ISWC OpenIR  > 水保所科研产出--SCI  > 2017--SCI
Loess Landslide Inventory Map Based on GF-1 Satellite Imagery
Sun, Wenyi1; Tian, Yuansheng1; Mu, Xingmin1; Zhai, Jun2; Gao, Peng1; Zhao, Guangju1; Mu, XM (reprint author), Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China.
SubtypeArticle
2017
Source PublicationREMOTE SENSING
ISSN2072-4292
description.correspondentemailsunwy@ms.iswc.ac.cn ; m17791384850@163.com ; muxm2014@gmail.com ; zhaij@lreis.ac.cn ; gaopeng@ms.iswc.ac.cn ; gjzhao@ms.iswc.ac.cn
Volume9Issue:4
AbstractRainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies.
KeywordLoess Landslides Spectral Topography Gf-1 Satellite
Subject AreaRemote Sensing
DOI10.3390/rs9040314
URL查看原文
Indexed BySCI
Publication PlaceBASEL
Language英语
WOS IDWOS:000402571700013
PublisherMDPI AG
Funding OrganizationGovernmental Public Industry Research Special Funds for Projects [201501049]; National Natural Science Foundation of China [41501293, 41501022]; National Key Research and Development Program of China [2016YFC0402401]; Special-Funds of Scientific Research Programs of State Key Laboratory of soil Erosion and Dryland Farming on the Loess Plateau [A314021403-Q2] ; Governmental Public Industry Research Special Funds for Projects [201501049]; National Natural Science Foundation of China [41501293, 41501022]; National Key Research and Development Program of China [2016YFC0402401]; Special-Funds of Scientific Research Programs of State Key Laboratory of soil Erosion and Dryland Farming on the Loess Plateau [A314021403-Q2]
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Document Type期刊论文
Identifierhttp://ir.iswc.ac.cn/handle/361005/8006
Collection水保所科研产出--SCI_2017--SCI
Corresponding AuthorMu, XM (reprint author), Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China.
Affiliation1.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China
2.Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China
Recommended Citation
GB/T 7714
Sun, Wenyi,Tian, Yuansheng,Mu, Xingmin,et al. Loess Landslide Inventory Map Based on GF-1 Satellite Imagery[J]. REMOTE SENSING,2017,9(4).
APA Sun, Wenyi.,Tian, Yuansheng.,Mu, Xingmin.,Zhai, Jun.,Gao, Peng.,...&Mu, XM .(2017).Loess Landslide Inventory Map Based on GF-1 Satellite Imagery.REMOTE SENSING,9(4).
MLA Sun, Wenyi,et al."Loess Landslide Inventory Map Based on GF-1 Satellite Imagery".REMOTE SENSING 9.4(2017).
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