä designed the statistical and vegetation data in the field. M. Middleton was analysis. A. Teirilä was responsible for conducting responsible for designing the data analysis meth- correlation analysis, mixed effects modeling, and ods. P. Närhi conducted the ordination analysis, making Figures 2, 4, and 5. M. Middleton inter- accuracy assessment with receiver operating char- preted the results and discussed them. acteristics curves, and made Figures 2, 3, and 5.

8 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

M. Middleton conducted the spatial analysis with tance correction. M. Middleton, H. Arkimaa, E. neural networks, and made the tables and Fig- Hyvönen, P. Närhi, and R. Sutinen designed the ures 1 and 4. The results were interpreted by M. sampling scheme and field measurements and Middleton and P. Närhi and conclusions were conducted the field work. Data analysis was de- drawn by all authors. signed by M. Middleton, P. Närhi, and Paul Treitz. P. Närhi conducted the ordination and ordination IV The research was initiated by Hilkka Arkimaa, related statistical including Tables 1–3 and Figures Eija Hyvönen, M. Middleton, and R. Sutinen. The 2–4. M. Middleton conducted the spatial classifi- HyMap data collection was organized by V. Ku- cation with support vector machines (Fig. 1) and osmanen and H. Arkimaa. She and E. Hyvönen its accuracy assessment (Table 4, Fig. 1 and 6). participated in conducting the geometric rectifi- The results were interpreted and discussed by M. cation and H. Arkimaa and V. Kuosmanen in the Middleton (Fig. 4 and 5). atmospheric correction and bidirectional reflec-

LIST OF ABBREVIATIONS

AISA Airborne Imaging Spectrometer OH hydroxyl ion for Applications OMC Organic Matter Content ANN Artificial Neural Networks PAM Partitioning Around Medoids AUC Area Under the receiver oper- PLSR Partial Least Squares Regression ating characteristics Curve PNN Probability Neural Network BRT Bagging Regression Trees RBFLN Radial Basis Functional Link Network ex situ out of original place RF Random Forests HyMapTM Hyperspectral Mapper RMSE Root Mean Square Error in situ in place ROC Receiver Operating Characteristics LWIR long wavelength infrared, 8–15 µm RVM Relevance Vector Machines MANOVA permutational multivariate analysis SVM Support Vector Machines of variance SWC Soil Water Content MARS Multivariate Adaptive Regression SWIR short wavelength infrared, Splines 1700–3000 nm MVC Multivariate Calibration Techniques UV ultraviolet, 10–350 nm MWIR mid wavelength infrared, 3–5 µm VIS visible, 350–700 nm NDVI Normalized Difference Vegetation VSWIR visible and short wavelength Index infrared, 350–2500 nm NIR near-infrared, 700–1700 nm XRD X-ray diffractometry

LIST OF SYMBOLS

Ɛ dielectric permittivity p statistical significance

σ electrical conductivity rs Spearman correlation coefficient l wavelength R2 coefficient of multiple determination nm nanometer Radj residual degrees of freedom based n number of observations adjusted coefficient of multiple µm micrometer determination

9 Geological Survey of Finland Maarit Middleton

1 INTRODUCTION

1.1 Background and research environment

Until recently soil properties have gained only mi- of moisture, nutrients, and also pH (Tahvanainen nor research attention as abiotic factors affecting et al. 2002, Närhi et al. 2010). the spatial distribution, abundance, and diversity Soils are a fundamental factor for the biosphere of species in the boreal previously glaciated land- and survival in the North. From the local econom- scapes in Lapland, northern Finland (Cajander ic perspective, forestry and reindeer husbandry 1926, Kujala 1929, Sutinen et al. 2002a, Sutinen benefit from soils in northern Finland followed et al. 2002b, Sutinen et al. 2007a, Mäkitalo 2009). by agriculture, peat production, infrastructure Climatic factors and effects of climate change have development, and mineral industry. Mapping the gained much more interest as factors contributing properties of soils and plant assemblages should be to biogeography of tree species (e.g., Holopainen facilitated to benefit the local industries and gov- et al. 1996, Veijola 1998, Kultti et al. 2006). Bed- ernment. More accurate and cost efficient ways are rock mineralogy and texture has a significant im- needed since the most common practices require pact on the spatial heterogeneity of glacial till soil extensive field work, sampling, and expensive hydrology and nutrient potential (Koljonen 1992, laboratory analysis. In this study, radiation sensed Sutinen et al. 2007a). The extremes of the till soil at close range or remotely in the visible and short gradients are coarse textured tills derived from fel- wavelength infrared (VSWIR, 400–2500 nm) range sic rocks and fine-grained tills derived from mafic of the electromagnetic range is explored. The study rocks (Lintinen 1995). The finer the till texture is, focuses on hyperspectral sensing which is consid- the higher the ionic exchange capacity and nutri- ered as a suite of techniques recording the inten- ent potential will be but also soil water content sity of emitted energy reflected from a surface in (Timoney et al. 1993). The edaphic properties such many narrow bands within the optical wavelength as moisture and nutrient potential are driving fac- range. The term close-range soil sensing, in this tors for spatial distribution, diversity and abun- study, is considered as ex situ stationary laboratory dance of tree species (Sutinen et al. 2002a, Sutinen spectroscopy. The ‘closeness’ (distance of centim- et al. 2007a, Mäkitalo 2009). Till has a significant eters or meters from a target) of the material and role in the species distribution in regional scales as the non-destructive rapid nature of the measure- glacial tills are the most common sediment type ment makes VSWIR spectroscopy a non-invasive on Fennoscandian forest floors with 75% aerial and relatively inexpensive active close-range soil coverage. Moreover, tills commonly exhibit high sensing tool. In addition, potentially several soil spatial variability, which emphasizes the spatial properties can be measured at one instance with complexity of soils as substrate for boreal vegeta- minimal sample preparation and without hazard- tion (Hänninen 1997, Penttinen 2000). ous chemicals. The glaciogenic sediments in Lapland act as The idea behind hyperspectral remote sensing substrate for the biosphere of the Scandinavian in soil studies is to measure reflectance spectra and Russian taiga ecoregion characterized by open in a spatially continuous way for the purpose of canopy coniferous forests with Scots pine (Pinus mapping the patterns of soil classes and properties sylvestris), Norway spruce (Picea abies L. Karst), cost-efficiently and consistently. To circumvent downy birch (Betula pubenscens Ehrh.), and their the vegetation canopy obscurence on exposed sur- understory dominated by shrubs, mosses and li- faces the published geologic and pedologic hyper- chens, and peatlands, i.e., organic wetlands or spectral applications have focused on arid deserts, mires. The dominant tree species thriving in dry steppes, and savannas where mineral top soils are nutrient poor tills are Scots pine and silver birch exposed (Ben-Dor et al. 2009, Mulder et al. 2011, (Betula pendula), whereas Norway spruce and Kruse 2012, Van der Meer et al. 2012). However, downy birch dominate in the wet and mesic nu- in densely vegetated areas, such as the boreal zone, trient-rich tills (Sutinen et al. 2007a). Similarly, the use of hyperspectral remote sensing is mostly understory species follow edaphic properties limited to vegetation, more specifically for inven- (Salmela et al. 2001, Närhi et al. 2011), as well as torying forest properties or land cover (Treitz et al. species on organic soils which follow patterns soil 2010). The notion of spatial interpolation of soil

10 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment properties between soil observation or samples to be a very promising tool for rapid estimation with the aid of remote sensing has been more suc- of multiple soil chemical, physical and biological cessful on sparsely vegetated areas than on densely properties both in field (Stevens et al. 2008, Ben- vegetated areas. On densely vegetated areas, the Dor et al. 2009) and in laboratory (Sørensen & use of optical spectroscopy relies on indirect re- Dalsgaard 2005, Cécillon et al. 2009). trieval using soil indicators such as plant function- Currently, VSWIR and mid wavelength infrared al groups (Schaepman et al. 2007), ‘optical types’ (MWIR, 3–5 µm) spectroscopy and chemomet- (Ustin & Gamon 2010), biogeographical gradients rics are highly active research fields, particularly (Schmidt & Hewitt 2004, Mahecha & Schmidtlein in precision agriculture (e.g., Muller & Décamps 2008), geobotany (Ustin et al. 1999), indicator spe- 2001, The Second Global Workshop on Proximal cies (Mücher et al. 2009), productivity changes Remote Sensing 2011). In recent years, the chemo- (Dobos 2006, Hansen et al. 2009), and Ellenberg metric methods have advanced greatly including indicator values (Schmidtlein et al. 2007). also the non-linear multivariate regression (MVR, The idea in using vegetation as a proxy of soil e.g., Artificial Neural Networks, ANN; Support assumes that the biogeographical and geobotani- Vector Machines, SVM, Viscarra Rossel & Beh- cal information could reveal underlying patterns rens 2010), besides linear MVRs (e.g., partial least of soil properties. This would be very helpful, e.g., squares regression, PLSR, Geladi & Kowalski 1986, in spatial assessment of selecting an appropriate Martens & Naes 1992, Wold et al. 2001) and single tree species for artificial regeneration. The suc- variate regression (e.g., Demattê et al. 2006), for cess rate of artificial regeneration for dry soil tol- empirical calibration of the spectra with subsam- erant Scots pine could be significantly improved ple of laboratory measured soil properties. Spec- by including a soil water based assessment of site troscopy has been shown to have high potential suitability in the reforestation planning process for predicting soil mineralogy, soil texture, organic especially in Lapland where forest compartment carbon (soil organic matter), soil moisture, nitro- sizes can be large. Previously it has been reported gen content, and clay content especially for agri- that 96.5% of the mature pine stands in Finnish cultural soils (see reviews in Ben-Dor et al. 2009, Lapland occur on dry sites with dielectric permit- Stenberg et al. 2010, Mulder et al. 2011, Sarkhot et tivity (Ɛ) less than 15 (water content 0.27 cm3/cm3, al. 2011). The various forms of C are probably the Sutinen et al. 2002a, Sutinen et al. 2002b). The in- most accurately predicted of all soil elements (e.g., direct link of soil-vegetation-remote sensing is a Viscarra Rossel et al. 2006, Ludwig et al. 2008, complex but fascinating research topic, and also Vasques et al. 2009, Sarkhot et al. 2011). On the rarely researched. Although there lies the potential other hand, variability in the success predicting, in hyperspectral sensing for detecting properties e.g., cation exchange capacity, base saturation, pH, of soils and other vegetation layers besides up- exchangeable bases, extractable phosphorus, and per forest canopy (Salmela et al. 2001) the topic is hydraulic properties is high (Santra et al. 2009). poorly explored, especially in the boreal. The weak nature of the absorption features, over- The limited exposure of soils in Lapland con- lapping of them, influence of constituents on the stricts the applications of direct hyperspectral absorption features of other constituents, too wide sensing of soils merely to a close-range ex situ ap- or narrow variance of concentrations, geological proach conducted in laboratory. The significance variability, coating of the bigger particles by a va- of lab spectrometry lays in cost reduction (see riety of compounds and grains, and variability in Nduwamungu et al. 2009) and selection of samples the quality of the predicted property are factors for more expensive soil measurements. The main contributing to highly variable success. idea behind spectroscopy and chemometrics, i.e., In northern Finland, close-range spectroscopy sciences of extracting information from spectro- would most prominently benefit mineral explora- chemical systems by data-driven means, is that the tion. Overall, the lack of information on suitability spectra are measured of all samples and they are of chemometric predictions for mineral soil prop- empirically calibrated with a conventionally de- erties prevents the use of VSWIR spectroscopy for termined subsample. The estimated quantities of the purpose of mineral exploration and forest soil the remaining samples are then obtained through research. Moreover, soil spectroscopy has been application of the calibrated model on the rest of shown to hold high potential as a mapping tool for the samples. VSWIR spectroscopy has been shown a variety of soil properties of agricultural soils but

11 Geological Survey of Finland Maarit Middleton the application of soil spectroscopy in geochemi- as the Ɛ measurements can be efficiently conduct- cal exploration and pedogeochemical research, ed and are immediately usable for model calibra- however, is very rare. For mineral exploration pur- tion. However, the relationship between soil Ɛ and poses till samples are usually taken from relatively spectral reflectance has not yet been reported. unweathered low-organic-matter podzolic C-ho- Besides upland mineral soil properties, hyper- rizon. As a matter of fact, the low organic matter spectral data are also expected to be applicable for content (OMC) would be beneficial for mineral- peatland studies. Peatland soils have global im- ogical and elemental quantification of soils with portance as a large carbon stock (Yu et al. 2011). spectroscopy as organic matter has a tendency to Emissions of carbon dioxide, methane and nitrous mask absorption features by other soil constitu- oxide are controlled by peatland ground water ents (Galvão & Vitorello 1998). The fine mineral and nutrient levels, and dominant species associa- powder fraction (<0.06 mm) of glacial tills is com- tions (Moore & Knowles 1989, Silvola et al. 1996). monly used in geochemical exploration in glaci- Understanding them, as a function of increasing ated terrains because compared to coarse fraction trophic level (nutrient-richness: oligotrophic

1.2 Objectives and scope

The main motive behind the research is the lack cantly higher potential of identifying mineral ma- of understanding of hyperspectral close-range terials and plant species. These methods of opti- and remote sensing in applications that are rel- cal spectroscopy, utilizing the wavelength range evant to research of soil properties. Compared 350–2500 nm, have shown potential in a variety to other optical datasets, hyperspectral data have of soil related applications across biomes on the superior spectral resolution, and thus has signifi- Earth. However, this study focuses on applications

12 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment relevant in the boreal environment of Finnish Lap- tions and indirect detection of soil properties are land. The aim of the study is on practical applica- imaged with airborne remote sensing. tions on site specific and target scales rather than on regional or global scales. In this study strictly The main research problem addressed in this dis- soil related abiotic factors, i.e., non-living chemical sertation is stated as follows: and physical factors in the environment, which affect ecosystems, were considered. Resource gradi- How can hyperspectral spectroscopy be used for ents (i.e., light, nutrient contents, carbon dioxide, quantifying and classifying soil properties and plant and oxygen), direct gradients (i.e., temperature) associations dependent on the soil properties in the or indirect gradients (i.e., distance to peatland boreal environment? margin, see Franklin, 1995, for explanation) are not considered in this study. The main interest is The problem is studied from the perspective of in soil properties such as soil moisture and nu- four potential practical applications of hyperspec- trient potential (measured as bulk soil electrical tral data based on which four research questions conductivity, σ, and elemental concentrations) of (RQ) were formed (Table 1). Each RQ is answered both mineral and organic soils. The hyperspectral with a journal article (Table 2). The contributions close-range soil sensing is used for bare mineral of the articles are combined in the dissertation soil spectra. Spatial patterns of species associa- discussion.

Table 1. Research questions.

RQ # Research question RQ 1 What is the most appropriate empirical modeling technique to predict bulk soil dielectric permittivity of glacial till soils from VSWIR spectral reflectance measurements? RQ 2 Which element concentrations of glacial till soils can be predicted with VSWIR spectral reflectance? RQ 3 Can soil moisture based suitability for artificial regeneration to Scots pine be mapped spatially on clear cuts using hyperspectral imaging of ground cover plant species patterns? RQ 4 How to determine an optimal peatland class hierarchal level for medium resolution hyperspectal imaging spectroscopic data?

Table 2. Overview of research papers.

Paper RQ # Title Journal I RQ 1 Response of reflectance to dielectric International Journal of properties of bare tills Remote Sensing II RQ 2 Quantification of glacial till chemical composition by Applied Geochemistry reflectance spectroscopy III RQ 3 Imaging spectroscopy in soil-water based site suitability ISPRS Journal of Photo- assessment for artificial regeneration to Scots pine grammetry and Remote Sensing IV RQ 4 Ordination and hyperspectral remote sensing approach to Remote Sensing of classify peatland biotopes along soil moisture and Environment fertility gradients

1.3 Research realization and dissertation structure

This research was initiated after years of work- hyperspectral applications suitable to boreal envi- ing with hyperspectral data in projects which ronments would be necessary to increase aware- examined the applicability of hyperspectral data ness of spectrometry and its applications which in geoenvironmental studies. During the work, it have a geological or ecological background or was realized that summarizing and reporting the significance. Close-range hyperspectral data were

13 Geological Survey of Finland Maarit Middleton acquired in laboratory conditions from soil sam- ods, and comparison of the results to existing ples gathered from the field. Mineralogical and literature. chemical analyses were conducted for referencing This compilation dissertation consists of four the hyperspectral data. Two airborne campaigns individual papers and the summary of their re- for acquiring the remotely sensed hyperspectral sults. This summary is organized as follows: data were organized. In situ field referencing was Chapter 2 presents the theoretical foundation of done with geophysical measurements and vegeta- VSWIR spectroscopy and each RQ. Chapter 3 tion analysis. Data were interpreted with statisti- shortly reviews the research methods and mate- cal, data mining, chemometric, and image pro- rials. The most important results answering each cessing methods. Finally, conclusions were drawn RQ are summarized in chapter 4. Finally, chapter 5 by reviewing the results against existing literature. discusses the results from theoretical and practical The research process typical to all four papers points of view, reviews the reliability and validity follows the sequence of: literature review, data of study, and suggests some future research. gathering, data analysis with quantitative meth-

2 THEORETICAL FOUNDATION

Soil and plant spectra are a sum of inherent scat- and spectral reflectance is explained, in addition tering and absorption properties of their constitu- the strengths and weaknesses of different regres- ents. The soil matrix is composed of mineral and sion approaches are discussed in chapter 2.4. To organic matter, and the air and water filling their answer RQ 2 the mineralogy of tills is discussed in pore spaces. Soil spectral response is primarily a chapter 2.5 as the prediction of chemical elements product of mineral particles as minerals approxi- concentrations is based on indirect information mately account for half of the soil volume (Hillel from the spectrally active mineralogical compo- 1998). Grain size, water, organic matter content, nents in till. Chapter 2.6 reviews the environmen- and soil precipitates also play a significant role in tal drivers for distribution of understory plant the overall soil spectra. Similarly, plant spectra are species and principles of soil hydrology relevant composed of a variety of plant properties and con- to understanding why remotely sensed spectra stituents: pigments, proteins, cellulose, lignin, wa- can be indirectly used for estimation of soil mois- ter, tissue structure, and canopy structure. There- ture in order to answer RQ 3. Steps towards ana- fore, any measured spectrum is a mix of spectrally lyzing the usability of satellite based hyperspectral active components. data and developing data classification methods The following two chapters 2.1 and 2.2 ex- are needed for the purpose of nationwide peat- plain the theoretical framework of soil and plant land mapping. To answer the RQ 4 particularly, spectra. Then brief summaries of hyperspectral chapter 2.7 explains the needs for a data process- close-range and imaging spectroscopy are given ing chain for resolving the optimal level of class in chapter 2.3. Finally, the four RQs are explained detail of the peatland site types. This would be ex- with respect to previous studies. As background tremely useful for the future goal of an ecological to the RQ 1, the relationship between soil water national wetland inventory.

