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Article

Loess Landslide Inventory Map Based on GF-1 Satellite Imagery

1
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
2
Satellite Environmental Center, Ministry of Environmental Protection, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zhong Lu, Chaoying Zhao and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 314; https://doi.org/10.3390/rs9040314
Received: 4 January 2017 / Revised: 20 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
(This article belongs to the Special Issue Remote Sensing of Landslides)
Rainfall-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. View Full-Text
Keywords: loess landslides; spectral; topography; GF-1 satellite loess landslides; spectral; topography; GF-1 satellite
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MDPI and ACS Style

Sun, W.; Tian, Y.; Mu, X.; Zhai, J.; Gao, P.; Zhao, G. Loess Landslide Inventory Map Based on GF-1 Satellite Imagery. Remote Sens. 2017, 9, 314. https://doi.org/10.3390/rs9040314

AMA Style

Sun W, Tian Y, Mu X, Zhai J, Gao P, Zhao G. Loess Landslide Inventory Map Based on GF-1 Satellite Imagery. Remote Sensing. 2017; 9(4):314. https://doi.org/10.3390/rs9040314

Chicago/Turabian Style

Sun, Wenyi, Yuansheng Tian, Xingmin Mu, Jun Zhai, Peng Gao, and Guangju Zhao. 2017. "Loess Landslide Inventory Map Based on GF-1 Satellite Imagery" Remote Sensing 9, no. 4: 314. https://doi.org/10.3390/rs9040314

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