期刊
REMOTE SENSING
卷 12, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/rs12142199
关键词
aboveground biomass; shrubs; vegetation indices; RGB; multispectral; canopy height model; UAS; rangelands; plains bison
类别
资金
- University of Calgary
- Natural Sciences and Engineering Council of Canada [RGPIN-2016-06502]
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 theBetulaandSalixgenera 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 R-2= 0.888; validation R-2= 0.774) nearly as well as the top multivariate linear regression models (calibration R-2= 0.896; validation R-2> 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.
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