Abstract Biochemical traits in forest vegetation are key indicators of leaf physiological processes, specifically photosynthetic and other photochemical light pathways, and are critical to the quantification of the terrestrial carbon cycle. Advances in remote sensing sensors and platforms are allowing multi-dimensional and continuous-spatial information to be acquired in a fast and non-destructive way to quantify forest biochemical traits at multiple spatial scales. Here we demonstrate the use of high spectral resolution, hyperspectral data combined with high density three-dimensional information from Light Detection and Ranging (LiDAR) both acquired from an unmanned aerial system (UAS) platform, to quantify and assess the three-dimensional distribution of biochemical pigments on individual tree canopy surfaces. To do so, a DSM based fusion method was developed to integrate the 3D LiDAR point cloud with hyperspectral reflectance data. Regression-based models were then developed to predict a number of biochemical traits (i.e., chlorophyll (Chl) a, b, total Chl and total carotenoids (Cars) content) from a suite of common spectral indices at three vertical canopy levels, and were evaluated using a leave-one-out cross-validation approach. One-way ANOVA and Duncan's multiple comparison post hoc tests were used to investigate the vertical distribution of biochemical pigments on individual tree canopy surfaces, and in response to age and species. Our results demonstrated that a number of vegetation indices, derived from the hyperspectral data, were strongly correlated with a number of biochemical traits (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%). In general, models fitted using leaf samples from the upper, middle and lower canopies separately (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%) had similar accuracy to the models developed with pooled data (Adj-R2 = 0.87–0.90; rRMSE = 5.21–6.11%). The differences between separate models and global models were not statistically significant (P > 0.05). However, the distribution of biochemical pigments across vertical layers varied significantly. For dawn redwood (Metasequoia glyptostroboides) and poplar (Populus deltoides), the results were consistent in that the lower component of the canopy (least light impacted) had the highest chlorophyll and carotenoids biochemical traits. Moreover, the vertical distribution of biochemical traits on individual tree canopy surfaces changed with age likely due to the growth variation from the photosynthetic activity of the canopy. This study indicates the potential of using fused 3D point cloud information with spectral data to monitor physiological activities of forest canopy for carbon accumulation estimation as well as precision forestry applications such as nutrition diagnosis, water regulation and subsequent productivity enhancement of these planted forest systems.

中文翻译：

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使用先进的遥感方法量化人工林物种生化性状的垂直剖面

摘要 森林植被的生化性状是叶片生理过程的关键指标，特别是光合作用和其他光化学光通路，对陆地碳循环的量化至关重要。遥感传感器和平台的进步允许以快速和非破坏性的方式获取多维和连续空间信息，以在多个空间尺度上量化森林生化性状。在这里，我们展示了使用高光谱分辨率、高光谱数据结合从无人机系统 (UAS) 平台获得的光探测和测距 (LiDAR) 的高密度三维信息来量化和评估个别树冠表面的生化色素。为此，开发了一种基于 DSM 的融合方法，以将 3D LiDAR 点云与高光谱反射率数据相结合。然后开发了基于回归的模型，以从三个垂直冠层水平的一组常见光谱指数预测许多生化性状（即叶绿素 (Chl) a、b、总 Chl 和总类胡萝卜素 (Cars) 含量），并且使用留一法交叉验证方法进行评估。单向方差分析和 Duncan 的多重比较事后检验用于研究生化色素在单个树冠表面的垂直分布，以及对年龄和物种的响应。我们的结果表明，来自高光谱数据的许多植被指数与许多生化性状密切相关（Adj-R2 = 0.85–0.91；rRMSE = 5.19–6.38%）。一般来说，分别使用来自上、中和下冠层的叶样本拟合的模型（Adj-R2 = 0.85–0.91；rRMSE = 5.19–6.38%）与使用汇总数据开发的模型具有相似的准确性（Adj-R2 = 0.87–0.90；rRMSE = 5.21–6.11%）。单独模型与全局模型之间的差异无统计学意义（P > 0.05）。然而，生化色素在垂直层中的分布差异很大。对于黎明红木 (Metasequoia glyptostroboides) 和杨树 (Populus deltoides)，结果是一致的，树冠的下部成分（受光影响最小）具有最高的叶绿素和类胡萝卜素生化特征。而且，个别树木冠层表面生化性状的垂直分布随年龄而变化，这可能是由于冠层光合活动的生长变化所致。这项研究表明，使用融合 3D 点云信息和光谱数据来监测森林冠层的生理活动以进行碳积累估计以及精确林业应用（如这些人工林系统的营养诊断、水分调节和随后的生产力提高）的潜力。