User-Driven Land Cover Change Prediction Map Tool for Land Conservation Planning

Background

Effective conservation planning requires forward-looking tools that anticipate land cover change, rather than relying solely on historical analysis. Rapid urbanization and land-use change threaten ecosystems and biodiversity, particularly in regions experiencing development pressure. Traditional models often lack accessibility for nontechnical users, limiting their application in real-world decision-making. Integrating machine learning with user-friendly platforms can enhance stakeholder engagement and improve conservation outcomes.

Goals and Methods

This study develops a user-driven, cloud-based tool for predicting land-cover change using machine-learning and deep-learning models. The tool processes Sentinel-2 imagery to classify land cover and predict future changes based on biophysical and socioeconomic variables such as vegetation productivity, population density, and infrastructure proximity. The model is trained using multi-year datasets and validated through accuracy assessments and expert review. A web-based interface allows users to adjust input variables and simulate development scenarios in real time.

Conclusions and Takeaways

The User-Driven Land Cover Change Prediction Map tool achieves high classification and prediction accuracy, demonstrating its effectiveness for conservation planning. Its interactive design enables stakeholders to explore future scenarios and assess potential environmental impacts without requiring technical expertise. This approach enhances proactive decision-making and supports efforts to minimize habitat loss. Practitioners can use this tool to integrate conservation considerations into development planning. Future work should focus on improving model accuracy, expanding geographic applicability, and strengthening stakeholder engagement in decision processes.