Forest cover change – local scale analysis based on globally available datasets

Human-driven forest cover changes impact carbon sequestration, climate, biodiversity, and various ecosystem services, being at the same time spatially diverse worldwide. Therefore, there is a need to monitor them at multiple scales. A wide range of globally available land cover datasets and products now enables detailed and comprehensive analysis of forest cover dynamics. For example, declassified spy-satellite imagery provides high-resolution insights into forest cover more than 50 years into the past, while contemporary remote-sensing products offer modern, high-resolution forest maps with varying level of thematic detail. In this study, we propose an optimized method for analysing forest cover change, by combining these two opportunities – historical satellite imagery and recent remote sensing thematic products. As a starting point, we tested different strategies to automatically analyse high-resolution CORONA imagery (1.8-7.5 meter spatial resolution) for 1960-1974 by using object based image analysis (OBIA). For the recent period, we evaluated the accuracy of 3 different contemporary global land cover products including: Google Dynamic World (GDW), ESA World Cover map (WC)  and Esri Land Cover (ELC). Our analysis was conducted in test areas in Poland, where the recent forest cover increase was substantial. We found that the optimal segmentation approaches and classification strategies offer high-quality CORONA-based forest mask (F1 score = 0.95). For the contemporary forest cover, the highest accuracy was achieved by combining three tested masks rather than by relaying on a single product. Our analysis shows that despite a land cover data deluge observed in the recent years there is a need for critical approach while analysing forest cover change over time.

 

Acknowledgements:

This research was funded in whole or in part by the National Science Centre, Poland (UMO-2024/53/N/ST10/02518). For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.