Understanding the impacts of climate variability and change on terrestrial ecosystems in Africa remains a critical issue for ecology as well as for regional and global climate policy making. However, acquiring this knowledge can be useful for future predictions towards improved governance for sustainable development. In this study, we analyzed the spatial–temporal characteristics of vegetation greenness, and identified the possible relationships with climatic factors and vulnerable plant species across Africa. Using a set of robust statistical metrics on the Normalized Difference Vegetation Index (NDVI3g) for precipitation and temperature over 34 years from 1982 to 2015, relevant results were obtained. The findings show that, for NDVI, the annual rate of increase (0.013 y⁻¹) was less than that of decrease (−0.014 y⁻¹). In contrast, climate data showed a sharper increase than a marked decrease. Temperature is increasing while rainfall is decreasing, both at a sharp rate in central Africa. In Africa, tree cover, broadleaved, deciduous, closed to open (>15%) and shrubland plant species are critically endangered. The tropical vegetation devastated by the climate variability, causes different plant species to gradually perish; some were cleared out from the areas which experienced degradation, while others were from that of improvement. This study provides valuable information to African governments in order to improve environmental sustainability and development that will lead to the sustainability of natural resources.

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (R_H) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future.

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R² = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

To achieve the goals of carbon peak and carbon neutrality, Xinjiang, as an autonomous region in China with large energy reserves, should adjust its energy development and vigorously develop new energy sources, such as photovoltaic (PV) power. This study utilized data spatiotemporal variation in solar radiation from 1984 to 2016 to verify that Xinjiang is suitable for the development of PV power generation. Then, the averages of the solar radiation, sunshine duration, and other data in the period after 2000 were used to assess the suitability of Xinjiang, based on spatial principal component analysis (SPCA). Finally, the theoretical power generation potential, fossil fuel reduction, and CO₂ emissions reduction were estimated. The results are as follows: (1) In terms of temporal variation, the solar radiation in Xinjiang decreased (1984–2002), increased (2002–2009), and decreased again (2009–2016), but the fluctuations were not statistically significant. In terms of spatial distribution, the Kunlun Mountains in southern Xinjiang had the highest solar radiation during the span of the study period. Hami and Turpan, in eastern Xinjiang, had sufficiently high and stable solar radiation. (2) The area in Xinjiang classed as highly suitable for solar PV power generation is about 87,837 km², which is mainly concentrated in eastern Xinjiang. (3) In the situation where the construction of PV power plants in Xinjiang is fully developed, the theoretical potential of annual solar PV power generation in Xinjiang is approximately 8.57 × 10⁶ GWh. This is equivalent to 2.59 × 10⁹ tce of coal. Furthermore, 6.58 × 10⁹ t of CO₂ emissions can be reduced. PV power generation potential is approximately 27 times the energy consumption of Xinjiang in 2020. Through the suitability assessment and calculations, we found that Xinjiang has significant potential for PV systems.

The Kunlun–Pamir Plateau is a globally irreplaceable biodiversity reserve, yet it is still unclear what causes the distribution of species richness. Here, we relied on the productivity and the water–energy dynamics hypotheses to investigate the distribution pattern of species richness (and its determinants) in the Kunlun–Pamir Plateau. The productivity hypothesis is mainly based on five MODIS products (NDVI, EVI, FPAR, LAI and GPP), which were calculated for three Dynamic Habitat Indices (DHIs): (1) cumulative productivity (CumDHI), (2) minimum productivity (MinDHI) and (3) intra-annual variation productivity (VarDHI). The CumDHI was applied to assess whether or not more energy has a higher species richness value. The MinDHI was used to determine and evaluate the higher minimums, leading to a higher species richness. The VarDHI was the annual variation value in productivity and was utilized to assess if the reduced intra-annual variability triggers a higher species richness. We found that the DHIs based on the FPAR correlated slightly higher with the mammal, bird, breeding bird and non-breeding bird richness (than those based on the other four DHIs, and the values were 0.24, 0.25, 0.24 and 0.01, respectively). The correlation between the climate variables and the mammals, birds, breeding birds and non-breeding birds was bigger at 0.24, 0.54, 0.54 and 0.02, respectively, and was mainly dominated by the precipitation-related climatic factors. The water–energy dynamic hypothesis is better suited to the Kunlun–Pamir Plateau than the productivity hypothesis. Our results might provide valuable information regarding the biodiversity conservation in this region.

The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km², whereas FATA showed the least increase at 0.2 ha/km² from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC.