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Forest mapping using remote sensing has made considerable progress over the past decade, but substantial uncertainties remain in complex regions, particularly where terrain and climate vary dramatically. Yunnan Province, China, represents such a challenging case, with its diverse climatic zones ranging from tropical to temperate and its topography spanning over 6500 m in elevation. These factors contribute to substantial variation in vegetation types, complicating the accurate identification of forest cover through remote sensing. This study aims to enhance forest mapping in Yunnan by leveraging multi-temporal remote sensing data from Sentinel-2 and Landsat 8/9 imagery, incorporating key phenological stages—such as the leaf greening (GRN) period, as well as the senescence, defoliation, and foliation (SDF) stages of deciduous forests—along with kNDVI and terrain factors. A random forest (RF) classifier was applied on the Google Earth Engine (GEE) platform to create a 10 m resolution forest map (LS2-RF). This map achieved an overall accuracy of 96.35% when validated with 1572 ground samples, significantly outperforming existing global datasets, such as Dynamic World (73.88%) and WorldCover (87.66%). These maps agreed well in extensive forested areas; discrepancies were noted in mixed land types, including farmland, urban areas, and regions with fragmented landscapes. In 2022, Yunnan’s forest cover was 60.40%, with higher coverage in the southwestern region and lower in the northeast. The largest forested area was found in Pu’er City, while the smallest was in Yuxi City. Forests were most abundant at elevations between 1500 and 2500 m (occupying 52.29% of the total forest area) and slopes of 15° to 25° (occupying 39.19% of the total forest area). Conversely, forest cover was lowest in areas below 500 m elevation (occupying 0.64% of the total forest area) and on slopes less than 5° (occupying 2.40% of the total forest area). The analysis also revealed a general trend of increasing forest cover with decreasing latitude and longitude, with peak forest coverage at mid-elevations and slopes, followed by a decline at higher elevations. The resultant forest map provides valuable data for ecological assessments, forest conservation initiatives, and informed policy decision-making. Full article

InSAR technology uses the geometry between antennas and targets to obtain DEM and deformation; therefore, accurate orbit information, which can provide reliable geometry, is the prerequisite for InSAR processing. However, the orbit information provided by some satellites may be inaccurate. Further, this inaccuracy will be reflected in the interferogram and will be difficult to remove, finally resulting in incorrect results. More importantly, it was found that the residual fringes caused by inaccurate orbit information vary unevenly throughout the whole image and cannot be completely removed by the existing refinement and re-flattening methods. Therefore, an interferogram re-flattening method based on local residual fringe removal and adaptively adjusted windows was proposed in this paper, with the aim being to remove the unevenly varying residual fringes. There are two innovative advantages of the proposed method. One advantage is that the method aims at the global inhomogeneity of residual fringes; the idea of combining local processing and residual fringe removal was proposed to ensure the residual fringes in the whole image can be removed. The other is that an adaptively adjusted local flattening window was designed to ensure that the residual fringes within the local window can be removed cleanly. Three sets of GaoFen-3 data and one pair of Sentinle-1A data were used for experiments. The re-flattening process shows that the local flattening and the adjustment of the local window are absolutely essential to the clean removal of time-varying and uneven residual fringes. The generated DEM and the estimated building heights are used to indirectly reflect the performance of re-flattening methods. The final results show that compared with mature refinement and re-flattening methods, the DEMs based on the proposed method are more accurate, which reflects that the proposed method has a better performance in the removal of time-varying and uneven residual fringes. Full article