Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images

作者：Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne B. Nicotra, Saul A. Cunningham, Matthew Brookhouse · 发表于：GIScience & Remote Sensing · 年份：2025 · DOI：10.1080/15481603.2025.2517922 · 被引用次数：7 · 研究领域：Remote Sensing in Agriculture、Fire effects on ecosystems、Remote Sensing and LiDAR Applications

Forests and woodlands worldwide appear to be increasingly vulnerable to decline and mortality, endangering forest-reliant biodiversity and ecosystem services. Consistent with this vulnerability, increased fire frequency and widespread insect-induced dieback has been reported across the subalpine woodlands of southeastern Australia. High-quality spatiotemporal information on the occurrence of the dieback is, however, scarce. This study aimed to develop a method to map distribution of subalpine woodlands and assess their canopy health using Sentinel-2 and Landsat series multispectral satellite imagery. Three-month growing-season geometric median surface reflectance was used for pixel-based supervised classification and regression. Forest masking models were trained on aerial LiDAR-derived forest cover. Canopy condition regression models were trained on ground-based assessment data surveyed at 85 vegetation plots. We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. Using Sentinel-2 surface reflectance, the best forest masking model was SVM (OA = 0.849 ± 0.003), while MLR performed best for canopy condition (r2 = 0.73 ± 0.07). The models built on Landsat surface reflectance performed similarly. The accuracy within our study area is greater than those of existing global/continental satellite-derived f...