ArticleScientific data2024
A global dataset of forest regrowth following wildfires.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A global dataset of forest regrowth following wildfires.Scientific data · 2024Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wildfires result in forest loss or degradation and release substantial emissions into the atmosphere. Forest regrowth following these fires allows for ecosystem repair and carbon replenishment. However, there is a lack of datasets explicitly characterizing the forest regrowth after fires. Here we employed multiple remotely sensed datasets to generate the first global maps of forest structure regrowth including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) following wildfires at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratio and rate at 5-year intervals, primarily from 2000 to 2020. The dataset developed in this study provides detailed insights into the characteristics of global forest regrowth following forest fires in both spatial and temporal dimensions, contributing to the assessment of forest ecology equilibrium and the quantification of forest carbon dynamics.
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