|Appears in Collections:||Biological and Environmental Sciences Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Quantifying structural diversity to better estimate change at mountain forest margins|
|Author(s):||Morley, Peter J|
Donoghue, Daniel N M
Jump, Alistair S
|Citation:||Morley PJ, Donoghue DNM, Chen J & Jump AS (2019) Quantifying structural diversity to better estimate change at mountain forest margins. Remote Sensing of Environment, 223, pp. 291-306. https://doi.org/10.1016/j.rse.2019.01.027|
|Abstract:||Global environmental changes are driving shifts in forest distribution across the globe with significant implications for biodiversity and ecosystem function. At the upper elevational limit of forest distribution, patterns of forest advance and stasis can be highly spatially variable. Reliable estimations of forest distribution shifts require assessments of forest change to account for variation in treeline advance across entire mountain ranges. Multispectral satellite remote sensing is well suited to this purpose and is particularly valuable in regions where the scope of field campaigns is restricted. However, there is little understanding of how much information about forest structure at the mountain treeline can be derived from multispectral remote sensing data. Here we combine field data from a structurally diverse treeline ecotone in the Central Mountain Range, Taiwan, with data from four multispectral satellite sensors (GeoEye, SPOT-7, Sentinel-2 and Landsat-8) to identify spectral features that best explain variation in vegetation structure at the mountain treeline and the effect of sensor spatial resolution on the characterisation of structural variation. The green, red and short-wave infrared spectral bands and vegetation indices based on green and short-wave infrared bands offer the best characterisation of forest structure with R2 values reported up to 0.723. There is very little quantitative difference in the ability of the sensors tested here to discriminate between discrete descriptors of vegetation structure (difference of R2MF within 0.09). While Landsat-8 is less well suited to defining above-ground woody biomass (R2 0.12–0.29 lower than the alternative sensors), there is little difference between the relationships defined for GeoEye, SPOT-7 and Sentinel-2 data (difference in R2|
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