KMS Institute of soil and water conservation Chinese Academy of Sciences
| 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. | |
| 文章类型 | Article |
| 2017 | |
| 发表期刊 | REMOTE SENSING
 |
| ISSN | 2072-4292 |
| 通讯作者邮箱 | sunwy@ms.iswc.ac.cn ; m17791384850@163.com ; muxm2014@gmail.com ; zhaij@lreis.ac.cn ; gaopeng@ms.iswc.ac.cn ; gjzhao@ms.iswc.ac.cn |
| 卷号 | 9期号:4 |
| 摘要 | 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. |
| 关键词 | Loess Landslides Spectral Topography Gf-1 Satellite |
| 学科领域 | Remote Sensing |
| DOI | 10.3390/rs9040314 |
| URL | 查看原文 |
| 收录类别 | SCI |
| 出版地 | BASEL |
| 语种 | 英语 |
| WOS记录号 | WOS:000402571700013 |
| 出版者 | MDPI AG |
| 项目资助者 | 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] ; 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] |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | sbir.nwafu.edu.cn/handle/361005/8006 |
| 专题 | 水保所科研产出--SCI_2017--SCI |
| 通讯作者 | 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. |
| 作者单位 | 1.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 |
| 推荐引用方式 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). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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