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Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments

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Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments Christian H ttich Dissertation zur Erlangung des naturwissenschaftlichen Doktorgrades der Friedrich-Schiller-Universität Jena Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments Dissertation zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) vorgelegt dem Rat der Chemisch-Geowissenschaftlichen Fakult t der Friedrich-Schiller-Universit t Jena von Dipl.-Geogr. Christian H&ttich geboren am 12. Mai 19,9 in Jena 1 2 Gutachter- 1. .rof. Dr. Christiane Schmullius 2. .rof. Dr. Stefan Dech (Universit t /&rzburg) 0ag der 1ffentlichen 2erteidigung- 29.04.2011 3 Acknowledgements I _____________________________________________________________________________________ Ackno ledgements 0his dissertation would not have been possible without the help of a large number of people. First of all I want to thank the scientific committee of this work- .rof. Dr. Christiane Schmullius and .rof. Dr. Stefan Dech for their always present motivation and fruitful discussions. I am e9tremely grateful to my scientific mentor .rof. Dr. Martin Herold, for his never-ending receptiveness to numerous questions from my side, for his clear guidance for the definition of the main research issues, the frequent feedback regarding the structure and content of my research, and for his contagious enthusiasm for remote sensing of the environment. Acknowledgements are given to the former remote sensing team of “BIO0A S&d?- Dr. Ursula Gessner (DFD-DLR), Dr. Rene Colditz (COAABIO, Me9ico), Manfred Beil (DFD-DLR), and Dr. Michael Schmidt (COAABIO, Me9ico) for e9tremely fruitful discussions, criticism, and technical support during all phases of the dissertation. I am very thankful for a number of impressive field campaigns in Aamibia and South Africa. I also have to thank a lot Dr. Jens Oldeland (University of Hamburg) for his motivation and valuable feedback to my manuscripts and very forthcoming brainstorming. I am very grateful to Ben Strohbach and Franziska Bangombe from the Aational Botanical Research Institute of Aamibia, for their e9tremely helpful discussions on vegetation ecology in Aamibia, for valuable refinements of the manuscripts, for the provision of the vegetation data, and the guidance during our field campaigns. I really appreciate Dr. Christopher Conrad for scientific support and to ensure a very open, friendly, and inspiring working atmosphere. I am really thankful to my colleagues from the Institute of Geography and GeologyCs Remote Sensing Unit in /&rzburg- Sarah Asam, Anna Cord, Sebastian Fritsch, 2itus Himmler, Dr. Doris Blein, .attrick Bn1fel, Dr. 0obias Landmann, Sylvia Le9, Fabian L1w, Dr. Miriam Machwitz, Dr. Aoellie Dahou R&th, Gunther Schorcht, Martin Schmidt, Dr. Matthias Schramm, Michael 0hiel, Dvonne /alz, Dr. Martin “Oggi? /egmann, Julian Eeidler. Special thanks to all of you for reviewing and improving the earlier manuscripts of the dissertation. I am really grateful to work in the FBiotopC with Ursula, Miriam, and Oggi, Finn, and later with Anna and Aoellie. /ithout their fellowship and the valuable help to a large number of specific problems this dissertation would not have been finished Get. Acknowledgements are also given to my student assistants- Sylvia Le9, Michael Gottschling, Michael /iesner, and .eter Eellner. 0hey did a great Gob during hours of remote sensing data pre-processing. I am deeply grateful to my parents Conny and Frank H&ttich. /ithout their motivation and financial support during my studies this dissertation would not have been realised. I am very thankful to my brothers Georg and Martin H&ttich, my grandmothers Marianne Frenzl and Renate H&ttich, and my friends for their tolerance and demonstrating me from time to time that there is a life besides remote sensing. 0his dissertation is dedicated to my wife Franziska, for her love, patience, and her understanding for too long office hours, my frequent absence during the field conference trips, and for her mental support. 0his dissertation has been carried out in the framework of the “BIO0A Southern Africa? proGect funded by the Federal Ministry of Education and Research (BMBF). Jena, April 2011 Christian H&ttich I II _____________________________________________________________________________________ II Abstract III _____________________________________________________________________________________ Abstract Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments CHRIS0IAA HH00ICH 0he characterisation and evaluation of biodiversity and land-cover in Southern AfricaCs Savannas is a maGor prerequisite for suitable and sustainable land management and conservation purposes. However, mechanisms for frequent update of the status and trends of biodiversity and land change processes are still missing. 