Department of Landscape Architecture & Environmental Planning, College of Environmental Design, University of California Berkeley, Berkeley, California, 94720-2000, USA.
Oak Ridge Institute for Science and Education (ORISE), U.S. Environmental Protection Agency, Washington, D.C., 20004, USA.
Ecol Appl. 2019 Oct;29(7):e01961. doi: 10.1002/eap.1961. Epub 2019 Jul 22.
The unprecedented global biodiversity loss has massive implications for the capacity of ecosystems to maintain functions critical to human well-being, urgently calling for rapid, scalable, and reproducible strategies for biodiversity monitoring, particularly in threatened ecosystems with difficult field access such as wetlands. Remote sensing indicators of spectral variability and greenness may predict the diversity of plant communities based on their optical diversity; however, most evidence is based on narrowband spectral data or terrestrial ecosystems. We investigate how spectral greenness and heterogeneity from publicly available broadband multi-spectral Landsat satellite imagery explain variation in vegetation diversity across different wetland types, ecoregions, and disturbance levels using 1,138 sites surveyed by U.S. EPA's National Wetland Condition Assessment. We found positive correlations of plant species richness and diversity with indicators of annual maximum spectral greenness and its spatial heterogeneity, explaining up to 43% variation within the global sample, 48% within wetland types or ecoregions, and up to 61% with abiotic covariates. The combined effect of spectral greenness and heterogeneity was stronger than the best-performing model using climatic, topographic, and edaphic factors alone. When compared among major U.S. watersheds and individual states, the fit of diversity-greenness models increased when more wetland types were included within the corresponding region's boundaries, up to 61% at the watershed and 77% at the state level, respectively, for diversity models and up to 73% and 80%, respectively, for richness models. Model outliers were characterized by a significantly greater diversity of nonnative species (P < 0.0001), suggesting that changes in model performance and greenness distributions could be used as indicators of shifts in plant community composition, particularly in tidal wetlands making the majority of outliers with significantly lower than predicted diversity. This study represents a first-time national-scale effort to use publicly available remote sensing, climatic, and topographic data to predict plant diversity in wetlands, which tend to be understudied compared to terrestrial ecosystems despite being among the most stressed ecosystems on Earth. Our study suggests that multi-temporal broadband satellite imagery could provide a low-cost assessment of regional and national wetland biodiversity for prioritization of conservation efforts and early detection of biodiversity loss.
前所未有的全球生物多样性丧失对生态系统维持人类福祉所需功能的能力产生了巨大影响，迫切需要快速、可扩展和可重复的生物多样性监测策略，特别是在湿地等难以进入的受威胁生态系统中。光谱可变性和绿色度的遥感指标可以根据其光学多样性预测植物群落的多样性；然而，大多数证据基于窄带光谱数据或陆地生态系统。我们研究了公共宽带多光谱陆地卫星图像的光谱绿色度和异质性如何解释不同湿地类型、生态区和干扰水平的植被多样性变化，使用美国环保署的国家湿地状况评估调查的 1138 个地点。我们发现，植物物种丰富度和多样性与年度最大光谱绿色度及其空间异质性的指标呈正相关，在全球样本中解释了高达 43%的变化，在湿地类型或生态区中解释了高达 48%的变化，与非生物协变量的解释高达 61%。光谱绿色度和异质性的综合效应强于仅使用气候、地形和土壤因素的最佳表现模型。当在主要美国流域和个别州之间进行比较时，当更多的湿地类型包含在相应区域的边界内时，多样性-绿色度模型的拟合度会增加，在流域和州一级分别高达 61%和 77%，用于多样性模型，以及分别高达 73%和 80%，用于丰富度模型。模型异常值的特征是外来物种的多样性明显更大（P<0.0001），这表明模型性能和绿色度分布的变化可以用作植物群落组成变化的指标，特别是在潮湿地带，大多数异常值的多样性明显低于预测值。本研究代表了首次利用公共遥感、气候和地形数据预测湿地植物多样性的全国性努力，与陆地生态系统相比，湿地往往研究不足，尽管它们是地球上受压力最大的生态系统之一。我们的研究表明，多时相宽带卫星图像可以为保护工作提供低成本的区域和国家湿地生物多样性评估，并及早发现生物多样性丧失。
