Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence
Impacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence
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DOI：
10.1016/j.foreco.2021.119648
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发表时间：
2021-11
影响因子：
3.7
通讯作者：
M. d'Oliveira;E. O. Figueiredo;D. R. Almeida;L. C. D. Oliveira;C. Silva;B. Nelson;Renato Mesquita da Cunha;D. Papa;S. Stark;R. Valbuena
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作者：
M. d'Oliveira;E. O. Figueiredo;D. R. Almeida;L. C. D. Oliveira;C. Silva;B. Nelson;Renato Mesquita da Cunha;D. Papa;S. Stark;R. Valbuena
可持续森林管理依赖于从地面调查和遥感相结合中获得的关于森林结构的良好知识。机载激光雷达能够探测森林地面和冠层元素，可以准确和精确地估计森林结构参数，但由于发展中国家偏远地区服务提供商的缺乏，仍然很难获得。或者，如果地表面高程已知（例如，从激光雷达），他们可以绑定到冠层表面模型从立体摄影测量使用RGB图像从无人驾驶飞行器（UAV）。在这里，我们评估了这样的摄影测量冠层测量是否提供地上生物量（AGB）和干扰的影响估计，从伐木，激光雷达相媲美，以及是否使用这两个序列可以提供一个有效的收获后监测系统。具体而言，通过森林清查地面图，机载LiDAR数据和UAV-RGB相机系统的组合，我们（i）自动定位和测量伐木引起的树冠干扰，（ii）比较单独使用LiDAR和LiDAR（地形高程模型）和RGB摄影测量（森林表面模型）的组合产生的AGB模型，（iii）估计伐木造成的AGB存量损失。这项研究是在位于巴西亚马逊西南部的安蒂马里国家森林进行的。我们的研究结果表明，使用RGB摄影测量的地区，地形海拔已经估计可以是一种有效的方法来快速识别选择性伐木，并准确地监测其影响。
Sustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact.