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Soil Spectroscopy As a Tool to Assess Organic Carbon, Iron Oxides, and Clay Content in the Subtropical Thicket Biome of the Eastern Cape Province of South Africa

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Soil Spectroscopy As a Tool to Assess Organic Carbon, Iron Oxides, and Clay Content in the Subtropical Thicket Biome of the Eastern Cape Province of South Africa Centre for Geo-Information Thesis Report GIRS-2009-13 Soil spectroscopy as a tool to assess organic carbon, iron oxides, and clay content in the Subtropical Thicket Biome of the Eastern Cape province of South Africa Marco Nocita Date: 02/07/2009 Assigned and supported by: Soil spectroscopy as a tool to assess organic carbon, iron oxides, and clay content in the Subtropical Thicket Biome of the Eastern Cape province of South Africa Marco Nocita Registration number 800327606130 Supervisors: Lammert Kooistra Martin Bachmann A thesis submitted in partial fulfillment of the degree of Master of Science at Wageningen University and Research Centre, The Netherlands. Date: 02/07/2009 Wageningen, The Netherlands Thesis code number: GRS-80439 Thesis Report: GIRS-2009-13 Wageningen University and Research Centre Laboratory of Geo-Information Science and Remote Sensing Abstract In the subtropical thicket biome of the Eastern Cape province of South Africa, heavy browsing by goats, which remove shrub biomass more rapidly than it is replaced, transforms the dense closed-canopy shrubland into an open savanna-like system. This transformation causes a lot of changes, among which, soil fertility depletion. This document presents a project dealing with organic carbon (OC), iron oxides, and clay content assessment, in the degraded thicket biome, through the combination of soil spectroscopy and partial least square regression (PLSR) techniques. The study area is a transect crossing in direction south east-north west the Eastern Cape province of South Africa, from latitude -33.57 to -32.59 and longitude 25.38 (eastern extreme) to 25.26 (western extreme). The study area has been selected based on a GIS analyses, realized overlaying vegetation type, rainfall and topography data sets. A total of 113 points have been visited over a distance of 130 km. At every point field spectroscopy measurements were realized and soil samples of the first cm (topsoil) and of the 0-20 cm have been collected. The soil samples have been chemically and spectrally analyzed. The present study models the relationships between soil spectral reflectances, measured in situ and in the laboratory, and the soil parameters taken in consideration. The PLSR models developed with laboratory and field spectra gave good predictions of OC, with a root mean square error of validation (RMSEV) <0.6, and sufficient results for the iron oxides prediction (RMSEV always >0.55). The clay content prediction models didn’t produce enough accuracy. Results indicated that soil stoniness is an important variable to consider for the creation of soil properties prediction models.. The up-scaling process of the OC laboratory and field spectroscopy prediction models to the 232 EnMAP channels gave high level of accuracy, also including the noise component (signal to noise ratio=100). The promising results of this research study will serve as base for the future up-scaling processes of the obtained ground-based regression models to air-borne and space-borne hyperspectral data, in order to cover all the subtropical thicket biome of South Africa. IV Acknowledgments I would like to thank PRESENCE and Earthcollective for the assistance and dedication during the South African time. Special thanks to Silvia and Dieter for their patience and friendship and for the extraordinary short-time organization. I would like to specially mention Saint Edwill Moore from Patensie for believing always in the usefulness of the project and for solving problems just like WOLF in Pulp Fiction. Thanks also to my drivers and my workers, for coming with me around the subtropical thicket biome to get dirty with soil, and to Mike Powell, just to be a GIS magician. Thanks to Andreas Mueller and Martin Bachmann of the German Aero-Space Agency (DLR) for believing I’m not crazy and for giving me credits, possibility (money), and for replying to my phone calls from South Africa like: “Marco, are you feeling alone?”, and to send me the best surfer of Wurzburg University, Christian Huettich, together with the spectrometer. Thanks to Lammert Kooistra for accepting to supervise me. Thanks also to my girlfriend, friend, company for the life, and most beautiful swimmer in Munich, Lily, without that this project would not exist, and this life wouldn’t be so interesting. Last special thanks go to my mother and my brother for their support in difficult moments, and to my father, always with me, always with the guitar. V Table of contents Page Abstract………………………………………………………………..IV Acknowledgements………………………………………………….V Table of contents……………………………………………………..VI List of figures…………………………………………………………VIII List of tables…………………………………………………………..X Chapter: 1. Introduction…………………………………………………………….........1 1.1 Background of the Sub-tropical thicket biome……………………………….1 1.2 Problem description……………………………………………………………….5 1.3 Research objective and questions……………………………………………...7 1.4 Report overview……………………………………………………………...........8 2. Literature review…………………………………………………………….9 2.1 Organic carbon, Iron oxides, and clay content……………………………….9 2.2 Soil spectroscopy………………………………………………………………...10 2.3 Spectral data manipulations and statistical analyses……………………..12 2.4 VNIR-PLRS previous studies for soil properties estimation……………15 2.5 Ground Spectroscopy Up-scaling process to Imaging Spectroscopy………………………..