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

Autor:	David N. Laskin, Lucy G. Poley, Gregory J. McDermid
Rok vydání:	2020
Předmět:	Multivariate statistics 010504 meteorology & atmospheric sciences Science multispectral ved/biology.organism_classification_rank.species Multispectral image 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Shrub Bayesian multivariate linear regression canopy height model Spatial analysis 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing RGB Biomass (ecology) ved/biology Linear model Vegetation 15. Life on land rangelands shrubs vegetation indices plains bison General Earth and Planetary Sciences Environmental science UAS aboveground biomass
Zdroj:	Remote Sensing Volume 12 Issue 14 Pages: 2199 Remote Sensing, Vol 12, Iss 2199, p 2199 (2020)
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:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff0e5929a55e4a181f3e19d39782c5b3 https://doi.org/10.3390/rs12142199 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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