The implementation of ecological projects can largely change regional land use patterns, in turn altering the local hydrological process. Articulating these changes and their effects on ecosystem services, such as water conservation, is critical to understanding the impacts of land use activities and in directing future land planning toward regional sustainable development. Taking Zhangjiakou City of the Yongding River as the study area—a region with implementation of various ecological projects—the impact of land use changes on various hydrological components and water conservation capacity from 2000 to 2015 was simulated based on a soil and water assessment tool model (SWAT). An empirical regression model based on partial least squares was established to explore the contribution of different land use changes on water conservation. With special focus on the forest having the most complex effects on the hydrological process, the impacts of forest type and age on the water conservation capacity are discussed on different scales. Results show that between 2000 and 2015, the area of forest, grassland and cultivated land decreased by 0.05%, 0.98% and 1.64%, respectively, which reduces the regional evapotranspiration (0.48%) and soil water content (0.72%). The increase in settlement area (42.23%) is the main reason for the increase in water yield (14.52%). Most land use covered by vegetation has strong water conservation capacity, and the water conservation capacity of the forest is particularly outstanding. Farmland and settlements tend to have a negative effect on water conservation. The water conservation capacity of forest at all scales decreased significantly with the growth of forest (p < 0.05), while the water conservation capacity of different tree species had no significant difference. For the study area, increasing the forest area will be an effective way to improve the water conservation function, planting evergreen conifers can rapidly improve the regional water conservation capacity, while planting deciduous conifers is of great benefit to long-term sustainable development.

Although the MODIS Collection 5.1 Land Cover Type (MODIS v5.1 LCT) product is one of the most recent global land cover datasets and has the shortest updating cycle, evaluations regarding this collection have not been reported. Given the importance of evaluating global land cover data for producers and potential users, the 2010 MODIS v5.1 LCT product IGBP (International Geosphere-Biosphere Programme) layer was evaluated based on two grid maps at scales of 100-m and 500-m,which were derived by rasterizing the 2010 data from the national land use/cover database of China (NLUD-C). This comparison was conducted based on a new legend consisting of nine classes constructed based on the definitions of classes in the IGBP and NLUD-C legends. The overall accuracies of the aggregated classification data were 64.62% and 66.42% at the sub-pixel and pixel scales, respectively. These accuracies differed significantly in different regions. Specifically, high-quality data were obtained more easily for regions with a single land cover type, such as Xinjiang province and the northeast plain of China. The lowest accuracies were obtained for the middle of China, including Ningxia, Shaanxi, Chongqing, Yunnan and Guizhou. At the sub-pixel scale, relatively high producer and user accuracies were obtained for cropland, grass and barren regions; the highest producer accuracy was obtained for forests, and the highest user accuracy was obtained for water bodies. Shrublands and wetlands were associated with low producer and user accuracies at the sub-pixel and pixel scales, of less than 10%. Based on dominant-type reference data, the errors were classified as mixed-pixel errors and labeling errors. Labeling errors primarily originated from misclassification between grassland and barren lands. Mixed pixel errors increased as the pixel diversity increased and as the percentage of dominant-type sub-pixels decreased. Overall, mixed pixels were sources of error for most land cover types other than grassland and barren lands; whereas labeling errors were more prevalent than mixed pixel errors when considering all of the land cover data over China, due to the large amount of misclassification between the pure pixels of grassland and barren lands. Next, the accuracy of cropland/natural vegetation mosaics was assessed based on the qualitative (a mosaic of croplands, forests, shrublands, and grasslands) and quantitative (no single component composes more than 60% of the landscape) parts in the definition, which resulted in accuracies of 91.43% and less than 19.26%, respectively. These results are summarized with their implications for the development of the next generation of MCD12Q1 data and with suggestions for potential users of MCD12Q1 v5.1.

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

Southwest China has abundant grassland resources, but they are mainly scattered across fragmented mountainous terrain with frequently cloudy and rainy weather, making their accurate identification by remote sensing challenging. Therefore, the goal of this study was to generate prefecture-level city-scale mountainous grassland distribution data to support the development of sustainable grassland husbandry. Here, we proposed a sample selection method and comprehensively utilized multi-source data to obtain the quasi-10 m southwest grassland distribution data. The sample selection method was to first determine the sample selection range based on multi-source land use/cover database, and then to randomly select the samples under the constraint of secondary land use types, multiple factors of terrain and pure pixels. This method can deal with the difficulty in identifying the fragmented grassland distribution caused by steep mountains and hills. In addition, a multispectral time series dataset was constructed based on the fusion of Landsat 8 OLI and Sentinel-2A/B data due to cloudy and rainy weather and was used as one of the input features along with synthetic aperture radar Sentinel-1 time series data and the terrain multi-factor data. Finally, a remote sensing method to accurately identify grassland distribution in southwest China was constructed based on the Google Earth Engine (GEE) platform. Taking Zhaotong City, a prefecture-level city in Yunnan Province, as an example, a thematic map of grassland distribution with an overall accuracy of 88.21% was obtained using the above method. This map has been used by the local government of Zhaotong City in their planning of the development of sustainable grassland husbandry.

Inappropriate land use has caused a series of environmental disasters such as floods and sand storms, and some of them involved unused land changes with highly localized distributions in arid and semi-arid regions with fragile ecosystems. As the third-rank category of first-level land use/cover in China, unused land (UL) is now playing an increasingly important role in protection of the natural environment and sustainable utilization of land resources. In this article, we assessed the effects on regional eco-environments employing a quantitative EL (ecological effect index) model, which can be used to evaluate and represent the contribution of UL changes to the eco-environmental quality. Results show that UL changes generally contributed to the deterioration of eco-environmental quality during the study period. Some major contributors to improving eco-environmental quality were transformation of sandy land and saline-alkali lands to grasslands, expansion of water bodies in UL areas, and reclamation of farmland in UL areas (except for marsh lands). In contrast, the main contributors to worsening eco-environmental quality were grassland degradation to UL (except marshes), reclamation of marsh areas, and shrinkage of water bodies to leave desert or saline-alkali land. Some suggestions are provided about UL management, utilization, and protection issues.

Against the background of coordinated development of the Beijing–Tianjin–Hebei region (BTH), it is of great significance to quantitatively reveal spatiotemporal dynamics of urban expansion for optimizing the layout of urban land across regions. However, the urban expansion characteristics, types and trends, and spatial coevolution (including urban land, GDP, and population) have not been well investigated in the existing research studies. This study presents a new spatial measure that describes the difference of the main trend direction. In addition, we also introduce a new method to classify an urban expansion type based on other scholars. The results show the following: (1) The annual urban expansion area (UEA) in Beijing and Tianjin has been ahead of that in Hebei; the annual urban expansion rate (UER) gradually shifted from the highest in megacities to the highest in counties; the high–high clusters of the UEA presented an evolution from a “seesaw” pattern to a “dumbbell” pattern, while that of the UER moved first from Beijing to Tianjin and eventually to Hebei. (2) Double high speed for both UEA and UER was the main extension type; most cities presented a U-shaped trend. (3) Qinhuangdao has the largest difference between the main trend direction of spatial distribution of urban land, GDP and population; the spatial distribution of GDP is closer to that of urban land than population. (4) The area and proportion of land occupied by urban expansion varied greatly across districts/counties. BTH experienced dramatic urban expansion and has a profound impact on land use. These research results can provide a data basis and empirical reference for territorial spatial planning.