Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM  

作　　者：Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee 

机构地区：[1]Department of Geomorphology,Tarbiat Modares University,Tehran 14117-13116,Iran [2]Geoscience Platform Research Division,Korea Institute of Geoscience and Mineral Resources(KIGAM),124 Gwahak-ro Yuseong-gu,Daejeon 34132,South Korea [3]Department of Geophysical Exploration,Korea University of Science and Technology,217 Gajeong-ro Yuseong-gu,Daejeon 34113,South Korea [4]Department of Geography,The University of Burdwan,Bardhaman,West Bengal 713104,India [5]Soil Erosion and Degradation Research Group,Departament de Geografia,Universitat de València,Blasco Ibàñez,28,46010-Valencia,Spain

出　　处：《Geoscience Frontiers》2021年第6期129-146,共18页地学前缘（英文版）

摘　　要：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 i

关 键 词：Digital elevation model(DEM) Gully erosion susceptibility(GES) Advanced land observation satellite(ALOS) Cforest Cubist Elastic net 

分 类 号：P237[天文地球—摄影测量与遥感] P931[天文地球—测绘科学与技术]