Centre for Geo-Information Thesis Report GIRS-2005-24 Digital Soil Mapping in the Nioro du Rip Area, Senegal Bas Kempen June 2005 Digital Soil Mapping in the Nioro du Rip Area, Senegal Bas Kempen A thesis submitted in partial fulfillment of the degree of Master of Science at Wageningen University and Research Centre, The Netherlands. June 2005 Wageningen, The Netherlands Registration nr: 800303427040 Thesis code: GRS-80340 Thesis report: GIRS-2005-24 Supervision: Dr Ir Sytze de Bruin Centre for Geo-Information Dr Ir Gerard Heuvelink Laboratory of Soil Science and Geology / Alterra Dr Ir Jetse Stoorvogel Laboratory of Soil Science and Geology Examination: Prof. Dr Ir Arnold Bregt Centre for Geo-Information Dr Ir Sytze de Bruin Centre for Geo-Information Dr Ir Gerard Heuvelink Laboratory of Soil Science and Geology / Alterra Centre for Geo-Information Environmental Sciences Department Wageningen University Acknowledgements Inspired by a presentation by Gerard Heuvelink, I dived a year ago into the interesting world of pedometrics and digital soil mapping. A dive that gave me the opportunity to explore Senegal, that lead me to a feast of eating and drinking (in scientific circles referred to as the “Global Workshop on Digital Soil Mapping”) in Montpellier, France and that finally resulted in the report you are now looking at. A dive I do not regret to have made. During the past year many people got involved in this project. I would like to use this opportunity to thank them. I will start with my supervisors Sytze de Bruin, Gerard Heuvelink and Jetse Stoorvogel. Thank you very much for your help, advice, comments and dedication throughout the year and for the critical review of the draft report. Your efforts were greatly appreciated. In Senegal I could not do without the support of Bocar Diagana and Adrien Mankor of ISRA Dakar. Thank you very much for all arrangements you made to facilitate the fieldwork and for making this toubab feel a bit home. You were the solid base of the success of the fieldwork. Another person who I owe my deepest thanks to is Abibou Niang of ISRA St Louis and his family. Abibou, thank you for your incredible hospitality during my stay in St. Louis, for the help you continuously offered and for all the work you did for me, including the analysis of 187 soil samples. For the fieldwork I owe my thanks to my excellent field guide, moto-driver and assistant Mbaye Diaw, to the people of the villages in the Nioro du Rip area who gave us directions time and time again and shared their food and water with us and to the ISRA enumerators in Nioro du Rip for their company. Furthermore I would like to thank the driver Cheikh Mbaye and the director of ISRA Bambey Ousmane Ndoye and all other people of the staff of the ISRA departments in Dakar, St Louis and Bambey who assisted me in one way or another. Merci beaucoup! Of the staff of the Centre for Geo-Information of Wageningen University I would like to thank Harm Bartholomeus, Gerrit Epema, Michael Schaepman and John Stuiver for their technical and scientifical help and advice. The advice of Dick Brus concerning soil sampling strategy and the validation procedure was greatly appreciated. This thesis marks the end of a seven year period as a student at Wageningen University. Last but not least I would like to thank my family and friends for their interest and support. Even Maarten who kept me on the couch too long in the mornings by seducing me with fresh coffee (… ‘you can drink one more cup’….). And Hans and Bram who kept me too often around the kitchen table to battle for Middle Earth while I had to be behind my computer. I am especially greatful to my girlfriend Els for her support and her patience during the many months I was exploring the far corners of this planet the past years. And above all I owe my deepest thanks to my parents. Not only for the approximately 3000 kilometers they drove the past seven years to pick me up and bring me to the train station but for their unconditional support in every sense during this period of my life. Bas Kempen Wageningen, June 2005 3 4 Table of Contents ACKNOWLEDGEMENTS 3 ABSTRACT 7 1. INTRODUCTION 9 1.1 DIGITAL SOIL MAPPING 9 1.1.1 Background 9 1.1.2 Environmental variables and soil spatial prediction 9 1.1.3 Quantitative vs. qualitative models of soil variation 10 1.2 SOIL DATA NEEDS IN SUB-SAHARAN AFRICA 11 1.2.1 Trade-off analysis model 11 1.2.2 Importance of SOC and soil texture for agricultural production 11 1.2.3 Digital soil mapping as tool to meet soil data requirements 11 1.2.4 Research objective 12 2. MATERIALS AND METHODS 13 2.1 THE