Estimating individual‐level plant traits at scale
Estimating individual‐level plant traits at scale
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大规模估计个体水平的植物性状
DOI：
10.1002/eap.2300
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发表时间：
2021
影响因子：
5
通讯作者：
White, Ethan P.
中科院分区：
文献类型：
--
作者：
Marconi, Sergio;Graves, Sarah J.;Weinstein, Ben G.;Bohlman, Stephanie;White, Ethan P.
功能生态学越来越关注于基于特征（影响个体健康和表现的可测量特征）来描述生态群落。分析森林内和森林之间的特征分布可以显着提高对群落组成和生态系统功能的理解。从历史上看，性状分布的数据是通过（1）从少量树木中收集少量叶子产生的，这受到有限的采样，但产生基本生态单元的信息（个人），或（2）使用遥感图像来推断特征，在大区域连续产生信息，但作为地块（包含不同物种的多棵树）或像素，而不是个体。识别个体树木并估计其特征的遥感方法将提供这两种方法的好处，产生与生物个体相关的连续大规模数据。我们使用的数据从国家生态观测网络（氖）开发一种方法来扩大功能性状从160棵树到数百万棵树的空间范围内的两个氖网站。该管道包括三个阶段：（1）图像分割，以识别个体树木并估计结构特征;（2）使用高光谱特征推断叶质量面积（LMA），氮，碳和磷含量的模型集合，以及异速生长的DBH;以及（3）预测氖站点完整遥感足迹的分割树冠。保持试验数据的R2值范围为0.41至0.75。集成方法的性能优于单一偏最小二乘模型。与其他性状相比，碳表现较差（R2为0.41）。由于过度分割，牙冠分割步骤在管道中造成了最大的不确定性。管道产生了良好的DBH估计值（R2为0.62的保留数据）。性状预测冠执行显着优于可比的预测像素，导致在0.07和0.26之间的测试数据的R2的改善。我们使用管道为约500万个个体冠产生个体水平的性状数据，覆盖总面积约360平方公里。这个大的数据集允许测试景观尺度上的生态问题，揭示了叶片性状与结构性状和环境条件相关。
Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals’ fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote‐sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote‐sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large‐scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: (1) image segmentation, to identify individual trees and estimate structural traits; (2) an ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and (3) predictions for segmented crowns for the full remote‐sensing footprint at the NEON sites. TheR2values on held‐out test data ranged from 0.41 to 0.75 on held‐out test data. The ensemble approach performed better than single partial least‐squares models. Carbon performed poorly compared to other traits (R2of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over‐segmentation. The pipeline produced good estimates of DBH (R2of 0.62 on held‐out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement ofR2on test data of between 0.07 and 0.26. We used the pipeline to produce individual‐level trait data for ~5 million individual crowns, covering a total extent of ~360 km2. This large data set allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.