Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling. (December 2020)

• Record Type: Journal Article
• Authors: Xu, Wenru; He, Hong S.; Fraser, Jacob S.; Hawbaker, Todd J.; Henne, Paul D.; Duan, Shengwu; Zhu, Zhiliang
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Megafire -- Reconstruction -- Forest landscape model -- Remote sensing -- Field inventory; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104884
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: We presents a novel framework for spatially reconstructing the post-fire time-series of forest conditions. The framework generates detailed stand attributes that can be validated against field inventory data. The framework is highly scalable and can be applied to filling the gaps where inventory data are not available and to derive recovery trajectories across landscapes. The framework is applicable to other types of disturbances such as harvest and windstorm.