Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area’s normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.

Recently, the frequent occurrence of droughts has caused a serious impact on vegetation growth and progression. This research is based upon the normalized difference vegetation index (NDVI) from 2001 to 2020. The correlation between the NDVI and standardized precipitation evapotranspiration index (SPEI) at disparate time scales was used to assess the response of vegetation growth to drought in the Yinshanbeilu region. The drought levels of SPEI1, SPEI3, SPEI6, and SPEI12 increased prominently in the eastern region of the country, while the NDVI decreased significantly from east to west in spring, summer, and autumn but was reversed in the winter. The area with an upward trend (33.86%) was slightly lower than that with a downward trend (66.14%). The correlation coefficients between the NDVI and SPEI over the entire year increased with the SPEI timescale. The elevated values were concentrated in the southeastern and western regions of the survey region. Additionally, the best correlation timescales were SPEI6 and SPEI12. Grassland was the most sensitive vegetation type to the SPEI response in the NDVI. The correlation coefficients of NDVI and SPEI1–12 were 0.313, 0.459, 0.422, and 0.406. Both spring and summer were more responsive to SPEI12, whereas autumn and winter were more responsive to SPEI3. The correlation of disparate time scales exhibited complex soil texture features with respect to different seasonal scales, and the soil texture showed a strong response to vegetation in both summer and autumn. Loam, sandy loam, and silty loam all exhibited the highest response to SPEI12, with coefficients of 0.509, 0.474, and 0.403, respectively.

In the context of global warming, timely and accurate drought monitoring is of great importance to ensure regional ecological security and guide agricultural production. This study established the Drought Severity Index (DSI), based on the potential evapotranspiration (PET), evapotranspiration (ET) and normalized difference vegetation index (NDVI) data from 2001 to 2020, to compensate for the low accuracy of drought spatial and temporal evolution due to the uneven distribution of stations. The DSI index was established to reveal the spatial and temporal variation of droughts in Inner Mongolia in the past 20 years, using trend analysis, gravity shift and geographic probes, and to explore the influence of different factors on the DSI. The results were as follows. (1) The results showed that the spatial distribution of DSI in Inner Mongolia during 2001–2020 had strong spatial heterogeneity, and generally showed distribution characteristics of drought in the west and wet in the east. In addition, the changes in DSI all exhibited a rising tendency, with the highest tendency in deciduous broadleaf forests (DBF) and the lowest tendency in grassland (GRA). (2) The center of gravity of wet, normal and arid areas showed a migration trend from northeast to southwest, with migration distances of 209 km, 462 km and 826 km, respectively. (3) The four combinations of temperature and elevation, temperature and slope, temperature and land use, and temperature and rainfall contributed the most. The results obtained in this study are important for the scheduling of ecological early warnings and drought prevention and control.

Accumulated evidence reminds one that abrupt transitions between dry and wet spells, though attracting less attention, have harmful influences upon global continents as extensively investigated droughts and floods. This study attempts to incorporate dryness–wetness transitions into the current hazard assessment framework through bivariate frequency analysis and causal attribution from a teleconnection perspective. In the study, regional dry and wet conditions are monitored using the multivariate standardized drought index (MSDI) which facilitates the integrated evaluation of water deficits/surplus from a combined viewpoint of precipitation (largely denoting the received atmospheric water) and runoff (representing an important source of surface water). On such a basis, a copula-based method is subsequently utilized to calculate joint return periods of dryness–wetness combinations in three (i.e., moderate, severe and extreme) severity scenarios. The changing frequency of diverse dryness–wetness combinations is also estimated under a changing climate using a 25-year time window. Furthermore, the cross-wavelet transform is applied to attribute variations in dry and wet conditions to large-scale climate indices, which benefits the early warning of dryness–wetness combinations by providing predictive information. A case study conducted during 1952–2010 in the Huai River basin (HRB)—a typical climatic transition zone in China—shows that the HRB is subject to prolonged dryness with the highest frequency, followed by the abrupt transition from dryness to wetness. Spatially, abrupt dryness–wetness transitions are more likely to occur in the southern and central parts of the HRB than in the rest of the proportion. The past half-century has witnessed the dominantly higher frequency of occurrence of dryness–wetness combinations under three severity scenarios. In particular, the occurrence of the continued dry/wetness escalates more rapidly than transition events under climate change. Moreover, a preliminary attribution analysis discloses the link of the dry and wet conditions in the HRB with climate indices, such as the El Niño southern oscillation, the Pacific decadal oscillation and the Arctic oscillation, as well as sunspot activities. The results of the study enrich the current atlas of water-related hazards, which may benefit more effective hazard mitigation and adaptation.

Mining subsidence is a pervasive geohazard in coal basins, and precise and reliable deformation monitoring is essential to effective risk mitigation. Conventional time-series Interferometric Synthetic Aperture Radar (InSAR) suffers from vegetation-induced decorrelation and atmospheric delays. Most predictive models leverage only temporal information. We introduced an integrated DS InSAR + CNN LSTM framework for subsidence monitoring and forecasting. Forty-three Sentinel-1A scenes (2017–2018), corrected with Generic Atmospheric Correction Online Service for InSAR (GACOS) data, were processed to derive cumulative deformation, cross-validated against multi-view SBAS InSAR, and used to train a CNN LSTM network that predicts trends one year in advance. The findings indicate that (1) DS InSAR provides 2.83 times the monitoring density of SBAS InSAR, with deformation rate R² = 0.83, RMSE = 0.0028 m/a, and MAE = 0.0019 m/a at common pixels. The RMS average decrease in GACOS atmospheric delay phase correction is 2.52 mm. (2) High- and low-settlement zones comprise 0.11% and 92.32% of the area, respectively; maximum velocity reaches 190.61 mm/a, with a cumulative subsidence of −338.33 mm. (3) Across the five zones with the most severe subsidence, the CNN–LSTM model attains R² values of 0.97–0.99 and RMSE below 1 mm, markedly outperforming the standalone LSTM network. (4) Deformation correlated strongly with geological structures, groundwater decline (R² = 0.66–0.78), and precipitation (slope > 0.33), highlighting coupled natural and anthropogenic control.