Remote Sensing Framework for Evaluating Forest Landscape Restoration Projects: Enhancing Accuracy and Effectiveness

Abstract

Forest and landscape restoration (FLR) initiatives are essential for combating deforestation, preserving biodiversity, and mitigating climate change. Remote sensing emerges as a key tool in evaluating FLR projects by providing accurate and timely data for monitoring and assessment. This letter presents a framework for generating high-quality maps using remote sensing data to assess the biophysical impact of FLR projects. The framework was applied to evaluate the Katanino FLR Project in Zambia. The results showcase a remarkable increase in forest cover, with a forest classification accuracy exceeding 90%. Such encouraging outcomes underscore the efficacy of the project in achieving its restoration goals and highlight the tangible benefits of employing remote sensing tools in FLR evaluation. Moreover, comprehensive FLR assessment, when complemented with diverse evaluation methodologies, facilitates a holistic understanding of FLR project impacts, enabling informed decision-making for the sustainable management of forest landscapes worldwide.