2.1 Spectra of soil Soil VSWIR spectra is generally rather flat and are the main sources of absorption features in the characterized by a few broad weak absorption fea- VSWIR. In the visible (VIS, 350–700 nm) range, tures (Fig. 1A). They are caused by electronic pro- on the contrary, the primary process causing ab- cesses, including crystal field transitions, electron sorption is electronic excitation. Extensive reviews transfer between transition metal ions and charge of the soil absorption features caused by the main transfer between transition metal ions and anions, overtones of OH, SO4, and CO3, and combinations and molecular vibrational processes of the mate- of H2O and CO2 are presented by Clark (1999) and rial (Hunt 1977). The overtones and combinations Stenberg et al. (2010). of molecular fundamental vibrations in the MWIR The soil particles are larger than the VSWIR

14 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment wavelengths, which causes soils to exhibit diffuse 510 nm, 550 nm, and 900–1100 nm (e.g., goethite reflectance. When radiation interacts with dry soil in Fig. 1A, Hunt & Ashley 1979). The Fe ions can a portion of it reflects from particle surfaces and also be present in layered minerals such as biotite, the rest penetrates into the particles, where it is ei- muscovite, sericite, and mixed-layer clay miner- ther absorbed or scattered. The scattered spectral als such as mica-vermiculite and AlOH-interlay- reflectance is thus a sum of the specular reflec- er-vermiculite, which could be present in till soil tance and internal volume reflectance. Soils exhib- horizons (see Righi et al. 1997, Peuraniemi et al. it bidirectional reflectance properties (Cierniewski 1997). These secondary minerals may be prod- & Courault 1993) thus observation conditions ucts of podzolisation, which results in crystalline including illumination and observation direction Fe and amorphous Fe precipitates. They may coat impact soil reflectance. Surface roughness, sur- mineral grains and therefore mask the absorb- face aggregation, and the way the components are ance features of the primary minerals. In soils, Al arranged to form the structure of the soil, have a and Mg, which have an overall high VSWIR re- significant effect on the overall reflectance (e.g., flectance, can be present in secondary precipitates Wu et al. 2009). thus affecting the entire spectra. Electronic exci- The till spectra as a combination of their min- tations also by other transition metals ions such eral bearing constituents is not fully understood. as Cr, Co, and Ni can cause absorption in the VIS In till fine fraction, which was the interest of this range (Clark 1999). research, the rock forming primary minerals such Particle size has an effect on the depth of the as quartz, alkali feldspars, plagioclase, and amphi- absorption features and the level of the overall re- bole dominate. Secondary minerals such as kaolin- flectance. Larger mineral grains absorb more light ite, mixed-layer clay minerals such as vermiculite, than smaller grains causing decreased reflectance. chlorite, illite and swelling-lattice vermiculite may Absorption depth increases with an increase min- also be present (Pulkkinen 2004, paper II). Quartz eral grain size as with larger grains greater inter- is featureless in VSWIR having overall high reflec- nal path causes more light to be absorbed. With tance (Fig. 1B, Hunt & Salisbury 1970). Feldspars the smaller grains surface reflection dominates are also highly reflective (e.g., albite in Fig. 1B) but over internal scattering, which makes the absorp- plagioclase, in the presence of Fe(II), can have a tion features shallower compared to coarse grains. broad absorption around 1100–1300 nm (Crown (Clark 1999, Van der Meer et al. 2012) & Pieters 1987). The OH-bearing minerals such In the case of moist soils, water molecules are as amphiboles (e.g., termolite in Fig. 1B), chlorite, in the lattices of minerals and electrically bound talc, and illite are characterized by a number of on the surfaces of the particles in the matrix. Solid absorption features between 2000–2500 nm (Fig. particles can be surrounded by several layers of 1B). They are caused by combination vibrations of molecules until free water appears into the pore hydroxyl stretch and metal-OH bend of AlOH at spaces. This is observed as decreased reflectance 2200 nm, MgOH at 2300 nm (and FeOH at 2290 throughout the VSWIR spectrum (Fig. 1A, Bow- nm) for till bearing layered silicates such as chlo- ers & Hanks 1965, Whiting et al. 2004), and bet- rite, talc and illite (Farmer 1974, in Clark 1999). ter known as visual darkening (Ångström 1925, When present, carbonates strongly absorb at 2335 Planet 1970). Darkening is caused by multiple nm and 2500 nm, and weakly at 1870 nm, 1990 mechanisms some of them might still be unknown nm and 2160 nm (Clark 1999). Structural and (Twomey et al. 1986, Lekner & Dorf 1988, Zhang electrically bound molecular water may partially & Voss 2006). Soils darken with increasing soil mask the OH-absorption (Hunt 1977). water content (SWC) only until a certain moisture The Fe related absorption features in soils domi- level is reached. Beyond a so-called cut-off thick- nate the ultraviolet (UV, 10–350 nm) and VIS part ness the SWC-reflectance relationship reverts, of the spectrum. Features by Fe(III)- and Fe(II)- i.e., the reflectance starts to increase with surface bearing constituents are situated at approximately moisture. The cut-off thickness is defined as a 670 nm and 950 nm (Hunt et al. 1971). Iron(III) thickness of soil mixture that transmits only 5% produces crystal field transition at 450 nm, 550– of the incident light. This point is close to SWC at 650 nm and 750–950 nm (e.g., ferrihydrite in Fig. hygroscopic limit where water film covers all the 1A) whereas absorption bands caused by Fe(II) particles (Neema et al. 1987), and also SWC at the crystal field transitions can be found at 450 nm, field holding capacity, which typically lies within a

15 Geological Survey of Finland Maarit Middleton soil moisture range of 0.15–0.40 g/cm3 (Zhu 1984, C-H, C=O, and N-H (Coleman & Montgomery in Weidong et al. 2002). The dynamics of SWC- 1987, Henderson et al. 1992, Ben-Dor & Banin reflectance relationship seem to be closely related 1995, Clark 1999, Viscarra Rossel & Behrens to soil type and used wavelengths. At the shorter 2010, Stenberg 2010). wavelengths, where scattering processes dominate, 0.7 reflectance change rate is very rapid. At the longer A Goethite wavelengths (l>1900 nm), where water absorp- 0.6 Ferrihydrite Organic soil Till, Ɛ=3.9 tion have a greater impact, variation is more pro- 0.5 Till, Ɛ=6.8 gressive (Weidong et al. 2002). This supports the Till, Ɛ=21.5 0.4 an ce use of short wavelength infrared (SWIR, 1000– c t e l f

e 0.3

3000 nm) for soil moisture prediction especially at R low moisture levels. Shorter wavelengths would be 0.2 more appropriate for moisture estimation at high moisture levels. 0.1 In addition to the general darkening of the 0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 soils, the hydroxyl bands reveal the presence of Wavelength (nm) water in soils. They are seen as very strong specific overtone and combination absorption bands 1 B around at 1400 nm and 1920–1935 nm, and 0.9 weaker bands 450 nm, 574 nm, 760 nm, 986 nm 0.8 (942–1135 nm), 1200 nm, 1379–1450 nm, 1672 0.7 nm, 1800 nm, 2189 nm, and 2400 nm (Fig. 1A, 0.6 an ce 0.5 c t e l Hunt 1977, Bishop et al. 1994, Ben-Dor et al. f e 0.4 R 1999, Weidong et al. 2002, Demattê et al. 2006). Albite 0.3 Quartz The presence of the weaker bands is dependent Talc 0.2 Illite Chlorite on the mineralogy of the soils, i.e., the presence of 0.1 Tremolite mineral-OH bonds. The effect of increasing water 0 400 600 800 is also seen as widening and deepening of the ab- 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm) sorption features especially at 1400 nm but also less intensively at 1900 nm (Demattê et al. 2006). 0.60 C DoDwonwyn byi rbcirhc h(B (Beetutulala pub pubeescsceenns) In contradiction, hydroxyl absorption at 2200 nm SphSphaagngunumm m mossoss ( S(Sphaphaggnnuu m ffuscum)) ReRindeeindeeer lric lihechenn ( C(Clladoadonniaia rraanngiferiinana)) 0.50 has shown to exhibit a less evident behavior with CoCmommmonon h ahairciracpap m moossss ( (PPoollyyttrriichhuum ccoommmmuneune) ) Juniper haircap moss (Polytrichum juniperinum) Juniper haircap moss (Polytrichum juniperinum) Litter Litter increasing water content (Ben-Dor et al. 1999, 0.40 Charcoaled wood Demattê et al. 2006). Another fundamental water Charcoaled wood an ce c t

e 0.30 l

absorption band is centered at 2800 nm and an f e OH combination band at 3100 nm. They are lo- R cated beyond the SWIR range considered in this 0.20 paper but they have a wing effect on the spectra at 0.10 λ<2500 nm (Bishop et al. 1994). Similarly to water, soil organic compounds 0.00 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 have their fundamental absorption features in Wavelength (nm) MWIR, and their overtones and combinations appear as numerous weak absorption features Fig. 1. Example spectra of common minerals in tills, till with throughout the VSWIR (Fig. 1A, Stenberg et al. varying soil dielectric permittivity values (ε=3.9–21.5, 1B), 2010). However, overall decrease in reflectance organic soil (1A), plant species, and ground cover compo- due to OMC is generally observed in the VIS nents (1C). Mineral spectra of goethite, ferrihydrite (1A), albite, quartz, tremolite, talc, illite, and chlorite (1B) were range as organic matter has a broad absorption retrieved from the USGS Digital Spectral Library (Clark et in those wavelengths. Several bands have been al. 2007). Till spectra were modified from paper I. Spectra recognized to be important for OMC in SWIR of organic soil was taken from Lang et al. (2002) and Sphag- num moss from Arkimaa et al. (2009). Samples of plant and but especially 1000 nm, 1600 nm, 1799 nm, 1800 ground cover component spectra were gathered at study site nm, 2000 nm, and 2200–2400 nm are related to of original paper III and measured with spectroradiometer organic molecules and proteins with O-H, C-C, in laboratory.

16 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

2.2 Spectra of plant species in shrub, herb and bryophyte layers

Plant spectra are characterized by low reflectance level, the effect of leaf area index on SWIR can be (<5%) in the VIS, and especially around 680 nm, up to 60% around 1450 nm and 1950–2500 nm caused by absorption of plant pigments (Fig. 1C). where the absorption coefficient of water is high Radiative transfer modeling results indicate that (Jacquemoud et al. 2009). chlorophyll a and b contents causes about 60% Despite the ability of hyperspectral remote sens- of the canopy reflectance variation in VIS wave- ing to discriminate between landscape compo- lengths (Jacquemoud et al. 2009). Spectroscopy nents, difficulties arise when separating vegetation has been used for quantification of vegetation species. Within species reflectance varies depend- biochemical constituents such as plant pigments: ing on factors such as season, illumination, nutri- chlorophyll a and b, carotenoids, anthocyanides, ent regime, and moisture availability. These factors and nitrogen at the leaf and canopy levels success- contribute to the changing of single species reflec- fully (see summary Ustin et al. 2009). Pigments tance in space and time. The main contributors to have absorbance features in the VIS but nitrogen is the failure to separate and identify plant species is found to occur in chlorophyll molecules thus ena- the spectral similarity of plant species, and the in- bling its detection through cross-correlation with adequacy of hyperspectral sensors in spatial, spec- chlorophyll absorption (Daughtry et al. 2000). The tral, and temporal resolutions (Treitz et al. 2010). most unique property of plant spectral reflectance Besides the green vegetation components, sev- occurs at red-edge, between 680 nm and 730 nm, eral other ground cover components contribute which is characterized as the most extreme reflec- to the reflectance in the boreal zone. Some ex- tance gradient in naturally occurring materials ample spectra are presented in Figure 1C. The (Fig. 1C). Broadening and deepening of chloro- samples were gathered at the study site of paper phyll absorption feature between 660–759 nm as III and were measured in laboratory with a Field- a result of the increase in chlorophyll content is Spec spectroradiometer (see chapter 3.3.1). Moss related to a shift of the red edge towards the longer spectra resembles green vegetation spectra but wavelengths (Vogelmann & Moss 1993). The high are highly variable between species, within spe- spectral reflectance (<50%) in near-infrared (NIR, cies, and even within an individual according to 700–1700 nm) is associated with scattering from genotype, light, nutrient, and water status (Fig. 1C, the internal tissue structure, i.e., air-water inter- Harris et al. 2006b, Van Gaalen et al. 2007, Arki- face of the leaves (e.g., Danson 1995). Plants with maa et al. 2009). Lichens, on the other hand, have compact leaves and thicker cell walls have fewer negligible red-edge as NIR reflectance is lowered air spaces, less water, and reduced scattering from compared to green vegetation (Fig. 1C, Rees et al. air-water interfaces, and consequently lower NIR 2004, Peltoniemi et al. 2005). Plant litter, similarly reflectance. On the canopy level, leaf angle distri- to organic matter (Coûteaux et al. 2003), is char- bution and leaf area index (effect of dry matter) acterized by several broad and narrow absorption both have been estimated to contribute equally up features in the NIR and SWIR caused by cellulose, to 40% of the NIR reflectance (Jacquemoud et al. hemicellulose, lignin, starch, pectins, waxes or 2009) causing the correlation between normalized other structural compounds (Fig. 1A, 1C, Elvidge difference vegetation index (NDVI) and amount 1990). Strong absorption features, associated to of biomass (Lillesand et al. 2008). At the same cellulose and lignin, are at 2100 nm and 2300 nm time, quantification of other leaf constituents such (Elvidge 1990, Nagler et al. 2000). Another diag- as water, protein, cellulose, and lignin has been nostic feature of the dry plant material is an ab- successful using NIR wavelengths (see summary sorption wing in the VIS portion of the spectrum Kokaly et al. 2009). In the SWIR, water on aver- produced by the intensive absorption of lignin and age contributes 50% to the reflectance (Jacque- humic acid in the blue and UV ranges. Collapse of moud et al. 2009). Similarly to soils, vegetation has cell structure, caused by desiccated water, induces the primary specific absorption features at 1450 reduced NIR reflectance compared to green leaves. nm, 1940 nm, and 2700 nm, and secondary fea- Burnt litter, organic soil, and wood with charcoal tures throughout the spectra at 480 nm, 680 nm, top gives a distinct ‘dark’ visual effect (Fig. 1C, 960 nm, 1120 nm, 1540 nm, 1670 nm, and 2200 Lang et al. 2002). nm (Wessman 1990, Carter 1991). On the canopy

17 Geological Survey of Finland Maarit Middleton

2.3 Spectroradiometry

Spectroradiometry is the technique of measuring sensing terminology NIR region covers only the the spectrum of radiation emitted by a source. The range 700–1700 nm which is followed by SWIR use of the term ‘hyperspectral’ is more vague and at 1700–3000 nm. NIRS was first developed for undefined in the field of spectroscopy indicating a analysis of agricultural commodities in the 1960’s strong need for a proper internationally acknowl- (Norris et al. 1976). The history of hyperspectral edged terminology definition. ‘Hyperspectral’ is a close-range detection of soil properties also dates ‘catch phrase’ which is established for scientific use. back to 1960’s (Obukhov & Orlov 1964, Bowers & In the strictest meaning, it is associated with high Hanks 1965, Huete & Escadafal 1991), and hyper- number spectrally narrow (i.e., low full-width- spectral remote sensing of mineral Earth materials half-maximum) spectral bands which are contigu- to the 1970’s and 1980’s (Abrams et al. 1977, Goetz ously spaced in the optical wavelength range. In et al. 1985). Development of imaging spectrome- the broad sense, the number of bands is common- ters closely relate to geology as they were primarily ly more than ten and the data are not necessarily developed for the purpose of mineral mapping of contiguous in the spectral domain. Most recently, soils and outcrops in the 1970’s and 1980’s (Goetz with the developing sensor technology, data at the 2009). Nowadays, hyperspectral sensing is used MWIR and long wavelength infrared (LWIR, 8–14 extensively for routine analysis of raw materials, µm) and ranges are also considered hyperspectral in-process materials, and quality control, e.g., in if bands are spectrally narrow and numerous. In the food, polymer, pharmaceutical, textile, and this study, hyperspectral data are considered in petroleum industries (Davies 1998). this broad sense, including all spectral data in the Several advantages lie in spectroscopy. The high VSWIR domain with a higher number of bands spectral resolution of hyperspectral data offers and narrow bandwidths compared to multispec- the potential to discriminate between materials tral data. which have subtle spectral signature differences. Remote sensing or remote detection is consid- Therefore, more information is potentially avail- ered as a collection of information about an ob- able from high-resolution spectra with a broad ject without making physical contact with it (Rees coverage of wavelengths. Compared to multispec- 2001). Remotely sensed hyperspectral data includ- tral data, hyperspectral data give a greater likeli- ing the spatial domain can be acquired with im- hood of making an optimal choice of wavebands aging spectroscopy, hyperspectral remote sensing, for discriminating between materials or relating ultraspectral imaging or hyperspectral imaging as the optical properties to the property of the sur- proposed by Goetz et al. (1985). They define im- face (Rees et al. 2004). Hyperspectral data are ac- aging spectrometry as “acquisition of images in quired non-invasively and non-destructively al- hundreds of contiguous, registered, spectral bands lowing monitoring of several surface properties such that for each pixel a radiance spectrum can at once. Although currently quite expensive and be derived”. Whereas Schaepman (2007) suggests difficult to obtain, the availability of hyperspectral the following definition for imaging spectroscopy: remote sensing systems is improving steadily. On “simultaneous acquisition of spatially co-regis- the contrary, the major disadvantage of hyperspec- tered images, in many narrow, spectrally continu- tral information is shallow penetration. The tech- ous bands, measured in calibrated radiance units, nology is still developing thus the most affordable from a remotely operated platform”. Often imag- data are often characterized by low signal-to-noise ing sensors are programmable to record a selected (S-N) ratio. Eventhough capable of deriving several number of bands, on selected spectral ranges with surface properties at once the properties also may variable bandwidths. In this work, spectrometry influence and obstruct detection of other surface conducted in tacked to a measurement target is properties. Hyperspectral data are currently lower considered as close-range sensing. in spatial resolution compared to high resolution In analytical chemistry hyperspectral close- multispectral data. Thus the field-of-view is often range spectroscopy is better known as near-in- a mixture of several features of interest, which re- frared analysis (NIRS). In chemical spectroscopy quires special image processing methods for ‘un- the NIR region is considered to cover the wave- mixing’ the data to acquire the needed informa- length range 750–2500 nm whereas in remote tion. Similarly to other optical methods, spectral

18 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment reflectance is dependent on the soil-target-sensor from different platforms, configurations, illumina- geometry making it difficult to compare the data tion settings, and timing of acquisition.

2.4 Prediction of soil water content from VSWIR spectra

Estimating SWC from a single spectral reflectance could also be done by measuring the dry spectra variable by regression analysis has been shown to of each soil sample (Weidong et al. 2002). How- be statistically feasible using a variety of reflectance ever, this requires prior information regarding the factors (e.g., Haubrock et al. 2008). The overtone soil type or dehydration of the soil sample for a absorption feature around 1900 nm has proven to dry spectrum measurement limiting the applica- be especially powerful for estimating SWC in the tion to laboratory use. One of the most promising laboratory (Weidong et al. 2002). Several studies approaches is presented by Whiting et al. (2004). reveal the dependence between reflectance and Their approach relies on the water absorption fea- SWC being non-linear, often exponential, regard- ture at 2800 nm which decreases the reflectance less of moisture being expressed as volumetric over the entire range of NIR and SWIR (Bishop water content (Lobell & Asner 2002, Weidong et et al. 1994, Whiting et al. 2004, Haubrock et al. al. 2002), gravimetric water content (Whiting et 2008). A Gaussian curve is fit though the convex al. 2004), or degree of saturation (Lobell & As- hulls and the slope of the curve is regressed to ner 2002). However, linear relationships are often SWC successfully. reported but on a narrow range of SWCs (<35%, The idea behind using multiband approaches Muller & Décamps 2001). A problem using this in chemometric regressions is that incorporation relationship for practical purposes arises when of multiple bands generally makes the regression SWC at the cut-off thickness is unknown. Differ- models more robust compared to using only single ent models should be constructed for predicting band reflectance. Multiband approaches also nor- soil moisture on both sides of this critical point malize the effect of overall reflectance variation but it would be practical only for SWC monitor- due to soil co-variates (Haubrock et al. 2008). The ing. When mapping SMC the spatial variation in SWC has been modeled from multiband reflec- soil co-variates (e.g., texture, OMC, mineralogy) tance measurements in several different ways. Soil also means spatial variation in the position of the moisture indexes have been successfully used for cut-off thickness. In other words, the inflection modeling the SWC (e.g., Nocita et al. 2011). Hau- point of the SWC-reflectance relationship varies. brock et al. (2008) review the common soil mois- Therefore, using single variable models for map- ture indices and other quantification methods ap- ping spatial distribution of SWC might not be ef- plying two or more bands which are mostly used fective. The use of single reflectance values as well for laboratory data. The advantage of the MVR as overall albedo, when estimating moisture for a techniques is that the entire spectra can be input to set of field samples, is unreliable since co-variates model SWC. It is argued that different wavelengths can affect soil brightness significantly, especially in can be more valuable for predicting low SWCs the VIS-NIR region. than the high SWCs. The longer wavelengths are Utilizing water absorption depth in soil mois- better suited for estimating volumetric moisture ture estimations is only possible for monitoring contents at >20% as reflectance saturates at much soil moisture of a specific sample in laboratory lower moisture levels at VIS-NIR wavelengths than conditions and when other co-variates of soil stay on SWIR wavelengths (Lobell & Asner 2002, Hau- constant. Several workarounds for the dilem- brock et al. 2008). This trend is valid for several ma are presented. Continuum removal could to soil classes but with varying intensities. Weidong be performed prior to modeling in order to get et al. (2002) present the successful use of longer relative absorption band depths (Clark & Roush SWIR wavelengths (>1900 nm) for soil moisture 1984). Demattê et al. (2006) utilized the area of an prediction, especially at low moisture levels (<0.3 absorption feature. They found a linear relation- g/cm3), similarly to Neema et al. (1987). Short- ship between SWC and the area of the water ab- er wavelengths would be more appropriate for sorption features around 1400 nm, 1900 nm, and moisture estimation at high moisture levels. The 2200 nm. Removing the effect of soil co-variates fact that the most optimal wavelength ranges are

19 Geological Survey of Finland Maarit Middleton different for predicting soil moisture at low and techniques such as boosted regression trees (BRT) high water contents supports the use of the entire are shown to be effective for soil predictions, spe- measured spectral range for SWC predictions. An cifically for large datasets and wide range of val- additional advantage of all multiband techniques ues (Brown et al. 2006, Viscarra Rossel & Behrens is that water absorption bands, which are influ- 2010). Although the number of input variables can enced by the atmospheric water vapor, can be ex- be high in spectroscopic data, the number of ob- cluded. This enables the use of the prediction tech- servations might still remain relatively low. There- niques in field conditions. A few studies yet show fore, a MVR technique which could also produce the superiority of MVR and, especially the non- stable results with a range of sample sizes would linear data mining techniques, for predictions be optimal for predicting SWC spatially. It would of soil properties (Mouazen et al. 2010, Viscarra also be important to consider if till-specific models Rossel & Behrens 2010). are needed or if a global model would be accurate The classical partial least squares regression enough. The value of finding a robust prediction (PLSR) has been used in chemometrics to pre- method and understanding the Ɛ-reflectance rela- dict concentrations of constituents for a couple of tionship would be evident in Lapland, e.g., when decades now (Geladi & Kowalski 1986, Martens artificial regeneration to dry soil tolerant Scots & Naes 1992, Wold et al. 2001). The idea is to in- pine is being considered on clear-cuts with high put an appropriate spectral range, which can in- soil moisture variability. The SWC of mechanically clude an unlimited number of spectral variables. exposed soil pixels in high spatial resolution opti- The currently most advanced chemometric MVR cal data could be modeled.

