Abstract 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.

中文翻译：

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卫星图像纹理捕捉植被异质性并解释鸟类丰富度的模式

摘要 解决全球生物多样性下降问题需要准确评估决定物种多样性模式的关键环境属性。植被的空间异质性强烈影响物种多样性模式，激光雷达和卫星图像纹理分析得出的植被结构测量与物种丰富度相关。我们的目标是通过将基于实地的植被结构测量与图像纹理和鸟类丰富度直接联系起来，更好地理解图像纹理解释鸟类丰富度的原因。此外，我们询问了图像纹理与基于激光雷达的冠层高度可变性相比如何，以及传感器分辨率如何影响图像纹理的解释能力。我们从 2017 年到 2019 年的 30 m（Landsat 8）和 10 m（Sentinel-2）分辨率增强型植被指数 (EVI) 图像中生成了纹理度量。我们将纹理与来自 27 个国家生态观测网络 (NEON) 的植被度量和鸟类丰富度数据进行了比较) 横跨美国大陆的陆地现场。30 和 10 m 分辨率纹理指标都与基于激光雷达的冠层高度变异性密切相关（|r| 分别为 0.64 和 0.80）。纹理与基于实地的指标中度相关，包括植被高度和树干直径的变异性，以及树叶高度的多样性（范围 |r| = 0.31–0.52）。通常，与 30 m 分辨率纹理相比，10 m 分辨率纹理与激光雷达和基于场的指标的相关性更强。在鸟类总丰富度的单变量线性模型中，10 m 分辨率纹理度量也比 30 m 纹理度量（高达 R2adj = 0.31）具有更高的解释力（高达 R2adj = 0.45）。在所有评估的指标中，10 m 的均匀性纹理是鸟类总丰富度的最佳单变量预测因子。在将纹理与基于激光雷达的冠层高度可变性和基于字段的度量相结合的多元鸟类丰富度模型中，30 m 和 10 m 分辨率纹理度量均在排名靠前的模型中被选中，并独立贡献了解释能力（高达 R2adj = 46%） . 基于激光雷达的冠层高度变异性也在鸟类总丰富度排名靠前的模型中被选择，但独立地仅贡献了解释方差的 15%。我们的结果显示卫星图像纹理表征了结构和组成植被异质性的多个特征，补充了鸟类丰富度模型中更常用的指标，对于一些行会而言，其表现优于基于激光雷达的冠层高度变异性和基于实地的植被测量。我们的研究是第一项将图像纹理与植被异质性的特定组成部分以及跨多个生态区和空间分辨率的鸟类丰富度直接联系起来的研究，从而揭示了图像纹理与生物多样性之间强相关性背后的栖息地特征。