气候变化领域集成服务门户(CCPortal): Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds

CCPortal

DOI	10.1016/j.rse.2021.112300
	Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds
	Fu X.; Zhang Z.; Cao L.; Coops N.C.; Goodbody T.R.H.; Liu H.; Shen X.; Wu X.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Assessing changes in forest structure over time is crucial for monitoring forest resources, supporting sustainable forest management practices, and providing key insights into changes in the terrestrial carbon cycle. Recent research interest and rapid growth of unmanned aerial vehicle (UAV)-based digital aerial photogrammetry (DAP) technology principally due to its low cost and timeliness, is providing high-spatial resolution data for enhancing forest inventories and forest dynamics monitoring. The increasing prevalence of these UAV acquired datasets in forestry promotes a need for better understanding of how DAP-based point clouds change over time, and how these changes may relate to changes in forest structure. In this study, we utilized bi-temporal DAP data to investigate changes in forest structure over a 3-year period in subtropical planted forest stands throughout Dongtai Yellow Sea National Forest Park, Jiangsu Province, China. To do so, we evaluated both direct (i.e., structural parameter changes estimated using the differences between bi-temporal DAP metrics) and indirect (i.e., structural parameters were modelled for each date, and their changes predicted as their differences) methods to estimate the changes in forest structure. In addition, once models were developed, changes in Lorey's mean height and volume were extrapolated across the entire study site and examined related to the forest type and age. Our assessments of the different approaches showed that the direct approach (R2 = 0.54–0.78) resulted in improved accuracy compared to the indirect method (R2 = 0.51–0.73). The distributional metrics, namely, height percentiles (e.g., H75 and H95) and canopy return density (e.g., D7 and D5), and the Weibull-fitted metrics (e.g., α) were found to be sensitive to changes in structural parameters, whereas canopy volume-related metrics had relatively low predictive capabilities. Overall, the predicted changes in Lorey's mean height and volume were mapped over the entire study area, and indicated that Lorey's mean height increased mostly in middle-aged and young forest stands. Over-mature stands showed the lowest height increment. This study proved the capability of using bi-temporal point clouds from UAV-based DAP for enhancing forest inventories and promoting sustainable forest management. © 2021 Elsevier Inc.
英文关键词	Bi-temporal; Digital aerial photogrammetry; Forest growth; Forest structural parameters; Subtropical forest; Unmanned aerial vehicle
语种	英语
scopus关键词	Aerial photography; Antennas; Photogrammetry; Unmanned aerial vehicles (UAV); Aerial photogrammetry; Height increments; High spatial resolution; Predictive capabilities; Recent researches; Structural parameter; Sustainable forest management; Terrestrial carbon cycle; Forestry
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178960
作者单位	Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, 159 Longpan road, Nanjing, Jiangsu 210037, China; Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
推荐引用方式 GB/T 7714	Fu X.,Zhang Z.,Cao L.,et al. Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds[J],2021,255.
APA	Fu X..,Zhang Z..,Cao L..,Coops N.C..,Goodbody T.R.H..,...&Wu X..(2021).Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds.Remote Sensing of Environment,255.
MLA	Fu X.,et al."Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds".Remote Sensing of Environment 255(2021).