Forest regrowth dataset for globally key deforestation regions
Abstract
Deforestation-driven forest loss substantially alters the global carbon budget and degrades ecosystem services, while subsequent forest regrowth is critical for ecosystem recovery and carbon sequestration. However, comprehensive datasets explicitly characterizing post-deforestation forest regrowth remain lacking. Here, we integrate multiple remote sensing products to develop the first spatially explicit dataset quantifying forest structural regrowth following deforestation across globally important deforestation regions at 30 m resolution. The dataset characterizes regrowth dynamics of forest height, aboveground biomass (AGB), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR). For each structural attribute, regrowth ratios and rates are provided at 5-year intervals, primarily spanning 1985–2020. This dataset enables a detailed assessment of post-deforestation forest regrowth across spatial, temporal, and structural dimensions, supporting improved quantification of forest carbon budgets and enhanced evaluation of forest ecosystem services.
Fields of Study & Subjects
Remote Sensing and LiDAR Applications
Remote Sensing in Agriculture
Forest ecology and management
Publication details
- Journal / source
- EGU General Assembly Conference Abstracts
- Publication date
- 13 March 2026
- Language
- EN