Manned aircraft versus small unmanned aerial system-forestry remote sensing comparison utilizing lidar and structure-from-motion for forest carbon modeling and disturbance detection

编号 030024402

推送时间 20200622

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Manned aircraft versus small unmanned aerial system-forestry remote sensing comparison utilizing lidar and structure-from-motion for forest carbon modeling and disturbance detection

来源期刊 JOURNAL OF APPLIED REMOTE SENSING

期 第244期

发表时间 20190809

关键词 Lidar;  small unmanned aerial system;  forestry;  remote sensing;  airborne lidar;  structure-from-motion; 

摘要 Sustainable forest management relies on the acquisition of timely (change detection) and accurate structural information of forest landscapes. Light detection and ranging (lidar) remote sensing platforms enable rapid, three-dimensional (3-D), structural data collection with a high spatial resolution. This study explores a functional carbon model applied to a dense, closed deciduous forest. Data are collected by manned airborne systems and unmanned aerial system, producing both lidar and structure-from-motion (SfM) 3-D mapping. A hybrid approach combining cost-effective SfM-generated data with lidar-derived digital elevation models also is explored, since the SfM fails to produce adequate terrain returns. Carbon modeling results are comparable to those achieved by the initial developers (r(2) = 0.64 versus r(2) = 0.72), despite the challenging uneven-aged forest environment. Vertical profiles, mapped utilizing a volumetric point density from the manned airborne lidar, are leveraged to train a binary classifier for disturbance detection. Producer's accuracy, user's accuracy, and Kappa statistic for disturbance detection are 94.1%, 92.2%, and 89.8%, respectively, showing a high likelihood of detecting disturbances (harvesting). The results bode well for the use of unmanned aerial system (UAS) systems, and either lidar or SfM, to assess forest stocking. Although disturbance detection is successful, further study is required to validate the use of UAS, and especially SfM, for this task. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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