NSF Award
1926454
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. <br/><br/>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.<br/><br/>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.
