Multi-criteria characterization of recent digital soil mapping and modeling approaches

Grunwald, Sabine

doi:10.1016/j.geoderma.2009.06.003

Cited by 296 publications

(174 citation statements)

References 106 publications

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“…Soil maps can be used to provide information to support government and private sector decisions for promoting the use and conservation of this poorly-known, threatened ecosystem and for guiding future research (Miles et al, 2006). Soils vary both horizontally and vertically in the landscape, and are continuously modified by internal and external factors, generating complex spatial patterns (Grunwald, 2009). According to Boucneau et al (1998), soil spatial predictions can be obtained either by: soil property estimation, using mean and range values from a choropleth soil map, for example; or spatial interpolation, using kriging, for instance.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Vasques

Coelho

Dart

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to compare ordinary kriging with regression kriging to map soil properties at different depths in a tropical dry forest area in Brazil. The 11 soil properties evaluated were: organic carbon content and stock; bulk density; clay, sand, and silt contents; cation exchange capacity; pH; water retention at field capacity and at permanent wilting point; and available water. Samples were taken from 327 sites at 0.0-0.10, 0.10-0.20, and 0.20-0.40-m depths, in a tropical dry forest area of 102 km 2 .Stepwise linear regression models for particle-size fractions and water retention properties had the best fit. Relief and parent material covariates were selected in 31 of the 33 models (11 properties at three depths) and vegetation covariates in 29 models. Based on external validation, ordinary kriging obtained higher accuracy for 21 out of 33 property x depth combinations, indicating that the inclusion of a linear trend model before kriging does not necessarily improve predictions. Therefore, for similar studies, the geostatistical methods employed should be compared on a case-by-case basis.Index terms: caatinga, digital soil mapping, gamma radiometric survey, geostatistics, pedometrics. Mapeamento de carbono, frações granulométricas e água do solo em Floresta Tropical Seca no BrasilResumo -O objetivo deste trabalho foi comparar krigagem ordinária com regressão-krigagem para mapear atributos do solo, em diferentes profundidades, em área de Floresta Tropical Seca no Brasil. Os 11 atributos do solo avaliados foram: conteúdo e estoque de carbono orgânico; densidade do solo; conteúdos de argila, areia e silte; capacidade de troca catiônica; pH; retenção de água na capacidade de campo e no ponto de murcha permanente; e água disponível. As amostras foram retiradas de 327 locais a 0,0-0,10, 0,10-0,20 e 0,20-0,40 m de profundidade, em área de Floresta Tropical Seca de 102 km 2 . Os modelos de regressão linear "stepwise" tiveram o melhor ajuste para as frações granulométricas e as propriedades de retenção de água. Foram selecionadas covariáveis de relevo e material de origem em 31 dos 33 modelos (11 atributos em três profundidades) e de vegetação em 29 modelos. Com base na validação externa, a krigagem ordinária obteve maior acurácia para 21 das 33 combinações atributo vs. profundidade, o que é indicativo de que a inclusão de um modelo linear de tendência antes da krigagem não necessariamente melhora as predições. Portanto, para estudos semelhantes, os métodos geoestatísticos empregados devem ser comparados caso a caso.Termos para indexação: caatinga, mapeamento digital de solos, levantamento gamarradiométrico, geoestatística, pedometria.

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Section: Introductionmentioning

confidence: 99%

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Vasques

Coelho

Dart

et al. 2016

Pesq. agropec. bras.

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“…In their study, the topography was heterogeneous, varying from plain to mountainous. Since there is no defined protocol for DSM (Grunwald, 2009), we hypothesized that in an area with more homogeneous relief, the limitation described by ten Caten et al (2011) could not occur in our area of study. Furthermore, soils from these classes occur contiguously in the entire region, predominantly in the geoforms with divergent shoulders and convergent slopes, which hinders the differentiation by our predictive model, which is entirely based on terrain attributes.…”

Section: Resultsmentioning

confidence: 99%

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski

Dalmolin

Flores

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km 2 RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km 2 physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

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“…Due to the simultaneous richness of spatial inference methods and the potentially available auxiliary environmental information (Grunwald, S. 2009;Hengl, T. 2009;Mulder, V.L. et al 2011), there is a high versatility of possible approaches for the compilation of a given soil (related) map.…”

Section: Formation Of Digital Soil Mappingmentioning

confidence: 99%

Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

et al. 2015

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Due to former soil surveys and mapping activities signifi cant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfi lled with these available datasets either by their direct usage or aft er certain specifi c and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable imperfection as for the accuracy and reliability of the delivered products. With a recently started project we would like to signifi cantly extend the potential, how soil information requirements could be satisfi ed in Hungary. We started to compile digital soil maps, which fulfi l optimally the national and international demands from points of view of thematic, spatial and temporal accuracy. In addition to the auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened incorporating and eventually integrating geostatistical, data mining and GIS tools. Regression kriging has been used for the spatial inference of certain quantitative data, like particle size distribution components, rootable depth and organic matt er content. Classifi cation and regression trees were applied for the understanding of the soil-landscape models involved in existing soil maps, and for the post-formalization of survey/compilation rules. The relationships identifi ed and expressed in decision rules made the compilation of spatially refi ned category-type soil maps (like genetic soil type and soil productivity maps) possible with the aid of high resolution environmental auxiliary variables. In our paper, we give a short introduction to soil mapping and information management concentrating on the driving forces for the renewal of soil spatial data infrastructure provided by the framework of Digital Soil Mapping. The fi rst results of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project are presented in the form of brand new national and regional soil maps.

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Multi-criteria characterization of recent digital soil mapping and modeling approaches

Cited by 296 publications

References 106 publications

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

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