Supraglacial debris cover considerably influences sub-debris ablation patterns and the surface morphology of glaciers by modulating the land–atmosphere energy exchange. Understanding its spatial distribution and temporal variations is crucial for analyzing melting processes and managing downstream disaster mitigation efforts. In recent years, the overall slightly positive mass balance or stable state of eastern Pamir glaciers has been referred to as the “Pamir-Karakoram anomaly”. It is important to note that spatial heterogeneity in glacier change has drawn widespread research attention. However, research on the spatiotemporal changes in the debris cover in this region is completely nonexistent, which has led to an inadequate understanding of debris-covered glacier variations. To address this research gap, this study employed Landsat remote sensing images within the Google Earth Engine platform, leveraging the Random Forest algorithm to classify the supraglacial debris cover. The classification algorithm integrates spectral features from Landsat images and derived indices (NDVI, NDSI, NDWI, and BAND RATIO), supplemented by auxiliary factors such as slope and aspect. By extracting the supraglacial debris cover from 1994 to 2024, this study systematically analyzed the spatiotemporal variations and investigated the underlying drivers of debris cover changes from the perspective of mass conservation. By 2024, the area of supraglacial debris in eastern Pamir reached 258.08 ± 20.65 km², accounting for 18.5 ± 1.55% of the total glacier area. It was observed that the Kungey Mountain region demonstrated the largest debris cover rate. Between 1994 and 2024, while the total glacier area decreased by −2.57 ± 0.70%, the debris-covered areas expanded upward at a rate of +1.64 ± 0.10% yr⁻¹. The expansion of debris cover is driven by several factors in the context of global warming. The rising temperature resulted in permafrost degradation, slope destabilization, and intensified weathering on supply slopes, thereby augmenting the debris supply. Additionally, the steep supply slope in the study area facilitates the rapid deposition of collapsed debris onto glacier surfaces, with frequent avalanche events accelerating the mobilization of rock fragments.

In recent years, land subsidence in the Northern Anhui Plain has become increasingly pronounced, posing serious risks to infrastructure and groundwater management. However, quantitative assessments of its driving mechanisms remain limited. This study focuses on Bozhou, a typical resource-based city, and employs 186 Sentinel-1 SAR images and SBAS-based interferometric analysis to retrieve the spatiotemporal evolution of land subsidence from 2022 to 2024. Results show that the peak subsidence rate reaches 102 mm/year and has its major distribution in the central and eastern sectors of Bozhou. Temporally, the subsidence pattern shows an initial intensification followed by gradual stabilization. Furthermore, a GeoDetector-based analysis indicates that excessive groundwater extraction and coal mining are the dominant factors, with significant interactive enhancement effects. These findings provide crucial insights for the prevention and mitigation of land subsidence in resource-based cities.

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts.

Debris-covered glaciers, with their intricate thermal dynamics and significant spatial heterogeneity, play a pivotal role in elucidating glacier ablation processes and their responses to climate change. However, existing research on their energy balance predominantly focuses on short-term or localized processes, while the long-term evolution of energy fluxes and the combined effects of debris cover and ice cliffs remain underexplored. This study, focused on the Lirung glacier in the Central Himalaya, leverages multi-source remote sensing data (Landsat 8, MODIS, Planet) in conjunction with meteorological observations and an energy balance model to investigate the spatiotemporal variations in the glacier’s surface energy balance from October 2017 to August 2019. Key findings are as follows: (1) Net radiation flux emerges as the predominant energy driver for ablation, reaching its peak during May–June and substantially outpacing both sensible and latent heat fluxes in magnitude; (2) The energy balance exhibits pronounced spatial heterogeneity, with lower-altitude regions receiving enhanced energy inputs and displaying reduced albedo, thereby magnifying the local ablation flux; (3) The average debris thickness is quantified at 0.55 ± 0.02 m, with thicker debris layers mitigating ablation, while thinner layers exacerbate it; (4) Ice cliffs are characterized by significantly elevated ablation fluxes, with certain areas recording values as high as 1.73 times the glacier-wide mean; (5) The proglacial lake has expanded by 21.1 ± 11.4%, with its temporal variations closely tracking the fluctuations in net radiation flux. These findings provide crucial insights into the energy balance and climate responses of debris-covered glaciers in the Central Himalaya.

Accurate agricultural object extraction from high-resolution remote sensing imagery requires not only high regional segmentation accuracy but also reliable object geometry. Yet most semantic segmentation frameworks are optimized primarily for pixel- or region-level agreement, so high overlap scores can coexist with boundary discontinuities, object merging, and structurally inconsistent predictions. To address this mismatch, we propose Attention-enhanced DeepLab V3+ with Local-to-Global Shape Consistency (ADLSC), a local-to-global framework that coordinates semantic discrimination, adaptive boundary representation, and complete-object geometric consistency. Target-aware multiscale context strengthens target–background discrimination, contour-adaptive representation adjusts sampling to local boundary variation while preserving narrow inter-object separation, and a staged hierarchical objective extends optimization from regional agreement to curvature- and shape-related consistency. On cropland parcel and greenhouse facility datasets, ADLSC improves IoU by 8.28 and 15.92 percentage points and matching rate by 24.32 and 42.88 percentage points, respectively, relative to DeepLab V3+, while reducing curvature and shape errors. The larger gains in object matching than in regional overlap show that explicitly coupling local contour adaptation with object-level geometric constraints improves aspects of segmentation quality that pixel-oriented objectives alone do not capture, supporting more reliable object-based agricultural mapping and spatial analysis.