Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

• Main Authors: Lucy G. Poley, David N. Laskin, Gregory J. McDermid
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Remote Sensing
• Subjects: aboveground biomass; shrubs; vegetation indices; RGB; multispectral; canopy height model
• Online Access: https://www.mdpi.com/2072-4292/12/14/2199

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the <i>Betula</i> and <i>Salix</i> genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R² = 0.888; validation R² = 0.774) nearly as well as the top multivariate linear regression models (calibration R² = 0.896; validation R² > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.