Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model

Arabameri, A; Tiefenbacher, JP; Blaschke, T; Pradhan, B; Bui, DT

Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model

Arabameri, A Tiefenbacher, JP Blaschke, T Pradhan, B

Bui, DT

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Remote Sensing, 2020, 12, (5)
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Arabameri, A
dc.contributor.author	Tiefenbacher, JP
dc.contributor.author	Blaschke, T
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Bui, DT
dc.date.accessioned	2021-03-07T19:54:49Z
dc.date.available	2021-03-07T19:54:49Z
dc.date.issued	2020-03-01
dc.identifier.citation	Remote Sensing, 2020, 12, (5)
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/146864
dc.description.abstract	© 2020 by the author. Licensee MDPI, Basel, Switzerland. The morphometric characteristics of the Kalvari 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 Kalvari 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 456.3m3km-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.
dc.language	English
dc.publisher	MDPI AG
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs12050874
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0909 Geomatic Engineering
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-03-07T19:54:47Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	5

Abstract:

© 2020 by the author. Licensee MDPI, Basel, Switzerland. The morphometric characteristics of the Kalvari 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 Kalvari 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 456.3m3km-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.

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http://hdl.handle.net/10453/146864