Yanjun Su
Chinese Academy of Sciences
99 Papers
150 Citations
Yanjun Su is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Lidar & Environmental science. The author has an hindex of 21, co-authored 66 publications. Previous affiliations of Yanjun Su include University of California, Merced.
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Papers
Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas
TL;DR: Li et al. as mentioned in this paper proposed an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions.
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Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species
TL;DR: It is demonstrated that the newly-launched and freely-available Sentinel-2 (S2) sensor can accurately map mangrove extent and basically discriminate mangroves species communities, but for the latter, one should be cautious due to the complexity ofMangrove species.
168
Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data
TL;DR: This study mapped the global forest AGB density at a 1-km resolution through the integration of ground inventory data, optical imagery, Geoscience Laser Altimeter System/Ice, Cloud, and Land Elevation Satellite data, climate surfaces, and topographic data and showed good agreements with these regional AGB products, but some of the regional A GB products tended to underestimate forest A GB density.
Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data
Xiaoqiang Liu,Yanjun Su,Tianyu Hu,Qiuli Yang,Bingbing Liu,Y. Deng,Hao Tang,Zhiyao Tang,Jingyun Fang,Qinghua Guo +9 more
TL;DR: Wang et al. as mentioned in this paper developed a novel neural network guided interpolation (NNGI) method to map forest canopy height by fusing GEDI, ICESat-2 ATLAS, and Sentinel-2 images.
160
Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects
Shichao Jin,Xiliang Sun,Fangfang Wu,Yanjun Su,Yumei Li,Shiling Song,Kexin Xu,Qin Ma,Frédéric Baret,Frédéric Baret,Dong Jiang,Yanfeng Ding,Qinghua Guo +12 more
TL;DR: Three main challenges in lidar-based phenotypes development are identified: developing low cost, high spatial–temporal, and hyperspectral lidar facilities, moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and embracing open source and big data.
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