Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM
The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic...
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Elsevier
2021-11-01
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| Series: | Geoscience Frontiers |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987121000943 |
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| author | Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee |
| author_facet | Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee |
| author_sort | Alireza Arabameri |
| collection | DOAJ |
| description | The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately influences on gully occurrences. Therefore, terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled with machine learning (ML) methods has been used for GES mapping in the parts of Semnan province, Iran. Current research focuses on the comparison of predicted GES result by using three types of DEM i.e. Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) in different resolutions. For further progress of our research work, here we have used thirteen suitable geo-environmental gully erosion conditioning factors (GECFs) based on the multi-collinearity analysis. ML methods of conditional inference forests (Cforest), Cubist model and Elastic net model have been chosen for modelling GES accordingly. Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods (Cforest = 21.4, Cubist = 19.65 and Elastic net = 17.08), followed by lithology and slope. Validation of the model’s result was performed through area under curve (AUC) and other statistical indices. The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs (AUC value of Cforest in ALOS DEM is 0.994, AW3D30 DEM is 0.989 and ASTER DEM is 0.982) used in this study, followed by elastic net and cubist model. The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area. |
| first_indexed | 2024-03-12T19:42:38Z |
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| language | English |
| last_indexed | 2024-03-12T19:42:38Z |
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| spelling | doaj.art-b872e32587d2440ca61f761d98df460a2023-08-02T03:46:17ZengElsevierGeoscience Frontiers1674-98712021-11-01126101230Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEMAlireza Arabameri0Fatemeh Rezaie1Subodh Chandra Pal2Artemi Cerda3Asish Saha4Rabin Chakrabortty5Saro Lee6Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran; Corresponding author.Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro Yuseong-gu, Daejeon 34132, South Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, South KoreaDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaSoil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Blasco Ibàñez, 28, 46010-Valencia, SpainDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaGeoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro Yuseong-gu, Daejeon 34132, South Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, South KoreaThe geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately influences on gully occurrences. Therefore, terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled with machine learning (ML) methods has been used for GES mapping in the parts of Semnan province, Iran. Current research focuses on the comparison of predicted GES result by using three types of DEM i.e. Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) in different resolutions. For further progress of our research work, here we have used thirteen suitable geo-environmental gully erosion conditioning factors (GECFs) based on the multi-collinearity analysis. ML methods of conditional inference forests (Cforest), Cubist model and Elastic net model have been chosen for modelling GES accordingly. Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods (Cforest = 21.4, Cubist = 19.65 and Elastic net = 17.08), followed by lithology and slope. Validation of the model’s result was performed through area under curve (AUC) and other statistical indices. The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs (AUC value of Cforest in ALOS DEM is 0.994, AW3D30 DEM is 0.989 and ASTER DEM is 0.982) used in this study, followed by elastic net and cubist model. The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.http://www.sciencedirect.com/science/article/pii/S1674987121000943Digital elevation model (DEM)Gully erosion susceptibility (GES)Advanced land observation satellite (ALOS)CforestCubistElastic net |
| spellingShingle | Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM Geoscience Frontiers Digital elevation model (DEM) Gully erosion susceptibility (GES) Advanced land observation satellite (ALOS) Cforest Cubist Elastic net |
| title | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM |
| title_full | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM |
| title_fullStr | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM |
| title_full_unstemmed | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM |
| title_short | Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM |
| title_sort | modelling of piping collapses and gully headcut landforms evaluating topographic variables from different types of dem |
| topic | Digital elevation model (DEM) Gully erosion susceptibility (GES) Advanced land observation satellite (ALOS) Cforest Cubist Elastic net |
| url | http://www.sciencedirect.com/science/article/pii/S1674987121000943 |
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