0he knowledge of the spatial distribution of vegetation types is an important information source for all social benefit areas. Remote sensing techniques are essential tools for mapping and monitoring of land-cover. 0he development and evaluation of conceptsC for integrated land-cover assessments attracted increased interest in the remote sensing community since evolving standards for the characterisation of land-cover enable an easier access and inter- comparability of earth observation data. Regarding the comple9ity of the savanna biome in terms of the spatiotemporal heterogeneity of the vegetation structure and rainfall variability, the main research needs are addressing the assessment of the capabilities and limitations of using satellite data for land-cover and vegetation mapping purposes. /hile integrating Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for mapping vegetation types in Aamibia, the temporal characteristics of semi-arid life-forms types were used for the classification of vegetation types in Aamibia. 0he Random Forest framework was applied and evaluated for classifying vegetation types using MODIS time series metrics as input features. 0he study region comprised the Balahari in the north-eastern communal lands of Aamibia. Regarding of the evolving global standardisation process for land-cover, there is the need to report the capabilities and limitations of the FAO and UAE. Land Cover Classification System (LCCS) in regional case studies. LCCS is evaluated in terms of the applicability in open savanna ecosystems and as ontology for the semantic integration of an in-situ vegetation database in a coarse scale mapping framework based on MODIS data. Further, the capabilities of the methodological setups of global land-cover mapping initiatives are assessed while using the results of the integrated vegetation type mapping framework. In order to assess the e9isting accuracy uncertainties of mapping savannas at global scales, the effects of composite length and varying observation periods were compared in terms of mapping accuracy. 0he implications for global monitoring were discussed. 0he determinants of precipitation amount and mapping accuracy were evaluated by comparing MODIS and 0ropical Rainfall Measuring Mission (0RMM) time series data. Integrating multi-scale land-cover information, such as life form, cover, and height of vegetation types ( in-situ ), vegetation physiognomy and local patterns (Landsat), and phenology (MODIS) in an ecosystem assessment framework, resulted in a fle9ible land-cover map including a broad structural-physiognomic and a phytosociological legend. 0he principle of classifiers and modifiers in LCCS proved to be applicable in dry savanna ecosystems and can be confirmed as overarching land-cover ontology. Analyses of time series classifications showed that mapping accuracy increases with increasing observation period. Small composite period lengths lead to increased mapping accuracies. 0he relationship between mapping accuracy and observation period was observed as a function of precipitation input and the magnitude of change between land-cover stages. 0he integration of in-situ data in a multi-scale framework leads to improved knowledge of the regionalisation of Aamibian vegetation types. On the one hand, the case study in the north-eastern Balahari showed that multi-data mapping approaches using in-situ to medium resolution MODIS time series data bear the potential of the wall-to-wall update of e9isting vegetation type maps. On the other hand, the global remote sensing community can e9tend the reference databases by integrating regional standardised biodiversity and ecotype assessments in calibration and validation activities. 0he studies point on the uncertainties of mapping savannas at global scales and suggest III I2 Abstract _____________________________________________________________________________________ possible solutions for improvements by adapting the remotely sensed feature sets, classification methods, and integrating dynamic processes of semi-arid ecosystems in the mapping framework. Keywords: 2egetation types, land-cover, savanna, MODIS time series, Random Forest, LCCS, 0RMM, Aamibia IV Deutsche Eusammenfassung 2 _____________________________________________________________________________________ Deutsche $usammenfassung Vegetationskartierung in der Trockensavanne Namibias unter Nut%ung botanischer Felddaten und multitemporaler Satellitendaten: Kon%epte %ur integrativen Erfassung der Landbedeckung CHRIS0IAA HH00ICH Die Erfassung und Bewertung des Eustandes
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