Land degradation destroys human habitats, and vegetation is a marker reflecting land degradation. In this article, the Balochistan Province of Pakistan, which has a fragile ecological environment, was selected as a typical case to analyze its land degradation over 21 years. Relevant studies that used the NDVI and remote sensing data to monitor land degradation already existed. Based on the data product of MODIS, this study obtained the spatio-temporal trends of the normalized difference vegetation index (NDVI) changes from 2000 to 2020 using the sen+ Mann–Kendall (MK) test and Hurst index and analyzed the driving factors of land degradation and restoration by employing the multiple stepwise regression method. The residual analysis method was an effective tool for distinguishing between anthropogenic and climatic impacts, given that not all regions have a significant correlation between the NDVI and rainfall. The main climatic drivers of the NDVI were derived based on the Geodetector analysis and stripped of the main climatic factors by residual analysis to explore the influence of anthropogenic factors on the NDVI. The results show the following: (1) Balochistan is dominated by land restoration. Land restoration is mainly dominated by climate as well as both climate and human factors, and land degradation is mainly dominated by climate and human factors. (2) The Geodetector-based study found high correlations between the NDVI and TMP, MAP, AET and PET, complementing most previous residual analyses that considered only precipitation and temperature. In Balochistan, TMP, AET, PET and MAP were the dominant climatic factors affecting the spatial distribution of the NDVI; TMP with MAP and TMP with AET were the main interactive factors in the spatial distribution of the NDVI. (3) The article quantifies the impact of the anthropogenic drivers on land degradation. Human activities positively influenced the NDVI in 91.02% of the area and negatively influenced it in 8.98% of the area. (4) The overall trend of the NDVI was mainly stable, with stronger improvement than degradation, and showed strong persistence. The above findings enrich our understanding of the climatic impacts of land degradation and human impacts in arid or semi-arid regions and provide a scientific basis for ecological engineering to achieve ecological conservation and quality development in Balochistan, Pakistan.

Soil organic carbon (C) and soil total nitrogen (N) show different degrees of spatial variability at different scales. Both are important components of soil nutrients and essential elements for plant growth and development, and are closely related to biogeochemical cycles. However, there is limited information on the regional spatial validity of SOC and TN and the associated drivers at the scale of the Central Asian drylands. Therefore, this study uses the ISRIC-WISE (International Soil Reference and Information Centre-Word Inventory of Soil Property Estimates) database to conduct soil sampling at the raster level, combined with relevant climatic and environmental datasets, to investigate the spatial distribution characteristics and drivers of soil C and N in the drylands of Central Asia using classical geostatistical methods and structural equation modelling (SEM). The results of this study show that the distributions of soil C and N contents in the dry zone of Central Asia have greater similarity, with C content mainly concentrated in the ranges of 0–5.5 g/kg and 11.1–15.9 g/kg; soil N content mainly concentrated in the range of 0.4–1.1 g/kg, and the soil C:N ratio mainly concentrated in the range of 12.2–28.9. Structural equation modelling showed that the main driver of soil C change was Aridity (−0.51); the main driver of soil N change was Mean Annual Temperature (MAT) (−0.44); and soil C:N change was most influenced by Aboveground biomass (AGB) (−0.25). An analysis of the relative importance contribution showed that Aridity had the highest relative importance with regard to the change in C (32%); MAT had the highest relative importance with regard to the changes in N and C:N (29% and 40%, respectively). The above findings provide a reference for the use of soil resources in drylands and provide a scientific basis for regional differences in the response of arid ecosystems to climate change.

Using a structural equation model (SEM), this paper investigates the response of soil nitrogen content of five typical grasslands in the middle line countries of China’s “Belt and Road” initiative to the changes of climate variables, soil pH value, and normalized vegetation index, and employs the principal component analysis method to determine the spatial variation characteristics and influencing factors of nitrogen reserves in different grasslands. Pontiac grassland (PS), Middle East grassland (MES), Kazakh grassland (KS), Kazakh forest grassland (KFS), and Kazakh semi-desert grassland (KFS) are the five grasslands in the research region (KSD). The results indicated that (1) the nitrogen reserves of the five grassland soils (0–100 cm) in the research area were 7.49 Pg, or approximately 5.7 percent of the total world nitrogen reserves. The sum of the five grasslands’ 0–30 cm and 0–50 cm N reserves accounted for 36.3 percent and 63.1 percent, respectively, of the total 0–100 cm N reserves. The density of nitrogen in the soil (0–100 cm) varied significantly between grasslands, ranging from 1.47 to 3.87 kg/m2, with an average of 3.10 kg/m2. (2) PCA analysis revealed a substantial positive correlation between soil N and MAP (p < 0.01), a negative correlation between soil N and Srad (p < 0.01), and a high degree of similarity between the three grassland samples, KFS, KS, and KSD. (3) The decision tree algorithm determined that MAP had the most relative importance for changes in soil nitrogen content in PS, MES, and KFS, whereas Srad had the greatest relative importance for changes in soil nitrogen content in KS and KSD. The pH showed the least proportional impact for variations in soil N concentration in all five grasslands. (4) Different factors influence the change in soil N content across diverse grasslands. The principal positive driving factor of soil N content in KS and KSD is Srad, with loads of −0.39 and −0.44, respectively. The principal negative driving factor of soil N content in PS and MES is Map, with loads of 0.38 and 0.2, respectively. In the SEM model of soil nitrogen content in KFS, no environmental variables had a significant effect on N content, and the model’s R2 value was 0.08, indicating an average fit.