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Article

Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model

1
Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran
2
Department of Geography, Texas State University, San Marcos, TX 78666, USA
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Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria
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Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, Faculty of Engineering and IT, University of Technology Sydney, Sydney, 2007 NSW, Australia
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Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
6
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 874; https://doi.org/10.3390/rs12050874
Received: 17 January 2020 / Revised: 28 February 2020 / Accepted: 2 March 2020 / Published: 9 March 2020
(This article belongs to the Special Issue Remote Sensing of Soil Erosion)
The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 m 3 km 2   year 1 , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R2 = 0.96), followed by runoff (R2 = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management. View Full-Text
Keywords: soil erosion; drainage network; morphometry; novel ensemble technique; Kalvārī Basin soil erosion; drainage network; morphometry; novel ensemble technique; Kalvārī Basin
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MDPI and ACS Style

Arabameri, A.; Tiefenbacher, J.P.; Blaschke, T.; Pradhan, B.; Tien Bui, D. Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model. Remote Sens. 2020, 12, 874. https://doi.org/10.3390/rs12050874

AMA Style

Arabameri A, Tiefenbacher JP, Blaschke T, Pradhan B, Tien Bui D. Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model. Remote Sensing. 2020; 12(5):874. https://doi.org/10.3390/rs12050874

Chicago/Turabian Style

Arabameri, Alireza, John P. Tiefenbacher, Thomas Blaschke, Biswajeet Pradhan, and Dieu Tien Bui. 2020. "Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model" Remote Sensing 12, no. 5: 874. https://doi.org/10.3390/rs12050874

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