STUDY AREA 13 2.1.1 Location and climate 13 2.1.2 Geology and geomorphology 13 2.1.3 Soils and land use 16 2.2 CONCEPTUAL MODEL 17 2.2.1 CLORPT-related processes that affect soil spatial distribution in the Nioro du Rip area 17 2.2.2 Mapping soil organic carbon and the fine fraction contents: model framework 19 2.3 DATA DESCRIPTION, PREPROCESSING AND GENERATION 20 2.3.1 Soil data 20 2.3.2 Remote sensing imagery 22 2.3.3 Digital elevation model 23 2.4 FROM CONCEPT TO APPLICATION: FROM QUALITATIVE TO QUANTITATIVE 26 2.4.1 Modelling soil organic carbon: model 1 26 2.4.2 Modelling soil organic carbon: model 2 27 2.4.3 Modelling soil texture: model 1 28 2.4.4 Modelling soil texture: model 2 29 2.5 SAMPLING AND VALIDATION 30 2.5.1 Sampling Strategy 31 2.5.2 Selection of sample points for validation 31 2.5.3 Sampling the soil 33 2.5.4 Validation procedure 34 3. RESULTS 37 3.1 MODEL INPUT DATA 37 3.1.1 Landsat ETM+ classification 37 3.1.2 Landscape unit mapping 40 3.2 MODEL RESULTS 42 3.2.1 Soil organic carbon mapping 42 3.2.2 Soil texture mapping 42 3.3 VALIDATION DATA ANALYSIS 45 3.3.1 Study area level 45 3.3.2 Landscape unit level 47 5 3.4 MODEL VALIDATION 49 3.4.1 Bias and goodness of fit 49 3.4.2 Statistical inference 50 4. DISCUSSION 57 4.1 PREDICTION MODEL DESIGN 57 4.2 MODEL INPUT: DATA QUALITY AND UNCERTAINTY 58 4.2.1 Digital elevation model 58 4.2.2 Landscape classification 58 4.2.3 Landsat 7 ETM+ image classification 59 4.2.4 Environmental predictor variables 60 4.3 SPATIAL PREDICTION OF THE SOIL ORGANIC CARBON AND FINE FRACTION CONTENTS 60 4.3.1 Spatial dependency 60 4.3.2 Other issues concerning spatial prediction 62 4.4 FINAL THOUGHTS 63 4.4.1 Digital soil mapping within the TOA context 63 4.4.2 Digital soil mapping in Africa 64 5. CONCLUSIONS 65 5.1 MODEL DESIGN AND SPATIAL PREDICTION 65 5.2 SPATIAL DISTRIBUTION OF SOC AND FF 66 5.3 CONCLUDING REMARKS AND RECOMMENDATIONS 66 REFERENCES 67 APPENDICES 71 APPENDIX A The soil data set collected during the fieldwork in 2005 73 APPENDIX B Summary statistics of the soil organic carbon and fine fraction contents on cluster level 76 APPENDIX C Scatterplots showing the observed vs. the predicted values for the three landscape positions 78 APPENDIX D Summary statistics of the PEs and SPEs of the two models on cluster level 80 6 Abstract Digital soil mapping techniques quantify the functional relationships between soil properties and more readily observed environmental variables, including land use. These relationships are used in quantitative models that predict soil properties at unvisited locations. This study applied digital soil mapping to meet quantitative soil data requirements in the Nioro du Rip area, Senegal. It aimed to integrate qualitative soil-landscape and environmental process knowledge in a catenary context with quantitative spatial prediction methods and to validate the results by statistical inference with an independent data set. The soil properties of interest were soil organic carbon and the fine fraction of the soil texture. A qualitative conceptual model of soil-landscape processes and soil spatial distribution was defined and translated into prediction models that use simple prediction rules. Four models were developed on basis of the available input data: two models for each soil property. These two models had a different level of detail. Environmental input data were derived from a digital elevation model and a Landsat 7 ETM+ image. The models were calibrated with a small soil data set. Validation showed that the model results were poor. All results were biased and the mean squared prediction errors were large. The main reason for the poor model performance was the lack of sufficient soil and environmental data to define the conceptual model and to calibrate the corresponding quantitative model. This resulted in models that were largely based on expert judgment and that did not have a solid empirical support. The insights gained and validation data collected in this study create a basis for more accurate quantitative soil property mapping in the Nioro du Rip area. The elaborate soil data set can, together with additional (high resolution) environmental data, result in a better understanding of the soil spatial distribution with which spatial prediction can be improved. Keywords: quantitative models, catena, Landsat, DEM, soil organic carbon, soil texture, West Africa. 7 8 1. Introduction 1.1 Digital Soil Mapping 1.1.1 Background Increasing population pressure, (human-induced) land degradation, pollution and climatic change are putting more and more pressure on our natural resources.