干旱区生态环境知识资源中心(Arid): Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)

Arid

DOI	10.3390/rs11212577
	Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)
	Arabameri, Alireza 1; Cerda, Artemi 2; Rodrigo-Comino, Jesus 2,3; Pradhan, Biswajeet 4,5; Sohrabi, Masoud 6; Blaschke, Thomas 7; Dieu Tien Bui 8
通讯作者	Dieu Tien Bui
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:21
英文摘要	Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province.
英文关键词	remote sensing land degradation sustainable development statistical model gully erosion land management
类型	Article
语种	英语
国家	Iran ; Spain ; Germany ; Australia ; South Korea ; Austria ; Vietnam
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000504716700118
WOS关键词	MULTICRITERIA DECISION-MAKING ; LOGISTIC-REGRESSION ; CERTAINTY FACTOR ; SOIL-WATER ; ENTROPY ; MODELS ; CATCHMENT ; INITIATION ; BIVARIATE ; ENSEMBLE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
EI主题词	2019-11-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/310559
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 3658117994, Iran; 2.Univ Valencia, Dept Geog, Soil Eros & Degradat Res Grp, Blasco Ibanez 28, Valencia 46010, Spain; 3.Trier Univ, Phys Geog, D-54286 Trier, Germany; 4.Univ Technol Sydney, Fac Engn & Informat Technol, CAMGIS, Sydney, NSW 2007, Australia; 5.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; 6.Islamic Azad Univ Urmia, Dept Civil Engn Geotech, Orumiyeh 5167678747, Iran; 7.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; 8.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Arabameri, Alireza,Cerda, Artemi,Rodrigo-Comino, Jesus,et al. Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)[J],2019,11(21).
APA	Arabameri, Alireza.,Cerda, Artemi.,Rodrigo-Comino, Jesus.,Pradhan, Biswajeet.,Sohrabi, Masoud.,...&Dieu Tien Bui.(2019).Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran).REMOTE SENSING,11(21).
MLA	Arabameri, Alireza,et al."Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)".REMOTE SENSING 11.21(2019).