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  • Modeling Primary Production in Temperate Forests Using Three-Dimensional Canopy Structural Complexity Metrics Derived from Airborne LiDAR Data
Title: Modeling Primary Production in Temperate Forests Using Three-Dimensional Canopy Structural Complexity Metrics Derived from Airborne LiDAR Data
Accurate and scalable estimation of forest production is essential for quantifying carbon sequestration, forecasting timber yields, and guiding climate change mitigation strategies. While prior studies established a positive linkage between net primary production (NPP) and canopy structural complexity (CSC) metrics derived from terrestrial LiDAR, the spatial coverage of ground-based surveys is limited. Airborne laser scanning (ALS) could offer a rapid and spatially extensive alternative to terrestrial scanning, but the predictive capacity of ALS-derived CSC metrics for estimating forest production remains insufficiently explored. To address this gap, we derived a suite of three-dimensional (3D) CSC metrics from small-footprint, high-density ALS data collected by the National Ecological Observatory Network’s Airborne Observation Platform. We evaluated relationships between CSC metrics and the NPP of plots nested within seven deciduous and evergreen temperate forests. Optimal metric combinations for predicting NPP within and across forest types were identified using partial least squares regression coupled with recursive feature elimination. ALS-derived CSC metrics explained 77% (RMSE = 11%) and 76% (RMSE = 13%) of the variance in deciduous and evergreen forest plot NPP, respectively. Our findings demonstrate that 3D CSC metrics derived from high-density ALS are robust predictors of plot-level NPP, offering performance comparable to terrestrial scanners while enabling greater scalability and more efficient data acquisition.  more » « less
Award ID(s):
2217817
PAR ID:
10660642
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Remote Sensing
Volume:
17
Issue:
16
ISSN:
2072-4292
Page Range / eLocation ID:
2817
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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