Mapping Annual Global Forest Gain From 1983 to 2021 With Landsat Imagery

Abstract

The world's forests are experiencing rapid changes due to land use and climate change. However, a detailed map of global forest gain at fine spatial and temporal resolutions is still missing. To fill this gap, we developed an automatic framework for mapping annual forest gain globally using Landsat time series, the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm, and the Google Earth engine platform. First, stable forest samples collected based on the first all-season sample set and an automated sample migrate method were used to determine annual normalized burn ratio (NBR) thresholds for forest gain detection. Second, with the NBR time series from 1982 to 2021 and LandTrendr algorithm, we produced a dataset of global forest gain year from 1983 to 2021 based on a set of decision rules. Our results reveal that over 60% gains occurred in Russia, Canada, the United States, Indonesia, and China, and approximately half of global forest gain occurred between 2001 and 2010. The forest gain map developed in this study exhibited good consistency with statistical inventories and independent regional and global products. Our dataset can be useful for policy-relevant research on the global carbon cycle, and our method provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Keywords

• Change detection
• forest disturbance
• land cover
• Landsat-based detection of trends in disturbance and recovery (LandTrendr)