……………………………….16 3. Methodology…………………………………………………………..........17 3.1 Study area………………………………………………………………………….18 3.2 Data collection…………………………………………………………………….19 3.2.1 Soil sampling campaign…………………………………………………….19 3.2.2 Field spectroscopy campaign……………………………………………..20 3.2.3 Soil samples analyses………………………………………………………21 3.3 Model construction……………………………………………………………….22 3.3.1 Datasets available……………………………………………………...........22 3.3.2 Spectral preprocessing……………………………………………………..22 3.3.3 PLSR model calibration and validation…………………………………..23 3.4 EnMAP up-scaling………………………………………………………………..24 3.4.1 Spectral resampling…………………………………………………………24 3.4.2 Noise simulation……………………………………………………………..24 3.4.3 Organic carbon model calibration and validation…………………..….25 VI 4. Results……………………………………………………………………….27 4.1 Laboratory analyses……………………………………………………………..27 4.2 Soil spectra interpretation………………………………………………………29 4.3 Models calibrations and validation……………………………………............31 4.3.1 Organic carbon……………………………………………………………….31 4.3.2 Iron oxides…………………………………………………………………….35 4.3.3 Clay content...…………………………………………………………………38 4.3.4 EnMAP simulations……………….…………………………………...........40 5. Discussion…………………………………………………………………..44 6. Conclusions and recommendations……………………………………51 7. References…………………………………………………………….........53 8. Appendixes………………………………………………………………….60 VII List of figures Page Figure 1: Location of the thicket biome in southern Africa…………………………………..2 Figure 2: Methodology overview…………………………………………………………….....17 Figure 3: map of the STB vegetation types…………………………………………………...18 Figure 4: map of the STB topography…………………………………………………………18 Figure 5: map of the STB rainfall…………………………………………………………….. .19 Figure 6: map of the STB study area…………………………………………………………..19 Figure 7: soil sampling scheme……………………………………………………………......21 Figure 8: Mean laboratory and field spectra obtained from topsoil and 0-20cm soil samples........................................................................................................................29 Figure 9: Topsoil laboratory and field spectra of plot 51, 52, and 53..................................30 Figure 10: ASD and resampled EnMAP topsoil laboratory and field spectra of plot 7...............................................................................................................................31 Figure 11: 0-20cm model, without water absorption bands, OC predicted vs. observed values………………………………………………………………………………….33 Figure 12: topsoil field without stones NIR-OC predicted vs. observed values..................33 Figure 13: topsoil field without stones NIR-OC predicted vs. observed values..................................................................................................................33 Figure 14: topsoil field with stones NIR OC predicted vs. observed values……………………………………………………………………………....33 Figure 15: variable importance for projection (VIP) representation related to topsoil laboratory (TL) organic carbon prediction model, considering the full spectral resolution (a) , and excluding the water absorption bands (b)………………….…………….. ……………............................................................34 Figure 16: variable importance for projection (VIP) representation of topsoil field stone (TFS) organic carbon prediction model, considering the full spectral resolution (a), and excluding the water absorption bands (b) ............................................35 Figure 17: topsoil field stone NIR iron content predicted Vs observed values....................37 Figure 18: B coefficients (VIP) of topsoil field stones iron oxides prediction model...........38 Figure 19: spectral loadings and loadings weights of topsoil field stones iron oxides prediction mode................................................................................................38 VIII Figure 20: EnMAP topsoil laboratory OC predicted Vs observed values............................41 Figure 21: B coefficients of EnMAP topsoil laboratory OC predicted Vs. observed values............................................................................................................41 Figure 22: EnMAP topsoil field, without stones, OC predicted Vs observed values...........42
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