Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling

Environmental Modelling and Software
By: , and 

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Abstract

Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal extents, which are difficult to collect from field inventories. Remote sensing provides an alternative but is limited in revealing post-fire recovery trajectories and the underlying processes that drive the recovery. We developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. We derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. We simulated the megafire and the post-megafire forest recovery from 1985 to 2015 using the LANDIS model. We demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling
Series title Environmental Modelling and Software
DOI 10.1016/j.envsoft.2020.104884
Volume 134
Year Published 2020
Language English
Publisher Elsevier
Contributing office(s) Geosciences and Environmental Change Science Center
Description 104884, 10 p.
Country China
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