2.5 Prediction of till soil chemical elements from VSWIR spectra

Although reflectance spectroscopy has proven to the development of the calibration models. The be useful in quantifying many properties of miner- fine fraction (<0.06 mm) of glacial tills in north- al and organic soils (Ben-Dor et al. 2009, Stenberg ern Finland is mainly composed of rock-forming 2010) the number of studies dealing with chemical minerals such as quartz, plagioclase, amphibole, element concentrations of mineral soils is few. The microcline and low amounts of secondary miner- overtones and combinations in the NIR are much als such as chlorite and illite, vermiculite, chlorite, weaker than the fundamental MWIR bands, and and swelling-lattice vermiculite (Pulkkinen 2004). can be difficult to distinguish. Their location might As discussed in chapter 2.1, silicates (quartz, pla- also have shifted from the theoretical location be- gioclase, microcline) are characterized by high re- cause molecules do not exhibit harmonic vibra- flectance and by lack of absorption features in the tion. Therefore, interpretations of VSWIR spectra VSWIR. Amphiboles and secondary minerals can can be difficult, particularly when the target spe- be distinguished by their absorption features. Thus cies is present in small amounts or when the soil predictions of till chemical element concentration property being studied does not exhibit absorp- merely rely on the spectrally active components. tion features but it can only be detected though The OH-bearing secondary minerals have a num- correlation to the target species. The low cost of ber of absorption features in 2000–2500 nm range. spectroscopy, however, is the driving force for ap- However, one of the few chemical elements that plying it to new research problems. has a pronounced absorption features at UV and In contradiction to the soil SWC predictions, VIS ranges is Fe. Electronic transfers of Fe ions chemical elements rarely produce absorption fea- cause absorption especially at 440 nm, 670 nm, tures. Absorption features of the chemical elements and 900–1100 nm (Crowley et al. 2003, Richter et appear through molecular bonding, e.g., mineral- al. 2009). Concave spectral shape around 850 nm ogy. Several rock forming minerals have spectral is related also to crystalline form of iron but amor- absorption features that allow for their quantifica- phous iron does not exhibit this particular feature tion. Mainly, it is the features associated with tran- (Demattê et al. 2006). sition metals, OH, and water and combinations of Even in the absence of elemental absorption and, CO3, PO4, BO3, and SO4 (Hunt 1977, Hunt & features, concentrations of several soil chemical Ashley 1979, Crowley et al. 2003) that allow for elements have been successfully predicted using

20 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

VSWIR. Stenberg et al. (2010) review the literature Na, Mg, P, and some micronutrients (Cozzolino on soil nutrient predictions for agricultural purpos- & Morón 2003, Stenberg et al. 2010). Prediction of es and conclude that determination of soil chemical contaminant metals has also been quite success- elements can be successful but not consistent. Con- ful (Kemper & Sommer 2002, Wu et al. 2007, Bray centrations of organic C (see Cozzolino & Morón et al. 2009, Stenberg et al. 2010). The occasionally 2003, Terhoeven-Urselmans et al. 2008), total and successful calibrations are attributed to locally pre- organic N (see Cozzolino & Morón 2003) are par- sent co-variation to spectrally active constituents. ticularly well estimated across a variety of soil types. This phenomena is called cross-correlation. Such Success has been reported to be highly variable for co-variations are naturally sample set specific. mineral N, available K, exchangeable K, Ca, Fe,

2.6 Mapping of site suitability for artificial regeneration of Scots pine from hyperspectral imaging of soil water based species patterns

The role of understory in forestry related hyper- climatic (precipitation, temperature, wind, snow spectral remote sensing studies has, thus far, been cover, photosynthetically active radiation), edaphic insignificant. Research has focused on inventory (coarse fraction content, aeration, soil temperature, of the upper canopy, whereas the focus regard- soil nutrients, and pH), biotic (competition), and/ ing understory has been on removing its effect on or orographic (slope, aspect) variables, as well as forest canopy estimations (e.g., Pisek et al. 2010). the state of the forest succession after natural and/ With the arrival of high spatial and spectral res- or anthropogenic disturbances (Archibold 1994, olution VSWIR data the interest in understory Wieser & Tausz 2007). As demonstrated on an uni- related applications has grown. Remote sensing form climatic area of central Lapland, Ca concen- based understory inventory has been suggested as tration and Ca:Al ratio are the main edaphic vari- a means to improve estimates of, e.g., site produc- ables determining forest vegetation composition tivity (Lieffers et al. 1996). Remotely sensed data and diversity (Närhi et al. 2011). However, a couple have also been demonstrated to improve mapping of examples in the Pacific Northwestern mountains of invasive understory species (Joshi et al. 2006) of the USA (Lookingbill et al. 2004), and in sub- and species vital to conserving the habitat of en- boreal (Wang 2000) and subarctic northwestern dangered mammals (Linderman et al. 2004). In Canada (Timoney et al. 1993) suggests survey- Finnish Lapland, the quality of reindeer pastures ing of understory species as a means of revealing has been evaluated with remotely sensed optical relative spatial differences in SWC. According to data (Kumpula 2006). Salmela et al. (2001) soil water can explain 42% of VSWIR detection of the understory has previ- a species coverage and 60% of a species occurrence ously been used as an indirect method to map soil in central Lapland. This may open up a possibil- conditions. McMichael et al. (1999) suggest that ity to map the spatial distribution of the understory spectrally described vegetation is an indirect in- species associations with remotely sensed methods dicator of soil environmental CO2 conditions in to reveal the underlying patterns of soil moisture. the undisturbed ecosystem of Alaska. Schmidtlein Soil moisture mapping could be turned into the- & Sassin (2004) and Schmidtlein (2005) modeled matic information supporting species selection in the Ellenberg indicator values from hyperspectral artificial regeneration of formerly Norway spruce- imagery in estimation of underlying soil quality in downy birch dominated sites which are spatially alpine meadow habitats. However, reports on soil heterogeneous in soil moisture. moisture modeling from remotely sensed patterns The basis for the artificial regeneration site of understory are rare. Ritari & Saukkola’s (1985) suitability analysis for Scots pine is the tempo- study, conducted in southern Lapland, aims to- ral persistence, i.e., time stability of the SWC wards this goal. They showed the spectral disparity patterns (Sutinen et al. 2007b). Time stability of between dry nutrient-poor sites and moist nutri- soil moisture means that well drained soils with ent-rich sites. high hydraulic conductivities are characterized by The occurrence, diversity and abundance of low SWCs throughout the year, whereas poorly plant species in the boreal zone is controlled by drained soils with low hydraulic conductivity

21 Geological Survey of Finland Maarit Middleton have higher SWCs regardless of the fact that till Lapland with low summer evaporation (50 mm soils in Lapland experience a seasonal hydrologi- lower than precipitation, Solantie 1987). cal cycle with spring snowmelt, thawing, and sat- In paper I it is suggested that soil surfaces ex- uration, summer drying and fall wetting (Sutinen posed by mechanical site-preparation could be ex- et al. 1997). Snowmelt saturation (ε>30) may last ploited in site suitability mapping for Scots pine. 2–8 weeks in fine-grained tills of Norway spruce Presently, site-preparation by plowing is merely re- stands, but in the case of sandy soils of Scots pine placed with less destructive soil mounding (Hunt stands snowmelt saturation is absent and sea- & McMinn 1988, Hagner 1999). As mounding ex- sonal SWC is low (Hänninen 1997, Sutinen et al. poses mineral soil surfaces to a lesser extent, an 1997). Heavy autumn rainfalls may also instigate assessment approach which does not require soil high SWC (Sutinen et al. 2007b). However, the exposure, would be more desirable for pine site time stability of soil implies that the magnitude suitability. In ideal circumstances, detecting the difference of SWCs between sites tend to follow understory species patterns rather than exposed a consistent spatial pattern from year to year re- soil mounds would also provide a more continu- gardless of changes in amount of precipitation. ous representation of the site suitability. As spe- Spatial soil water regimes of tills are controlled cies differentiation with optical remote sensing by physical properties of tills such as soil texture can be cumbersome (Treitz et al. 2010), Airborne and structure. Particularly at the study site for pa- Imaging Spectrometer for Applications (AISA) per III, macroporosity caused by the pebble and data with a high spectral resolution and with the boulder fractions (>6 mm) was found to be a vital highest possible spatial resolution available at the contributor to the spatial pattern of redistribution time were chosen for the task. If the site suitability and infiltration of soil water of the fine grained methodology was successful it would be an asset tills (Sutinen et al. 2007b). The spatially stable be- for artificial regeneration of Scots pine, in all cool havior of the soil water patterns is recognized also and humid regions throughout Fennoscandia and in different climates (e.g., Cosh et al. 2008, Hu et Eurasia. al. 2009) aside from the humid climate of central

2.7 Optimization of peatland class hierarchal level from hyperspectral remote sensing data

Spatially continuous peatland inventory encom- tuation are the primary factors for peatland spe- passing the heterogeneity of plant communities cies composition (Laitinen et al. 2007, Närhi et and environmental drivers, including information al. 2010). Moreover, soil pH (Sjörs & Gunnarsson on the trophic level and hydrology, would provide 2002, Tahvanainen et al. 2002) and nutrients, pri- a suitable data source for conservation planning marily N, P, Ca, and Mg (Wells 1996, Økland et and better means for quantifying greenhouse gas al. 2001, Bragazza & Gerdol 2002, Sjörs & Gun- emissions. National wetland inventories of the narsson 2002) determine the trophic level of peat- wetland surface properties are conducted for these lands. Soil fertility and hydrology (Malmer 1986, purposes using merely remotely sensed optical Bridgham et al. 1996) are included as the main or radar data, at least in Sweden (Boresjö Bronge environmental drivers for peatland site types in et al. 2006) and Canada (Fournier et al. 2007). It the Finnish floristic peatland classification sys- is hoped that hyperspectral data will be available tems (Cajander 1926, Eurola et al. 1984, Laine & from satellites during the 2010’s (Buckingham & Vasander 2005) providing a good basis also for Staenz 2008) to facilitate such nationwide map- national mapping. In those classification systems, ping also in Finland. Steps towards analyzing the peatlands are divided into swamps, spruce peat- usability of satellite based hyperspectral data and lands, pine peatlands, fens, and eutrophic fens on developing data classification methods are needed the basis of species associations at the top of the hi- for the purpose of nationwide mapping. In par- erarchy, but at a lower hierarchical level into 25–37 ticular, resolving the optimal level of class detail of site type classes. The process of combining them the peatland site types would be extremely useful. into higher level ecological classes with the tradi- Water table depth (and consequently surface tional spectral separability approach would be a soil moisture), and its magnitude of seasonal fluc- tedious task. In addition, a large variation in the

22 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment classification accuracies between high level hierar- environmental variables, and remotely sensed chal peatland classes (Huang & Sheng 2005, Töyrä spectra as inputs. Since a prior understanding of & Pietroniro 2005, Haapanen & Tokola 2007) and environmental drivers in northern boreal peatland low level classes (Dechka et al. 2002, Grenier et al. environments is available (Tahvanainen et al. 2002, 2007, Korpela et al. 2009) leads one to think that Närhi et al. 2010) constrained ordination could be each dataset, based on their spectral, spatial, and applied. Firstly, unsupervised classification and temporal resolution, has an optimal class level multivariate analysis of variance was applied in (Middleton et al. 2009). Therefore, it would be the ordination space in order to separate peat- beneficial to develop a methodology to resolve the land clusters which could also be distinguished by issue of the optimal class hierarchical level appro- their spectral reflectance. Secondly, a fuzzy clus- priate to each dataset. tering approach was employed on hyperspectral In order to make the classes meaningful eco- data to produce continuous representations of the logically, community structure and spectral sepa- peatland plant associations and map their extent rability of the plant associations could be solved by spatially. applying ordination as vegetation cover fractions,

2.8 Chapter summary

Based on the literature review of spectral proper- tion of understory species patterns would provide ties of soils it obvious that the water molecule has a more continuous representation to aid artificial several absorption features in the VSWIR but out regeneration. Literature of indirect mapping of of the chemical elements only Fe clearly and a few soil water through remote sensing of vegetation others exhibit identified absorption features. Thus patterns is rare and several environmental fac- single variate non-linear regression techniques tors contribute to the understory species patterns. might be possible for predictions of water content. However, time stability of soil moisture patterns The signature of chemical elements is ‘hidden’ in and relative high impact of soil moisture on species the spectra, as absorption features are caused by occurrence and abundance in the cool and humid molecular and mineral bonds. This phenomenon is boreal zone it may provide a relatively successful called cross-correlation. Thus sample-set-specific guideline. Similarly to mineral soils, species pat- multiband regression techniques are required. In terns on organic peatlands are also dependent on addition, SWC predictions may also be improved a range of environmental factors. Utilizing statisti- by MVR as the variation by the effects of soil co- cal techniques in the constrained ordination space variates, soil-specific cut-off thicknesses, and op- could aid in revealing the vegetation community timal wavelength ranges can be incorporated into structure, spectrally separable peatland site types, the models. and spectral feature selection for ecologically Close-range soil spectroscopy could be applied meaningful and optimized peatland site type map- for site suitability assessment of Scots pine but a ping. This methodology could aid a possible na- remote sensing based method through recogni- tional scale peatland site type mapping.

3 MATERIALS AND METHODS

In chapter 3.1 the ecological, geological, and cli- chapter 3.2. Thirdly, a short description of the data matic conditions in central and southern Lapland collected with close-range and airborne hyper- are shortly described. The data for the four original spectral instrumentation (chapter 3.3), and refer- research papers I, II, and III were mainly collected ence data collected with geophysical, geochemical, in central Lapland but the study area for paper particle size, and mineralogical techniques of soil IV was located in southern Lapland (Fig. 2). Sec- are given followed by a description of vegetation ondly, the sampling schemes, measurement tech- analysis (chapter 3.4). Fourthly, the numerical data niques, and application of the numerical methods processing techniques are described including the to answer each research question are explained in ordination techniques (chapter 3.5), classification

23 Geological Survey of Finland Maarit Middleton and hyperspectral feature extraction (chapter 3.6), data collection and Table 4 lists the statistical and and chemometric regression (chapter 3.7). Lastly, data mining techniques applied on the data to an- the validation measures calculated for efficiency of swer each RQ. For background and more detailed prediction and classification are reviewed in chap- descriptions of applying each method the reader is ter 3.8. Table 3 summarizes the devices used for advised to refer to the original papers.

3.1 Study sites

The study sites in central Lapland belong to the compilation of harmonized vegetation regions by northern boreal zone. Yet the study site of paper the Nordic countries (Arbetsgruppen för natur- IV belongs to the central boreal zone following the vårdsfrågor 1977, Fig. 2). The glacial landscapes in

24°0'0"E 25°0'0"E 26°0'0"E 27°0'0"E 28°0'0"E 29°0'0"E N N " " N " 0 0 ' ' ^ Paper I - Vuotsonkangas 0 ' 0 0 Paper II - Till 2 ° 0 °

" ° 8 8 8 6 6 ^ Paper I - Haipankuusikko !(" Paper II - Till 1 6 Paper I - Kuorajoki ^ ^ Paper III Paper I - Vaalolehto N " N " 0 ' 0 ' 0 0 ° ° 7 7 6 6

KEMIJÄRVI

ROVANIEMI ay rw No

n e d e w N N " " N S N " " 0 0 ' ' 0 0 ' ' R 0 0 0 0 ° ° ° ° u 6 6 6 6 6 s 6 6 Paper IV 6

Finland s i TORNIO a 0 12.5 25 50 75 100 km

24°0'0"E 25°0'0"E 26°0'0"E 27°0'0"E 28°0'0"E 29°0'0"E Fig. 2. Map of northern Finland study area. Study sites and areas specific to each original research paper are given in symbols and as black area for paper IV. On background light grey resembles peatlands, dark grey lakes and main road network. Names of main towns are given in capital letters. In lower right corner map colours represent vegetation regions (Arbetsgruppen för naturvårdsfrågor 1977). Dark green corresponds to northern boreal zone, beige to central boreal zone, light green to hemi- boreal zone, and blue to southern boreal zone. Base maps from the National Land Survey of Finland Topographic Database 03/2013 © NLS and HALTIK.

24 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment both areas are gently undulating with a few-me- Lapland sites are located on the Peräpohja Schist ter-thick drift cover superimposing the bedrock. Belt whose main rock type is phyllite (Perttunen The cental Lapland study sites are characterized 1991). The southern Lapland site is characterized by basal tills because of its unique location in the by an abundance of aapa mires which is a min- so-called ice divide zone of the Fennoscandia ice ertrophic peatland complex with concave surface sheet during the last Pleistocene glaciation (Tan- topography receiving supplementary nutrients ner 1915). Tills are local in origin and strongly from surrounding uplands. The boreal climate is reflect the lithology of the underlying crystalline typically cool and humid with low summer evapo- and weathered bedrock (see Lestinen 1980). The ration that is below precipitation by about 50 mm southern Lapland area is also dominated by tills, (Solantie 1987). The annual mean temperature is yet it is also characterized by active ice moraines +1 °C in southern Lapland and -1–-2 °C in central such as drumlins, and also fluvial and glaciofluvial Lapland. The precipitation sum is higher (550–600 sorted sediments (Nenonen et al. 2004, Nenonen mm) in southern Lapland than in central Lapland 2004). The Proterozoic bedrock types in the cen- (500–550 mm, Vajda & Venäläinen 2003) and tral Lapland sites vary from mafic greenstones roughly 40–50% of annual precipitation falls as of the Lapland Greenstone belt to felsic granitic snow (Heino & Hellsten 1983). intrusions (Lehtonen et al. 1998). The southern

3.2 Sampling and application of numerical techniques on research data

For paper I four undisturbed till soil samples were (150 samples at each site, Fig. 2) 60 km apart but taken (Fig. 2) from sites with varying underly- with similar metavolcanic lithology. Spectral re- ing lithology. After wetting the samples to near flectance was measured with the FieldSpec FR full saturation Ɛ of each sample was measured ex spectroradiometer and chemical element concen- situ in laboratory to acquire a longitudinal data- trations were assayed with inductively coupled set measured daily up to 3 months or until Ɛ=3–4 plasma atomic emission spectrometry (ICP-AES, (n=396, Table 5). Dielectric permittivity was meas- Table 3) from digestion of hot (90 °C) aqua regia. ured with a Percometer using a non-destructive The final population was narrowed to 217 when flat probe and spectral reflectance with a FieldSpec heterogeneous samples with standard deviation FR spectroradiometer (Table 3). This resulted in a <-1% (in reflectance) throughout the spectral total dataset of 95 for further processing of paper I range 350–2500 nm were removed. To predict data as two to six daily measurements per till were element concentrations from till spectra PLSR averaged and some measurements were removed was applied on continuum removed and untrans- due to small S-N ratio (Table 5). Particle size distri- formed spectra, and model efficiencies were com- bution was determined with wet sieving and sedi- pared to a separate validation set reporting R2 and mentation, and OMC colorimetrically (Metrohm RMSE (Table 4). To get an overview of the miner- spectrocolourmeter E 1009). Spectral transforma- alogy X-ray diffractometry (Philips X’Pert MPD) tions with continuum removal, derivatives, and was applied on a subset of the data (n=16, Table 3). wavelet transformation were applied (Table 4). To For paper III, the AISA instrument was flown in predict Ɛ from spectra, a single variate regression a twin-engine cargo plane owned by the Helsinki of a selected spectral bands was performed with University of Technology on the 14th of Septem- mixed effects modelling for paper I. In further ber, 1999, at 9.00 a.m. UTC (11.00 local time, Table processing of the data MVR models with BRT, 3). At the 46.9 ha study site (see Fig. 2), Ɛ measure- multivariate adaptive regression splines (MARS), ments were made with a Percometer (Table 3) on PLSR, random forests (RF), and relevance vector 573 micro sites, which were every 25 m on parallel machine (RVM) were constructed. The efficiency, lines spaced 50 m apart. Measurements were made tested with the calibration dataset, was reported between June 27th and 29th, 2000. In the summer as coefficients of multiple determination 2(R ) and 2007 a random selection of 51 these sites was cho- root mean square errors (RMSE, Table 4). sen for vegetation inventory (Table 3). Neural net- For paper II a complete set of 300 till soil sam- work models were built with a radial basis func- ples at depth of 0–20 cm were taken from two sites tional link network (RBFLN) and probabilistic

25 Geological Survey of Finland Maarit Middleton

Table 3. Summary table of measured quantities acquired to answer each research questions (RQ). Measured units (Ɛ dielectric permittivity, σ electrical conductivity), used measurements devices, their producers, and company locations are listed.

