## 3 Results
### AI for Environmental Monitoring and Mapping of Land Use, Vegetation, and Soils  
Regional coverage: This sub-section includes 15 studies that apply AI to ecological monitoring and land-use/soil mapping. The evidence is overwhelmingly concentrated in Asia (\(n=12\))--with case studies drawn from China (e.g., the Chongming Dongtan wetlands, Yellow River Delta, Inner-Mongolia grasslands, and Poyang Lake Basin) and South/South-East Asia (e.g., rural Bangladesh). Two studies are conducted in Africa (\(n=2\)), and two others adopt a global perspective (\(n=2\)). The advancement of artificial intelligence and remote sensing technologies has opened new frontiers in environmental monitoring and spatial analysis of terrestrial ecosystem dynamics. Of particular relevance are studies focused on land use, vegetation health, and  
Figure 6: Dynamics of changes in interests in topics over the past years. Source: compiled by authors. soil conditions--critical components of sustainable natural resource management in local ecosystems. The use of AI for assessing the condition and transformation of wetlands and vegetative communities has proven highly effective in analyzing the impacts of urbanization and climate change. For example, the K-ELM model applied to Landsat data from 1986 to 2013 enabled the analysis of spatiotemporal changes in the Chongming Dongtan wetlands, capturing the effects of urban expansion and generating forecasts of ecosystem shifts [16]. In another study, the spatiotemporal dynamics of groundwater-dependent ecosystems (GDE) were assessed using Landsat 8 imagery and the Random Forest algorithm [10]. This approach contributes to more accurate predictions of the implications of climate and land-use change for biodiversity. The integration of satellite data, remote sensing techniques, and cloud-based platforms--such as Google Earth Engine--with AI algorithms significantly enhances the mapping of terrestrial groundwater-dependent vegetation (TGDV) [17]. These technologies enable the monitoring of both seasonal and long-term ecosystem changes, while improving the understanding of climatic and anthropogenic impacts. Machine learning methods, including Extreme Gradient Boosting, have been applied to time series satellite data in the Yellow River Delta to precisely track the degradation and restoration of wetland ecosystems, supporting the development of evidence-based ecological management strategies [18]. The accuracy of spatiotemporal analysis of wetland plant communities has been further improved through the application of Random Forest and SHAP algorithms, facilitating sustainable management of ecosystem functions [19]. Additionally, Random Forest classifiers applied to Sentinel-1 and Sentinel-2 satellite data have been used to distinguish between rice paddies and wetland areas in the Poyang Lake Basin [20]. This approach allows for a more accurate interpretation of ecosystem phenological signals and the development of targeted land-use recommendations for sustainable resource management. AI is also employed to assess pastureland changes in Inner Mongolia, analyzing their responses to climatic variability and anthropogenic pressures [21]. This is critical for developing adaptive strategies for the sustainable use of land in areas vulnerable to degradation. A study on the spatiotemporal dynamics of oases in China over the period 1987-2017 leveraged AI tools to identify patterns of expansion and degradation. The research also assessed their impact on land use, including the increase in cropland and grazing areas. These insights are applicable for optimizing agricultural policy and mitigating resource depletion in arid regions [22]. Similarly, another study evaluated the impact of a large-scale infrastructure project (the Padma Bridge) on land-use change and urbanization in the rural ecosystems of Bangladesh [23]. The analysis used Random Forest and artificial neural networks combined with cellular automata to model satellite-derived land-use transitions up to 2033.