Heavy metal pollution in soils and sediments is becoming a matter of wide concern, this study was carried out in Dawa County of the Liaohe River Delta, with the aim of exploring the impacts of land use levels on heavy metal contamination of soil and sediment. A total of 129 soil samples were collected in different land use intensities (LUI). Soil metals (Fe, Mn, Cd, Cr, Cu, Ni, Pb and Zn) and soil salinity, pH, soil organic carbon (SOC), nitrate nitrogen (NO₃⁻-N), available phosphorus (AP) and grain sizes were analyzed. Correlation analysis indicated that SOC and grain size played important roles in affecting the heavy metal distribution. The factor analysis results indicated that heavy metal contamination was most probably caused by industrial and agricultural wastewater discharges, domestic sewage discharge and atmospheric deposition. Using ANOVA, it found that human activities significantly changed soil physic-chemical properties through soil erosion, leaching and fertilizer application, further affecting the behaviors of heavy metals in the soil and sediments. The anthropogenic factors could lead to potential environmental risk, as indicated by the Geo-accumulation index (I_geo) results of heavy metals. Overall, the heavy metals generally had approached or even exceeded moderately polluted (0 < I_geo < 1, 1 < I_geo < 2), but the Pb and Cu pollution level was low (I_geo < 0), and the Cd pollution level was moderately or strongly polluted (2 < I_geo < 3, 3 < I_geo < 4) in the five land use levels. This study will provide valuable information for appropriately determining how land should be used in future reclamation areas, as well as for the sustainable management of estuarine areas around the world.

Permafrost is extremely sensitive to climate change. The degradation of permafrost has strong and profound effects on vegetation. The permafrost zone of northeastern China is the second largest region of permafrost in China and lies on the south edge of the Eurasian cryolithozone. This study analyzed the spatiotemporal variations of the growing-season Normalization Difference Vegetation Index (NDVI) in the permafrost zone of northeastern China and analyzed the correlation between NDVI and ground surface temperatures (GST) during the years 1981–2014. Mean growing-season NDVI (MGS-NDVI) experienced a marked increase of 0.003 year⁻¹ across the entire permafrost zone. The spatial dynamics of vegetation cover had a high degree of heterogeneity on a per pixel scale. The MGS-NDVI value increased significantly (5% significance level) in 80.57%, and this increase was mostly distributed in permafrost zone except for the western steppe region. Only 7.72% experienced a significant decrease in NDVI, mainly in the cultivated and steppe portions. In addition, MGS-NDVI increased significantly with increasing growing-season mean ground surface temperature (GS-MGST). Our results suggest that a warming of GS-MGST (permafrost degradation) in the permafrost region of northeastern China played a positive role in increasing plant growth and activities. Although increasing ground surface temperature resulted in increased vegetation cover and growth in the short time of permafrost degradation, from the long term point of view, permafrost degradation or disappearance may weaken or even hinder vegetation activities.

Vegetation phenology does not only serve as a key index of terrestrial ecosystem response to worldwide climate change but also has a major influence on plant productivity and the carbon cycle. In the current research, the change of vegetation phenological parameters was studied and the impact exerted by climate change on phenological phases in northeast China for 1982–2014 was explored using the latest edition of the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS NDVI3g) dataset. The results showed that the start of the growing season (SOS) slightly advanced, the end of the growing season (EOS) showed a significant delay, and the length of the growing season (LOS) exhibited a significant prolonging at the regional scale. At the different vegetation types scale, there existed diverse responses of vegetation phenological phases to climate change for forest, grassland, and cultivated land. Significant decreasing trends in the SOS occupied 19.1% of the entire research area, whereas pixels with significantly increasing trends in the SOS accounted for 13.1%. The EOS was delayed in most of the study region (approximately 72.1%). As the result of the variations of SOS and EOS, the LOS was obviously enhanced (p < 0.05) in 29.7% of the research area. According to the correlation of vegetation phenology with climate factors, the SOS had a significant negative relationship with the average temperature in springtime, while the EOS was notably negatively connected to summer total precipitation at the regional scale. At the pixel scale, the correlation of phenological parameters with climate variables showed strong spatial heterogeneities. This study contributes to the comprehension of the responses of vegetation phenology to climate change.

Variations in the Normalized Difference Vegetation Index (NDVI) reflect the global land surface vegetation coverage, which is important for the analysis of the ecological environment. In this study, the spatiotemporal variation in global land surface vegetation NDVI and its climatic driving factors were analyzed for the period 1982-2014, using the GIMMS NDVI3g data set. The results show that the NDVI of global land surface vegetation was increasing during the study period. Although the NDVI of most areas in the southern hemisphere is generally higher than that of most areas in the northern hemisphere, the increase in NDVI in the northern hemisphere is higher than that in the southern hemisphere. Temperature and precipitation have different effects on NDVI at different spatiotemporal scales. Temperature is the driving factor for NDVI variation in most parts of the northern hemisphere, whereas precipitation is the driving factor for NDVI variation in the southern hemisphere.

Vegetation is an important component of the terrestrial ecosystem, and studying the rules of vegetation change and its driving factors is helpful to strengthen the ecological protection and sustainable development of regional vegetation. This study analyzes the changes in Normalized Difference Vegetation Index (NDVI) and its response to climate factors in the five regions of western Ordos and eastern Alxa in China between 2000 and 2020. The MODIS NDVI and meteorological data from 2000 to 2020 was used and the ordinary least squares, trend analysis, and correlation analysis methods were analyzed. The NDVI in this region shows spatial differentiation and is high in the east and low in the west. The overall NDVI has shown a significant increasing trend (p < 0.01), and the slope value of the rate of change also shows that the NDVI in 98.17% of the area is increasing. On a temporal scale, NDVI had a significant positive correlation with precipitation (p < 0.01), but no significant correlation with temperature changes. On a spatial scale, NDVI was positively correlated with precipitation, which accounted for 95.57% of spatial changes, of which a significant positive correlation accounted for 34.99% (p < 0.05). Meanwhile, the temperature and NDVI were negatively correlated but not significantly. A positive correlation accounted for 45.95% of the change, but the insignificant negative correlation accounted for 54.05%. Therefore, comprehensive analysis showed that precipitation played a leading role in the NDVI in the study area. The results are helpful to study the driving mechanism of vegetation growth and provide reference for vegetation protection in regions of western Ordos and eastern Alxa of Inner Mongolia, China.

Inner Mongolia’s vegetation dynamics are critical for understanding regional ecological responses to climate change. This study investigates the spatiotemporal patterns of the Normalized Difference Vegetation Index (NDVI) during the growing season (1999–2019) and explores its relationship with climatic factors to predict future vegetation changes. Using MODIS monthly NDVI products and meteorological data, we analyzed NDVI trends across Inner Mongolia’s distinct vegetation zones. Statistical methods (e.g., trend analysis, correlation tests) were applied to assess spatial-temporal NDVI variations and climate linkages, with significance thresholds set at p < 0.05 and p < 0.01. NDVI exhibited a northeast-to-southwest decline, reflecting regional vegetation gradients. Growing-season NDVI increased significantly (p < 0.05), with summer NDVI rising markedly (p < 0.01; +0.0022/yr). Precipitation significantly increased over 21 years, while temperatures remained stable. NDVI-climate correlations were spatially heterogeneous, with precipitation as the dominant vegetation growth factor. The findings highlight precipitation’s pivotal role in shaping Inner Mongolia’s vegetation dynamics. The stable temperature but increasing precipitation suggests a shift toward wetter conditions, potentially enhancing grassland productivity. These insights enable predictive modeling of vegetation responses to future climate variability, supporting regional ecological management.