文献类型：期刊文献

英文题名：Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area

作者：Chen, Bowei[1] Pang, Yong[1] Li, Zengyuan[1] Lu, Hao[2] Liu, Luxia[3] North, P. R. J.[4] Rosette, J. A. B.[4]

第一作者：Chen, Bowei

通信作者：Pang, Y[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China;[3]Univ Jinan, Sch Water Conservancy & Environm, Jinan 250022, Shandong, Peoples R China;[4]Swansea Univ, Dept Geog, GEMEO, Swansea SA2 8PP, W Glam, Wales

年份：2019

卷号：16

期号：9

起止页码：1447-1451

外文期刊名：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000484210100022)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 41871278 and Grant 31570546, and in part by the China Scholarship Council.

语种：英文

外文关键词：Ice; Cloud; and land Elevation Satellite (ICESat)-2; local outlier factor (LOF); photon classification; photon-counting LiDAR

摘要：The Ice, Cloud, and land Elevation Satellite (ICESat)-2 is the next generation of National Aeronautics and Space Administration (NASA)'s ICESat mission launched in September 2018. The new photon-counting LiDAR onboard ICESat-2 introduces new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we propose an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data sets from two types of photon-counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed that our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best results compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for ICESat-2 vegetation study.