Measured Measurement device Company Place RQ 1 RQ 2 RQ 3 RQ 4 property

Ɛ Percometer Adek Ltd. Tallinn, Estonia x x x σ Conductivity fork GTK Espoo, Finland x pH IQ160 pH meter IQ Scientific U.S.A. x Instruments, Inc. Percentage plant Visual interpretation within x x coverages 1 m2 quadrat

Partial leaching Hot aqua regia HCl + HNO3 x (3 + 1) Element iCAP 6500 Duo ICP Thermo Fisher Cambridge, UK x concentrations emission spectrometer Corp. Mineral Philips X´Pert MPD, XRD PANalytical B.V. Almelo, x composition Netherlands Particle size Wet sieving and x distribution sedimentation OMC spectrocolourmeter E 1009 Metrohm AG Switzerland x Reflectance FieldSpec FR Analytical Boulder, U.S.A. x x Spectral Devices Ltd. Radiance AISA VTT Espoo, Finland x Radiance HyMapTM Integrated Sydney, x Spectronics Pty Australia Ltd. neural network (PNN) functions (Table 4). Their cation (Middleton et al. 2009) and segmentation sensitivity to a variety of validation set properties of the HyMap imagery into spatially homogenous and their performance were compared with a sep- regions and choosing large enough segments arate calibration dataset of field Ɛ-measurements. for placing the field plots. This was done to en- Receiver operating characteristics (ROC, Park et sure that the field observations would match the al. 2004) analysis was used in model validation remotely sensed data as an up to 30 m spatial (Table 4). Non-metric multidimensional scaling deviation was observed for the HyMap data at (NMDS) ordination and correlation analysis were maximum. Data for validation were gathered in used for revealing the understory community August-September 2005 at 115 sites using a strat- structure and their dependence on soil variables ified sampling limited to locations reasonably ac- (Table 4). cessible by road (Närhi et al. 2010). In these data- For paper IV the Hyperspectral Mapper sets Ɛ was measured with the Percometer, σ with a (HyMap) sensor was flown in a Dornier 288 air- conductivity fork, and pH with a IQ160 pH meter craft operated by the German aerospace center (Table 3). Plant species coverages and site types in the middle of the growing season on the 29th were also determined (Table 3). For spatial classi- of July, 2000, between 11.35 a.m.–2.30 p.m. local fication of HyMap with SVMs and the validation time (8.35 a.m. UTC, Table 3). The calibration of the SVM result with ROC analysis (Table 4) data were gathered at 61 sites representing a va- the sample plots were spatially extended based on riety of peatland site types in July 2007. The sam- visual interpretation of the HyMap data. pling scheme was based on unsupervised classifi-

26 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment x x x x x x RQ 4 x x x x x x x RQ 3 x x x x RQ 2 x x x x x x x x x x x x RQ 1 Vienna, Austria U.S.A. Place Reno, U.S.A. Zürich, Schwitcherland Espoo, Finland Zürich, Switzerland U.S.A. Vienna, Austria McLean, U.S.A. Ostend, Belgium Berlin, Germany Redlands, U.S.A. Reno, U.S.A. R Development Team Core MathSoft Inc. Organization University of Nevada, University of Zürich VTT ReSe Applications, University of Zurich MathSoft Inc. R Development Team Core Exelis MedCalc Software Hulboldt-University of Berlin ESRI University of Nevada, R Development Core Team R Development Core Reference Looney & Yu 2001, C. G. Looney, et al. 2004., 2001, C. G. Looney, Looney & Yu Sawatzky et al. 2009 Richter 2003 Oksanen et al. 2011 & Ripley 2012 2012, Wand Terhoeven-Urselmans Mäkisara et al. 1994a, 1994b Terhoeven-Urselmans 2012, Wand & Ripley 2012 2012, Wand Terhoeven-Urselmans Therneau et al. 2012, Peters & Hothorn 2012 Schläpfer 2001 Hothorn et al. 2012 Team R Development Core Green et al. 1988 Green Sing et al. 2005, 2007, 2012 Hanley & McNeil 1983 Milborrow et al. 2012 Milborrow Oksanen et al. 2009 Karatzoglou et al. 2011 Chang & Lin 2003, van der Linden et al. 2010 Aldrich 2012, Terhoeven-Urselmans 2012 Aldrich 2012, Terhoeven-Urselmans An overview of Geostatisical analyst 2008 Maechler 2012 Mevik et al. 2007, Mevik & Wehrens 2007, Mevik et al. 2007, & Wehrens Mevik et al. 2011 et al. 2004., 2001, C. G. Looney, Looney & Yu Sawatzky et al. 2009 R S-Plus 4.5 Professional Release 2 Software GeoXplore v. 4.1 in v. GeoXplore ArcSDM ATCOR4 1.17-7 in R vegan ver. KernSmooth 1.4 in R soil.spec ver. GEOVIS PARametric GEocoding PARametric KernSmooth 1.4 in R soil.spec ver. rpart ver 4.0-1 0.9-0 in R ver. ipred S-Plus 4.5 Professional S-Plus 4.5 Professional Release 2 party ver. 1.0-3 in R party ver. R ENVI ROCR ver. 1.0-2 and ROCR ver. 1.0-4 9.4.1. MedCalc ver. earth in R 1.15-4 in R vegan ver. kernlab ver. 0.9-14 kernlab ver. 2.1 imageSVM v. utilizing LIBSVM wavelets 1.4 in R soil.spec ver. Geostatistical analyst in 9.3 ver. ArcGIS cluster ver. 1.14-3 cluster ver. pls ver. 2.1 and 2.2-0 pls ver. in R 4.1 in v. GeoXplore ArcSDM 2 adj Data processing Data processing technique R RBFLN Atmospheric correction CCA Continuum removal Geometric correction 1st derivative 2nd derivative BRT Mixed-effects Mixed-effects modeling RF RMSE MNF ROC ROC curve comparison MARS NMDS RVM SVM Wavelet Wavelet transformation Ordinary kriking Ordinary PAM PLSR PNN R Table 4. Summary table of the statistical methods used to analyze the data to answer each research question (RQ). Used software literature references, software companies, and company company and companies, software references, literature software Used (RQ). question research each answer to the data methods used the analyze statistical to of 4. Summary table Table also given. are locations

27 Geological Survey of Finland Maarit Middleton

Table 5. Descriptive statistics of dielectric permittivity (Ɛ) and spectral data used in thesis synopsis to answer research question I. Number (n) of measurements made of each till sample and the final population used for regression models are given along with descriptive statistics of till soil Ɛ. Sample sizes and statistics of Ɛ are also reported for calibration and validation datasets combined of four tills (Haipankuusikko, Kuorajoki, Vaalolehto and Vuotsonkangas).

Vuotson- Vaalolehto Kuorajoki Haipan- All All All kangas till till till kuusikko till calibration validation till 1 till 2 till 3 till 4 n of 25 152 83 136 396 measuraments final n 12 28 29 26 95 47 48 mean Ɛ 13.1 13.3 16.9 16.8 15.3 15.4 15.3 median Ɛ 15.0 12.8 18.0 18.1 15.5 15.2 16.0 standard 5.9 6.9 6.1 5.2 6.3 6.3 6.3 deviation Ɛ minimum Ɛ 3.6 3.9 3.7 4.9 3.6 3.6 3.7 maximum Ɛ 21.4 26.3 25.5 25.8 26.3 26.3 25.8

3.3 Hyperspectral instrumentation and data

3.3.1 Close-range spectroscopy for this work. The Hyperspectral Mapper imaging spectrometer (HyMapTM, Cocks et al. 1998, Table The soil spectral reflectance between 350–2500 3) was programmed to record a true hyperspectral nm was measured with a FieldSpec FR spectrora- dataset with 126 10-to-22-nm-wide (full-width- diometer (see Table 3 for instrument details). The half-maximum) bands across the 437–2485 nm samples were artificially illuminated from narid spectral range. The Airborne Imaging Spectrom- with a tungsten lamp configuration (Makita Inc., eter for Applications (AISA, Mäkisara et al. 1993, U.S.A.) with an approximately 25o viewing angle of Table 3) instrument was programmed to record the an optical fiber. The fiber optic cable had a 25° full first nine spectral bands in the VIS (448.99–652.27 angle cone of acceptance field-of-view. The dis- nm) and eight bands in the NIR (667.46–874.04 tance between the head of the fibre optic cable and nm) 7–8 nm in width. For the HyMap data a flight the sample was 7.1 cm on average. With this con- altitude of 2000 m, a ground speed of 278 km/h, a figuration the measured surface area was estimat- 60˚ field of view, and 2.5 mrad along- and 2.0 mrad ed to be 5 cm2. The spectroradiometer sampling across-track instantaneous fields of view resulted resolution was 1.4 nm but the data were internally in a spatial resolution of 5 m for a 2 km ground resampled to 1-nm-resolution. The instrument swath. For the AISA data an average flight altitude was calibrated to a white reference spectrum (i.e., of 1300 m and speed of 200 km/h resulted in 1.1 m Spectralon plate, Labsphere, NH, U.S.A.) between spatial resolution. each sample, and the reflectance readings for each The AISA and HyMap datasets were preproc- wavelength were expressed in relative reflectance essed with radiometric and geometric corrections, readings. For improved S-N ratio the instrument geocoding, and mosaicking of the flight stripes. was allowed to warm up for 20 min prior to data For the raw radiance HyMap data, supplied by collection. Figure 1 in this thesis synopsis, Figure imaging service provider Hyvista Corp. (Sydney, 1 in paper I, and Figure 3 in paper II illustrate the Australia), geometric rectification was performed FieldSpec FR measured spectra of tills with vary- with PARametric GEocoding (Table 4). The Hy- ing moisture contents and varying concentrations Map data were not optimal due to the unavailabil- of elements. ity of the inertial movement unit (IMU) data and lack of clearly identifiable ground control points. 3.3.2 Airborne hyperspectral imaging Georectification was performed using elevation, spectrometry position, and one tie point for each 100 pixels along a scan line (points/flight swath >50) as in- Two airborne datasets with different imaging puts resulting in a mean root mean square error spectrometers and spectral settings were acquired (RMSE) of 15 m and 30 m maximum deviation.

28 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

The AISA data were first geometrically corrected campaign. Pseudo invariant features were supplied as GPS navigation data and data gathered with an as inputs into ATCOR4 (Table 4), which was also IMU as inputs, and geocoded based on 1:20000 used for bidirectional reflectance correction with digital topographic map and 25 m pixel size digi- nadir normalization procedure. The atmospheric tal elevation model by the National Land Survey of correction was done assuming a flat terrain model Finland with GEOVIS software (Table 4). since the terrain was gently undulating (6–102 For the AISA data, the spectrometer recorded m a.s.l., 99% of peatlands 13–44 m a.s.l.). Finally, digital numbers were radiometrically normalized continuous coverages of both datasets were creat- to correspond to 9.00 a.m. UTC assuming a Lam- ed by mosaicking flight swaths. HyMap spectra of bertian surface model and converted to raw radi- different peatland site types are illustrated in Fig- ance values using instrument and band-specific ure 5 of paper IV. AISA spectra of some landscape gain and offset values. The HyMap data were also components on the study area of paper III can be subjected to atmospheric correction, for which viewed in Figure 4 of Siira et al. (2000a) and close- reflectance was measured at five pseudo invariant range spectra in Figure 1C of this thesis synopsis. features with a spectroradiometer during the flight

3.4 Reference field data

3.4.1 Geophysical measurements centrations from soil solution 0.2 g/3 mL (mineral sample/HCl + HNO3 dilution) were analyzed with The soil root zone bulkƐ (as a measure of volumet- ICP-AES (Table 3). The particle size distribution ric water content, Roth et al. 1992) was acquired the standard wet sieving followed by sedimenta- with an electrical capacitance probe. The device tion with the areometric floatation method was measures the change in the electrical capacity of applied (Gee & Bauder 1986). The OMC was de- the electrode due to the influence of the soil the termined spectrocolorimetrically (Table 3). The probe is inserted (Plakk 2008, Table 3). The de- mineralogy of the till samples was determined vice operates at a frequency of 30–50 MHz. The with X-ray diffractometry (XRD, Table 3). Speci- measurement volume of the tube probe, used in mens were prepared by grinding the mineral pow- situ, was estimated to 500 cm3, and approximately der to remove clumps, and were then fixed on a 100 cm3 for the 5-cm-diameter flat probe, which glass slide with acetone. For clay mineralogy, ori- was used ex situ. The σ (as a rough estimate of the ented samples were prepared by mixing with water soil solute content, Rhoades & Oster 1986) was and allowing the mixture to settle before submerg- measured with a conductivity fork with a galvanic ing a glass slide into the suspension. four electrode Wenner configuration having 15 cm steel rods and 16 cm spacing (Puranen et al. 3.4.3 Vegetation analysis 1999, Table 3). With this configuration the soil σ measurements represent approximately the top 30 The in situ species inventory was done by estimat- cm of the soil. Soil water pH was measured with ing species coverages in terms of cover-% within an IQ160 pH meter (Table 3). Three to five meas- a 1m-by-1m sampling frame (Table 3). Species urements were made at each sampling site and the cover fractions were estimated in herb and bryo- mean values were used as inputs into the statisti- phyte (i.e., bottom and field) layers separately. Al- cal analysis to avoid bias caused by pebbles and ternatively, the coverages of all species were also boulders in the soil. estimated as one layer, total coverage being 100%. The coverages of grass and moss litter were also 3.4.2 Geochemical, particle size and estimated. On peatlands the classification system mineralogical analysis described by Eurola et al. (1984) and Eurola et al. (1995) was followed. The partial leaching mineral soil samples were digested with hot (90 °C) aqua regia. Element con-

29 Geological Survey of Finland Maarit Middleton

3.5 Ordination techniques

The purpose of ordination techniques, common- 3.5.2 Canonical correspondence analysis ly applied in ecology, is to reveal the community structure and its relation to the environmental var- Direct gradient analysis can be chosen when there iables such as soil properties and, in this work, also is a priori understanding of the environmental var- the spectral variables. Ordination techniques are iables that constrain the vegetation composition. visual in nature representing multivariable veg- The constrained canonical correspondence analy- etation and environmental data in a few dimen- sis (CCA, Ter Braak 1986, Ter Braak 1987, Table sions, so that distances in the ordination reflect 4) was used in this study. As a constrained ordina- similarities in vegetation composition between tion, the CCA does not try to display all variation sample sites. Values of the coordinates are not es- in the data, yet only the part that can be explained sential since they only achieve the desired spacing. by the utilized constraints. The CCA is calculated The variables are given as lines in the ordination using reciprocal averaging from the correspond- graphics, each line indicating the direction of a ence analysis such that at each cycle of the averag- gradient, and length indicating strength of corre- ing process, a multiple regression is performed on lation between the variable and ordination. (Jong- the sample scores of the environmental variables. man et al. 1995) New site scores are calculated based on this regression, and then the process is repeated, continuing 3.5.1 Non-metric multidimensional scaling until the scores stabilize (Jongman et al. 1995). Compared to another common constrained ordi- Non-metric multidimensional scaling (NMDS, nation technique, redundancy analysis, the CCA Kruskal 1964) using Bray-Curtis dissimilarities has been demonstrated to be more robust in ex- with Wisconsin double standardization (Bray & tracting variation in species cover fractions in re- Curtis 1957, Table 4) was applied to vegetation lation to the environmental variables, specifically data in this study to analyze soil variables con- in cases, where relations among species variables tributing to vegetation composition. The NMDS and environmental attributes are non-linear (Ter is an unconstrained method based on vegetation Braak 1986, Närhi et al. 2010). dissimilarities only, no attempt to maximize correlations with environmental variables is made. The 3.5.3 Partitioning around medoids Kruskal’s NMDS is constructed by first calculating a distance matrix, then randomly selecting the It is suggested that classification in ordination initial configuration of samples in n-dimensions, space should be applied (Feoli & Orlóci 1991) to and finally calculating the ‘stress’ and moving the reveal natural community clusters reliably. Thus samples in the direction of the stress. Stress is con- unsupervised classification method partition- sidered as a mismatch between the rank order of ing around medoids (PAM, Van der Laana et al. distances in the data and the rank order of dis- 2003, Table 4) was applied in this study. PAM is tances in the ordination. The stress is iteratively a k-medoids clustering algorithm technique. In recalculated until the stress appears to reach mini- comparison to the commonly used k-means it is mum. The final configuration of points may be considered more resistant to noise and outliers. rotated. NMDS is demonstrated superior to other The reason for it is that it minimizes a sum of pair- unconstrained ordination methods when applied wise dissimilarities instead of a sum of squared to ecological data (Kenkel & Orlóci 1986, Minchin Euclidean distances and uses the most centrally 1987). The significance of the correlations between located objects in clusters, i.e., medoids, instead vegetation and environmental variables were tested of the cluster means. (Van der Laana et al. 2003, with several thousand permutations. Kaufman & Rousseeuw 2008)

30 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

3.6 Classification and hyperspectral feature extraction

3.6.1 Feature extraction composed of synapses that connect the input signals to a synaptic weight, a component that sums Minimum noise fraction (MNF) is a feature extrac- the weights, and a non-linear activation function tion or selection algorithm performed to reduce that combines the summed weights to the output dimensionality of hyperspectral data (Green et al. signals. The neurons are organized as several inter- 1988, Table 4). Noise can be effectively removed connected layers, some of them hidden, where the from multispectral data by transforming the origi- signals can propagate forward or backward (Kanel- nal hyperspectral data to the MNF space, and then lopoulos et al. 1997). Two supervised ANN algo- rejecting the most noisy components by selecting, rithms were applied for hyperspectral image classi- e.g., MNF bands with eigenvalues greater than five fication in this study: RBFLN and PNN. The RBFLN (similarly to, e.g., Harris et al. 2006a). The benefits is a three-layer feed-forward network where full of MNF are not only in feature extraction but also propagation is used in calibration (Looney 2002, saving in computational costs. Because of the ef- Table 4). The PNN is a multilayer feed-forward fective noise removal MNF also improves vegeta- network that uses sums of Gaussian distributions tion classification (Yang et al. 2009, Zhang & Xie to estimate the probability density function for a 2012). calibration dataset (Specht 1990, Table 4). SVMs, on the other hand, deal with class sepa- 3.6.2 Supervised non-parametric classification ration by attempting to find optimal hyperplanes to differentiate discrete classes in the multidimen- Both supervised non-parametric classification sional feature space (Burges 1998). Generalization methods used in this study, artificial neural net- power of SVMs relies on maximizing the margins works (ANN) and support vector machines of the hyperplane by determining support vectors, (SVM), determine the association between cali- i.e., a subset of calibration points which define the bration points and datasets, and provide a fuzzy position of the hyperplane margins. The SVMs deal membership value between 0 and 1 as an output. with linearly non-separable classes by mapping the The advantage of non-linear classifiers is that a data into a higher dimensional feature space by a single output class can be comprised of a series of kernel function. The kernel approach enables fit- spectral clusters in the spectral feature space. For ting of a linear hyperplane between classes in the a successful result, the SVMs and ANNs can be new variable space. The SVMs outperform tradi- trained with a lower number of calibration points tional statistical classifiers because of their ability compared to, e.g., statistical classifiers (e.g., Mas & to generalize based on a limited number of cali- Flores 2008). Furthermore, it is possible to use the bration pixels, higher robustness to noise, higher non-linear classifiers on data without atmospheric learning efficiency, and in general, less require- correction, especially if a study area was small (pa- ment for prior knowledge (see Bishop 2006 and Du per III, 46.9 ha in size) and atmospheric proper- et al. 2012 for summary). The method has proven ties influencing the spectra were, therefore, fairly successful in processing of remotely-sensed data constant. (Mountrakis et al. 2011). In this study the image- The ANN architecture is inspired by the brain SVM software, which utilized the LIBSVM for the structure. The main processing unit is a neuron, calibration of the SVMs, was used (Table 4).

3.7 Chemometric techniques

3.7.1 Spectral transformations shifts and overall curvature of samples, and to ‘nor- malize’ the spectral reflectance. The derivatives, The purpose of spectral pretreatments is to cor- continuum removal and wavelet transformation rect for non-linearity, measurement and sample were applied using routines in an R package ‘soil. variations, and noisy spectra (Stenberg et al. 2010). spec’ (Table 4). The ‘soil.spec’ package uses respec- Three different spectral transformations were ap- tive function ‘locpoly’ for derivatives and ‘chull’ for plied in further processing of the data in paper I continuum removal by convex hull method from to enhance the absorbance peaks, reduce baseline a package ‘KernSmooth’. To smooth and compress

31 Geological Survey of Finland Maarit Middleton the spectra also wavelet coefficients were applied tion method is applied to each bootstrap sample (Percival & Walden 2000). The ‘dwt’ function in and the results are then combined by averaging to ‘soil.spec’, used for the wavelet transformation, obtain an overall prediction with reduced variance also refers to an R package ‘wavelets’ (Table 4). (Sutton 2005). BRT has been reported to be very effective in reducing variance and error in high 3.7.2 Partial least squares regression dimensional datasets. In comparison to one-tree regression tree approaches, better estimates of er- Partial least squares regression (PLSR, Wold et al. rors are provided by bootstrapping, and the bias 2001) is the most popular technique in chemomet- component of the error is marginally better than rics for quantitative analysis of reflectance spectra. for single regression trees. Difficulty comes from It is used to construct predictive models when interpretation of the results as a large number of there are many predictor variables (spectral varia- trees are averaged for the final result (Prasad et al. bles at a wavelength range) that are highly colline- 2006). In this study, recursive partitioning algo- ar. The PLSR algorithm integrates data compres- rithm ‘rpart’ was bagged in an R package ‘ipred’ sion and regression steps and it selects successive (Table 4). orthogonal factors that maximize the co-variance between predictor and response variables. The 3.7.4 Multivariate adaptive regression splines number of factors to use in the models is selected by cross-validation. By fitting a PLSR model, one Multivariate Adaptive Regression Splines hopes to find a few PLSR factors that explain most (MARSTM, Friedman 1991, Table 4) produce a of the variation in both predictors and responses non-parametric regression model. MARS is a gen- (Martens & Naes 1992). In the current study, an eralization of recursive partitioning regression ap- R package ‘pls’ was used (Table 4). Overview of proaches, which generate piecewise linear models the chemometrics in spectroscopy, its history and instead of piecewise constant models. The adaptive main concepts are provided by Geladi (2003). The piecewise linear regressions are called basis func- more recent MVR methods, presented hereafter, tions. Changes in slope of the basis functions oc- have emerged in chemometrics from the fields of cur at points called knots. The regression can bend data mining and machine learning. at the knots, which mark the end of one region of data and the beginning of another with different 3.7.3 Bagging regression trees behavior of the function. Knots are established in a forward-backward stepwise way. A model which In recursive partitioning the feature space of pre- clearly overfits the data is produced first. In subse- dictor variables is recursively partitioned into a set quent steps, knots that contribute least to the ef- of decision rules, i.e., smaller groups with binary ficiency of the model are discarded by backwards splits based on a single predictor variable. Splits of pruning steps. The best model is selected via cross- all of the predictors are examined by an exhaus- validation, a process that applies a penalty to each tive search procedure and the split that maximizes knot added to the model to keep low complexity the homogeneity of the two resulting groups with values (Muñoz & Felicísimo 2004). By applying respect to response variable is chosen. The output linear basis functions between the splits of the par- is a tree diagram with the branches determined by titioned space, MARS overcomes the restriction of the splitting rules and a series of terminal nodes the regression trees of returning discontinuous re- that contain the mean nodes. Initially, maximal sponse surfaces. Hence, prediction accuracy can trees are grown and then cross-validations are ap- be expected to be higher, output smoother and less plied to prune the tree to avoid overfitting (Brei- coarse grained with MARS than with regular tree man et al. 1999). based methods. The downsides of MARS are the Bootstrap aggregation, or bagging, proposed by local nature of the model, which can lead to un- Breiman (1996) is used with regression trees to re- stable predictions, and cumbersome selection of duce the variance associated with prediction, and the input parameters (Friedman 1991, Prasad et al. thereby improve the prediction process. In bag- 2006). ging regression trees (BRT) many bootstrap samples are drawn from the available data. A predic-

32 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

3.7.5 Random forests truly probabilistic predictions through Bayes- ian inference whereas SVM can provide pseudo- Random forests (RF, Breiman 2001, Table 4) are probabilistic outputs only through postprocessing. similar to BRTs in that bootstrap samples are Its decision function is more sparse (i.e., depends drawn to construct multiple trees but the differ- on fewer input data) than a comparable SVM, be- ence is that each tree is grown with a randomized cause SVM minimizes the calibration error under subset of predictors. A large number of trees are the constraint of maximum smoothness, requiring grown, i.e., forest of trees. Each tree is grown to more decision points (Bishop & Tipping 2000). maximum size without pruning and these mul- Moreover, the number of relevance vectors in an tiple predictions are aggregated by averaging the RVM is not proportional to the number of sam- individual tree outputs for the final regression ples. The benefit of a sparser classifier is decreased prediction. Out-of-bag samples can be used to overfitting and thus RVM models are more gener- calculate an unbiased error rate and variable im- alized than SVM predictions. Therefore, the RVM portance eliminating the need of a calibration set has been shown to provide equivalent and often or cross-validation. Diaz-Uriarte & Alvarez (2006) superior results to the SVM both in generaliza- conclude that the most important advantages of tion ability and sparseness of the model (Gómez- RFs are suitability for datasets with many predic- Chova et al. 2009). tors and few samples, robustness to noise and irrelevant features, they are unlikely to overfit if trees 3.7.7 Mixed-effects modeling are carefully sized, random selection keeps bias low, and almost no fine-tuning of the parameters Mixed-effects models make it possible to take into is necessary to produce good predictions. Because account for the dependencies in data by including of the additional complexity compared to BRT, it more than one source of random variation, i.e., is usually considered even more of a ‘black-box’ in more than one random effect. Non-linear mixed- interpretability (Prasad et al. 2006). effects models are mixed-effects modes in which some, or all, of the fixed and random effects occur 3.7.6 Relevance vector machines non-linearly in the model functions. Non-linear mixed effects models provide a tool for analyzing The relevance vector machine (RVM, Tipping repeated-measurements data in which the rela- 2001) is another sparse kernel based learning ma- tionship between the explanatory variable and the chine that has the same functional form as the response variable can be modeled as a single func- SVM. It uses Bayesian inference to obtain parsimo- tion, allowing the parameters to differ between nious solutions for regression and classification but individuals (Zuur et al. 2009). The model used in it is used only for chemometric regression in this this study was formulated by (Lindstrom & Bates study. An RVM is formed as a linear combination 1990). In paper I, the mixed-effects models were of data-centered basis functions, called relevance created to compare regressions of Ɛ-reflectance for vectors, which can be non-linear. RVM provides different tills (treatments).

