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- W3040994259 abstract "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 Betula and Salix 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." @default.
- W3040994259 created "2020-07-16" @default.
- W3040994259 creator A5028672336 @default.
- W3040994259 creator A5074204306 @default.
- W3040994259 creator A5088816429 @default.
- W3040994259 date "2020-07-09" @default.
- W3040994259 modified "2023-10-16" @default.
- W3040994259 title "Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery" @default.
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- W3040994259 doi "https://doi.org/10.3390/rs12142199" @default.
- W3040994259 hasPublicationYear "2020" @default.
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