Under global climate change, the ecological vulnerability issue in Mongolia has become increasingly severe. However, the change process of the ecological environment and the dominant driving factors in different periods and sub-regions of Mongolia are not clear. In this paper, we propose a new ecological vulnerability index for Mongolia using MODIS data, combined with the Geographical Detector and the gravity center model, to reveal the spatiotemporal changes and driving mechanisms of ecological vulnerability in Mongolia from 2000 to 2022. The results show the following: (1) the newly proposed remote sensing ecological vulnerability index has high applicability in ecosystems mainly in Mongolia, with an accuracy rate of 89.39%; (2) Mongolia belongs to the category of moderate vulnerability, with an average ecological vulnerability index of 1.57, and the center of vulnerability is shifting toward the southwest direction; (3) Tmax is the leading driving factor of ecological vulnerability in Mongolia, especially at high altitudes and in arid regions, where it directly affects vegetation growth, desertification, and water availability. The dominant interactive factors have shifted from Tmax ∩ Tmin to Tmin ∩ PRE, with PRE being the leading factor in the eastern, central, and southern regions of Mongolia, Tmax being the leading factor in the western region, and Tmin being the leading factor in the northwestern region. This study provides an index system for constructing the ecological vulnerability system in Mongolia and offers scientific references for the regional protection of the ecological environment in Mongolia.

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R²_BTH = 0.58, R²_SP = 0.66, and R²_CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T_2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) _BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) _BTH = 0.265 and q(T_2m ∩ POP) _SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Studying long-term precipitation trends is crucial for sustainable development, as the proper utilization of water resources is essential for maintaining a sustainable water supply. The objective and novelty of this paper was to reveal the gradual mutation process of precipitation in China over a century. This study utilized monthly precipitation data from 1901 to 2022 (at a century scale) to analyze and explore the spatiotemporal variability in precipitation across different time scales and regions with a trend analysis, an abrupt change analysis, and gravity center models. The findings indicate that (1) from 1901 to 2022, the precipitation in China generally decreased from the southeast coastal areas toward the northwest inland regions. (2) There were significant differences in the migration of precipitation gravity centers among the different study regions, with the least dispersion being observed in the Liao River basin, while the Hai River basin, various river basins in the northwest, and the Pearl River basin exhibited certain regularities in gravity center movement, and other regions showed periodic variations. (3) Over the period from 1901 to 2022, there was a trend of transitioning from lower to higher precipitation levels. (4) According to continuous long-term abrupt change tests, the timing of precipitation shifts varied across different basins. Precipitation, as a crucial component of natural resources, directly impacts various aspects of socio-economic life. Research findings provide decision support for regional flood control and disaster reduction and offer scientific decisions for ecological security.

Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought conditions concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances theoretical understanding of drought propagation and lag mechanisms in arid and semi-arid ecosystems.