文献类型：期刊文献

英文题名：Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

作者：Chen, Bowei[1,2] Pang, Yong[1] Li, Zengyuan[1] North, Peter[2] Rosette, Jacqueline[2] Sun, Guoqing[3] Suarez, Juan[4] Bye, Iain[2] Lu, Hao[5]

第一作者：Chen, Bowei

通信作者：Li, ZY[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Swansea Univ, Dept Geog, GEMEO, Swansea SA2 8PP, W Glam, Wales;[3]Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA;[4]Forest Res, Northern Res Stn, Roslin EH25 9SY, Midlothian, Scotland;[5]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

年份：2019

卷号：11

期号：7

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20193107252347);Scopus(收录号：2-s2.0-85069814990);WOS:【SCI-EXPANDED(收录号:WOS:000465549300121)】；

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

语种：英文

外文关键词：ICESat-2; photon counting LiDAR; forest parameters; LiDAR metrics

摘要：ICESat-2 is the new generation of NASA's ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2.