Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR
Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR
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DOI：
10.3390/rs11070817
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发表时间：
2019-04-01
期刊：
影响因子：
5
通讯作者：
Aragao, Luiz E. O. C.
中科院分区：
文献类型：
--
作者：
Dalagnol, Ricardo;Phillips, Oliver L.;Aragao, Luiz E. O. C.
伐木，包括有选择的非法活动，十分普遍，影响到热带森林的碳循环和生物多样性。然而，使用甚高分辨率卫星数据（1米空间分辨率）来准确跟踪大面积和偏远地区这些小规模人为干扰的自动化方法并不容易获得。执行这种类型的分析的主要限制是缺乏空间上准确的树尺度验证数据。在这项研究中，我们评估了VHR卫星图像的潜力，以检测树冠树木损失有关的选择性砍伐在封闭的树冠热带森林。为此，我们将WorldView-2和GeoEye-1卫星的树木损失检测能力与机载LiDAR进行了比较，后者在巴西亚马逊的Jamari国家森林获得了伐木前后的数据。我们发现，伐木导致树冠高度变化范围为-5.6至-42.2 m，平均减少-23.5 m。一个简单的激光雷达高度差阈值为-10米，足以映射97%的砍伐树木。与LiDAR相比，使用VHR卫星图像和随机森林（RF）模型可以检测到树木损失，平均精度为64%，同时绘制60%的树木损失总量。树木损失与大间隙开口或高大的树木更成功地检测。一般而言，RF模型最重要的遥感度量是标准差统计，特别是从可见波段（R、G、B）的反射率和阴影分数中提取的标准差统计。虽然大多数小的林冠空隙在2年内关闭，但在较长的时间内仍然可以观察到较大的空隙。然而，建议使用年度图像，以达到可接受的可探测性。我们的研究表明，VHR卫星图像具有潜在的监测在热带森林采伐和检测热点的自然干扰，在区域尺度上的低成本。
Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre- and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from -5.6 to -42.2 m, with a mean reduction of -23.5 m. A simple LiDAR height difference threshold of -10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within similar to 2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale.