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Identifying vertical characteristics of mountainous vegetation distribution is necessary for studying the ecological environment quality and biodiversity and for evaluating its responses to climate change. However, producing fine vegetation distribution in a complex mountainous area remains a huge challenge. This study developed a framework based on multi-source high-resolution satellite images to strengthen the understanding of vertical features of vegetation distribution. We fused GaoFen-6 and Sentinel-2 data to produce 2 m multispectral data, combined with ALOS PALSAR digital elevation model (DEM) data, and used an object-based method to extract variables for establishing a classification model. The spatial distribution of vegetation types in Wuyishan National Park (WNP) was then obtained using a hierarchical random forest classifier. The characteristics of different vegetation types along the elevation gradient and their distribution patterns under different human protection levels were finally examined. The results show that (1) An overall accuracy of 87.11% and a Kappa coefficient of 0.85 for vegetation classification was achieved. (2) WNP exhibits obviously vertical differentiation of vegetation types, showing four compound dominant zone groups and five dominant belts. (3) The composition of vegetation types in the scenic area differs significantly from other regions. The proportions of Masson pine and Chinese fir exhibit a noticeably decreasing trend as the distance increases away from roads, while the changes in broadleaf forest and bamboo forest are less pronounced. [ABSTRACT FROM AUTHOR]

Many studies have investigated the effects of spectral and spatial features of remotely sensed data and topographic characteristics on land-cover and forest classification results, but they are mainly based on individual sensor data. How these features from different kinds of remotely sensed data with various spatial resolutions influence classification results is unclear. We conducted a comprehensively comparative analysis of spectral and spatial features from ZiYuan-3 (ZY-3), Sentinel-2, and Landsat and their fused datasets with spatial resolution ranges from 2 m, 6 m, 10 m, 15 m, and to 30 m, and topographic factors in influencing land-cover classification results in a subtropical forest ecosystem using random forest approach. The results indicated that the combined spectral (fused data based on ZY-3 and Sentinel-2), spatial, and topographical data with 2-m spatial resolution provided the highest overall classification accuracy of 83.5% for 11 land-cover classes, as well as the highest accuracies for almost all individual classes. The improvement of spectral bands from 4 to 10 through fusion of ZY-3 and Sentinel-2 data increased overall accuracy by 14.2% at 2-m spatial resolution, and by 11.1% at 6-m spatial resolution. Textures from high spatial resolution imagery play more important roles than textures from medium spatial resolution images. The incorporation of textural images into spectral data in the 2-m spatial resolution imagery improved overall accuracy by 6.0–7.7% compared to 1.1–1.7% in the 10-m to 30-m spatial resolution images. Incorporation of topographic factors into spectral and textural imagery further improved overall accuracy by 1.2–5.5%. The classification accuracies for coniferous forest, eucalyptus, other broadleaf forests, and bamboo forest can be 85.3–91.1%. This research provides new insights for using proper combinations of spectral bands and textures corresponding to specifically spatial resolution images in improving land-cover and forest classifications in subtropical regions. [ABSTRACT FROM AUTHOR]