Ecological risk assessment plays an important role in ecosystem management and conservation. Conventional landscape-level assessment can only estimate the ecological risk level. It does not define ecological risk types, resulting in a lack of targeted regulation methods. This study establishes a model for identifying ecological risk-related “source-sink” landscape functions according to (1) “source-sink” landscape theory, (2) the responses of landscape types to ecological risks, and (3) the key influences on ecological risk. Four ecological risk “source-sink” landscape functions were mapped as a grid to understand their distribution. Natural and human activity factors were analyzed to determine their effects. After comprehensively considering the ecological risk levels, types of ecological risk, “source-sink” landscape functions, and their influencing factors, six principles and twenty-four targeted regulation strategies were proposed. Take the Liaoning province, China, as an example. The results prove that more than 80% of the grids were affected by the ecological risk “sink” landscape function for different and multiple ecological risks in the study area. Landscapes with the “source” function were mainly located in central cities and coastal areas. About 65% of the grids with “sink” landscape functions had medium, moderate-high, and high ecological risks. More than 75% of the grids with “source” landscape functions had medium, moderate-low, and low ecological risks. Local terrain features, vegetation, and climate were closely related to the “source” or “sink” landscape function of a grid. The land use type converted to artificial surface had the highest driving effects (q value) on multiple ecological risk “source-sink” landscape functions, and had a significant difference between other factors. The driving effects of land use type converted to artificial surface and road network density gradually increased with the risk level. The influences of GDP and population density gradually weakened with the level. The influence of interaction between any two factors was stronger than the influence of a single factor on ecological risk. The proposed assessment model can help to identify specific ecological risk at the grid level, and combined with the regulation strategy, the scientific basis can be provided for the regulation and management of different ecological risks.

Coal-grain overlap areas (CGOA) with high groundwater levels are vulnerable to subsidence and water logging during a series of mining activities, which have adverse impacts on crop yields. Such damage requires full reports of disturbed boundaries for the agricultural reimbursement and ongoing reclamation. Since direct measurements are difficult in such a case because of vast, unreachable areas, so it is necessary to be able to identify out-of-production boundary (OB) and reduced-production boundary (RB) in the corresponding region. In this study, OB was extracted by setting thresholds through characteristics of the cultivated land elevation based on UAV-generated digital surface model (DSM) and digital orthophoto map (DOM). Meanwhile, aboveground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI), were used to select the appropriate vegetation indexes (VI) to perform a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR) and random forest (RF) algorithms. Finally, an improved OTSU segmentation algorithm was applied to extract mild RB and severe RB. The results show elevation threshold segmentation method and the improved OTSU segmentation method can accurately recognize and extract disturbed boundaries, which are consistent with the tonal difference after crop damage in the image. This study provides reference methods and theoretical supports for disturbed boundaries determination in CGOA with high groundwater levels for further agricultural compensation and reclamation processes.

Coal mining disturbances in semi-arid grasslands affect land surface phenology (LSP), impacting ecosystem functions, restoration target setting, and carbon sequestration; however, the magnitude and spatial extent of these disturbances and their detectability across vegetation indices (VIs), remain insufficiently constrained. We developed and applied a streamlined quantitative framework to delineate the extent and intensity of mining-induced phenological disturbance and to compare the sensitivity and stability of commonly used VIs. Using Harmonized Landsat Sentinel (HLS) surface reflectance data over the Yimin mine, we reconstructed multitemporal VI trajectories and derived phenological metrics; directional phenology gradients were used to delineate disturbance, and VI responsiveness was evaluated via mean difference (MD) and standard deviation (SD) between affected and control areas. Research findings indicate that the impact of mining extends to an area approximately four times the size of the mining site, with the start of season (SOS) in affected areas occurring about 10 days later than in unaffected areas. Responses varied markedly among VIs, with the Modified Soil-Adjusted Vegetation Index (MSAVI) exhibiting the highest spectral stability under disturbance. This framework yields an information-rich quantification of phenological impacts attributable to mining and provides operational guidance for index selection and the prioritization of restoration and environmental management in semi-arid mining landscapes.