3.8 Model validation

The root mean square error (RMSE) was reported values and the predicted response values, were as a prediction error estimate for the estimations also calculated for regression goodness of fit. Due of soil Ɛ and element concentrations from spectra. to a small sample size, in paper I and further pro- RMSE is the square root of the sum of the squared cessing of the data, the calibration R2 and RMSE differences between predicted (y) and measured values were compared for the till-specific models, (x) values divided by the number of fitted spectra meaning that the same sample set was used for (n). The following formula was applied: model construction and goodness of fit and error estimations. Validation goodness of fit and error 1 2 RMSE= n∑(y−x) estimations could be calculated for the universal fits and results in paper II as the population sizes The coefficients of multiple determination2 (R ), were large enough to be split into calibration and i.e., square of the correlation between response validation sets. Calibration R2 and RMSE were also

33 Geological Survey of Finland Maarit Middleton recorded for the generic fit so that the values could per left corner of the ROC space, representing TPR be comparable to the till-specific fits. For paper of 1(no false negatives) and FPR of 0 (no false pos- I, residual degrees of freedom based adjusted R2 itives). Such a model would produce a perfect clas-

(Radj) was also calculated as it is suitable for com- sification with a AUC values of 1. A model which is paring nested models. Although commonly used no better than a random guess (AUC=0.5) would for goodness of fit, R2 does not directly character- yield a point along a change diagonal that is a line ize accuracy as the values measure the strength of from the left bottom to the top right corners. In the relationship between spectral and the soil vari- case of two models with equal AUCs but different ables. For the purposes of characterizing uncer- shapes, the better model reaches high TPR values tainty, the RMSE may thus be preferable (Wood- at low FPR values. cock 2006). In paper IV, the class-specific prediction prob- The receiver operating characteristics (ROC, abilities were also turned into discrete classifica- Park et al. 2004) curves and their area under the tion results such that each pixel was assigned into curve (AUC) values were used to assess the predic- a class with the highest output probability. Thus tion efficiency of classifiers which produced a real accuracy could also be assessed with measures de- number class probability as an output as recom- termined from an error matrix. Besides overall ac- mended by Manel et al. (2001). An ROC curve is a curacy and kappa values, for the individual classes plot of test sensitivity (i.e., true positive rate, TPR) both user’s and producer’s accuracies were calcu- on the Y axis and 1-specificity (i.e., false positive lated (Congalton 1991). All classification accura- rate, FPR) on the X axis generated using a set of cies were calculated using independent validation varying cut-off points. The TPR equals to the frac- sets. For comparison of the spatial patterns of Scots tion of true positives in the validation dataset out pine suitability created in paper III a probability of all positives in the classification result. Similarly, surface was also created from in situ Ɛ measure- the FPR equals to the fraction of false positives out ments with ordinary kriking in ArcMap (Cressie of all the negatives in the classification result. The 1988, Table 4). best possible model would yield a point in the up-

4 RESULTS

In this chapter the key results to answer each re- ability assessment and the sensitivity of the ANN search question are presented. Chapter 4.1 sum- model performance to parameterization and cali- marizes the results related to prediction of glacial bration set properties are described. In addition, till soil Ɛ from close-range VSWIR spectra and the results of the NMDS are interpreted to explain comparison of the prediction efficiency between the dependence of the ground cover components the different regression techniques. In chapter on soil Ɛ. Finally, results related to the peatland site 4.2 the mineralogical description of the studied type mapping are given in chapter 4.4. The logic tills is first given and then the differences in the in conducting the peatland thematic class level PLSR prediction efficiencies between the two stud- optimization with CCA is described and the SVM ied tills in relation to variation and correlation of classification results of the peatland are given. chemical element concentrations are explained. In For a more detailed description of the results the chapter 4.3, the selection and utilization of the best reader is advised to refer to the original research performing ANN model for Scots pine site suit- papers I–IV.

4.1 Predicting till soil dielectric permittivity from reflectance spectra

Paper I and further data processing for the paper tance statistically significantly. It also better fits I data answer the RQ 1. The key finding of paper the dry end of the gradient (Ɛ=3–15) compared I is that a three parameter exponential function, to other non-linear functions. The shape of the y = aebx + c, where x=Ɛ and y=reflectance, explains function is such that reflectance sharply decreas- the relationship between the Ɛ and VSWIR reflec- es as a function of the Ɛ from Ɛ=3 to Ɛ=15. The

34 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment function becomes linear when Ɛ=15–40 leveling values usually produced more optimistic results off at reflectance values between 0.04–0.15 (660– than the validation with an independent sample. 2320 nm) with a slope of a function close to 0. The The mixed effects modeling results in paper I results of the mixed effects modeling showed that a showed that the generic exponential model was statistically significant generic exponential model, not statistically valid for the tills with OMC>1.7% y = 0.564e-0.205x + 0.072, explains the relationship (dag/kg) and especially when a reflectance variable between Ɛ and reflectance, e.g., when mean of from the VIS range was chosen (mean reflectance reflectance between 1444-1464 nm was used as a at 640–660 nm, Radj= 0.585). Although the generic reflectance variable (Radj=0.847). models estimate Ɛ statistically significantly, the The data used in paper I was also further pro- till-specific models are usually more appropriate cessed with MVR methods such as PLSR, MARS, (Fig. 3). When calibration stage goodness of fit BRT, RF, and RVM and compared against the sin- and errors are compared between the generic (Fig. gle-variate exponential function. Results are illus- 3B, D) and till-specific models (Fig. 3A, C) the trated in Figure 3. The further processing of the PLSR, RF, and RVM produced better predictions data with MVR techniques demonstrated that the for till-specific calibrations. However, with MARS till soil Ɛ could be predicted well not only with the and BRT the Ɛ could be better predicted for all exponential single variate model (mean of reflec- tills (generic fit) than for each till specifically. The tance between 1444–1464 nm, validation R2=0.77, spectral transformations did not have a significant RMSE=2.98, Fig. 3B, D) but also with MVR tech- impact on the results (Fig. 3). Only the RVM vali- niques: PLSR, MARS, BRT, RF and RVM (valida- dation results were improved by the continuum tion R2=0.81–0.94, RMSE=1.59–2.77, Fig. 3B, D). removal and wavelet transformations. In conclu- The highest goodness of fit 2(R =0.89–0.94) and sion, individual multivariate calibrations are not the lowest error (RMSE=0.99–1.23) were calcu- necessary for the studied tills (clay fraction con- lated for RVM predicted models, indicating that tent 2.4–5.5%, fine fraction content 23.5–47.1%), the RVM outperformed the other methods. When which are typical for northern Finland. According the till-specific calibration sets with smaller sam- to the results of paper 1 for single variate predic- ple size were also used, PLSR and RF produced tions of Ɛ the reflectance variable should definitely equally good predictions of Ɛ compared to RVM be chosen outside of VIS (l>700 nm) and rather at (Fig. 3A, C). It was also seen from the Figure 3 (B, SWIR (l>1740 nm) especially when dealing with D) that the validation goodness of fit and error tills with high OMC >1.7% (dag/kg).

35 Geological Survey of Finland Maarit Middleton

EXP ref Till 1 A EXP 1.der Till 2 EXP 2.der Till 3 EXP cr Till 4 EXP wt PLSR ref PLSR 1.der PLSR 2.der PLSR cr PLSR wt MARS ref MARS 1.der MARS 2.der MARS cr MARS wt BRT ref BRT 1.der BRT 2.der BRT cr BRT wt RF ref RF 1.der RF 2.der RF cr RF wt RVM ref RVM 1.der RVM 2.der RVM cr RVM wt

0 0.7 0.8 0.9 1 R2

EXP ref Calibration EXP 1.der Validation B EXP 2.der EXP cr EXP wt PLSR ref PLSR 1.der PLSR 2.der PLSR cr PLSR wt MARS ref MARS 1.der MARS 2.der MARS cr MARS wt BRT ref BRT 1.der BRT 2.der BRT cr BRT wt RF ref RF 1.der RF 2.der RF cr RF wt RVM ref RVM 1.der RVM 2.der RVM cr RVM wt

0 0.7 0.8 0.9 1 R2 Fig. 3. Coefficients of multiple determination 2(R , 3A, 3B) and root mean squared error (RMSE, 3C, 3D) values for models of till dielectric permittivity predicted from reflectance spectra (350–2500 nm) with exponential (EXP), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), bagging regression trees (BRT), random forests (RF), and relevance vector machines (RVM). Till-specific models (3A,3B) were constructed on Vuotsonkangas till (till 1), Haipankuusik- ko till (till 2), Vaalolehto till (till 3) and Kuorajoki till (till 4), and generic models (3B, 3D) for four tills combined. For generic models (3B, 3D) both calibration and validation R2 and RMSE values are given. Original reflectance spectra (ref) was pre- treated with transformations such as first derivative (1.der), second derivative (2.der), continuum removal (cr), and wavelet transformation (wt). Figures edited by Paavo Närhi, GTK.

36 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

EXP ref Till 1 C EXP 1.der Till 2 EXP 2.der Till 3 EXP cr Till 4 EXP wt PLSR ref PLSR 1.der PLSR 2.der PLSR cr PLSR wt MARS ref MARS 1.der MARS 2.der MARS cr MARS wt BRT ref BRT 1.der BRT 2.der BRT cr BRT wt RF ref RF 1.der RF 2.der RF cr RF wt RVM ref RVM 1.der RVM 2.der RVM cr RVM wt

0 1 2 3 5.5 RMSE

EXP ref Calibration EXP 1.der Validation D EXP 2.der EXP cr EXP wt PLSR ref PLSR 1.der PLSR 2.der PLSR cr PLSR wt MARS ref MARS 1.der MARS 2.der MARS cr MARS wt BRT ref BRT 1.der BRT 2.der BRT cr BRT wt RF ref RF 1.der RF 2.der RF cr RF wt RVM ref RVM 1.der RVM 2.der RVM cr RVM wt

0 1 2 3 5.5 RMSE

37 Geological Survey of Finland Maarit Middleton

4.2 Predicting till soil element concentrations from reflectance spectra

The results of paper II answer the RQ 2. Accord- with R2≥0.8 were Al, Co, Cr, Cu, Fe, Mg, Mn, and ing to the XRD measurements, the till 1 was com- Zn. Predicting concentrations of the till 1 with the posed of plagioclase feldspar (XRD sample n=9, model created for the till 2, and vice versa, was not mean 48.3%), quartz (41.3%), amphibole (9%) and possible. clinochlorite (1.6%). Till 2 (XRD samples n=7) The Figure 4 demonstrates the dependence of was very similar in composition but had slightly the predictive accuracy (error expressed as RMSE) higher quartz content (44.9%), lower amphibole on standard deviation of the element concentra- content (5.4%), and had less than 1% of talc and tions. The predictive error of the PLSR model is illite. Based on the literature it is known that a higher for elements with larger variation in con- number of other soil chemical/mineralogical concentration. For example, the prediction error for stituents that were not analyzed, could be present Fe, which occurs in high concentrations and has in these soil samples (Righi et al. 1997, Peuraniemi large variation in concentration, is predicted with et al. 1997). For example, soil precipitates and clay a large error. On the other end, e.g., Be (Till 2, Fig. mineralogy which have absorption features were 4C) with low standard deviation in concentration, not quantified nor identified. has a small prediction error. There is a very slight When the PLSR models were built for a com- difference in standard deviations between the bined dataset of till 1 and till 2, concentration of till 1 and till 2, if concentrations of till 1, having several elements were predicted: Al, Ba, Be, Co, Cr, smaller variation, is compared to till 2 (compare Cu, Fe, K, La, Mg, Mn, Ni, P, S, Sc, Sr, Ti, V, Y, and Fig. 4B and 4C). However, Spearman correlation Zn (Fig. 4A). The statistically significantly (valida- analyses indicated statistically significant inter- tion R2≥0.5) predicted elements were bolded in the correlation between concentrations of analytes. Figure 4. For till 1, only concentration of Fe could When absolute values of the Spearman correlation be predicted with spectra (Fig. 4B), whereas con- coefficients (n=217) between all analysed elements centrations of Al, Ba, Co, Cr, Cu, Fe, K, La, Mg, (n=25) were summed together a value of 114.1 was Mn, Ni, S, Sc, Ti, V, Y, and Zn could be predicted received. The corresponding value for till 1 was for till 2 (Fig. 4C). The most significantly (valida- 115.3 and for till 2 it was 159.7. This means the ele- tion R2≥0.8) predicted element for till 1 was Fe but ment concentrations are more inter-correlated in for till 2 Ba, Co, Cu, Mg, and V could be predicted till 2 compared to till 1 and the combined dataset with the highest significance (validation 2R ≥ 0.8). (till 1 + 2). For the combined till data, elements predicted

4.3 Mapping site suitability for artificial regeneration to Scots pine based on hyperspectral imaging data

The RQ 3 is answered with the results of paper This model was chosen to produce the final dis- III. The success of the pine suitability assessment crete map with the distribution of the ‘suitable’ with ANN prediction from hyperspectral data and ‘unsuitable for pine’ classes (Fig. 5). If little could be considered moderate. The best perform- risk regarding the anticipated pine survival rate ing model was an RBFLN model trained with 15 is required, the class is delineated to low true and calibration points representing the ‘suitable’ and false positive rates. In case A (Fig. 5), 34.4% (true 15 points representing ‘unsuitable for pine’ class positive rate) of all suitable for pine (Ɛ≤15) sites (0.64 points/ha). The soil Ɛ-limit between the would be correctly identified as suitable for pine, ‘suitable’ and ‘unsuitable for pine’ classes was set and 17.9% (false positive rate) of all unsuitable to Ɛ=15 (later referred to as 15/15) and the result- for pine (soil Ɛ>15) sites would be incorrectly ing fuzzy membership band was then median re- identified as such. At higher risk rates, larger true sampled to 14 m pixel size. This particular model (68.9%) and false (33.9%) positive rates are cho- produced the highest area under the ROC curve sen to threshold the class (case B in Fig. 5). There- value (AUC = 0.741) and model performance was fore a larger area would be subjected to Scots pine better in the low false positive rate range than regeneration. Compared to a prediction map cre- the five next best models (see Park et al. 2004). ated with ordinary kriking the ANN classification

38 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

Fe A Al 1000

Mg Ca Ti K 100 P -1 S Mn

RMSE (mg kg ) Ba Cr VZn Li Ni Cu AsLa Sr 1 Y Co BSc

0.1 Be

0.1 1 10 100 1000 10 000 -1 Standard deviation (mg kg )

B AlFe 1000

Mg Ca Ti K 100 -1 S P Mn 10 Cr RMSE (mg kg ) VBa Zn Li Cu Ni La Sr Co 1 Y Sc As

B 0.1 Be

0.1 1 10 100 1000 10 000 -1 Standard deviation (mg kg )

Fe C Al 1000

Mg Ca Ti K 100 P -1 Mn S

10 RMSE (mg kg ) ZnBa As Li Cr V La Cu B Sr Ni 1 Co Y Sc

0.1 Be

0.1 1 10 100 1000 10 000 -1 Standard deviation (mg kg )

Fig. 4. Element-specific root mean squared errors (RMSE) of partial least squares regression models for till soil element concentrations from reflectance spectra (350–2500 nm) plotted against the standard deviation of each element concentration. For till 1 (4B) concentration of only one element (Fe, bolded) could be predicted statistically significantly (validation 2R ≥0.5) whereas for till 2 (4C) concentrations of 17 elements. For the combined dataset of till 1 and till 2 (4A) concentrations of 16 elements could be predicted. Figures edited by Paavo Närhi, GTK.

39 Geological Survey of Finland Maarit Middleton

Case A - low risk Ordinary kriking of Ɛ =<15

Ground dielectric permittivity !( Ɛ>15 !( Ɛ<=15 Case B - high risk Ordinary Kriging Prediction Map Ɛ<= 15 Probability 0.0 - 0.1 0.1 - 0.2 0.2 - 0.3 0.3 - 0.4 0.4 - 0.5 suitable for pine 0.5 - 0.6 unsuitable for pine 0.6 - 0.7 0.7 - 0.8 b 0.8 - 0.9 0 100 200 400 600 800 N Meters 0.9 - 1.0

Fig. 5. Two representations of Scots pine regeneration suitability assessments by neural network classification (Case A and B). Interpretation of class extents is based on accuracy analysis with receiver operating characteristics curves. In case A, smaller area is regenerated to pine and thus lower risk, concerning the regeneration success, is taken compared to case B. Spatial patterns of ‘suitable for pine’ class are similar to ordinary kriking result created from ground measurements of dielectric permittivity. Dielectric permittivity threshold of 15 was used in neural network classification and ordinary kriking to represent suitability for Scots pine regeneration.

results resemble very much the spatial patterns of =0.61, meanPNN2m=0.52). A Spearman rank cor- probability for soil Ɛ≤15 (Fig. 5). relation coefficient of 0.66 (p<0.001, n=78) was The sensitivity of the classification on the cali- obtained between the RBFLN and PNN. This in- bration set Ɛ limits (‘suitable’ Ɛ≤13 and ‘unsuitable’ dicated that, besides the ANN model, also the cali- Ɛ>17 or 15/15), calibration set size (0.64 points/ bration set had a great impact on the accuracy. ha, 2.43 points/ha, 6.06 points/ha), ANN model In addition, the best AUCs were obtained with selection (PNN or RBFLN) and resampling of the same calibration set (15/15, 0.64 points/ha) the output probability bands with different meth- both with PNN and RBFLN. It indicated that a ods (mean or median) into different pixel sizes (4 very small number of calibration points could also m, 6 m, 8 m, 10 m, 12 m, 14 m) was tested sta- be sufficient for successful calibration if the points tistically. The results show that these variables were representative of the classes. The greatest po- can have a profound impact on the efficiency of tential to produce the most accurate RBFLN model prediction as the variation of the AUCs was large was with the calibration set which was produced set- (n=156 combinations of ANN classification vari- ting a threshold between the ‘suitable’/’unsuitable’ ables, minAUC=0.43, maxAUC=0.74, meanAUC=0.62, classes to Ɛ>15/Ɛ≤15 (compared to 13/17), and stdAUC=0.09). The RBFLN models overall produced which had calibration point density of 2.43 points/ higher accuracies than the PNN models as tested ha (compared to 0.64 points/ha and 6.06 points/ with the Mann-Whitney test of the AUC values ha). The classification could be further improved

(U=702, p<0.001, nRBFLN=78, nPNN=78, meanRBFLN2m by resampling the output ANN probability channel

40 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment from 2 m to 12 m or 14 m pixel size (compared to Carex brunnescens, Polytrichum commune, Rhodo-

4 m, 6 m, 8 m, 10 m, U=241, p<0.001, n2-4-6-8-10m dendron tomentosum, and Vaccinium uliginosum =24, n12-14m=54). Statistical difference between were oriented in the direction of Ɛ in the NMDS mean and median resampling was not found ordination showing that they are associated with

(U=281, p=0.89, nMEAN, 12-14m=24, nMEDIAN, 12-14m mesic and wet soils. The following ground cover =24, meanMEAN=0.64, meanMEDIAN=0.65. components were more abundant on dry soils The NMDS ordination analysis (Fig. 6) and than on wet or mesic soils: Solidago virgaurea correlation of the species cover percentages with (rs=-0.36), Epilobium angstifolium (rs=-0.40), Poly- Ɛ showed that the most common ground cover trichum juniperinum (rs=-0.31), rocks (rs=-0.55), components are somewhat dependant on the Pohlia nutans (rs=-0.35), and Cladonia (rs=-0.55) soil Ɛ. Positive correlation was found between Ɛ since their abundance was negatively correlated to and cover-% of Betula pubenscens (n=51, p<0.05, Ɛ. Bare soil, Cladina, Epilobium angustifolium, Hy- rs=0.51), Vaccinium uliginosum (rs=0.35), Luzula locomium splendens, litter, Lycopodium clavatum, piloza (rs=0.34), Deschampsia flexuosa (rs=0.34), Nephroma arcticum, Pleurozium schreberi, and Salix phylicifolia (rs=0.34), Equisetum sylvaticum rocks were oriented to the opposite direction from (rs=0.41), Empetrum nigrum (rs=0.32), Carex Ɛ because they are most common on dry soils. Out globularis (rs=0.35), and Polytrichum commune of the most common species in the study site, only (rs=0.77) indicating that these components were Vaccinium myrtillus can tolerate a wider range of more abundant on mesic and wet sites than on SWCs as it is not correlated to soil Ɛ. dry sites. Moreover, Betula nana, Betula pubescens,

Polytrichum juniperinum

NDVI

Betula Carex σ pubescens brunnescens ε 15-17 12-14 Rhododendron tomentosum Polytrichum Betula commune nana Vaccinium uliginosum

11 Deschampsia Epilobium flexuosa angustifolium rocks Cladonia 5 sp. 8-10 7 6 4 2-3 Lycopodium clavatum soil Pleurozium detritus Hylocomium schreberi splendens Nephroma arcticum

Fig. 6. Non-metric multidimensional scaling ordination of micro sites (diamonds) at paper III study site in northern Finland based on field measured coverages of plant species and ground cover components. Indicative variables are displayed by lines including AISA radiance bands 2–17 (448.99−874.04 nm), normalized difference vegetation index (NDVI), electrical conductivity (σ), and soil dielectric permittivity (Ɛ). Figure edited by Paavo Närhi and Viena Arvola, GTK.

