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Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas 被引量:14
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作者 Chen Guo Qiang Xu +4 位作者 Xiujun Dong Weile Li Kuanyao Zhao Huiyan Lu yuanzhen ju 《Journal of Earth Science》 SCIE CAS CSCD 2021年第5期1079-1091,共13页
Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory m... Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging(Li DAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the Li DAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne Li DAR DEM derivatives. Quasi-3 D relief image maps are generated based on the sky-view factor(SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km^(2) study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of Li DAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m^(2). Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of Li DAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses. 展开更多
关键词 GEOHAZARD geohazard inventory airborne Li DAR sky view factor remote sensing interpretation complex mountainous areas
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Application of deep learning in ecological resource research:Theories, methods, and challenges 被引量:7
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作者 Qinghua GUO Shichao JIN +9 位作者 Min LI Qiuli YANG Kexin XU yuanzhen ju Jing ZHANG Jing XUAN Jin LIU Yanjun SU Qiang XU Yu LIU 《Science China Earth Sciences》 SCIE EI CAS CSCD 2020年第10期1457-1474,共18页
Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial ... Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society.Advances in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source data.Ecological resource research is entering a new era driven by big data.Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data.Deep learning is a method for automatically extracting complex high-dimensional nonlinear features,which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data.To promote the application of deep learning in the field of ecological resource research,here,we first introduce the relationship between deep learning theory and research on ecological resources,common tools,and datasets.Second,applications of deep learning in classification and recognition,detection and localization,semantic segmentation,instance segmentation,and graph neural network in typical spatial discrete data are presented through three cases:species classification,crop breeding,and vegetation mapping.Finally,challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning.It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms,and enrich applications with the development of hardware. 展开更多
关键词 Ecological resources Deep learning Neural network Big data Theory and tools Application and challenge
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