Collaborative Research: MSA: Incorporating canopy structural complexity to improve model forecasts of functional effects of forest disturbance

合作研究：MSA：结合冠层结构复杂性来改进森林干扰功能效应的模型预测

• 批准号: 1926454
• 负责人: Jaclyn Matthes
• 金额: $ 2.96万
• 依托单位: Wellesley College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926454&HistoricalAwards=false
• 关键词: Collaborative; Research; MSA; Incorporating; canopy

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.

火灾、风暴和昆虫爆发等干扰会改变森林的物理结构并影响生长速度。严重的干扰常常会降低森林的生长速度；这些事件已得到充分研究，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来提高生长速度。生态系统模型通常无法预测森林对中等干扰的反应。部分原因是他们依赖于简单化的森林结构概念。遥感方法可以检测干扰并测量对森林结构的影响。该项目结合了这些方法来改进森林对中度干扰响应的模型预测。更好的模型预测将有助于改善森林管理实践。该研究将利用数十年的陆地卫星图像来绘制 NEON 森林站点最近（5 年前）的干扰情况。 NEON 空中观测平台 (AOP) 的 LiDAR 将通过计算几个与生态系统功能相关的结构指标来表征这些地点的树冠结构。我们将通过比较受干扰地区与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，将为森林结构的干扰后果提供宏观系统层面的理解。我们将使用生成的干扰和冠层结构图来测试生态系统人口学 (ED2) 模型捕获干扰的结构和功能结果的技能。使用模型实验，我们将通过将 NEON AOP 结构数据同化到 ED2 来量化净初级生产和净生态系统生产的改进。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。