气候变化领域集成服务门户(CCPortal): Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness

CCPortal

DOI	10.1016/j.rse.2020.112175
	Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness
	Farwell L.S.; Gudex-Cross D.; Anise I.E.; Bosch M.J.; Olah A.M.; Radeloff V.C.; Razenkova E.; Rogova N.; Silveira E.M.O.; Smith M.M.; Pidgeon A.M.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (\|r\| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range \|r\| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity. © 2020 Elsevier Inc.
英文关键词	Biodiversity; Enhanced vegetation index (EVI); Landsat 8; Lidar; National Ecological Observatory Network (NEON); Sentinel-2; Vegetation composition; Vegetation structure
语种	英语
scopus关键词	Biodiversity; Birds; Forestry; Image enhancement; Learning to rank; Optical radar; Satellites; Textures; Vegetation; Enhanced vegetation index; Environmental attributes; Spatial heterogeneity; Spatial resolution; Specific component; Strong correlation; Vegetation measurement; Vegetation structure; Image texture; bird; heterogeneity; NDVI; satellite imagery; songbird; species diversity; species richness; texture; vegetation structure; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179037
作者单位	Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, United States; Department of Integrative Biology, University of Wisconsin-Madison, 250 North Mills Street, Madison, WI 53706, United States
推荐引用方式 GB/T 7714	Farwell L.S.,Gudex-Cross D.,Anise I.E.,et al. Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness[J],2021,253.
APA	Farwell L.S..,Gudex-Cross D..,Anise I.E..,Bosch M.J..,Olah A.M..,...&Pidgeon A.M..(2021).Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness.Remote Sensing of Environment,253.
MLA	Farwell L.S.,et al."Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness".Remote Sensing of Environment 253(2021).