## 3 Results
### AI for Monitoring, Forecasting, and Management of Aquatic Ecosystems
A notable contribution is the CPH-Fnnet model introduced in [78], which integrates Vision Transformer architecture and attention mechanisms to enable 3D reconstruction of forest environments using imagery from standard handheld devices such as smartphones. This high-precision model is well suited for forest cover assessment, monitoring, and inventory tasks. Additionally, [PERSON]. [79] utilized machine learning algorithms and Sentinel-2 data to classify 24 forest habitat types with an accuracy of 87%, thereby enhancing biodiversity monitoring and informing national conservation policies. Artificial intelligence is increasingly employed to detect long-term changes in forest cover and assess ecosystem resilience. For example, [PERSON]. [80] conducted a spatiotemporal analysis of tropical forest decline in Jinghong, China, revealing a 25% decrease in forest cover between 1989 and 2018. Similarly, [PERSON]. [81] applied support vector machine (SVM) algorithms and Landsat satellite imagery to assess the reduction in argon forests in Morocco from 1985 to 2017. This study provided a quantitative evaluation of ecosystem degradation, including losses in biodiversity and cultural heritage. In Iraq, [PERSON]. [82] used an ensemble of deep learning models--comprising convolutional and fully connected neural networks--to forecast changes in forest cover, achieving the highest accuracy through integration of anthropogenic, climatic, soil, and topographical variables. Mangrove forests, as particularly vulnerable ecosystems, have also become a focal point for AI-driven analysis. In mountainous regions of China, [PERSON]. [83] employed Sentinel-1 and Sentinel-2 data combined with ensemble learning techniques to map forest cover and assess the influence of climatic and topographic factors. On Tuanaku Island in Indonesia, Ref. [84] utilized spectral indices and geospatial data to track mangrove dynamics over the 2010-2020 period, identifying phases of both expansion and degradation. In Colombia, Ref. [85] leveraged AI and satellite imagery to estimate above-ground biomass of mangroves along the Pacific coast, thereby improving predictions of climate change impacts. Furthermore, Ref. [86] applied XGBoost and multiple satellite datasets (Landsat 8, Sentinel-2, WorldView-2, and Zhuhai-1) to calculate the leaf area index (LAI), enabling effective monitoring of mangrove ecosystem recovery and resilience under changing climatic conditions. Thus, the integration of artificial intelligence into forest management encompasses a wide range of tasks, from fire prevention and degradation monitoring to biomass estimation and carbon stock assessment. AI enables a shift from fragmented interventions to comprehensive ecosystem management by incorporating ecological, climatic, and socio-economic parameters. The use of explainable AI techniques and advanced deep learning architectures enhances the transparency and adaptability of forest governance. Importantly, AI contributes to the resilience of both terrestrial and coastal forest ecosystems by supporting the development of strategies that align biodiversity conservation, reduction in anthropogenic pressures, and the fulfillment of climate commitments. In total, according to the regional classification, the following references pertain to Asia [72; 73; 77; 78; 80; 82; 83; 84; 86]; Africa [81]; Europe [75; 76; 79] and Latin America [85].