Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
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DOI：
10.1016/j.rse.2021.112582
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发表时间：
2021-07-23
影响因子：
13.5
通讯作者：
Brancalion, Pedro H. S.
中科院分区：
文献类型：
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作者：
de Almeida, Danilo Roberti Alves;Broadbent, Eben North;Brancalion, Pedro H. S.
遥感器、机载轨道平台、飞行器或无人机（UAV）已成为一种有前途的技术，可增强我们对森林生态系统组成、结构和功能变化的了解，提供森林恢复的多尺度监测。无人机系统可以生成高分辨率图像，提供有关森林生态系统的准确信息，以帮助恢复项目的决策。然而，无人机技术的进步已经超过了实际应用，因此，我们探索结合无人机载激光雷达和高光谱数据来评估恢复种植的多样性和结构。我们开发了新的分析方法，以评估12个13岁的恢复实验建立了20，60或120个本地树种在巴西大西洋森林的地块。我们评估了（1）激光雷达和高光谱衍生变量的一致性和互补性，（2）它们区分树木丰富度水平的能力和（3）它们预测地上生物量（AGB）的能力。我们分析了三个结构属性来自激光雷达数据冠层高度，叶面积指数（LAI），林下叶面积指数和18个变量来自高光谱数据-15植被指数（维斯），两个组成部分的最小噪声分数（光谱组成）和光谱角（光谱变异性）。我们发现，维斯与LAI低LAI值呈正相关，但稳定的LAI大于2平方米/平方米。LAI和结构维斯随物种丰富度的增加而增加，高光谱变异与物种丰富度显著相关。虽然激光雷达得出的冠层高度比高光谱得出的维斯更好地预测AGB，它是无人机搭载的高光谱和激光雷达数据的融合，允许有效的共同监测森林结构属性和树木多样性恢复种植。此外，将激光雷达和高光谱数据放在一起考虑，更广泛地支持了生物多样性理论的预期，表明多样性增强了恢复过程中的生物量捕获和冠层功能属性。在联合国生态系统恢复十年期间，无人机遥感器的使用可以发挥重要作用，这需要以前所未有的规模进行详细的森林监测。
Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate highresolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data-canopy height, leaf area index (LAI), and understory LAI-and eighteen variables derived from hyperspectral data-15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale.