Collaborative Research: MSA: Incorporating Canopy Structural Complexity to Improve Model Forecasts of Functional Effects of Forest Disturbance

火灾、风暴和昆虫暴发等干扰会改变森林的物理结构并影响生长速度。严重的干扰往往会降低森林增长率；这些事件得到了很好的研究，但很少见。大多数干扰都比较温和，可以通过重塑森林树冠来增加光捕获和碳吸收，从而提高增长速度。生态系统模型往往无法预测森林对中度干扰的反应。这在一定程度上是因为他们依赖于森林结构的简单化概念。遥感方法可以探测干扰并测量对森林结构的影响。该项目将这些方法结合在一起，以改进森林对中等干扰反应的模型预测。更好的模型预测将有助于改进森林管理实践。这项研究将利用几十年的陆地卫星图像来绘制最近(5年前)森林霓虹灯遗址的干扰图。来自霓虹灯空中观测平台(AOP)的激光雷达将通过计算几个结构指标来表征这些地点的树冠结构，这些结构指标与生态系统功能有明显的联系。我们将通过比较受干扰地区和附近未受干扰地区的树冠结构来识别几种森林干扰的结构特征。干扰类型和严重程度的范围，再加上森林类型的广泛多样性，将有助于从宏观系统层面了解干扰对森林结构造成的后果。我们将使用生成的干扰和树冠结构图来测试生态系统人口统计学(ED2)模型在捕获干扰的结构和功能结果方面的技能。利用模型实验，我们将通过将霓虹灯AOP结构数据同化到ED2中来量化净初级生产力和净生态系统生产力的改善。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Disturbances such as fire, storms, and insect outbreaks change the physical structure of forests and affect growth rates. Severe disturbances often reduce forest growth rates; these events are well-studied but rare. Most disturbances are more moderate and can boost growth rates by reshaping forest canopies to increase light capture and carbon uptake. Ecosystem models often fail to predict forest responses to moderate disturbances. This is partly because they rely on simplistic concepts of forest structure. Remote sensing methods can detect disturbances and measure impacts on forest structure. This project combines these methods to improve model predictions of forest responses to moderate disturbance. Better model predictions will help improve forest management practices. The research will leverage decades of Landsat satellite imagery to map recent (5 years old) disturbances at forested NEON sites. LiDAR from the NEON Aerial Observation Platform (AOP) will characterize canopy structure at these sites by calculating several structural metrics with demonstrated links to ecosystem functions. We will identify structural signatures of several forest disturbances by comparing canopy structure in disturbed areas to nearby undisturbed areas. The range of disturbance types and severity, in combination with a wide diversity of forest types will provide a macrosystem-level understanding of disturbance consequences for forest structure. We will use the resulting maps of disturbance and canopy structure to test the skill of the Ecosystem Demography (ED2) model at capturing the structural and functional outcomes of disturbance. Using model experiments, we will quantify the improvements in Net Primary Production and Net Ecosystem Production by assimilating NEON AOP structural data into ED2.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.