The Qinghai–Tibet Plateau has seen decades of changes in land use/cover as a result of urbanization and regional planning policy. Research on the efficiency of social development aids in the pursuit of social and environmental sustainability. Based on CLUD and socioeconomic statistical data, this study systematically analyses the spatiotemporal evolution characteristics of urban land use in the Qinghai–Tibet Plateau and evaluates its social development efficiency from three perspectives—the holistic, the municipal, and urban hierarchy—by using indicators such as the Moran index, land use efficiency, and urban expansion speed and proportion. Results show that the urbanization rate climbed from 21.26% to 54.95%, and the area of urban lands increased from 201.93 km² to 796.59 km² from 1990 to 2020, with urban lands expanding from the Lanzhou–Xining City Area to the central and south of the Qinghai–Tibet Plateau. The holistic urban land use efficiency grew from 1.14 to 1.53, while the UPD decreased slightly from 1.44 to 1.31, and the UED increased steadily from 1.40 to 12.97 per decade. Moreover, we should pay attention to the rational allocation of land in human, social and ecosystem terms to comprehensively improve the quality of urbanization across the plateau.

The Zoige alpine wetland of eastern Qinghai–Tibetan Plateau is one of the most important wetlands in the world, which suffered stronger human regulations of decades’ drainage and recent restoration. It is so fragile that great efforts should be made to strengthen wetland protection. Based on wetland type classification for seven periods by the object-oriented classification method, we quantitatively discussed dynamically changes of wetland cover types and their driving forces. Conclusions show that alpine meadows accounts for 44.59% of the whole area of the county, and the transition between alpine meadows and swamp meadows mostly occurs in the process of wetland landscape changes. Changes of wetland cover types have taken place in Zoige County from 1990 to 2018 in which wetland loss was mainly caused by the decrease of marsh areas during 1995–2005. Particularly, shift from alpine meadows to swamp meadows led to wetland slightly expansion in two periods, i.e., 1990–1995 and 2005–2018. There is a significant negative correlation between marsh area and average temperature in growing season, average wind speed and rural population, and a significant negative correlation between water area and maximum wind speed. The increase of air temperature and wind speed lead to an enhancement of surface evapotranspiration and a decrease of water retention capacity. Whereas, positive feedback of ecosystem lead to an increase in air temperature and a decrease of marsh area. Both climatic changes and human activities have caused changes in wetland cover type, especially temperature in growing season, wind speed, agricultural development, and animal husbandry.

Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management.

The warming climate and increasing extreme weather events are transforming ecological backgrounds, which is bringing new challenges to herders’ livelihood in grassland areas. To understand the practical ecological risks and the current resilience situations of herders’ behaviors and government correspondence measures, we built a conceptual framework of community resilience in pastoral areas, selected different kinds of steppes along the ecological gradient (desert steppe, typical steppe and meadow steppe) and took household surveys to recognize the difference in ecological risks and enhancing strategies in different grassland types. The results show that: (1) Herders in desert steppe, with the lowest precipitation and the worst grassland condition, turn out to have more experience in perceiving droughts and mitigating loss from disaster, but received the most attention from government assistance (28.0%) to getting through drought; (2) Typical steppe, with traditionally better pastural husbandry environment, suffered most broadly through droughts (85.7%) and have worst household livestock loss (26.7%) through snow storms; (3) Meadow steppe has the highest catastrophic snow storm ratio (65.0%) and affected ratio (95.0%), but the least assistance from the government (22.22%). The results revealed that originally high ecological vulnerability gradually encouraged herder’s livelihood adaptive capability. However, the government assistance and attention are more inclined to the local original ecological vulnerability. In addition, the increasing extreme climate events are bringing new challenges to adaptive knowledge systems of indigenous herders under good ecological condition. There is a clear need to combine the efforts of local pastoralists, policymakers and scientific community together to construct a more resilient socio-ecological pastoral systems under the global climate change. This research provides an in-depth understanding of community resilience in pastoral areas along the ecological gradient while facing the slow-onset climate change impacts. Practical recommendations on climate risk management and adaptation in pastoral areas are discussed.

Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to monitoring and evaluating the carbon sequestration functions of soil and water conservation vegetation. This study focuses on an artificial locust forest planted in the early 1990s in Caijiachuan Basin, Jixian County, Shanxi Province. A drone equipped with LiDAR was used to obtain point cloud data and generate a canopy height model. A watershed segmentation algorithm was used to identify tree vertices and extract individual trees. A relationship model between tree height, diameter at breast height, and biomass, combined with sample survey data, was established to explore the spatial distribution of biomass in the artificial locust forest at the level of the entire basin. The results show the following: (1) the structural parameters of locust extracted using UAV point cloud data have a good degree of fit and accuracy, and the recall rate is 72.7%; (2) the average error rate of the extracted maximum tree height value of locust is 7%, that of the minimum tree height value is 14%, and that of the average tree height value is 18%; (3) the average error rate of the extracted maximum diameter at breast height of locust is 15%, that of the minimum diameter at breast height is 37%, and that of the average diameter at breast height is 36%; and (4) the average error rate of the biomass estimation of locust calculated using point cloud data is 16.0%.