Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest
Mapping three-dimensional variation in leaf mass per area with imaging spectroscopy and lidar in a temperate broadleaf forest
复制标题
DOI：
10.1016/j.rse.2020.112043
复制
发表时间：
2020-12
影响因子：
13.5
通讯作者：
A. Chlus;E. Kruger;P. Townsend
中科院分区：
文献类型：
--
作者：
A. Chlus;E. Kruger;P. Townsend
成像光谱学是在景观和更大尺度上绘制森林生态系统冠层叶片特征的重要工具。迄今为止，大多数努力都涉及到特征的二维映射，通常代表冠层顶部的条件。然而，林冠垂直剖面的特征及其相关的生物学功能各不相同，因此，纳入有关垂直模式的信息可能会改善初级生产力等生态系统过程的建模。2016年和2017年，利用美国国家生态观测站网络（NEON） 5域（五大湖）森林的大量野外数据，研究了与植物生长和防御相关的重要叶面性状——叶面积质量（LMA）的垂直变化特征。现场工作与NEON机载观测平台（AOP）飞越同时进行，收集了成像光谱和激光雷达数据。利用成像光谱技术绘制冠层LMA，利用激光雷达模拟透射率垂直梯度，建立了温带阔叶林LMA的三维模式。利用偏最小二乘回归（PLSR）估算冠层顶部LMA (R2: 0.57, RMSE 10.8 g m−2)，并结合激光雷达衍生的透光率和高度指标，采用多水平回归模型模拟冠层内LMA （R2: 0.78, RMSE 8.3 g m−2）。耦合模型在不考虑物种组成的情况下准确估计了整个冠层的LMA （R2= 0.82, RMSE: 8.5 g m−2）。
Imaging spectroscopy is a valuable tool for mapping canopy foliar traits in forested ecosystems at landscape and larger scales. Most efforts to date have involved two-dimensional mapping of traits, typically representing top-of-canopy conditions. However, traits and their associated biological functions vary through the canopy vertical profile, such that incorporating information about vertical patterns may improve modeling of ecosystem processes like primary productivity. In 2016 and 2017, we collected extensive field data in forests in Domain 5 (Great Lakes) of the National Ecological Observatory Network (NEON) to characterize the vertical variation in leaf mass per area (LMA), an important foliar trait related to plant growth and defense. Fieldwork was coincident with NEON Airborne Observation Platform (AOP) overflights which collected imaging spectroscopy and lidar data. Using imaging spectroscopy to map top-of-canopy LMA and lidar to model vertical gradients of transmittance, we developed a method to map three-dimensional patterns in LMA in temperate broadleaf forests. Partial least squares regression (PLSR) was used to estimate top-of-canopy LMA (R2: 0.57, RMSE 10.8 g m−2), which, along with lidar-derived metrics of light transmittance and height, was used in a multilevel regression to model within-canopy LMA (R2: 0.78, RMSE 8.3 g m−2). The coupled models accurately estimated LMA throughout the canopy without taking into account species composition (R2= 0.82, RMSE: 8.5 g m−2).