Forest regrowth dataset for globally key deforestation regions

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Authors: Jinlong Zang
Cited by: 0

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