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Building capacities for digital soil organic carbon mapping

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The presentation was given by Mr. Mario Guevara during the GSOC map training that was held in Aguascalientes Mexico, 26-30 June 2017.

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Building capacities for digital soil organic carbon mapping

  1. 1. Building capacities for digital soil organic carbon mapping M. Guevara, G. F. Olmedo E. Stell, J. G. De Lira, C. Cruz, R. Vargas
  2. 2. The University of Delaware, Plant and Soil Sciences La Comision Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO Instituto Nacional de Tecnología Agropecuaria, Ar. Instituto Nacional de Estadistica y Geografia Mx. Many thanks to:
  3. 3. Topics Conceptual basis The SCORPAN model Digital terrain analysis Harmonization of databases Modeling SOC Validating and predicting Uncertainty of SOC estimates
  4. 4. http://www.stockholmresilience.org/research/planetary-boundaries/planetary-bound aries/about-the-research/the-nine-planetary-boundaries.html Planetary boundaries that must not be transgressed are closely related to soil organic carbon (SOC)
  5. 5. How can we quantify the spatial variability of SOC and predict in places where no information is available?
  6. 6. We use digital soil mapping to ananlyze the spatial variability of SOC and its associated uncertainty aiming to provide a baseline estimate useful for accounting and monitoring soil carbon. Digital Soil Mapping (DSM) is the creation and the population of a geographically referenced soil database, generated at a given resolution by using field and laboratory observation methods coupled with environmental data through quantitative relationships (see http://digitalsoilmapping.org/). We assume that different DSM models will capture different portions of SOC variability. We combine linear models with data driven statistics for variable selection, prediction to new data and uncertainty estimation.
  7. 7. Land modeling efforts to predict climate change rely on SOC estimates but no uncertainty assessment is available. High uncertainties and significant differences of SOC estimates associated with the number of covariates, the pixel size and the statistical performance of the modeling approaches. We coupled remote sensing, geomorphometry and climate surfaces to generate a multiscale and multisource set of prediction factors. We model, cross validate and estimate uncertainty of SOC variability models.
  8. 8. Overreaching goal To provide cyber-infrastructure tools and develop country-specific, technical, analytical and institutional capacities for digital SOC mapping across Central America and the Caribbean . To better inform and empower producers and protect the functioning of the productive landscapes of our territories Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations Folberth et al. (2017) Country-specific information is required by regional-to-global academic and policy relevant research efforts (GlobalSoilMap.net, FAO-GSM cookbook 2017).
  9. 9. Working principles and specific objectives Reproducible research Transparency in methods Open source platforms To teach the participants how to generate a high quality digital SOC maps based on state of the art techniques including uncertainty of SOC estimates The SOC resulting maps could be used as baseline estimates for reporting, monitoring and verification of SOC stocks
  10. 10. Working objectives To generate a regional, interpretable and predictable model of SOC variability Country specific SOC map representative of the first 30cm of depth at the spatial resolution of 1x1km, derived using digital soil mapping techniques and including the uncertainty of SOC estimates.
  11. 11. Baseline datasets (SOC ground thruth) SOC Point data (e.g. Mexico ): http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/vectorial_seriei.aspx http://www.inegi.org.mx/geo/contenidos/recnat/edafologia/vectorial_serieii.aspx Global: http://www.earth-syst-sci-data.net/9/1/2017/ http://www.isric.org/sites/default/files/isric_report_2015_03.pdf
  12. 12. Baseline datasets (SOC covariates 1x1km) Digital terrain parameters Climate surfaces Remote sensing Legacy and polygon maps (i.e., soil, geology, land use/cover) SoilGrids250m system Pelletier et al. 2016, Shangguan et al., 2017, Hengl et al. 2017, Fick and Hijmans, 2017
  13. 13. McBratney et al 2000
  14. 14. Targulian y Goryachkin 2004
  15. 15. Conceptual basis Dokuchaev 1883 Jenny 1941 Troeh 1964 Walker 1968 Legros and Bonneric 1979 Moore et al 1993 Heuvelink and Webster 2001
  16. 16. McBratney et al 2003 Grunwald et al. 2009
  17. 17. Grunwald et al. 2011
  18. 18. Guevara et al. NACP, 2017
  19. 19. f, a data driven decision ‘I will keep my eye on the quantile culture to see what develops’. Breiman 2001 Quantile regression forests, Meinshausen 2006 The full conditional distribution, a surrogate of uncertainty Kriging, Support Vector Machines, Cubist, Kernel weighted nearest neighbors https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726 http://robertmarks.org/Classes/ENGR5358/Papers/NFL_4_Dummies.pdf http://onlinelibrary.wiley.com.udel.idm.oclc.org/doi/10.1111/2041-210X.12397/abstract http://jmlr.org/papers/volume15/delgado14a/delgado14a.pdf https://www.analyticsvidhya.com/blog/2017/02/introduction-to-ensembling-along-with-implementation-in-r/
  20. 20. Examples of large scale SOC mapping Viscarra Rossel et al 2014, Hengl et al. 2017 ...
  21. 21. Digital terrain analysis (DTA) in SAGA GIS Topography is a major driver of soil variability Topography can be represented by digital elevation models We have digital elevation models globally available Using DTA we can derive topographic attributes (e.g. Slope, aspect, wetness index). Derive terrain parameters in SAGA GIS using a digital elevation model of 1x1 km. http://www.saga-gis.org/
  22. 22. Global-to-local and local-to-global SOC mapping All models are wrong but some of they are useful Models should not compete but inform each other Different models (and modeling cultures) will capture different portions of SOC There are no best method on digital soil mapping (no silver bullets) Ensemble learning should be part of the pedometrics agenda The eternal question is, how inform nationwide policy decision based on the best information available? Box 1976, Breiman 2001, Ho and Pepyne 2002,, Finke 2012, Qiao et al. 2015
  23. 23. The quantile regression forests
  24. 24. Harmonization of datasets To generate a geographical information system of all available data sets. For points and grids, same geospatial reference For grids, same pixel size, 1km. (note: Rasterize first polygon maps) http://spatialreference.org/ Exercise 2.1 Harmonize SOC predictors in a regular grid of 1x1km and all available information Exercise 2.2 Generate a regression matrix of available SOC data and the values of the predictors at observation points.
  25. 25. SOC covariates
  26. 26. Information sources (some) http://worldclim.org/version2 http://onlinelibrary.wiley.com.udel.idm.oclc.org/doi/10.1002/2015MS000526/pdf www.soilgrids.org http://worldgrids.org/doku.php/wiki:layers http://cci.esa.int/
  27. 27. Working folder 1. List of soil covariates available to 1km from ISRIC’s worldgrids.org 2. List of additional covariates 3. Limit of each country http://www.gadm.org/country 4. References (McBratney et al. Batjes et al, Hengl et al.) 5. Code (considering FAO DSM cookbook, http://www.fao.org/3/a-bs901e.pdf) 6. Regression matrix 7. Covariates matrix 8. Digital elevation model processing steps (derive terrain parameters in SAGA GIS)

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