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Spatially Explicit Reconstruction of Post-Megafire Forest Recovery Through Landscape Modeling

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Spatially Explicit Reconstruction of Post-Megafire Forest Recovery Through Landscape Modeling Environmental Modelling and Software 134 (2020) 104884 Contents lists available at ScienceDirect Environmental Modelling and Software journal homepage: http://www.elsevier.com/locate/envsoft Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling Wenru Xu a, Hong S. He a,*, Jacob S. Fraser b, Todd J. Hawbaker c, Paul D. Henne c, Shengwu Duan a, Zhiliang Zhu d a School of Natural Resources, University of Missouri, 203 ABNR Bldg., Columbia, MO, 65211, USA b USDA Forest Service, Northern Research Station, 202 ABNR Bldg., Columbia, MO, 65211, USA c U.S. Geological Survey, Denver Federal Center, MS 980, Denver, CO, 80225, USA d U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston, VA, 20192, USA ARTICLE INFO ABSTRACT Keywords: Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities Megafire across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal Reconstruction extents, which are difficultto collect from fieldinventories. Remote sensing provides an alternative but is limited Forest landscape model in revealing post-firerecovery trajectories and the underlying processes that drive the recovery. We developed a Remote sensing novel framework to spatially reconstruct the post-firetime-series of forest conditions after the 1987 Black Dragon Field inventory 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-firestand dynamics and that it is applicable to other types of disturbances. 1. Introduction with low or moderate severity burns, while fecund, light-seeded broadleaf species (e.g., Betula spp.) are wind dispersed and are more Forest fire is a primary disturbance in many forest ecosystems, likely to colonize in areas with high severity burns (Greene et al., 2007; influencing succession dynamics and carbon storage (Lecomte et al., Johnstone et al., 2010). Megafires can also create large high-severity 2006; Bowman et al., 2009). Fires that burned large areas with high burn patches that could delay tree regeneration and prolong early intensity (areal extent > 100 km2, often called megafires) can cause seral conditions by limiting the reach of seed dispersal (Johnstone et al., abrupt changes to ecosystems and have distinctly different ecological 2016), which may even trigger a shift from forest to shrub- or effects from other fires (Bradstock, 2008; Keane et al., 2008; Stephens grass-dominated cover types due to seed limitation and climate-induced et al., 2014). Post-fire recovery is an important variable for under­ regeneration failure (Collins and Roller, 2013; Savage et al., 2013; standing fireeffects on forest ecosystems, which is mainly determined by Harvey et al., 2016). Even with similar burn severity and sufficientseed burn severity and species regeneration strategies (Johnstone et al., availability, germination and establishment can be affected by toler­ 2010; Halofsky et al., 2011). Megafires often result in a heterogeneous ances to temperature and moisture that vary by species (Petrie et al., mosaic of burn severities across a wide range of environmental condi­ 2016; Davis et al., 2018) and microsite conditions, which can influence tions; consequently, the vegetational response can be complex. Seed­ the success of tree establishment and regeneration, with fewer tree lings regenerated after the fire vary strongly among areas with seedlings found on harsh sites (Broncano and Retana, 2004; Bonnet contrasting burn severities due to species-specific differences in et al., 2005; Kemp et al., 2019). The complex vegetation responses to dispersal, seed size, shade tolerance and parent tree locations. megafires make assessments of post-fire recovery challenging. Large-seeded species (e.g., Pinus spp.) have higher regeneration rates Assessment of post-fire forest recovery is traditionally completed under partial shade, and thus have higher regeneration rates in areas with plot-based field inventories. This method can provide relatively * Corresponding author. E-mail address: [email protected] (H.S. He). https://doi.org/10.1016/j.envsoft.2020.104884 Accepted 29 September 2020 Available online 6 October 2020 1364-8152/© 2020 Elsevier Ltd. All rights reserved. W. Xu et al. Environmental Modelling and Software 134 (2020) 104884 accurate and detailed measurements of post-fire plant communities, burn severity of the Black Dragon fire and the post-fire time series of which can be used to quantify burn severity and recovery based on the forest conditions (i.e., forest composition, structure and aboveground time the plots were surveyed after the fire(e.g., Johnstone et al., 2004; biomass) and (2) evaluate whether the reconstructed forest conditions Turner et al., 2016). However, field-based inventories generally cover could realistically capture the post-firerecovery (e.g., density and basal small spatial extents and provide plot-based information on burn area) at the level of individual tree species under different burn sever­ severity and recovery but not about the size and shape of burned patches ities. Spatiotemporal reconstruction of the post-megafire forest condi­ (e.g., Crotteau et al., 2013). Since megafires burn large areas across a tion provides a platform to investigate the recovery rate and trajectories range of environmental gradients and a mix of burn severities, it is through model simulations and thus improve realism and reduce challenging to capture the heterogeneous burn severities and post-fire uncertainties. recovery patterns using field-based methods alone. In addition, forest inventories before and immediately after megafires,and the subsequent 2. Data and methods monitoring of vegetation recovery, are often lacking. These limitations hinder field-based approaches for assessing megafire effects and 2.1. Study area post-fire recovery. Remote sensing is effective in capturing burn severity patterns and Our study area is located in the Great Xing ’an Mountains and en­ ◦ 0 ◦ 0 ◦ 0 monitoring post-fire vegetation recovery for megafires (French et al., compasses approximately 8.46 × 104 km2 (50 10 N, 121 12 E to 53 33 ◦ 2008; Gitas et al., 2012; Chu and Guo, 2014). Remote sensing-based N, 127 00’ E) in Northeast China. (Fig. 1). The area is hilly and moun­ vegetation indices such as the normalized burn ratio (NBR) (García tainous (altitudes ranging from 134 to 1511 m) and falls within the and Caselles, 1991; Epting et al., 2005) and its derivatives, differenced continental cold temperate climate zone with long and severe winters ◦ NBR (dNBR) and relative differenced NBR (RdNBR) (Key and Benson, but short summers. The average annual temperature is 3.9 C with an ◦ 2005; Miller and Thode, 2007), have been widely used for detecting average temperature of 33 C in the coldest month (January), and an ◦ burn severity patterns (Eidenshink et al., 2007). The normalized dif­ average temperature of 17.5 C in the hottest month (July). The annual ference vegetation index (NDVI), enhanced vegetation index (EVI) and cumulative precipitation ranges from 400 to 500 mm. More than 60% of soil adjusted vegetation index (SAVI) have been used for monitoring the annual precipitation occurs in the summer season from June to post-fire recovery (van Leeuwen et al., 2010; Gitas et al., 2012; Vera­ August (Zhou, 1991; Xu, 1998). Vegetation in this region is represen­ verbeke et al., 2012). However, a great deal of uncertainty exists when tative of cool boreal coniferous forests that cover 83% of the study area. using these vegetation indices to assess post-fire recovery in terms of The canopy species composition is relatively simple. Dahurian larch species composition and forest structure. Forest recovery assessments (Larix gmelini (Rupr.) Kuzen, hereafter “larch”), a deciduous conifer, and using vegetation indices can become complicated when different vege­ white birch (Betula platyphylla Suk.), a deciduous broadleaved species, tation recovery states have similar vegetation index values (Glenn et al., are dominant, covering more than 80% of the study area. Other tree 2008; Chu and Guo, 2014). For instance, young (e.g., two years species include the evergreen conifers, Korean spruce (Picea koriensis post-fire)broadleaf forest pixels may exhibit the same NDVI value as the Nakai, hereafter “spruce”) and Scots pine (Pinus sylvestris var. mongolica unburned coniferous forest pixels that are on a very different succes­ Litvinov, hereafter “pine”), and the deciduous broadleaved species, sional stage (Idris et al., 2005; Cuevas-Gonzalez et al., 2009; Cai et al., aspen (Populus davidiana Dole and P. suaveolens Fischer), willow (Cho­ 2018). The limited availability of cloud-free satellite images during the senia arbutifolia (Pall.) A. Skv), Asian black birch (Betula davurica Pall., growing season can also impede continuous assessment of post-fire hereafter “black birch”), and Mongolian oak (Quercus mongolica Fisch.ex forest recovery (Ju and Roy, 2008). In addition, remote sensing-based Ledeb.). Black birch and Mongolian oak are mainly distributed in the vegetation indices are limited in their ability to monitor demographic southeastern low elevation part of
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