文献类型：期刊文献

英文题名：Forest Canopy Gap Dynamics based on Time-Series of Airborne Lidar Data

作者：Li, Shiming[1] Liu, Qingwang[1] Li, Zengyuan[1] Qi, Zhiyong[1] Si, Lin[1] Wang, Ning[2]

第一作者：李世明

机构：[1] Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, No.2 Dongxiaofu, Haidian District, Beijing, 100091, China; [2] China Academy of Launch Vehicle Technology, Postbox9200, Beijing, 100076, China

年份：2022

卷号：2022-July

起止页码：6119-6121

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20224313008234)

语种：英文

摘要：Identifying forest canopy gaps and monitoring gap dynamics are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. However, it is still difficult to observe and measure canopy gaps extensively in both space and time using field measurements or bi-dimensional remote sensing images, particularly in large extensive boreal forests. In this study, we investigate the feasibility to map boreal canopy gaps of different sizes with airborne lidar datasets and derive the characteristics of gap dynamics. Two co-registered canopy height models (CHMs) of 1m resolution were created from lidar datasets acquired respectively in 2012 and 2016. Canopy gaps are automatically delineated using fixed-height threshold method. Further, two gap layers are compared and the changing information of canopy gaps are delineated to provide information on the area of old and new gaps, gap expansions, new random gap openings, gap closure due to disturbance and regeneration. The result shows airborne lidar is an efficient and effective tool for rapidly extracting detailed and spatially extensive short-term dynamics of canopy gaps. ? 2022 IEEE.