Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach

Lemercier, Blandine; Lacoste, Marine; Loum, Macoumba; Walter, Christian

doi:10.1016/j.geoderma.2011.03.010

Cited by 68 publications

(52 citation statements)

References 39 publications

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“…Most covariates were terrain attributes derived from the digital elevation model (DEM). This is in accordance with Campling et al (2002), who found topography important in general, and Lemercier et al (2012), who showed that a topographic wetness index was among the most important covariates. Local depression at various scales (concave curvature, basins in TPI, sites with accumulation by erosion, terrain wetness) increased the probability of poorly drained soils and the presence of waterlogged horizons.…”

Section: Model Structuresupporting

confidence: 91%

“…347). Campling et al (2002) reported a κ of 0.705, Kidd et al (2014) found κ values of 0.27 and 0.31 for the two study regions, Lemercier et al (2012) reported a κ of 0.27 and Peng et al (2003) found a κ of 0.59 for predictions of three drainage levels. The κ values computed for the models of this study ranged between 0.37 and 0.5 for modelling the presence of waterlogged horizons and was 0.48 for predicting the three levels of drainage class.…”

Section: Predictive Performance Of Fitted Modelsmentioning

confidence: 92%

“…Three studies with up to seven drainage levels achieved PC of 52 to 78 %, and Zhao et al (2013) had 36 % of correctly classified sites. Kidd et al (2014) found PC of 53 and 55 % for two study regions, and Lemercier et al (2012) reported PC of 52 % for a four-level drainage response. The presented models (Table 4 and 5) are almost as good with PC of 78 to 82 % for predicting the presence of waterlogged horizons and PC of 78 % for predicting the three drainage class levels.…”

Section: Predictive Performance Of Fitted Modelsmentioning

confidence: 98%

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Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Nussbaum

Walthert

Fraefel

et al. 2017

SOIL

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Abstract. High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soilforming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task.We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting.We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.

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Section: Model Structuresupporting

confidence: 91%

Section: Predictive Performance Of Fitted Modelsmentioning

confidence: 92%

Section: Predictive Performance Of Fitted Modelsmentioning

confidence: 98%

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Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Nussbaum

Walthert

Fraefel

et al. 2017

SOIL

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“…Nesse aspecto, o Mapeamento Digital de Solos (MDS) tem se tornado uma alternativa para geração desses mapas por meio de métodos matemáticos computacionais associados a variáveis ambientais derivadas do Modelos Digital de Elevação (MDE), do uso da terra, do material de origem, entre outros, em sistemas de informação geográfica (McBratney et al, 2003;Hengl et al, 2007).…”

Section: Introductionunclassified

Expansão de mapas pedológicos para áreas fisiograficamente semelhantes por meio de mapeamento digital de solos

Bagatini

Giasson

Teske

2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi realizar a expansão de mapas pedológicos pela extrapolação de mapas preexistentes para áreas fisiograficamente semelhantes. Foram utilizados mapas de solos, em escala 1:50.000, das bacias hidrográficas dos rios Santo Cristo e Arroio Portão, no Rio Grande do Sul, e a extrapolação foi feita com uso do algoritmo de árvores de decisão "simple cart", treinado nas áreas previamente mapeadas. As bacias foram divididas em duas partes, uma para o treinamento e outra para a validação do modelo. A partir do modelo digital de elevação Aster-GDEM, foram gerados sete mapas de variáveis preditoras dos solos na paisagem. A amostragem de dados foi aleatória, com densidade de três pontos por hectare. O treinamento dos modelos foi realizado no programa Weka, e as acurácias foram calculadas a partir de matriz de erros. Para ambas as bacias, a acurácia geral do mapa de solos predito foi maior na área de treinamento do que na área de validação, a qual apresentou valores de 50 e 54%. Os mapas produzidos pelo modelo preditor apresentaram acentuada diferença na distribuição espacial das unidades de mapeamento, comparados com o mapa de solos original, indício de que a técnica de mapeamento digital utilizada é pouco eficiente para extrapolar mapas de solos preexistentes para outras áreas fisiograficamente semelhantes.Termos para indexação: acurácia geral, árvores de decisão, mineração de dados, pedometria, simple cart, sistema de informação geográfica. Expanding pedological maps to physiographically similar areas with digital soil mappingAbstract -The objective of this work was to expand pedological maps by extrapolating existing soil maps to physiographically similar areas. Soil maps were used at the scale of 1:50,000, for the watersheds of the rivers Santo Cristo and Arroio Portão, in the state of Rio Grande do Sul, Brazil, and the extrapolation was done using the "Simple Cart" decision tree algorithm, trained in the previously mapped areas. The watersheds were divided into two parts, one used for model training and the other for model validation. From the digital elevation model Aster-GDEM, seven maps of soil predicting variables in the landscape were generated. Sampling was random and performed with sampling density of three points per hectare. Model training was performed in the Weka software, and model accuracies were calculated using the error matrix. For both watersheds, the overall accuracy of the predicted soil map was higher in the training area than in the validation area, and showed values of 50 and 54%. The maps produced by the predictive model showed acute differences in the spatial distribution of mapping units, compared with the original soil map, indicating that the used digital mapping technique has low effectivity for the extrapolation of pre-existing soil maps to other physiographically similar areas.

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“…BRT is a relatively new and improved data mining technique applied in various fields including ecology (De'ath, 2007;Elith et al, 2008;Esther et al, 2014;Hale et al, 2014), epidemiology (Cheong et al, 2014, Ellis et al, 2013Neuman et al, 2004), soil mapping (Jafari et al, 2014;Lemercier et al, 2012), agriculture (Etter et al, 2006;Gellrich et al, 2008;Müller et al, 2013), and fisheries (Froeschke, 2011). Unlike random forests, the BRT method produces the assembly of trees with a slow learning rate and in a sequential manner to extract more variability in the data.…”

Section: Data Mining Approachesmentioning

confidence: 99%

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

Saha¹

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Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach

Cited by 68 publications

References 39 publications

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Expansão de mapas pedológicos para áreas fisiograficamente semelhantes por meio de mapeamento digital de solos

Improved Criteria for Estimating Calibration Factors for Highway Safety Manual (HSM) Applications

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