文献类型：期刊文献

英文题名：Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data

作者：Cao, Lin[1,2] Coops, Nicholas C.[2] Innes, John L.[2] Sheppard, Stephen R. J.[2] Fu, Liyong[3] Ruan, Honghua[1] She, Guanghui[1]

第一作者：Cao, Lin

通信作者：Cao, L[1]

机构：[1]Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestiy Southern China, 159 Longpan Rd, Nanjing 210037, Jiangsu, Peoples R China;[2]Univ British Columbia, Dept Forest Resources Management, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;[3]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2016

卷号：178

起止页码：158-171

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;EI(收录号：20161302165202);Scopus(收录号：2-s2.0-84961778404);WOS:【SCI-EXPANDED(收录号:WOS:000375508200013)】；

基金：We acknowledge grants from the National Natural Science Foundation of China (No. 31400492). We also acknowledge grants from the Natural Science Foundation of Jiangsu Province (No. BK20151515) and "Adaptation of Asia-Pacific Forests to Climate Change" (Project #APFNet/2010/PPF/001) funded by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation. This research was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Special thanks to Dr. Peter Marshall (UBC) for expert advices on the experimental design of tree core sampling.

语种：英文

外文关键词：Multi-temporal; Airborne LiDAR; Subtropical forest; Biomass change; Forest growth; Canopy height; Direct approach

摘要：Estimating the change in the magnitude and distribution of forest biomass is important for monitoring carbon dynamics and understanding the implications on the terrestrial carbon cycle. In this study, we assessed the capacity of multi-temporal airborne Light Detection and Ranging (LiDAR) data to estimate forest biomass dynamics at a mixed subtropical forest of southeast China, and analyzed the magnitude and spatial patterns of the above ground biomass (AGB) change over a 6-year period. To do so, we evaluated different approaches to estimate change in AGB, specifically direct (i.e., predicting biomass change using the differences in LiDAR metrics) and indirect (i.e., modeling biomass for each point in time and predicting the change as their differences) approaches. Once models were developed, the change in AGB was mapped across the study site and examined in relation to forest type and age. Our results demonstrated that the direct approach (CV-R-2 = 0.63, CV-rRMSE = 25.64%) produced more accurate estimates in change of AGB than the indirect approach (CV-R-2 = 0.59, CV-rRMSE = 28.35%). Canopy height metrics of delta mean height (Delta h(mean) 1 and a number of upper percentile heights, (i.e., Delta h(75) and Delta h(95)), calculated from the LiDAR first returns, were found to be most sensitive to changes in biomass, whereas metrics based on maximum canopy height or canopy density had less predictive power. Furthermore, differences in point cloud densities did not significantly impact the estimations of AGB and change of AGB at the plot-scale (30 x 30 m). Spatial extrapolation of the AGB change indicated that, in general, most of the forest area had an overall gain in biomass. The middle-aged stands (typically dominated by Quercus acutissima between 41 and 60 years old) show marked growth of biomass (median Delta AGB = 2.29 Mg ha(-1) year(-1)) followed by mature (typically dominated by Pinus massoniana between 31 and 60 years old) (median Delta AGB = 1.72 Mg ha(-1) year(-1)) and young stands (typically consisting of regenerated broadleaved trees younger than 40 years old) (median Delta AGB = 1.48 Mg ha(-1) year(-1)), whereas over-mature stands (typically dominated by Cunninghamia lanceolate older than 35 years) show the lowest growth (median Delta AGB = 0.76 Mg ha(-1) year(-1)) This study demonstrates benefits of using multi-temporal LiDAR data to help identify specific areas for management interventions, to enhance forest productivity, maintain biomass stocks and optimize carbon sequestration. (C) 2016 Elsevier Inc. All rights reserved.