Long-term dynamic monitoring of forest area changes with the aid of Google Earth Engine

Abstract

Remote sensing has become the most effective tool for monitoring and evaluating forest resources. Owing to the development of big data, current research on forest resource information mainly focuses on a regional, national, or global scale. However, there are still certain limitations on small regional scales. We used supervised classification based on the Google Earth Engine cloud platform to extract county land use information and a Whittaker smoother to extract vegetation indices to monitor forest area and vegetation indices in the study areas from the 1980s to the 2020s. The results show that the classification accuracy is improved for the study area where the image quality is affected by clouds after adding complementary datasets of greenness and water bodies. The overall accuracy (OA) increases from 0.82 to 0.89, and the kappa accuracy increases from 0.77 to 0.85. The OA of the classification results was 0.88, and the forest area increased and decreased with an overall increase of 225.35 km2. The building area continued to increase with an increase of 34.88 km2. The cropland area first decreased and then increased with a complete decrease of 243.6 km2. The bare soil area decreased by 16.2 km2. The area of the water bodies was relatively stable. The vegetation index indicated good forest growth. Jiangle County’s forest area and forest growth stability cannot be achieved without the protection of national and local policies.