Vegetation cover is important to the stability of regional ecosystems and is a focus of research on the relationship between natural and human environments. Although some studies have investigated the association between changes in vegetation cover and various influencing factors, these have shortcomings in quantifying direct and indirect effects. In this study, MOD13Q1 enhanced vegetation index (EVI) data for Anhui Province, China, were acquired between 2000 and 2020. The univariate linear regression, coefficient of variation and Hurst index methods were used to analyze spatial and temporal trends and fluctuations in the EVI between 2000 and 2020 and predict future trends. The impact of land-use change on EVI change was explored using 2000 and 2020 land-use data. Finally, a structural equation model (SEM) was used to quantify the effects of topography, annual average temperature, annual precipitation and human activity changes on EVI variation in Anhui Province. The results show that (1) from 2000 to 2020, the overall EVI in Anhui Province showed a fluctuating trend that increased at a rate of 0.0181·10a⁻¹, and 67.1% of the study area showed a greening trend. The EVI was relatively stable in most regions, with regions of fluctuating EVI being mostly affected by urbanization. For a period after 2020, the overall EVI change will exhibit anti-sustainability and will likely decrease. (2) Among the regions of EVI increase, 72.2% had no change in land-use type, while 10.8% and 6.6% changed to farmland and woodland land uses, respectively. Among the regions where EVI decreased, 69.9% had no change in land-use type, while 13.7% changed from farmland to construction land. (3) Overall, human activity change was the main influence on EVI change, which was mainly reflected in the negative impacts of accelerated urbanization. Topography had direct and indirect effects on EVI variations in Central and Southern Anhui. Annual precipitation change had a stronger impact on EVI variation in Northern and Central Anhui than in Southern Anhui, while annual average temperature change had a small impact in the entire province. Compared with other study methods, SEM provides a new approach to quantifying the influences of vegetation cover dynamics. In addition, the results of this study have important implications for ecological environmental protection and sustainable development in Anhui Province.

The increasing frequency of active fires worldwide has caused significant impacts on terrestrial, aquatic, and atmospheric systems. Polar regions have received little attention due to their sparse populations, but active fires in the Arctic cause carbon losses from peatlands, which affects the global climate system. Therefore, it is necessary to focus on the spatiotemporal variations in active fires in the Arctic and to assess the fire risk. We used MODIS C6 data from 2001 to 2019 and VIIRS V1 data from 2012 to 2019 to analyse the spatiotemporal characteristics of active fires and establish a fire risk assessment model based on logistic regression. The trends in active fire frequency based on MODIS C6 and VIIRS V1 data are consistent. Throughout the Arctic, the fire frequency appears to be fluctuating and overall increasing. Fire occurrence has obvious seasonality, being concentrated in summer (June–August) and highest in July, when lightning is most frequent. The frequency of active fires is related to multiple factors, such as vegetation type, NDVI, elevation, slope, air temperature, precipitation, wind speed, and distances from roads and settlements. A risk assessment model was constructed based on logistic regression and found to be accurate. The results are helpful in understanding the risk of fires in the Arctic under climate change and provide a scientific basis for fire prediction and control and for reducing fire-related carbon emissions.

The recent rapid economic development in the Yangtze River Delta (YRD) has led to atmospheric destruction; therefore, it is imperative to solve the issue of atmospheric environmental pollution to ensure stable long-term development. Based on the NO₂ column concentration observed by the TROPOMI (a tropospheric monitoring instrument) on the Sentinel-5P, the spatial–temporal distribution characteristics of the NO₂ column concentration in the YRD from 2019 to 2020 were analyzed using the Google Earth Engine (GEE) platform, and the Geographical Detector (Geodetector) model was used to determine the driving factors of the NO₂ column concentration. The results show that the correlation between the NO₂ column concentration and the ground-monitored NO₂ concentrations reached 70%. The annual variation trend of the NO₂ column concentration exhibited a ‘U’-shaped curve, with the characteristics of ‘high in winter and low in summer, with a transition between spring and autumn’. It exhibited obvious agglomeration characteristics in terms of the spatial distribution, with a high-value agglomeration in the central region of the YRD, followed by the northern region, and a low-value agglomeration in the southern region, with higher altitudes. The change in the NO₂ column concentration in the YRD was affected by both physical geographical factors and socio-economic factors; it is clear that the influence of socio-economic factors has increased.

The rapid development of remote sensing image sensor technology has led to exponential increases in available image data. The real-time scheduling of gigabyte-level images and the storage and management of massive image datasets are incredibly challenging for current hardware, networking and storage systems. This paper’s three novel strategies (ring caching, multi-threading and tile-prefetching mechanisms) are designed to comprehensively optimize the remote sensing image scheduling process from image retrieval, transmission and visualization perspectives. A novel remote sensing image management and scheduling system (RSIMSS) is designed using these three strategies as its core algorithm, the PostgreSQL database and HDFS distributed file system as its underlying storage system, and the multilayer Hilbert spatial index and image tile pyramid to organize massive remote sensing image datasets. Test results show that the RSIMSS provides efficient and stable image storage performance and allows real-time image scheduling and view roaming.

The PM_2.5 and O₃ pollution situation in the Yangtze River Delta (YRD) region is increasingly complex. Existing coordinated prevention and control programs are demarcated according to administrative regions, making fine-scale pollution control difficult. This study proposed a method for delineating regional control and pollution types based on the analysis of PM_2.5 and O₃ pollution characteristics. Four key indicators were used to comprehensively evaluate regional pollution trends: PM_2.5 concentration ρ(PM_2.5), O₃ concentration ρ(O₃), and the rates of change r(PM_2.5) and r(O₃). Our results demonstrate that from 2015 to 2020, cities in the YRD show a transition in the main pollution type from PM_2.5 to O₃. By 2020, the main types of pollution in the YRD region are co-pollution (ρ(PM_2.5) > 35 µg/m³ and ρ(O₃) > 100 µg/m³), O₃ pollution (ρ(PM_2.5) < 35 µg/m³ and ρ(O₃) > 100 µg/m³), and low pollution (ρ(PM_2.5) < 35 µg/m³ and ρ(O₃) < 100 µg/m³), accounting for 41.9%, 26.3%, and 30% of the study area, respectively, which is roughly consistent with the remote sensing results (40.4%, 25.6%, and 33.7%, respectively). The proposed method can predict future trends in pollution and assist in decision-making for the coordinated prevention and control of PM_2.5 and O₃ pollution.