Soil and bedrock distribution estimated from gully form and frequency: A GIS-based decision-tree model for Lebanon

Kheir, Rania Bou; Chorowicz, J.; Abdallah, Chadi; Dhont, Damien

doi:10.1016/j.geomorph.2007.03.010

Cited by 44 publications

(20 citation statements)

References 19 publications

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“…Different types of predictive variables (categorical and continuous) can be integrated into the model (Selle et al 2007). The machine-learning, probabilistic, non-parametric decision-tree method has been extensively exploited for vegetation mapping, ecological modeling, and remote sensing studies such as land use classification based on threshold values of various band data (Bou et al 2008).…”

Section: Methodsmentioning

confidence: 99%

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Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

Cheng

Zhang

Wang

et al. 2008

Environ Monit Assess

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The classification and regression tree (CART) model integrated with geographical information systems and the assessment of heavy-metals pollution system was developed to assess the heavy metals pollution in Fuyang, Zhejiang, China. The integration of the decision tree model with ArcGIS Engine 9 using a COM implementation in Microsoft Visual Basic 6.0 provided an approach for assessing the spatial distribution of soil Zn content with high predictive accuracy. The Zn concentration classes estimated by CART assigned the right classes with an accuracy of near 90%. This is a great improvement compared to the ordinary Kriging method for the spatial autocorrelation of the study area severely destroyed by human activities. Also, it can be used to investigate the inter-relationships between the heavy metals pollution and environmental and anthropogenic variables. Moreover, the research presents model predictions over space for further applications and investigations.

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Section: Methodsmentioning

confidence: 99%

“…3b), since a simpler tree is easier to understand and faster to use, and more importantly, smaller trees provide greater predictive accuracy for unseen data (Bou et al 2008). The maximum tree depth or maximum purity also can be specified in the module (Fig.…”

Section: Existing and Disused Smelting Factories (Sm)mentioning

confidence: 99%

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

Cheng

Zhang

Wang

et al. 2008

Environ Monit Assess

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“…Gordon et al, 2007;Bou Kheir et al, 2008). La escasez de estudios de modelización de la escorrentía y pérdida de suelo en cárcavas se debe, principalmente, a las limitaciones de los modelos aplicados, que en su mayoría representan procesos de erosión por salpicadura, laminar, en regueros y cárcavas efíme-ras, sin atender a la erosión que se produce en canales, cárcavas permanentes, "piping" o movimientos en masa (de Vente et al, 2008).…”

Section: Introductionunclassified

Escorrentía y pérdida de suelo en cárcavas : modelización en un agrosistema del Prepirineo oscense

López‐Vicente

Navas

2013

CIG

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“…Efficiency of using CTA for predictive soil landscape mapping was demonstrated in a few studies at regional and subregional scale (Moran and Bui, 2002;Scull et al, 2005). Recent studies showed their potential for land cover mapping from remote sensing images analysis (Friedl et al, 1999;Lawrence et al, 2004), geomorphological mapping (Luoto and Hjort, 2005) and soil erosion occurrence (Bou Kheir et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

Kheir

Bøcher

Greve

2010

Hydrol. Earth Syst. Sci.

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Abstract. Accurate information about organic/mineral soil occurrence is a prerequisite for many land resources management applications (including climate change mitigation). This paper aims at investigating the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes in unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to explain organic/mineral field measurements in hydromorphic landscapes of the Danish area chosen. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morphological/hydrological) parameters only, (3) selected pairs of parameters and (4) excluding each parameter one at a time from the potential pool of predictor parameters. The best classification tree model (with the lowest misclassification error and the smallest number of terminal nodes and predictor parameters) combined the steady-state topographic wetness index and soil type, and explained 68% of the variability in organic/mineral field measurements. The overall accuracy of the predictive organic/inorganic landscapes' map produced (at 1:50 000 cartographic scale) using the best tree was estiCorrespondence to: R. Bou Kheir (rania.boukheir@agrsci.dk) mated to be ca. 75%. The proposed classification-tree model is relatively simple, quick, realistic and practical, and it can be applied to other areas, thereby providing a tool to facilitate the implementation of pedological/hydrological plans for conservation and sustainable management. It is particularly useful when information about soil properties from conventional field surveys is limited.

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Soil and bedrock distribution estimated from gully form and frequency: A GIS-based decision-tree model for Lebanon

Cited by 44 publications

References 19 publications

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

Integrating classification and regression tree (CART) with GIS for assessment of heavy metals pollution

Escorrentía y pérdida de suelo en cárcavas : modelización en un agrosistema del Prepirineo oscense

The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data

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