A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions

作者：Conor McMahon, Dar A. Roberts, John C. Stella, Anna T. Trugman, Michael Bliss Singer, K. K. Caylor · 发表于：Remote Sensing of Environment · 年份：2024 · DOI：10.1016/j.rse.2024.114056 · 被引用次数：17 · 研究领域：Remote Sensing in Agriculture、Fire effects on ecosystems、Remote Sensing and LiDAR Applications

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral infor...