Research progress on retrieving forest canopy height and sub-canopy topography from spaceborne photon-counting LiDAR data

被引：0
|
作者：
Li Y. [1 ]
Zhu J. [1 ]
Fu H. [1 ]
Gao S. [1 ]
Wu K. [1 ]
机构：
[1] School of Geosciences and Info-physics, Central South University, Changsha
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 11期
基金：
中国国家自然科学基金;
关键词：
forest height; photon cloud filtering; research progress; space borne photon-countiong LiDAR ICESat-2; sub-canopy topography;
D O I：
10.11817/j.issn.1672-7207.2023.11.016
中图分类号：
学科分类号：
摘要：
Forest height is an important parameter to measure forest biomass and carbon sink of the forest ecosystem. The topography under the forest(sub-canopy topography) is a strategic information resource supporting national infrastructure construction and disaster monitoring. The new generation space-borne lidar ICESat-2/ ATLAS adopts a multi-beam micro-pulse photon counting technology for the first time, with a repetition frequency of 10 kHz to the ground. Compared with ICESat-1/GLAS, ICESat-2/ATLAS has a higher spatial sampling rate and insensitivity to slope and is currently important data for inverting the forest canopy height of forest ecosystems and sub-canopy topography. Some main indicators of ICESat-2/ATLAS were introduced and the influence of various errors on ATL08 products were summarized. The applicability of various photon point cloud filtering methods sub-canopy topography inversion and forest canopy height inversion were analyzed. The research progress and application prospects on photon point cloud filtering, sub-canopy topography inversion, and forest canopy height retrieval were put forward. © 2023 Central South University of Technology. All rights reserved.
引用
收藏
页码：4380 / 4390
页数：10
相关论文
共 45 条
  • [1] HOUGHTON R A., Aboveground forest biomass and the global carbon balance[J], Global Change Biology, 11, 6, (2005)
  • [2] KURZ W A, SHAW C H, BOISVENUE C, Et al., Carbon in Canada's boreal forest: a synthesis[J], Environmental Reviews, 21, 4, (2013)
  • [3] ZHAO Yanhong, HOU Peng, JIANG Jinbao, Et al., Progress in quantitative inversion of vegetation ecological remote sensing parameters, Journal of Remote Sensing, 25, 11, (2021)
  • [4] PANG Yong, LI Zengyuan, CHEN Erxue, Et al., Lidar remote sensing technology and its application in forestry, Scientia Silvae Sinicae, 41, 3, (2005)
  • [5] GOETZ S, DUBAYAH R., Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change[J], Carbon Management, 2, 3, (2011)
  • [6] POPESCU S C, ZHAO K., A voxel-based lidar method for estimating crown base height for deciduous and pine trees[J], Remote sensing of environment, 112, 3, (2008)
  • [7] GLENN N F, NEUENSCHWANDER A, VIERLING L A, Et al., Landsat 8 and ICESat-2: performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass[J], Remote Sensing of Environment, 185, (2016)
  • [8] SIMARD M, PINTO N, FISHER J B, Et al., Mapping forest canopy height globally with spaceborne lidar, Journal of Geophysical Research: Biogeosciences, 116, G4, (2011)
  • [9] TIAN Jinyan, WANG Le, LI Xiaojuan, Sub-footprint analysis to uncover tree height variation using ICESat/GLAS [J], International Journal of Applied Earth Observation and Geoinformation, 35, (2015)
  • [10] MARKUS T, NEUMANN T, MARTINO A, Et al., The ice, cloud, and land elevation satellite-2(ICESat-2): science requirements, concept, and implementation[J], Remote sensing of environment, 190, (2017)
12345