Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?
Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?
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DOI：
10.1111/geb.13516
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发表时间：
2022-05
影响因子：
6.4
通讯作者：
A. Kamoske;K. Dahlin;Quentin D. Read;S. Record;Scott C. Stark;S. Serbin;P. Zarnetske;Maria Dornelas
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文献类型：
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作者：
A. Kamoske;K. Dahlin;Quentin D. Read;S. Record;Scott C. Stark;S. Serbin;P. Zarnetske;Maria Dornelas
目标快速的全球变化正在影响树种的多样性以及森林的基本生态系统功能和服务。因此，了解和预测树种多样性在森林生物群落内部和之间的空间分布情况至关重要。卫星遥感平台几十年来一直被用来绘制森林结构和功能图，但由于空间分辨率相对较低，而且在实地观察生物多样性的不同层面时尺度复杂，因此其监测变化的能力有限。最近，利用被动高光谱分辨率（即，高光谱）和主动激光雷达数据已投入使用，从而有机会弄清从实地观测到更大尺度的生物多样性模式如何在空间和时间上发生变化。到目前为止，大多数研究都集中在单个站点和/或一种传感器类型;在这里，我们询问来自国家生态观测网络的空中观测平台（氖AOP）的多种传感器类型如何在氖田间小区尺度（即，40 m × 40 m）.位置美国东部.时间段2017 - 2018. Taxa studiedTrees. MethodsWith a fusion of hyperspectral and lidar data from the氖AOP，we assess the ability of high-resolution remotely sensed metrics to measure biodiversity variation across the east US temperate forests.我们研究如何分类，功能和系统发育的α多样性的措施在空间上的变化和评估在何种程度上遥感指标与原位生物多样性metrics.ResultsModels使用估计的森林功能，冠层结构，地形多样性比模型包含每个类别单独进行。我们的研究结果表明，冠层结构多样性，而不仅仅是光谱反射率，是预测生物多样性的关键。主要结论我们发现，一种方法，联合利用光谱特性相关的叶片和冠层功能性状和森林健康，激光雷达衍生的估计森林结构，精细分辨率地形多样性，需要仔细考虑生物群落内部和之间的生物地理差异，以准确地从上方绘制生物多样性变化。
AimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).LocationEastern USA.Time period2017–2018.Taxa studiedTrees.MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.