AI-DRIVEN GEOSPATIAL ANALYSIS IN ECOSYSTEM MANAGEMENT: INTEGRATION OF MACHINE LEARNING METHODS

The escalating complexity of environmental pressures, including climate change, loss of biodiversity, and land degradation, demands new and integrated, science-based solutions for managing ecosystems. In this study, it is explored how the integration of machine learning (ML) and geospatial analysis is a truly transformative approach to understand, monitor, and manage ecosystems. Through reviewing a wide spectrum of recent research and cases, the paper evaluates the integration of ML processes—e.g., Random Forest, Support Vector Machines, Artificial Neural Networks, and deep learning models—alongside geospatial technologies like GIS, remote sensing, and satellite image technology. Importantly, the research focuses on Python and R as strong programming platforms for developing ecological AI solutions and underscores their importance for data preprocessing, predictive models, visualization, and decision support. A select group of fifteen case studies illustrates successful applications across various ecological scenarios, including assessments of deforestation, biodiversity conservation, land use and land cover classification, and evaluation of ecosystem services. These articles were selected to illustrate methodological variety, geographic coverage, and applied relevance of advances in ecosystem management. Furthermore, the review covers significant methodological progress, toolkits, and platform integration (such as Google Earth Engine and TensorFlow), which are used across geospatial AI workflows. It also highlights ongoing issues—e.g., data quality, computational demand, model interpretability, and ethical considerations—and possible avenues for future research and interdisciplinary collaboration and outlines future directions to foster cross-disciplinary research and sustainable AI application. This synthesis illustrates the promise of the consolidation of AI and geospatial analysis to support adaptive, transparent, and efficient ecosystem management amid the rapid global change.