41 Geological Survey of Finland Maarit Middleton

4.4 Peatland site type mapping from hyperspectral imaging data

Paper IV answers the RQ 4. The CCA ordination ate analysis of variance (MANOVA) showed that (Fig. 7) showed that soil Ɛ and σ were significant- the classes did not separate spectrally. The process ly (p<0.01) correlated with the peatland vegeta- of merging the PAM clusters without a non-signifi- tion composition similarly to soil pH (p<0.001) cant difference by their HyMap reflectance created thus peat soil Ɛ, σ and pH represent as important four final clusters: swamp, eutrophic fen, sedge fen, drivers for vegetation composition. The permuta- and bog (Fig. 7, F=4.9−29, r2=0.19−0.53, p<0.05). tion test also showed the HyMap bands between According to the MANOVA these clusters also de- 570−1324 nm and 1530−1793 nm were signifi- viated by their species abundances (F=94.8−24.9, cantly correlated (p≤0.001) with the vegetation r2=0.29−0.45, p<0.001). When compared to the CCA ordination showing that that entire HyMap in situ subjectively determined generalized four spectral range contributes to the spectral separa- classes the overall accuracy was 82.0% (kappa tion of aapa peatland site types but the VIS and 0.737) indicating moderate success. Contrast be- NIR plateaus provide the most information. tween the in situ and CCA-PAM-MANOVA clas- The CCA biplot revealed that the fifteen floristic sifications existed especially in the transitions in situ peatland site type classes (Eurola et al. 1984) between site types. could not be separated in the CCA ordination. The The CCA ordination is very helpful in interpret- unsupervised PAM classification of the sites by ing the four spectrally separable classes ecologi- their CCA scores showed that eight classes com- cally. In the CCA ordination (Fig. 7) the ‘bog’ class posed compact clusters. However, a pairwise com- consisted of a distinct cluster primarily dominated parison of the classes by permutational multivari- by Sphagnum fuscum, Empetrum nigrum, Rubus

1.53-1.79

1.15-1.32 Empetrum nigrum Sphagnum fuscum Carex lasiocarpa pH Rubus chamaemorus Trichophorum cespitosum Sphagnum σ angustifol. moss littergrass litter 1.14 Comarum palustre Sphagnum majus 1.05-1.12 Carex 0.72-1.03 rostrata

0.71 Eriophorum vaginatum 0.69 10.57-0.68 Bog ε Sedge Fen Eutrophic fen

Sphagnum lindbergii Swamp

Fig. 7. Canonical correspondence analysis (CCA) ordination of 1-m-quadrats showing CCA axes one (horizontal) and two (vertical). CCA was based on vegetation composition (only significant species, p<0.01) and soil pH, dielectric permittivity (Ɛ), and electrical conductivity (σ). Symbols illustrate the final result of the four generalized peatland classes based on unsupervised classification of the CCA scores with partitioning around medoids and separability analysis of the clusters with permutational multivariate analysis of variance (based on HyMap reflectance and species coverages). Figure edited by Paavo Närhi and Viena Arvola, GTK.

42 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment chamaemorus, and Sphagnum angustifolium. The cluding the water absorption feature around 1300 ‘bog’ class was located far from the soil pH, Ɛ and nm was significantly (p≤0.001–0.1) correlated to σ indicating that bog was a dry nutrient-poor CCA ordination. Thus CCA contributed to feature acidic site type. In the opposite ordination direc- selection of HyMap bands such that only the bands tion ‘sedge fen’ sites were clustered in the direction between 449–1793 nm and 2064–2358 nm were of high Ɛ. They were associated with water on the input into the SVM classifier. The bands at ranges surface and with low nutrient content (Eurola et 570–1324 nm and 1530–1793 nm were most sig- al. 1984). ‘Sedge fens’ have the highest abundances nificantly (p≤0.001) correlated to the CCA ordina- of Sphagnum lindenbergii (mean 47.9%) and Erio- tion indicating that those spectral ranges would be phorum vaginatum (mean 4.6%). The nutrient-rich the most important for separation of the peatland basic site types, i.e., ‘swamp’ and ‘eutrophic fen’, site types. clustered together with high pH and σ. The CCA The spatial distribution of the four classes was biplot (Fig. 7) and statistics (see Table 1 in paper then predicted with a fuzzy multiclass SVM clas- IV) revealed that ‘eutrophic fens’ were dominated sification from the HyMap data (Fig. 8). The SVM by Carex lasiocarpa and Trichophorum cespitosum, results exhibited high efficiencies of prediction: the and swamps with Comarum palustre, Sphagnum most accurate model returned by a ROC analysis majus, and Carex rostrata. Coverage of grass litter gave AUC=0.946 for ‘bog’, AUC=0.951 for ‘sedge (mean 22.8% and 27.3% respectively) was high in fen’, and AUC=0.999 for ‘eutrophic fen’. The over- both classes. Furthermore, ‘eutrophic fens’ were all accuracy varied between 78.5−87.8% (kappa also characterized by high coverages of moss litter 0.611−0.781) when each pixel was assigned into a (mean 12.3%). class by the highest pixel probability. Efficiency of A permutation test run for the HyMap bands re- prediction was not available for the ‘swamp’ class vealed that the entire measured spectral range ex- due to the low number of calibration and valida-

1.00

0.95 80

0.90 75 AUC

0.85 70 Overall accuracyOverall (%)

0.80 65 Eutrophic fen Sedge fen Bog Accuracy (%) 60 0.75 0 20 40 60 80 100 Percentage of training pixels

Figure 8. Area under the curve (AUC) values derived from receiver operating characteristics curves (left axis) and overall accuracy for all three classes (right axis, grey diamonds) for accuracy assessment of support vector machine prediction for the peatland biotope classes. Accuracy of prediction was at its highest when only 10% of all calibration pixels were used for training. Figure edited by Paavo Närhi, GTK

43 Geological Survey of Finland Maarit Middleton tion areas in the study area. All accuracy measures specific producer’s accuracy for: ‘bog’ was 89.1%; indicated that the SVM provided good prediction ‘sedge fen’ 84.8%; and ‘eutrophic fen’ 96.0%. Simi- accuracies with calibration sets of all sizes, yet the larly user’s accuracy for: bog was 88.1%; ‘sedge highest prediction success was gained with 10% fen’85.1% and; ‘eutrophic fen’ 96.0% when 10% of of the complete calibration set (pixel n=2308): all calibration area pixels were used for training AUC=0.946 for ‘bog’, AUC=0.951 for ‘sedge fen’ the SVMs. and AUC=0.999 for ‘eutrophic fen’. The class-

4.5 Chapter summary

The results of this study showed that glacial till soil works on airborne AISA data and validation of it Ɛ and concentrations of several chemical elements with receiver operating characteristics curves was could be estimated statistically significantly with found as an appropriate data processing solution close-range VSWIR spectroscopy. The relationship because the element of risk could thus be incor- between till soil Ɛ and reflectance at a single narrow porated into the suitability assessment. The neu- spectral range was well predicted with an expo- ral network model performance was significantly nential function, however, multivariate regression impacted by optimization of training set size, rep- techniques, commonly used in chemometrics and resentativeness of the training set, and NN model machine learning, turned out to be most robust. selection. Also post processing with filtering was Especially, the estimations with relevance vector found necessary in order to improve the classifi- machines performed slightly steadier compared cation accuracy for high spatial resolution AISA to partial squared regression and random forests. data (1.1 m) which spatial accuracy was relatively The spectroscopic-chemometric predictions were poor. Very high efficiencies of classification were only possible for tills with low organic matter con- gained for peatland site type mapping with fuzzy tent (<1.7 dag/kg) but could be improved by se- support vector machine classification and receiver lecting spectral features only at SWIR wavelengths operating characteristics curve validation. The key (>1740 mm) as inputs into the models. Prediction to success was optimization of the peatland class of chemical element concentrations in till soil the hierarchical content with canonical correspond- fine fraction with partial least squares regression ence analysis, finding the natural clusters in the was statistically significant for Al, Ba, Be, Co, Cr, ordination space, merging the clusters based on Cu, Fe, K, La, Mg, Mn, Ni, P, S, Sc, Sr, Ti, V, Y, and multivariate analysis of variance, and naming the Zn. However, the success of the predictions was final clusters with ecologically significant nomen- highly sample set specific and most prosperous for clature. As a visual technique the ordination ap- sample sets with highly inter-correlated element proach is very helpful in interpreting the spectrally concentrations. separable classes ecologically, but also as a feature The soil moisture based site suitability assess- selection method of spectral bands from the used ment for Scots pine was moderately successful. medium resolution HyMap data. Applying fuzzy classification with neural net-

5 DISCUSSION AND CONCLUSIONS

The purpose of the chapter 5.1 is to discuss and the results are made. Then the reliability and valid- conclude the results of soil close-range sensing ity of hyperspectral data and data processing steps and imaging spectroscopy of vegetated surfaces are explained in chapter 5.3. Finally, recommenda- from theoretical point. In chapter 5.2, concluding tions for future research are suggested in chapter remarks concerning the practical implications of 5.4.

44 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

5.1 Theoretical implications

5.1.1 Close-range detection of till soil water Ɛ=3.6–26.3, clay fraction (<0.002 mm) 2.4–5.5%, content and chemical element concentrations fine fraction (<0.063 mm) 23.5–47.1%, and OMC 0.6–5.8% (dag/kg). For datasets with a larger spread Statistically significant predictions ofƐ (3.6–26.3) the results might be different. For example, Viscar- were made with chemometric modeling of close- ra Rossel & Behrens (2010) concluded that ANN, range VSWIR spectroscopic measurements in SVM, MLR, MARS, and PLSR outperformed RF laboratory conditions (Fig. 3). The soil Ɛ could be and BRT when predicting C (0.01–13.9%) and clay predicted well with the exponential single variate contents (2.8–79.20%) and pH (4.8–10). model (mean of reflectance between 1444–1464 The prediction errors of Ɛ (validation RMSE nm, validation R2=0.77, RMSE=2.98) and also with <2.5 Ɛ units) were somewhat in the same range several MVR techniques (validation R2=0.81–0.94, compared to results of Liang et al. (2010) who RMSE=1.59–2.77). The MVR methods PLSR, also used MVR, specifically ANN, to predict soil MARS, BRT and RF, and RVM produced slightly moisture. They report RMSE-% as low as 1.04 (g/g, higher accuracies than single variate exponential equals app. 1 Ɛ unit according to Saarenketo 2006) functions. Overall, RVM slightly outperformed the and maximum absolute errors of 2.66% (g/g, app. other methods. RVMs produced the highest vali- 1 Ɛ units) when using ANN regression with reflec- dation goodness of fit and the smallest error values tance from three wavelengths (1314 nm, 1475 nm, for the generic models including all four tills (Fig. 1979 nm, n=115) chosen by correlation analysis. 3B, D). Other MVR models did almost equally The soil moisture Gaussian model by Whiting et well (Fig. 3A, B, C, D). Especially, RF models had al. (2004) produced an RMSE of 0.026 g/g at the virtually equal RMSEs to RVM models (calibra- low gravimetric water contents (range 0–0.32 g/g, tion RMSE<1.5, validation RMSE<2.5 Ɛ units, Fig. n=2503), which is a magnitude lower error than 3B, D). Although data mining techniques, and es- gained with the MVR methods reported here. In pecially tree based methods, are designed for large this respect, the soil moisture Gaussian model sample sizes, RF and RVM also performed well might be even superior to the MVRs when pre- predicting Ɛ also for datasets with small sample dicting SWC from VSWIR spectra for soils at low sizes (n=12–29, Fig. 3A, C). A regression method water contents, i.e., below the critical water con- which could produce reliable methods with a wide tent (cut-off thickness). Even though SWC is better range of sample sizes would be ideal in chemom- predicted with methods utilizing ranges of wave- etry to avoid unnecessarily large sample sizes, and lengths in VSWIR, the results of paper I also show thus enabling the use of spectrometry in a variety that monitoring of SWC in laboratory is also pos- of soil moisture monitoring and mapping applica- sible by utilizing the well known physically sound tions. To answer the RQ 1, when predicting the till water absorption bands, especially the absorption soil Ɛ from VSWIR it might be advisable to use the feature at 1444–1464 nm. For field measured spec- data mining techniques. tra, feature selection by inputting reflectance val- The relatively large variation between the model ues from the ranges of 500–1300 nm, 1600–1850 performances (Fig. 3A, 2C) might not be only a nm, and 2100–2500 nm, to avoid the atmospheric product of the selected regression technique but water absorption features, should be done prior to also the relatively small sample sizes in the till- regression modelling. specific predictions. With small sample sizes, such Spectral transformations had no significant ef- as in case of this dataset, the uncertainty in pa- fect on predicting till soil Ɛ whatsoever (Fig. 3). rameter estimation increases compared to larger Only a slight increase in validation efficiency was sample sizes. Till 1 (Vuotsonkangas till, n=12) produced with continuum removal and wavelet had the smallest population followed by till 4 transformation, and also with second derivatives (Haipankuusikko till, n=26), till 2 (Vaalolehto till, when RVM was applied (Fig. 3D). Some studies n=28), and till 3 (Kuorajoki till, n=29). This is not suggest that wavelets might produce a slight im- ideal for comparison of MVRs. For further evalu- provement to the prediction efficiency (Viscarra ation of the models larger sample sizes are needed. Rossel & Lark 2009, Viscarra Rossel & Behrens Similarly, the results are valid only for the studied 2010). Mouazen et al. (2010) showed that feature tills which have a relatively small spread of data: selection by PLSR prior to ANN regression pro-

45 Geological Survey of Finland Maarit Middleton duced up to a 0.2 improvement in R2 values when centration of Fe could be predicted with spectra predicting soil element concentration. At the same (Fig. 4B), whereas concentrations of a number of time, Brown et al. (2005) express their concern elements could be predicted for till 2. In paper II, about the use of derivatives as it can be unstable it was argued that the low standard deviation of due to changing spectral contributions of vari- elemental concentrations would be the main rea- ous soil minerals. Shifts in absorption features can son for this difference. However, the Figures 4B occur when other compounds are added or their and 4C reveal that the concentration differences concentration increases as demonstrated, e.g., in are minor. Further processing of the paper II data paper I for water at 1900 nm. Currently, there is revealed that the overall correlation between the no consensus on the use of transformations in pre- concentrations of elements is much higher for till processing of the soil spectra. It would be neces- 2 than for till 1. The inter-correlation of elements sary to systematically study all preprocessing pro- is a determining factor for the number of elements cedures in order to draw further conclusions on which concentration can be simultaneously pre- the advantages of feature selection, noise removal, dicted with VSWIR spectroscopy. It is also the key corrections or enhancement of spectral features issue in understanding why prediction of soil con- for prediction of soil properties. stituents can be highly variable in success (Sten- The masking effect of OMC>1.7% (dag/kg) co- berg et al. 2010). incides well with the finding by Galvão & Vitorello For modeling any soil property generic, i.e., (1998) who conclude that OMC>1.7% (dag/kg) global empirical models would be more desirable tends to obliterate the effect of chemical constitu- than till-specific models. The results of predicting ents in VIS. Organic matter has weak absorption both soil Ɛ and chemical elements concentrations features throughout the VSWIR spectra, and be- indicate that generic models can be statistically cause of the relatively good spectral response OMC significant (Fig. 3). Overall till-specific models can be quantified quite well in many cases (see were, however, more accurate than generic mod- summary in Stenberg et al. 2010). However, the els when predicting soil Ɛ (Fig. 3). In case of till results of this study indicate that quite low quanti- chemical element concentrations, cross-modeling ties of organic matter may already impede accurate was not statistically feasible (Fig. 4). Modeling ele- quantification of other soil constituents. In case of mental concentrations of tills from one area with a forest soils in Lapland, the organic matter content model created with data from another area was not in illuvial and eluvial horizons is usually insignifi- tangible. However, applying the generic model to cant. The OMC of parent tills has been observed to site-specific modeling was not attempted. In con- be less than 1.5%, and 80% of data from plowing clusion, successful application of generic models depth (0.3–0.5 m) indicate OMC to be less than 2% is more likely for predicting SWC than concentra- (Sutinen et al. 1993). Thus organic matter does not tions of elements due to the presence of absorp- pose a major problem for applicability of VSWIR tion features of water and weak features of min- spectroscopy for the shallow northern boreal soils. erals which are indirectly correlated to chemical To answer the RQ 2, the chemical element con- element concentrations. centrations of till fine fraction were predicted with 2 PLSR statistically significantly (R ≥0.5) for Al, Ba, 5.1.2 Remote sensing of boreal ground cover Be, Co, Cr, Cu, Fe, K, La, Mg, Mn Ni, P, S, Sc, Sr, plant associations Ti, V, Y, and Zn. The rock forming minerals in tills, i.e., quartz, feldspars, and amphibolites are spec- The ordination results of the papers III and IV con- trally merely featureless (Clark et al. 2007) but the firm the findings of some earlier studies (Salmela spectral contribution for statistically significant et al. 2001, Tahvanainen et al. 2002, Närhi et al. detection of elemental concentrations are due to 2011) about the edaphic contribution of moisture spectrally active crystalline and amorphous sec- (Ɛ) and nutrient potential (σ) on distribution and ondary minerals. Prediction of mineral soil chem- abundance of several vascular species and lichens istry is only possible in the presence of spectrally on mineral soil in central Lapland (Fig. 7). Accord- active mineral materials and cross-correlation of ing to the NMDS and correlation analysis, 12 spe- elements with the spectrally active or extraneous cies were found to be abundant on wet and mesic soil components. The results of paper III demon- sites, and nine species of dry sites. Some species, strate this issue very well. In till 1 only the con- e.g., Vaccinium myrtillus can tolerate a wide range

