Grassland is the second largest terrestrial ecosystem and a fundamental land resource for human survival and development. Although grassland degradation is a recognized and crucial ecological problem, there is no consensus on the area, scope, and degree of its global degradation trends, making the implementation of Sustainable Development Goals (SDG) 15.3 for achieving a land degradation-neutral world uncertain. This study quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth and its response to climate change. Furthermore, the driving factors behind these trends were analyzed, especially in hotspots. Results show that the improvement in global grassland has been remarkable since 2000, with a 1.92 times larger area than degrading grassland, amounting to 372.47 × 10⁴ and 193.57 × 10⁴ km², respectively. Africa and Asia lead in global grassland degradation and improvement, respectively. Globally, the combined effects of climate change and human activities are the main driving factors for grassland degradation and improvement, accounting for 84.72 and 87.76%, respectively. Notably, human activities played a crucial role in reversing the trend of grassland degradation in some hotspots. Finally, this study provides an essential scientific reference and support for realizing SDG 15.3 on global and regional scales.

Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes.

Overgrazing directly leads to grassland degradation, which is a serious constraint to the sustainable development of animal husbandry. In drylands, grassland biomass is highly heterogeneous in space and time. It is difficult to achieve sustainable utilization of grassland resources by focusing only on the average annual carrying capacity assessment obtained from grass yield. Here, we proposed a novel approach for assessing grassland carrying capacity, taking Zhenglan Banner (County) in Inner Mongolia as the study area. First, monthly grass yield at 30 m spatial resolution was estimated, derived from Carnegie–Ames–Stanford Approach (CASA) model and spatial and temporal adaptive reflectance fusion model (STARFM). Then, based on the degree of sand mobility and degradation condition of typical steppe, the utilization patterns for sandy land and typical steppe in different grazing seasons were developed separately to obtain available grass yield. Finally, the carrying capacity at the Gacha (Village)-scale was estimated and the current livestock carrying status was evaluated to facilitate the grassland refined management. In Zhenglan Banner, the carrying capacity was 237.46 thousand cattle-units in summer. The grassland resources are being overgrazed, with an overload rate of 19.32%. At Gacha-scale, the maximum reasonable stock density was ranged from 0.06 cattle-unit/ha to 0.42 cattle-unit/ha. Fifty-one Gachas exhibited livestock overload. This study is expected to provide technical support and scientific reference data for ecological conservation and grassland management in the study area, as well as in dryland pastoral areas of northern China.

The sparse Ulmus pumila L. woodland in the Otingdag Sandy Land of China is indispensable in maintaining the ecosystem stability of the desertified grasslands. Many studies of this region have focused on community structure and analysis of species composition, but without consideration of spatial distribution. Based on a combination of spectral and multiscale spatial variation features, we present a method for automated extraction of information on the U. pumila trees of the Otingdag Sandy Land using very high spatial resolution remote sensing imagery. In this method, feature images were constructed using fused 1-m spatial resolution GF-2 images through analysis of the characteristics of the natural geographical environment and the spatial distribution of the U. pumila trees. Then, a multiscale Laplace transform was performed on the feature images to generate multiscale Laplacian feature spaces. Next, local maxima and minima were obtained by iteration over the multiscale feature spaces. Finally, repeated values were removed and vector data (point data) were generated for automatic extraction of the spatial distribution and crown contours of the U. pumila trees. Results showed that the proposed method could overcome the lack of universality common to image classification methods. Validation indicated the accuracy of information extracted from U. pumila test data reached 82.7%. Further analysis determined the parameter values of the algorithm applicable to the study area. Extraction accuracy was improved considerably with a gradual increase of the Sigma parameter; however, the probability of missing data also increased markedly after the parameter reached a certain level. Therefore, we recommend the Sigma value of the algorithm be set to 90 (±5). The proposed method could provide a reference for information extraction, spatial distribution mapping, and forest protection in relation to the U. pumila woodland of the Otingdag Sandy Land, which could also support improved ecological protection across much of northern China.