Wetlands are a distinctive terrestrial ecosystem that benefits living things, including people, in various ways. Sustainable wetland ecosystem resources are needed to protect the global environment. Wetlands in China have undergone positive and negative changes in response to several factors, but studies documenting their long-term dynamicity have been few, particularly in Guangling County. This study examines the change of wetlands area based on remotely sensed data while exploring trends associated with climate variations and economic growth in Guangling County, China. Analysis of remotely sensed imagery, mainly in hilly and nonhomogeneous environments is problematic, largely as a result of interference and their high spectral non-homogeneity. We conducted experiments using five classical machine learning algorithms based on the Google Earth Engine (GEE) and obtained the greatest robustness and accuracy using a Support Vector Machine (SVM)—Radial Basis Function (RBF) kernel approach, with overall accuracy and kappa statistics ranging from 86% to 98.1% and from 0.789 to 0.960, respectively. Based on the SVM-RBF model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends. We found that 45.71 km2 of wetland area was lost over the past 3.7 decades (January 1984–December 2020), or 81.82% of wetland area coverage. In this paper, we explore how factors such as county economic growth (GDP), humidity, and temperature variations are tightly linked with wetland change.

The interplay of specific weather conditions and human activity results due to haze. When the haze arrives, individuals will use microblogs to communicate their concerns and feelings. It will be easier for municipal administrators to alter public communication and resource allocation under the haze if we can master the emotions of netizens. Psychological tolerance is the ability to cope with and adjust to psychological stress and unpleasant emotions brought on by adversity, and it can guide human conduct to some extent. Although haze has a significant impact on human health, environment, transportation, and other factors, its impact on human mental health is concealed, indirect, and frequently underestimated. In this study, psychological tolerance was developed as a psychological impact evaluation index to quantify the impact of haze on human mental health. To begin, data from microblogs in China’s significantly haze-affected districts were collected from 2013 to 2019. The emotion score was then calculated using SnowNLP, and the subject index was calculated using the co-word network approach, both of which were used as social media evaluation indicators. Finally, utilizing ecological and socioeconomic factors, psychological tolerance was assessed at the provincial and prefecture level. The findings suggest that psychological tolerance differs greatly between areas. Psychological tolerance has a spatio-temporal trajectory in the timeseries as well. The findings offer a fresh viewpoint on haze’s mental effects.

This study prioritises management options and assesses the risk of soil erosion in the Midhagdu Watershed in eastern Ethiopia. The themed map was developed using satellite data including SRTM-DEM, Landsat OLI, rainfall data, and soil data. The RUSLE model as well as GIS and remote sensing methods were used in the experiment. The experiments revealed that the factors that affect soil erosion risk such as rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and anthropogenic soil erosion control practises factor values were distributed spatially and ranged in values from 41.365 to 43.793MJ mm ha-1yr-1, 0.26 to 0.31t ha-1MJ-1mm-1, 0 to 220.512, 0.21 to 0.87, and 0.11 to 1, respectively, and the most powerful factor that influences soil erosion risk was topography(LS) with a value of 0.885. The results of the grid cell-based RUSLE model showed that 52.24 percent of the Midhagdu watershed (28.37 km2 out of 54.3 km2) had low to moderate soil erosion levels and that 47.76 percent (25.94 km2 out of 54.3 km2) had high to extremely high soil erosion risk levels. By taking into account regions and priority classes based on soil erosion risk levels, the conclusions of this article suggest an early intervention to better plan soil erosion risk management.