46 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment of moisture conditions but is one of the most com- be improved by median or mean resampling of mon species. In addition, pH was a driver of veg- the resulting fuzzy membership image to at least etation composition on organic soils of aapa peat- 12 m pixel size. These options are dataset specific lands in southern Lapland (Fig. 7). Based on CCA but demonstrate that calibration set selection, size, ordination analysis, 11 species were related to the resampling pixel size, and ANN algorithm can constrained ordination, where five of them were have a profound impact on the classification ac- associated with high pH and σ, and one with high curacy. These results agree with the earlier studies Ɛ (Fig. 7). These species are also the most abun- which suggest that 1) ANN algorithms train better dantly occurring species on the peatlands. The if the calibration vectors cover the entire distribu- contribution of the soil gradients on the species tion of values (Hepner et al. 1990, Foody 2000), distribution of the most abundant species is the 2) a higher number of calibration points produces primary requirement of successful application of increased accuracies to a certain extent (Aurora VSWIR remote sensing in detection of the under- & Foody 1997), and 3) optimum number of cali- lying root zone soil properties. Moreover, several bration points are very much algorithm, network inorganic and senescenced ground cover compo- complexity, and data dimension dependent (Kav- nents such as a soil, rock, and litter attributed to zoglu & Mather 2003). This means reoptimizing of the spectra and thus to the successful recognition the parameters for each dataset and ANN classifier of the Scots pine suitability classes and peatland independently. site types. This is further demonstrated by correla- The question remains whether optical remote tion of the hyperspectral bands to the ordination sensing of the soil moisture by spatial species pat- space (Fig. 6, 7). The ordination approach might terns is applicable in practice. The applicability of also provide means for feature extraction of the the method remains untested in different habitats, hyperspectal bands. e.g., on areas recently clear-cut but not yet disturbed The results of the study demonstrated moderate by site-preparation nor burning. Acquiring re- success (AUC=0.741) of soil moisture based suita- motely sensed data immediately after clear-cutting, bility assessment for artificial regeneration to Scots but before other site treatments, would be the most pine (Fig. 5). To answer the RQ 3, the clear-cut logical from a practical perspective. The study site treated with prescribed burning and mechanical was a formerly Hylocomium-myrtillus type Norway site-preparation could be mapped into ‘suitable’ spruce-downy birch mixed forest but site-prepared and ‘unsuitable for pine’ subareas using hyper- and treated with prescribed burning ten years prior spectral imaging of ground cover plant species pat- to the hyperspectral data collection. The compari- terns. The results suggest that applying MNF trans- son of the species abundances in surrounding for- formation of spectral AISA radiance channels and est and the clear-cut demonstrated that completely careful calibration of the RBFLN function, instead different species dominate in the forest than on the of PNN, would produce consistently the most ac- clear-cut. Many of the most abundant ones, how- curate results. The classification accuracies were ever, are indicators of underlying soil moisture not compared between AISA radiance and those (Salmela et al. 2001). Secondly, the true success of transformed with MNF but several papers show methodology can be verified on the study site by that reduction of the spectral dimensionality with inventorying of the artificially regenerated Scots MNF improves classification by extracting noise pine approximately 20 years after the seeding that from data (Harris et al. 2006a, Zhang & Xie 2012). took place in 1989 and 1992 (Sutinen et al. 2002b). The comparison of PNN and RBFLN showed that, Eventually, the costs of the approach should be sub- although RBFLN is much slower than PNN, RB- jected to economical calculations which would also FLN is capable of generalizing more efficiently than consider method uncertainties. PNN with heterogeneous spectral data, such as The ability of the ordination methods to reveal remotely sensed understory vegetation patterns. the community structure was utilized in this study Sensitivity testing of the classification vari- to cluster peatland site types which were spectrally ables showed that the RBFLN should be trained separable. A constrained CCA ordination was first with >2.43 calibration points/ha and Ɛ class divi- constructed and the field determined site types sion between ‘suitable’/’unsuitable for pine’ classes were plotted in the ordination (Fig. 7). Then natural should be set to Ɛ≤15/Ɛ<15 rather than Ɛ≤13/Ɛ≥17 clusters of the study sites were revealed with un- for optimal performance. The final accuracy can supervised PAM clustering with the ordination

47 Geological Survey of Finland Maarit Middleton scores, of which the spectral separability was test- sideration that SVM is fairly reliable, even though ed with MANOVA. Based on the MANOVA the SVMs have difficulties dealing with noisy data and peatland site type classes were combined until four data with extremely high dimensionalities (Moun- generalized spectrally separable biotopes were left. trakis et al. 2011). Often SVMs have outperformed The field determined classes were also generalized ANNs (Atkinson & Tatnall 1997, Mas & Flores to match the hierarchal level of the purely data 2008) but recent developments in ANN tech- driven CCA-PAM-MANOVA classes. The disa- niques have improved their capabilities to equal greement between the CCA-PAM-MANOVA and the SVMs. For example, adaptive learning vector in situ subjectively determined generalized classes quantization by Zhang & Xie (2012) and a super- (overall accuracy 82.0%, kappa 0.737) was visual- vised classifier based on the learning vector quan- ized, especially in the transition between classes. tization by Kohonen (2001) have been demon- It indicates that at this scale, clusters of spectrally strated to perform equally well to SVMs (Trinder separable cohesive communities existed but they & Salah 2011). Advanced versions of SVMs, such gradually changed from one plant association to as adaptive binary tree, which creates a binary tree another. automatically based on Jeffries-Matusita distances, Three of the four classes were then mapped with are recently introduced (Du et al. 2012). Individual SVMs to produce a continuous fuzzy representa- classifiers are shown to perform well in some situ- tion of the class extents (Fig. 8). Moderate-to-good ations and fail under other conditions (Dudoit et successes was calculated with the class-specific al. 2002). The identification of the best procedure ROC analysis: 0.946 for ‘bog’, 0.951 for ‘sedge fen’, is often rather difficult, especially when the -per and 0.999 for ‘eutrophic fen’, and overall accura- formance of a number of candidates is evaluated cy: 87.8%, and kappa: 0.781. These data process- for learning samples of limited size. In conclusion, ing steps combine the benefits of feature selection the results of this study demonstrate that SVMs and optimization of class hierarchical level. Thus and ANNs can be considered a valuable tool for spectrally separable classes can be named in an processing of hyperspectral data (e.g., Bruzzone & ecological context. For these reasons the proposed Melgani 2004, Van der Linden et al. 2007). approach can be considered an improvement in Because of the distinctly clustered appearance combining the ecological in situ and remotely but continuous community variation of the peat- sensed information. Compared to the traditional land and clear-cut canopies, fuzzy classification separability analysis of spectral classes with the can be considered the most suitable for mapping ordination approach the ecological basis of com- of the class extents. Fuzzy classification with ANN, bining the classes can be more readily considered SVM or other non-linear classifier could predict and provides a substantial improvement to the both spatially sharp boundaries and transitional signature separability analysis. To answer the RQ zones between peatland site types. In contrast to 4, the success of the SVM classification is a result discrete classifiers, fuzzy approach allows visuali- of optimizing the class levels with the CCA-PAM- zation of gradual transition of plant communities MANOVA approach, and can thus be recom- which can be viewed as low and/or equal class mended as a processing step in future remote sens- membership values, homogeneity of communities ing based classifications of peatlands. Ordination with abrupt transitions between stands, and with- based methods are visual and thus the ecological in stand heterogeneity (see Schmidtlein et al. 2007 interpretation of a combination of ecological-en- for discussion). Fuzzy membership bands can be vironmental-spectral data may be more readily ac- visualized in a way that they reveal uncertainties complished with them than with other statistical as low class membership scores or as high spatial separability analysis only. variation of membership scores, which could in- The two common non-linear classifiers, ANNs dicate possibly an unconsidered thematic class in and SVMs were applied to classify the vegetation the study area. Therefore, local variation in species in shrub, herb, and bryophyte layers on organic composition and the degree of softness of the con- and mineral soils in this study. The aim was not crete boundaries can be visualized more readily to compare the classification methods but to select with the fuzzy than the discrete approaches, and an approach that can generalize complex hyper- thus are preferred to sharp boundaries. spectral data. Our results support the general con-

48 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

5.2 Practical implications

5.2.1 Close-range detection of till soil water performance of prediction approaches. Yet, only a content and chemical element concentrations few studies compare the data mining algorithms in chemometry (Viscarra Rossel & Behrens 2010). This research demonstrates the potential of Overall best performance was by ANN and the VSWIR spectroscopy in predicting SWC in labo- worst by RF and BT for a large dataset (n=1104) ratory conditions with high spectral resolution which had a large spread of soil C, clay content, data. In the paper I the focus was on determin- and pH. However, the differences between the ing the statistical relationship between Ɛ and re- models really were not very significant and also flectance. Thus most of the reflectance variables varied by spectral transformation (Viscarra Rossel were selected on the basis of theoretical absorp- & Behrens 2010). The MVR comparison should be tion features centered at 942–1135 nm, 1379–1450 intensively conducted by the research community nm, 1800 nm, and 1920–1935 nm (Curcio & Petty to gain understanding of the MVR algorithms in 1951, Hunt 1977) caused by absorption of water chemometry (Nduwamungu et al. 2009) but the molecules. Because atmospheric water interferes most important issues are quality of the spectral with wave propagation in remotely sensed data es- data and understanding the meaning of cross-cor- pecially at the strongest water absorption features relation between spectrally active soil components at 1379–1450 nm and 1920–1935 nm it would not and the predicted variable. be ideal to use these wavebands when airborne or Empirical SWC modeling with VSWIR spectra spaceborne data are applied. In addition, predic- over a wide SWC range in theory is restricted to tion efficiency is deteriorated in the VIS, which monitoring a single sample. This is because the is caused by the masking effect of organic matter critical point, i.e., cut-off thickness, is soil depend- (Galvão & Vitorello 1998). Therefore, remote sens- ent (Weidong et al. 2002). The critical point has ing of soil moisture in the SWIR bands approxi- been shown to differ according to field capacity mately around 1100–1300 nm, 1600–1850 nm, and porosity. Thus SWC can be spatially estimated and 2100–2500 nm would be most suitable for re- with spectra over a range of samples only up to the mote sensing of soil moisture. lowest critical point of all samples. These facts lim- The results of further processing of data in pa- it the practical use of spectroscopy only to applica- per I promote the use of MVR techniques for pre- tions which allow higher uncertainty or for initial dicting soil moisture. They are also easy to use, and investigations which are further going to be im- require little knowledge about the spectra of the proved with another approach. Increase in till soil measured material. They can be applied quickly reflectance at highƐ was not observed but more of to produce a predictive model. But chemometric leveling off the reflectance atƐ >15. Once Ɛ>15 the methods are also ‘a black box’ for an average user soil reflectance is almost constant and water in- and may be prone to overfitting if appropriate care duced information is lost within the variability in is not taken with regard to the independency and reflectance caused by other factors contributing to proper size of calibration and validation dataset the soil reflectance, e.g., organic matter, mineral- (Prasad et al. 2006). From a practical point of view, ogy, and surface roughness. Reflectance increase at it would be essential to establish rough guidelines high Ɛ was not observed because we did not reach for MVR technique selection. New improved MVR full saturation of the samples (max. Ɛ=42). Full techniques emerge constantly in data mining. At saturation of surface soils is rare during the grow- the same time, applicability of the present MVRs ing season in the boreal climate (Hänninen 1997, to chemometric modeling is not evident based Sutinen et al. 1997). Thus at least two moisture on theory. The applicability of each algorithm classes: Ɛ≤15 and Ɛ>15 could be separated based depends on the dataset, i.e., soil property to be on VSWIR data. predicted, ranges of these response variables and The practical application of theƐ -reflectance re- spectrally active variables, existence of the spec- lationship would be assessing subareas of clear-cut trally active variables, spectral range, pretreatment and mechanically site-prepared forest compart- of samples, spectral instrument, and illumination ments which would be suitable for artificial regen- setup. Hastie et al. (2009) conclude that nature of eration to Scots pine. The exponential nature of re- the target function has a strong influence on the flectance decrease with increasingƐ demonstrated

49 Geological Survey of Finland Maarit Middleton in this study for till soils enables such a classifi- diffuse reflectance Fourier transform spectroscopy cation approach. A simple single band cut-off (Reeves & Smith 2009) and with diffuse reflectance point at reflectance corresponding to Ɛ=15 could spectroscopy (Cohen et al. 2007). On the other be used to divide a dataset of extracted soil pixels hand, results reported by Brown et al. (2006) sug- from optical high spatial resolution data and field gest that there is great potential for worldwide cali- measured Ɛ to produce a spatial representation of brations of spectroscopic methods and exhaustive site suitability for Scots pine. Another alternative global spectral libraries. Combining large databas- to thresholding would be classification as demon- es with many different calibrations (Sankey et al. strated by Siira et al. (2000b). First pure soil pixels 2008) might facilitate the use of global models in were extracted from digitized high resolution (0.8 predicting elemental concentrations of tills. Glob- m) false color aerial photographs. The maximum al databases have been proposed as a solution (e.g., likelihood classification with field referencing McCarty et al. 2002, Brown et al. 2006). Several of Ɛ measurements produced the overall highest studies, however, note that for large datasets with a user’s (up to 87%) and producer’s (up to 92%) ac- large range in soil properties the prediction accu- curacy measures for the suitable and unsuitable racy drops because of non-linearity in the relation Scots pine regeneration areas. Two unsupervised between reflectance and the soil properties. This classification were used and validated with in situ would limit the use of large databases in local scale Ɛ measurements. The isodata clustering and k- studies. Thematic or spectral stratification of the means classifier provided almost identical results large dataset into smaller subset have been shown of 77% overall accuracy. Both approaches could to improve the estimates of several soil properties serve as effective alternatives to traditional soil (Bartholomeus et al. 2011, Ge et al. 2011). The im- survey methods when determining soil suitability plementation of VSWIR in close-range soil sens- for Scots pine during forest regeneration on site- ing is limited because of the low robustness of the prepared clear-cuts on formerly Norway spruce- chemometric techniques, and consequently many downy birch clear-cuts in Fennoscandia. local calibrations. Currently, most models perform The key question is whether calibrations in field better under controlled conditions and yield better conditions would be accurate enough for quicker results on a local scale. more cost-efficient data processing in practical Several other practical issues have to be solved applications such as geochemical work or forest before VSWIR spectroscopy can be applied in pre- soil studies. Although concentrations of several dicting mineral soil element concentrations. There elements measured from partially leached solutes is a great contradiction in sample preparation by were predicted statistically significantly (R2>0.5, wetting to improve the predictions of element Fig. 4A, B), the confidence of the predictions may concentrations and other soil properties. The best still be too low for practical applications. The ele- estimations are often gained with dried samples. ments with a large range of concentrations were For example, Bogrekci & Lee (2006) found that re- also predicted with higher RMSE errors (Fig. moval of water from soil samples prior to spectral 4A, B). PLSR models with R2>0.9, according to measurements significantly improved the predic- Reeves & Smith (2009), would provide sufficiently tions of P by measuring the SWC and artificially high accuracy. The usability can be estimated by removing the effect of water on the spectra. Simi- interpreting the element-specific RMSE values, lar results are also demonstrated by Sudduth & which are application specific. For example, in Hummel (1993) for organic C, and by Chang et al. mineral exploration, metals which often occur in (2005) for a variety of soil properties. At the same low quantities should be accurately predicted as time, some mineral absorption features which can they are most interesting from the viewpoint of be indirect indicators of presence of elements are geochemical exploration. False positives should be perhaps accentuated or may not appear until the avoided as they initiate unnecessary target drilling soil is wetted. Demattê et al. (2006) shows that this and then resources are wasted. On the other hand, phenomena is especially pronounced in the pres- false negative anomalies would mean undiscov- ence of hydroxide bearing minerals such as kao- ered mineral resources. linite and montmorillonite. Kusumo et al. (2008) Applicability of global chemometric prediction demonstrate quite accurate predictions of total models is currently unresolved. Universal geo- C and N despite moisture differences between chemical calibrations have proven impossible with samples. Viscarra Rossel et al. (2009) propose the

50 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment method of ‘spiking’ the dataset by including sam- medium resolution (≥12 m) data might be more ples with variable moisture contents. However, appropriate than high resolution data. In addition, Minasny et al. (2009) and McBratney et al. (2011) NDVI turned out to be a significant variable in showed that the use of External Parameter Or- the NMDS ordination space. Thus medium spatial thogonalisation (EPO-PLSR) was a more powerful and spectral resolution satellite data might be ad- tool for removing the effect of moisture when pre- equate for optical remote sensing based Scots pine dicting soil C in field conditions. The EPO-PLSR suitability assessment already before hyperspectral quite successfully removes an effect of an external satellite data will be available at affordable prices. parameter which has an impact on the spectra. For soil moisture estimations several other po- The desirability of spectroscopy is primarily tential remote sensing methods also exist. A re- based on cost-savings. Cohen et al. (2007) estimate mote sensing based method could be based on the cost of a VSWIR measurement to be less than thermal inertia or microwave frequency backscat- 10% of the traditional wet soil chemical measure- ter. In physical principles they might be more ap- ment costs. However, only a few papers deal with propriate than the use of VSWIR data, e.g., water the optimal sample sizes for calibration and vali- strongly affects the bulk soilƐ and thus the back- dation. The results of Brown et al. (2005) found scatter of microwave radiation (reviews by Stafford that ~20–35% of the full sample size is needed for 1988 and Wang & Qu 2009). Due to longer elec- adequate calibration of PLSR modes. This would tromagnetic wavelengths the penetration depth mean that the technique is not as cost efficient as of microwave and LWIR is also better compared intended. Moreover, greater care has to be taken to VSWIR. However, the limitation of LWIR and for selecting the validation dataset as random se- microwave data is the poor spatial resolution. The lection can overestimate the predictive accuracy data from cost-efficient satellite based sensors are (Brown et al. 2005). currently too coarse for site-specific studies of soil moisture (see Kaleita et al. 2005, Mulder et al. 5.2.2 Remote sensing of boreal ground cover 2011). For this reason VSWIR domain is the most plant associations operational as the data are most available and affordable (Muller & Décamps 2001). To be accepted as a standard forest management The goal in paper IV was to study the applicabil- practice, a SWC based Scots pine suitability as- ity and develop processing methods of hyperspec- sessment methodology should meet the data re- tral imaging data for peatland site type mapping quirements of affordability, high accuracy, and keeping in mind the future prospective national large spatial extent. A few methods to map SWC wetland inventory. Such spatial representation of for site suitability exist. Classification of low alti- peatlands across Finland is missing in contrast tude airborne radiometric gamma data has been to other peatland rich countries, such as Sweden suggested for regional mapping of site suitability (Boresjö Bronge et al. 2006) and Canada (Fournier (Hyvönen et al. 2007). In central Lapland, on the et al. 2007), which are currently conducting the basis of this low resolution mapping heterogene- work based on satellite optical and microwave ity of forest compartments could be estimated data. The high hierarchal class levels including fen, but it cannot considered detailed enough for rec- eutrophic fen, bog, and swamp matches well with ognition of the spatial soil water patterns within the classes suggested for the Canadian wetland forest compartments. Several dielectric ground inventory including bog, fen, marsh, swamp, and methods have been applied to map site suitabil- shallow water. Therefore, four might be close to ity at the compartment level (Hänninen 1997). To an optimal number of thematic classes. Thus four cut down the number of field measurements and classes may be appropriate for the national level costs (Siira et al. 2000b) proposed classification of inventory when hyperspectral data from satellite exposed mineral surface SWC from inexpensive platforms become available. digital high resolution false colour aerial photo- A widely applicable outcome of paper IV was the graphs. In comparison to aerial photography, the ordination based spectral signature separation pro- airborne hyperspectral data used in this study are cedure combined with fuzzy mapping. Previously, currenty too costly to be applied in practice. Re- correspondence analyses combined with unsuper- sampling of the classification results into larger vised clustering and indicator species methods, pixels sizes (original 1.1 m) demonstrated that have been employed for a meaningful class division

51 Geological Survey of Finland Maarit Middleton of peatland environments (Thomas et al. 2003), Although hyperspectral close-range and remote on arctic tundra (Atkinson & Treitz 2012), and sensing of plant and soil media have been avail- landscape-forest level (Malik & Husain 2006) with able since the 1970’s and became available for the moderate success. A non-classificatory continuous wider scientific community since the 1990’s, their mapping of ordination scores has been performed progress in becoming widely used research tools by predicting the ordination axis score values from has not been as rapid and successful as was sug- spectral channels and then interpreting the patterns gested. Issues such as complexity of the datasets, spatially as single axis scores (Ohmann & Gregory radiometric and atmospheric corrections and se- 2002, Schmidtlein & Sassin 2004, Thessler et al. lection of appropriate data modeling and classifi- 2005, Schmidtlein et al. 2007, Feilhauer et al. 2011). cation techniques, high cost of the data, shortage The advantage of the CCA-PAM-MANOVA ap- of people working in the hyperspectral science proach compared to the previously suggested ones community, and educational opportunities limit is that it combines community clustering and con- the expansion of imaging spectroscopy for more tinuous nature of community transition in the spa- common use. Many authors also agree that low tial domain. The CCA-PAM-MANOVA approach S-N of the imaging spectrometers (Ben-Dor et al. is only applicable in situations where environmen- 2009, Van der Meer et al. 2012) and low spatial tal variables are well known as constrained ordina- resolution (Price 1997) hinders the separation of tion was used. However, unconstrained ordination viewed materials. Advances in algorithm research could also be applied in a variety of environments are constantly published (e.g., Chanussot et al. and applications. 2010) but the time lag until the methods are avail- In the case of peatlands, several remotely sensed able for a user might be significant. Ben-Dor et al. datasets acquired during one summer season might (2009) concludes some practical aspects of imag- improve the classification accuracy. Flooding pe- ing spectrometry that require development in or- riod in the spring should be avoided as differences der for imaging spectroscopy to expand. These in- in surface moisture between site types have a great clude breakthroughs in electro-optics technology impact on the final success. Van der Meer et al. to obtain near laboratory quality spectra with high (2012) are also asking for more attention to process S-N ratio, enlarging the spectral window to UV related studies with hyperspectral remote sens- and LWIR, obtaining temporal coverage, training ing to improve the efficiencies of classification. In of specialists, encouraging commercial activity, northern Finland acquisition of temporal datasets, educating the market, reducing prices, provid- however, is difficult because of the low frequency ing nearly real-time services, and developing new of cloud free days during the short growing season. successful applications. Therefore, incorporation of temporal datasets into vegetation classification may be operational only occasionally.

