A methodology for digital soil mapping in poorly-accessible areas

Abstract Effective soil management requires knowledge of the spatial patterns of soil variation within the landscape to enable wise land use decisions. This is typically obtained through time-consuming and costly surveys. The aim of this study was to develop a cost-efficient methodology for digital soil mapping in poorly-accessible areas. The methodology uses a spatial model calibrated on the basis of limited soil sampling and explanatory covariables related to soil-forming factors, developed from readily available secondary information from accessible areas. The model is subsequently applied in the poorly-accessible areas. This can only be done if the environmental conditions in the poorly-accessible areas are also found in the accessible areas in which the model is developed. This study illustrates the methodology in an exercise to predict soil organic carbon (SOC) concentration in the Limpopo National Park, Mozambique. Readily-available secondary data was used as explanatory variables representing the soil-forming factors. Conditions in the accessible and poorly-accessible areas corresponded sufficiently to allow the extrapolation of the spatial model into the latter. The spatial variation of SOC in the accessible area was mostly described by the sampling cluster (71.5%) and the landscape unit (46.3%). Therefore ordinary (punctual) kriging (OK) and kriging with external drift (KED) based on the landscape unit were used to predict SOC. A linear regression (LM) model using only landscape stratification was used as control. All models were independently validated with test sets collected in both accessible and poorly-accessible areas. In the former the root mean squared error of prediction (RMSEP) was 0.42–0.50% SOC. The ratio between the RMSEP in the poorly-accessible and accessible areas was 0.67–0.72, showing that the methodology can be applied to predict SOC in poorly-accessible areas as successful as in accessible areas. The methodology is thus recommended for areas with similar access problems, especially for baseline studies and for sample design in two-stage surveys.

[1]  K. Shepherd,et al.  Global soil characterization with VNIR diffuse reflectance spectroscopy , 2006 .

[2]  Peter Frost,et al.  Competing Claims on Natural Resources: What Role for Science? , 2008 .

[3]  Tomislav Hengl,et al.  Finding the right pixel size , 2006, Comput. Geosci..

[4]  Yuanfang Huang,et al.  Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP , 2008 .

[5]  Peter Scull,et al.  Predictive soil mapping: a review , 2003 .

[6]  M. Jacobs,et al.  Comparison of Methods for Interpolating Soil Properties Using Limited Data , 2001 .

[7]  R. Lark.  Optimized spatial sampling of soil for estimation of the variogram by maximum likelihood , 2002 .

[8]  Yong Li,et al.  Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information , 2010 .

[9]  L. P. van Reeuwijk,et al.  Procedures for soil analysis , 1995 .

[10]  D. J. Brus,et al.  Sampling for Natural Resource Monitoring , 2006 .

[11]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[12]  G. Metternicht,et al.  Testing the performance of spatial interpolation techniques for mapping soil properties , 2006 .

[13]  M. Stalmans,et al.  Plant communities and landscapes of the Parque Nacional do Limpopo, Moçambique , 2004 .

[14]  R. Mapa,et al.  Variability of soil properties in a tropical Alfisol used for shifting cultivation , 1996 .

[15]  Alex B. McBratney,et al.  An overview of pedometric techniques for use in soil survey , 2000 .

[16]  Gerard B. M. Heuvelink,et al.  About regression-kriging: From equations to case studies , 2007, Comput. Geosci..

[17]  Fabrizio Ungaro,et al.  Assessing and mapping topsoil organic carbon stock at regional scale: A scorpan kriging approach conditional on soil map delineations and land use , 2010 .

[18]  H. Jenny,et al.  The soil resource. Origin and behavior , 1983, Vegetatio.

[19]  Alex B. McBratney,et al.  Diffuse Reflectance Spectroscopy as a Tool for Digital Soil Mapping , 2008 .

[20]  Gerard B. M. Heuvelink,et al.  Small scale digital soil mapping in Southeastern Kenya , 2008 .

[21]  V. Jetten,et al.  Developing a minimum data set for characterizing soil dynamics in shifting cultivation systems , 2006 .

[22]  Vincent Chaplot,et al.  Estimating carbon stocks at a regional level using soil information and easily accessible auxiliary variables. , 2010 .

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  H. Elsenbeer,et al.  Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis , 2008 .

[25]  S. de Bruin,et al.  Implementation and evaluation of existing knowledge for digital soil mapping in Senegal , 2009 .

[26]  R. Waring,et al.  The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations , 1994 .

[27]  Sue J. Welham,et al.  Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood , 2006, Comput. Geosci..

[28]  M. Z. Martin,et al.  Evaluation of Different Soil Carbon Determination Methods , 2009 .

[29]  Christer Nilsson,et al.  Linking Flow Regime and Water Quality in Rivers: a Challenge to Adaptive Catchment Management , 2008 .

[30]  Antoine Stevens,et al.  Assessment and monitoring of soil quality using near‐infrared reflectance spectroscopy (NIRS) , 2009 .

[31]  David G. Rossiter,et al.  Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique , 2012 .

[32]  G. Heuvelink,et al.  Optimization of sample patterns for universal kriging of environmental variables , 2007 .

[33]  C. Deutsch,et al.  Teacher's Aide Variogram Interpretation and Modeling , 2001 .

[34]  P. Goovaerts.  Geostatistics in soil science: state-of-the-art and perspectives , 1999 .

[35]  Pierre Goovaerts,et al.  Fine-resolution mapping of soil organic carbon based on multivariate secondary data , 2006 .

[36]  Budiman Minasny,et al.  A conditioned Latin hypercube method for sampling in the presence of ancillary information , 2006, Comput. Geosci..

[37]  Budiman Minasny,et al.  Mapping and comparing the distribution of soil carbon under cropping and grazing management practices in Narrabri, north-west New South Wales , 2010 .

[38]  Martial Bernoux,et al.  National and sub-national assessments of soil organic carbon stocks and changes: The GEFSOC modelling system , 2007 .

[39]  Igor V. Florinsky,et al.  Prediction of soil properties by digital terrain modelling , 2002, Environ. Model. Softw..

[40]  K. Shepherd,et al.  Development of Reflectance Spectral Libraries for Characterization of Soil Properties , 2002 .

[41]  Guðrún Gísladóttir,et al.  Land degradation control and its global environmental benefits , 2005 .

[42]  Edzer J. Pebesma,et al.  Multivariable geostatistics in S: the gstat package , 2004, Comput. Geosci..

[43]  Paul E. Gessler,et al.  Modeling Soil–Landscape and Ecosystem Properties Using Terrain Attributes , 2000 .

[44]  Gerard B. M. Heuvelink,et al.  Modelling soil variation: past, present, and future , 2001 .

[45]  R. M. Lark,et al.  Robust estimation of the variogram by residual maximum likelihood , 2007 .

[46]  A. Gitelson,et al.  Remote sensing of chlorophyll concentration in higher plant leaves , 1998 .

[47]  F. J. Pierce,et al.  Soil Carbon Maps , 2003 .

[48]  S. Grunwald,et al.  Upscaling of Dynamic Soil Organic Carbon Pools in a North‐Central Florida Watershed , 2010 .