Quantifying understory vegetation density using multi-temporal Sentinel-2 and GEDI LiDAR data

Abstract

Understory vegetation contributes considerably to biodiversity and total aboveground biomass of forest ecosystems. Whereas field inventories and LiDAR data are generally used to estimate understory vegetation density, methods for large-scale and spatially continuous estimation of understory vegetation density are still lacking. For an evergreen coniferous forest area in southern China, we developed and tested an effective and practical remote sensing-driven approach for mapping understory vegetation, based on phenological differences between over and understory vegetation. Specifically, we used plant area volume density (PAVD) calculations based on GEDI data to train a support vector regression model and subsequently estimated the understory vegetation density from Sentinel-2 derived metrics. We produced maps of PAVD for the growing and non-growing season respectively, both performing well compared against independent GEDI samples (R2 = 0.89 and 0.93, p < 0.01). Understory vegetation density was derived from the differences in PAVD between the growing and non-growing season. The understory vegetation density map was validated against field samples from 86 plots showing an overall R2 of 0.52 (p < 0.01), rRMSE = 21%. Our study developed a tangible approach to map spatially continuous understory vegetation density with the combination of GEDI LiDAR data and Sentinel-2 imagery, showing the potential to improve the estimation of terrestrial carbon storage and better understand forest ecosystem processes across larger areas.

Keywords

• understory vegetation
• gedi lidar data
• plant area volume density
• support vector regression