| Autor: |
Lucy G. Poley, David N. Laskin, Gregory J. McDermid |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 12, Iss 14, p 2199 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs12142199 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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