Forests are the most important part of terrestrial ecosystems. In the context of China’s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area’s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha⁻¹. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha⁻¹ of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development.

The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.

The Loess Plateau is an important grain-producing area and energy base in China and is an area featuring dramatic changes in both surface and underground processes. However, the associations between surface deformation and groundwater storage changes in different landscape types in the region are still unclear. Based on Sentinel-1 and GRACE (Gravity Recovery and Climate Experiment) data, this study monitored and verified the surface deformation and groundwater storage changes in different landscape types, such as those of the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area, in the Loess Plateau of China from 2020 to 2021. Through time series and cumulative analysis using the same spatial and temporal resolution, the associations between these two changes in different regions are discussed. The results show that: (1) the surface deformation rates in different landscape types differ significantly. The minimum surface deformation rate in the Kubuqi Desert is −5~5 mm/yr, while the surface deformation rates in the Hetao Irrigation District, the open-pit mine recovery area in the Jinbei Mining Area, and the Shendong Mining Area are −60~25 mm/yr, −25~25 mm/yr, and −95.33~26 mm/yr, respectively. (2) The regional groundwater reserves all showed a decreasing trend, with the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area declining by 359.42 mm, 103.30 mm, 45.60 mm, and 691.72 mm, respectively. (3) The surface elasticity deformation had the same trend as the temporal fluctuation in groundwater storage, and the diversion activity was the main reason why the temporal surface deformation in the Hetao Irrigation District lagged behind the change in groundwater storage by 1~2 months. The measure of “underground water reservoirs in coal mines” slows down the rate of collapse of coal mine roof formations, resulting in the strongest time-series correlation between mild deformation of the surface of the Shendong mine and changes in the amount of groundwater reserves (R = 0.73). This study analyzes the associations between surface deformation and groundwater storage changes in different landscape areas of the Loess Plateau of China and provides new approaches to analyzing the dynamic associations between the two and the causes of changes in both variables.

With the rapid development of urbanization, many environmental problems in this stage have seriously threatened human survival and health. The over-exploitation and unreasonable use of water resources increase the uncertainty and risk of future water resources utilization. China, as an important developing country, is in the stage of rapid urbanization. As two important urban agglomerations in China, the Yangtze River Delta and Pearl River Delta deserve to be studied especially in the process of their urbanization.In this paper, GeoSOS-FLUS software was used to simulate the forest loss in the Yangtze River Delta and Pearl River Delta in 2023 during urbanization process, and InVEST model was used to evaluate the water yield in 2013 and 2023 after simulation. It was found that the forest land in both areas decreased and the water yield in Yangtze River Delta decreased considerably. The distribution of forest land in 2023 changed slightly compared with that in 2013, and the total amount increased. In addition, the Yangtze River Delta has increased a lot.