5.3 Reliability and validity

A study is reliable if the results are consistent over representation derived from remotely sensed data time and if the results accurately represent the to- consists of uncertainties related to the input vari- tal population under study (Golafshani 2003). In ables, used models, their parameterization, spatial quantitative research, such as remote sensing, the support of the data, and position (Dungan 2002). concept of reliability is commonly considered as In the following, the most significant error sources uncertainty. In statistical terminology, uncertainty in these uncertainty categories are discussed. consists of accuracy, bias, and precision (Atkinson The quality and usability of the spectroscopic & Foody 2002). In this study the uncertainty is re- signature is largely determined by the S-N per- ported as accuracy, which is considered a sum of formance, uniformity and stability of imaging unbias and precision. Uncertainty of a model de- systems, and also the atmospheric interferences. rived from remotely sensed spatial data consists of The measurement devices used in this study were inaccuracies related to the measurement process spectrally and radiometrically calibrated prior to and ones added by the processing steps (Schott the surveys. The AISA instrument was calibrated 2006). The total uncertainty of a thematic spatial by the Finnish Forest Research Institute (Mäkisara

52 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment et al. 1993) and HyMap by Hyvista corp. (Cocks et ods. Emphasis was put in the sensitivity analysis al. 1998). A long data preprocessing chain apply- of the model parameters in paper III. The conclu- ing a variety models is needed to correct for the sion was that with ANN algorithms the quality hyperspectral remotely sensed data, which in turn of the calibration set and the choice of the ANN causes error accumulation by assumptions related algorithm had a greater impact on classification to models and their parameters. accuracy rather than optimizing the ANN calibra- Datcu et al. (1998) suggest that model selection parameters (number of functions and num- tion might be one of the largest sources of uncer- ber of iterations) to perfection. Although this does tainty. In the preprocessing phase, the raw digital not apply until a sufficient number of calibration numbers from the instruments were first turned points is exceeded. The quantity of calibration to on-board-radiance values according to the in- might also be very low if the calibration is repre- strument-specific calibration functions provided sentative of the classes. The kernel based SVM used by the instrument suppliers. Further, bidirectional in paper IV is similar such that the optimal perfor- correction with nadir normalization was conduct- mance of the algorithm is determined in the cali- ed on both datasets to compensate for the viewing bration stage by using calibration areas. Over- and and solar illumination geometries. To compensate underfitting are avoided by optimizing the kernel for the signal interference by atmosphere the Hy- width parameter and regularization parameter in a Map data also went through an atmospheric cor- cross-validation process. Testing of sensitivity for rection. The atmosphere was assumed to be con- the calibration set could not be performed in such stant on the small AISA flight area and, therefore, detail as there was a lack of calibration sites of all atmospheric correction was skipped as it was not four peatland site type classes. In the chemometric expected to improve the classification. Geometric regression, models were also constructed based on correction and geocoding were done to correct for calibration sets, which were chosen from the com- the distortions caused by the aircraft movements. plete datasets randomly. The MNF transformation was applied to paper III Spatial support in remotely sensed data refers data to remove as much of the noise in the data to the area covered by an effective resolution ele- as possible. The affects of MNF transformation ment, which is a function of the instantaneous field were not tested in this research, and MNF was not of view, flight variables, and atmospheric effects applied to HyMap data in paper IV. (Dungan 2002). The cell size or ground instan- Uncertainty was also introduced by the classifi- taneous field of view are used as approximations cation and regression models which were applied of the spatial support. From the user perspective, to produce the thematic end products. Often times the spatial support of airborne data is provided by model selection is not performed as it can be labo- the manufacturer and flight operator and they do rious. In this work, ANN models were compared not usually estimate the uncertainties involved in in paper III and regression models in further pro- the spatial support. The spatial resolution is a de- cessing of the paper I data. In paper III RBFLN termining factor for the model performance such was clearly found to be more efficient than PNN. that the thematic class hierarchical level is deter- In further processing of paper I data, the samples mined by cell size. In paper III optimization of the sizes were small and validation could not be per- cell size was attempted for the two class classifica- formed with an independent dataset for till-spe- tion and the best accuracies were gained with cell cific models. For the generic Ɛ-reflectance model sizes >10 m. In paper IV the number and thematic proper validation was performed. The RVMs per- content of peatland classes were optimized to formed slightly better than other models but this match the cell size with the CCA-PAM-MANOVA is in contradiction to some other studies such as approach. Both approaches significantly improved Viscarra Rossel & Behrens (2010) performed with the accuracies. larger sample size. In conclusion, the model selec- A major source of uncertainty for the HyMap tion can be a major source of uncertainty but very data was created as the IMU data were missing difficult to control as selection of models and their due to a human error causing a 30 m discrepancy parameters are strongly dataset dependent. between true coordinate locations and the HyMap Parametric uncertainty in this study was accom- data. The lack of ground control points in sparsely plished using calibration data in the supervised or uninhabited study sites such as in papers III and ANN and SVM approaches and the MVR meth- IV is a problem. In case of the AISA data the lack

53 Geological Survey of Finland Maarit Middleton of ground control points caused an RMSE of 20 m. study and are often ruled out by limited resources In addition, spatial distortion in the HyMap data but, in fact, they should be conducted both for ref- was highly variable from place to place. The spatial erence data and spectral data to produce a com- inaccuracies are very common in remotely sensed plete estimate of uncertainty. data. When combining in situ data with remotely Validity, on the other hand, is a more gener- sensed data the positional uncertainly can be im- al term determining whether the research truly proved by averaging pixel values around expected measures what it was intended to measure or field locations. In paper III, pixel values from a how truthful the research results are (Golafshani same size window were extracted and averaged to 2003). Heavy assumptions of causality between represent each 5 m by 5 m field site. To better com- vegetation reflectance and soil properties have pensate for the spatial discrepancy it might have strong implications on the validity of this study. been beneficial to choose a larger window. In paper However, it is demonstrated in papers III and IV IV, spatial discrepancy was better compensated by with statistical methods and with accuracy as- conducting a pixel based unsupervised classifica- sessments that a statistical co-variation between tion and also a segmentation prior to planning of SWC, nutrients and pH, and vegetation compo- the sampling scheme. sitions exists. Moreover, it was also shown that More accurate predictions can be considered the spatial species patterns have distinct spectral to be drawn from close-range spectral data than responses and that these spatial patterns can be from remote sensed spatial data. In close-range mapped with remotely sensed hyperspectral data. sensing the uncertainties related to spatial support In paper III, moderate success was gained for the and position are non-existent or irrelevant. With soil moisture site suitability for Scots pine assess- remotely sensed data the correction steps taken to ment when the accuracy was assessed towards compensate for the atmospheric effects, platform measurements of Ɛ. However, an issue of validity movements, and geocoding cause increase in the remains. Although remotely sensed spatial pat- total uncertainty of the model due to the uncer- terns of the understory are adjusted to the time tainties in the parameterization of each data pro- stable patterns of soil moisture variations, the cessing step. Therefore, models constructed on field measured Ɛ represent soil moisture only in close-range sensing data such as Ɛ and element one point of time. Due to the time stability of soil concentrations may be less uncertain than the Ɛ those measurements made late in June repre- models build on remotely sensed data. Factors sent the magnitude difference of SWCs between such as relatively low S-N ratio of remotely sensed sites during the growing season. Although soil data compared to laboratory data, spatial variabil- Ɛ is used only as a proxy of pine survival it all ity in surface roughness, viewing angles, the bidi- comes down to the uncertainty of the prediction. rectional reflectance property of soils (Cierniewski The true uncertainty can be evaluated after the & Courault 1993) and illumination conditions, pine seedlings have exceeded the critical 20 years and atmospheric effects on wave propagation of a plantation to stabilize (Hansson & Karlman would increase the uncertainty of remote sensing 1997). Such long term studies should be con- of soil properties of exposed soils. ducted in order to evaluate the true uncertainty It is a common practice in spectroscopy to vali- of the approach. Although, the mortality of pine date the information derived from hyperspectral saplings has been related to excess SWC and in- data against observations acquired by other meas- cidences of concomitant diseases, Gremmeniella urement techniques or by visual in situ observa- abietina in particular the causality is still unex- tions. Uncertainty measures of those reference plained (Witzell & Karlman 2000, Sutinen et al. data should also be reported in order to truly 2002b, Mäkitalo 2009). Despite all of the uncer- monitor the uncertainty of the results. For exam- tainties related to the site suitability approach, it ple, in geochemical research uncertainty related to still might serve as the most effective approach in geochemical, sampling, and analytical variability assessing soil moisture based site suitability for are frequently monitored with measures of un- Scots pine on the forest compartment level. certainty calculated against field duplicates and An issue of validity was also present in paper reference samples (e.g., Ramsey 1998). Such qual- II where the till soil chemical element concentra- ity control and assurance aspects of field or labo- tions were predicted from spectra. The absorption ratory reference data were not considered in this properties of soils are merely caused by molecular

54 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment processes, i.e., mineralogy, although electronic to be able to quantify some spectrally active sec- processes also cause soil absorption features. Thus ondary minerals such biotite, muscovite, sericite the elemental information extracted from spectra and mixed-layer clay minerals in tills in order to is largely indirect mineralogical information, and draw further conclusion of the location of the de- the success of quantification of elements is highly tected elements in the till soil. However, the study resultant from cross-correlation between elements was lacking such proper pretreatment of the till due to their co-existence in minerals or of miner- samples followed by XRD analyses. als. Therefore, it would have been very important

5.4 Recommendations for future research

The close-range soil sensing with VSWIR and ch- radiative transfer models have the potential to pro- emometry is a relatively young branch of science, duce more accurate and consistent predictions of and thus several research steps are needed to make pigment interactions because the radiative trans- conclusions concerning its usefulness. One of the fer models are based on physics and the use of full major drawbacks of spectroscopy-chemometrics spectrum. Therefore, calibrations would not be re- with respect to soil geochemistry is that the cali- quired for each dataset. Compared to vegetation, bration and validation datasets should cover the less modeling work has been conducted in the field entire range of concentrations as extrapolation of soil transfer models implying that the theoretical would cause unreliable concentrations. In geo- approach might bring new insight into modeling chemical work the out of line samples are usually quantities of soil constituents. Radiative transfer the most interesting and in mineral exploration modeling has been applied extensively to lunar may potentially be the ones indicating underlying surface research. Applying Hapke’s (2005) radia- mineralizations. Samples on the flanks of the dis- tive transfer theory Li & Li (2011) were able to use tribution are usually few and thus models might inverse modeling to predict FeO, mineral abun- not be able to predict those values accurately. dances, and particle size with reasonable success. An additional issue related to distribution is the Liang (1997) proposes numerical radiative transfer geological range. Some studies state that devel- modeling to understand the optical depth of soil opment of reliable VSWIR prediction models for but also notes that for large particle soils, e.g., sandy soil chemical properties should be limited to geo- soils, geometric-optical models might be more ap- logically homogenous areas (Chodak et al. 2002, propriate. In addition, the microtopographical is- Udelhoven et al. 2003). Others suggest that there sues caused by macrotopography of agricultural is great potential for spectroscopic methods with soils (tilling) may have a significant influence on worldwide calibrations and exhaustive worldwide the reflectance and should be considered in mod- spectral libraries would solve much of these prob- elling (Cierniewski et al. 2010). Overall, more re- lems (e.g., Brown et al. 2006). Future research in search should be conducted on radiative transfer geochemical studies should involve using spec- modeling of soils. troscopy as a screening tool for more expensive Nduwamungu et al. (2009) discuss the devel- laboratory analyses such as suggested by Chang & opments in soil spectroscopy practices which are Laird (2002) when predicting soil C and N. Ad- needed to make it fully operational. According to ditional research is also needed to develop spec- them the key issues for better comparison of the tral similarity measures to detect spectral outliers model performance is standardization of sample (e.g., Ramírez–López et al. 2011) and use princi- preparation, measurement techniques, spectrum pal component analysis to plot the observations acquisition, spectral pretreatment techniques, cali- into a standardized principal component space to bration statistics, and reporting of the metadata. identify spectrally unique or dissimilar observa- An interesting observation by Stenberg (2010) tions that might be difficult to model (Shepherd & suggest that rewetting of a soil sample to 20-30% Walsh 2002, Brown et al. 2005). before spectral measurement would improve the Another weak point of empirical models is the prediction of especially organic C but also clay. difficulty in extending predictive equations to Although the mechanisms are yet unknown, other datasets. Ustin et al. (2009) concludes that further studies are needed for exploring its us- compared to empirical models, physically based ability. Overall, uncertainties in modeling for soil

55 Geological Survey of Finland Maarit Middleton properties might be too large with current VSWIR classification is also available for wide user com- sensors but improvement of sensor technology at munities when processing hyperspectral data. It MWIR and LWIR ranges hold a high potential of has already been demonstrated to be very powerful improving the soil predictions (Linker 2011). in, e.g., subtropical wetland studies (Zhang & Xie Besides the constant advances in non-linear 2012). However, when working with transitional classifiers, object based image analysis methods community structure the applicability of an object will also become more common in hyperspectral based approach has to be carefully evaluated. Hy- data processing. Currently SVMs are already im- perspectral classifiers are advanced to include con- plemented in the commercial object based image textual information. The spectral-spatial classifiers analysis software packages (e.g., eCognition, Trim- are reported as being successful for data with large ble Geospatial Imaging, Munich, Germany). In- homogenous regions and where spectral signatures corporation of object based texture into vegetation of multiple classes overlap (Chanussot et al. 2010).

5.5 Chapter summary

The main conclusions of this thesis were: 4) Prediction of concentrations of several ele- 1) Hyperspectral spectroscopy was feasible in ments was possible with SWIR spectra for fine novel geoenvironmental applications in the bo- fraction of glacial tills but only in the presence real region of Finnish Lapland. Quantification of spectrally active mineral material and cross- of glacial till soil dielectric permittivity and con- correlation of elements with the spectrally centrations of several elements was statistically active or extraneous components. significant using single and multivariate regres- 5) The sample set specificity of the empirical cali- sion techniques. Classifying ground cover plant brations, great prediction uncertainties related associations for peatland site type mapping and to global models, and lack of physical models assessment of site suitability for artificial regen- currently restrict the applicability of VSWIR- eration of Scots pine from hyperspectral imag- chemometric modeling in estimation of till soil ing spectroscopic data was also successful. These chemical element concentrations. applications of imaging spectroscopy are pro- 6) Hyperspectral imaging of soil moisture driven gressed furthest towards being applied in prac- spatial plant community patterns for soil mois- tice but significantly more research is needed to ture based site suitability assessment for Scots solve the issues related to prediction of chemical pine was moderately successful on a previously element concentrations of glacial till soils. Norway spruce-downy birch dominated clear- 2) Dielectric permittivity of glacial till soils can cut in central Lapland. The success was related be slightly better predicted from close-range to the soil moisture subjectibility of several sensing of VSWIR spectra with RVM and RF abundant understory species. compared to exponential single variate regres- 7) Using medium spectral and spatial resolution sion. Although this study was conducted on optical remote sensing data for the suitability small samples set with relatively narrow spread assessment could be a cost efficient way for for- of data (i.e., Ɛ=3.6–26.3, clay fraction 2.4–5.5%, est managers to minimize risk related to Scots fine fraction 23.5–47.1%, OMC 0.6–5.8%). pine regeneration. Economic analyse are still Thus further studies with datasets having large needed to assess the final cost-benefit and spread and variability are needed for more gen- applicability to a variety of site types. eralized conclusions. 8) A constrained ordination based statistical ap- 3) The exponential soilƐ -VSWIR reflectance re- proach was developed for optimizing the class lationship and leveling-off reflectance atƐ ~15 hierarchal level of aapa peatland site types from would enable classifying exposed soil pixels on medium resolution hyperspectral imaging data. mechanically prepared forest compartments in The future intent would be to use this approach order to assess the soil moisture based suitabil- as a preprocessing step for mapping of peat- ity for artificial regeneration of dry soil tolerant lands, e.g. for national wetland inventories to (Ɛ<15) Scots pine. High spatial resolution opti- support conservation planning and modeling cal remote sensing data and ground Ɛ calibra- of greenhouse gas emissions. tion are required for this task.

56 Hyperspectal close-range and remote sensing of soils and related plant associations. Spectroscopic applications in the boreal environment

ACKNOWLEDGEMENTS

This thesis is a product of years of interacting, com- Närhi, Saara Salmela (WSP Finland Ltd.), and municating and sharing ideas between people who Oddvar Skre (Skre Nature and Environment). I have a passion for nature, science and sophisticat- have enjoyed working with all of you and all the ed risk taking in order to push the boundaries of ones I forget to mention at GTK and elsewhere. ‘the traditional’. I remain in debt to my instructor Kent Middleton revised the language of all the and mentor Raimo Sutinen for introducing spec- papers several times. Thank you also to Ed Clou- troscopy and interdisciplinary thinking when I en- tis (University of Winnipeg), Neal Scott (Queen’s tered work life as a young university student, and University) and several anonymous reviewers for for the long term encouragement and support over constructive comments on the manuscripts of the the years. Thank you also to my unofficial instruc- research papers. My sincere gratitude also goes to tor Markus Törmä for promptly giving feedback in the reviewers Angela Harris and Alexander Mc- the writing stages of this work. Although he mostly Bratney for their thorough commenting of this ‘sensed’ the work ‘remotely’ due to the significant thesis synopsis. geographic distance between us, this approach felt Over the years I have also been privileged to very appropriate for the research topic. Sincere study remote sensing with the Canadian academ- thanks also go to my supervisor professor Henrik ia. Lectures and discussion with professors Mryka Haggrén for accepting me as his student without Hall-Beyer and Steven E. Franklin at the Universi- a background in engineering and for guiding me ty of Calgary in 1998-1999 inspired me greatly, and through the practical stages of my studies. reconfirmed my desire to study remote sensing. I want to express my sincere gratitude to the Professor Paul Treitz provided me with a peaceful co-authors Hilkka Arkimaa, Eija Hyvönen, Viljo place to retreat with proper facilities and a great Kuosmanen, Paavo Närhi, Raimo Sutinen (Geo- research environment in the LARSEES laboratory, logical Survey of Finland, GTK), Ari Teirilä (Ka- at the Queen’s university Geography Department jaani University of Applied Sciences), and Paul in 2010-2011 where I was able to concentrate on fi- Treitz (Queen’s university) for their contribution nalizing this work. Warm hearted wishes go to the to the original research papers. Paavo Närhi and LARSEES crowd including Karin van Ewijk, Greg Ari Teirilä devoted their statistical skills for bring- McQuat, Adam Collingwood, and Fiona Gregory, ing this work up to the publishable standards. and all the faculty, staff and students at the depart- Hilkka Arkimaa, Eija Hyvönen, Viljo Kuosmanen, ment for making my year memorable. Raimo Sutinen, and Paul Treitz shared data, ideas This thesis was financed by several sources. and their precious time for guiding me. The ex- Emil Aaltosen säätiö, Seppo Säynäjäkankaan pertise of GTK personnel Terttu Aaltonen, Viena Tiedesäätiö and GTK international fund provided Arvola, Timo Hirvasniemi, Pentti Hölttä, Jukka financial support for my ‘writing sabbatical’ to Laitinen, Päivi Kuikka-Niemi, Eira Kuosmanen, Queen’s university. The AISA data were acquired Tapio Muurinen, Markku Mäkilä, Vesa Nykänen, as part of the HYDO (Hyperspectral remote detec- Marja Liisa Räisänen, Mia Tiljander, and Kimmo tion and mapping of geological objects) – project Virtanen has contributed to data processing, in- between the GTK, National Technology Agency terpretation of the results and editorial work. of Finland and mining industry in 1998-2001. Instructions also given by Gary Raines (University The HyMap data were acquired as a part of the of Nevada, USGS), Carlos Roberto de Souza Fil- MINEO project (Monitoring and assessing the ho (State University of Campinas), Kai Mäkisara environmental Impact of mining in Europe us- (Finnish Forest Research Institute), and Lea ing advanced Earth Observation Techniques, EU Hämäläinen (Labtium Oy) contributed to data funded, IST–1999-10337, 2000-2003). Viljo Kuos- acquisition and interpretation. Special thanks go manen was the engine for both of these projects. to GTK personal Paula Haavikko, Matti Piekkari, The laboratory spectroscopic work was initiated in and Markku Virtanen and to Mari Jakonen and the Academy of Finland project Mosaic patterns Mari Kuoppamaa (University of Oulu), for con- of forest soils (1997-2000) and continued in the ducting field work, sample preparation, spectral GTK funded project Global change and forest soils measurements, and some data preprocessing. For (2007-2012) managed by Raimo Sutinen. I want to the vegetation inventories I sincerely thank Paavo express my gratitude to all of my present and past

57 Geological Survey of Finland Maarit Middleton managers at GTK for enabling me to conduct a ing there for me. I will now stop neglecting you majority of the work on salary. with the excuse of the thesis. My loving thoughts The greatest thanks of all go to my husband go especially to my parents Pirkko and Kari Siira Kent who’s been running our family of four for the always promoting education to me. Thanks dad past few years. Thanks to Sofia and Benjamin for for letting me know already years back that once keeping me busy also on times off. Thank you also I will get my Ph.D. you will dig into your pocket to my Finnish and Canadian families, especially to one more time for the celebrations. Hey, it’s time my mother-in-law Rita Middleton and my sister to party! Marjo Siira, and all my friends near and far for be-

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68 Hyperspectral close-range and remote sensing of soils and related plant associations - ... • Maarit Middleton ISBN 978-952-217-282-2 (PDF version without articles) without version 978-952-217-282-2 (PDF ISBN 978-952-217-281-5 (paperback) ISBN ) and peatland Pinus sylvestris site types. The predictions were produced with statistical and The predictions were produced site types. The results demonstrate that glacial machine learning techniques. can be statistically till soil element concentrations and moisture efficiency of soil analyses. significantly predicted to improve cost applicable for Hyperspectral remotely sensed data is readily as part of forest soil moisture based site suitability assessment planning or management practices. In the future, conservation could benefit from estimation of green house gas emissions ecological peatland site type mapping. This Ph.D. thesis comprises of a synopsis and four original papers of a synopsis and four original This Ph.D. thesis comprises close- applications of hyperspectral dealing with geoenvironmental Visible relevant to northern Finland. range and remote sensing nm) close-range spectral and short wavelength infrared (350–2500 till soil water content data was used for quantification of glacial sensed imaging and chemical element concentrations. Remotely of site suitability for spectroscopic data was applied in mapping artificial regeneration to Scots pine ( [email protected] www.gtk.fi www.gtk.fi