Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review

Abstract

Wildland fires dramatically affect forest ecosystems, altering the loss of their biodiversity and their sustainability. In addition, they have a strong impact on the global carbon balance and, ultimately, on climate change. This review attempts to provide a comprehensive meta-analysis of studies on remotely sensed methods and data used for estimation of forest burnt area, burn severity, post-fire effects, and forest recovery patterns at the global level by using the PRISMA framework. In the study, we discuss the results of the analysis based on 329 selected papers on the main aspects of the study area published in 48 journals within the past two decades (2000–2020). In the first part of this review, we analyse characteristics of the papers, including journals, spatial extent, geographic distribution, types of remote sensing sensors, ecological zoning, tree species, spectral indices, and accuracy metrics used in the studies. The second part of this review discusses the main tendencies, challenges, and increasing added value of different remote sensing techniques in forest burnt area, burn severity, and post-fire recovery assessments. Finally, it identifies potential opportunities for future research with the use of the new generation of remote sensing systems, classification and cloud performing techniques, and emerging processes platforms for regional and large-scale applications in the field of study.

Fire traps in the wet subtropics: New perspectives from Hong Kong

“…Despite evidence showing that degraded sites in the wet tropics are reasonably fire‐prone (Flores et al., 2016; Mata et al., 2022; Uhl et al., 1988), there is still a prevailing perception of these bioregions being ‘too wet to burn’. Existing studies on fire dynamics continue to focus on naturally fire‐adapted Mediterranean, boreal and Savanna ecosystems (Kibler et al., 2019; Kurbanov et al., 2022; Mallek et al., 2013; Qiu et al., 2021; van Butsic, Kelly and Moritz, 2015). Since wet tropical landscapes are rarely fuel‐limited and have distinctive fire‐vegetation dynamics (Tepley et al., 2018), it is questionable whether patterns observed in other ecosystems hold true in wet tropical and subtropical biomes.…”

“…Similarly, post‐fire recovery trajectories in the wet tropics are worth re‐evaluating as they determine whether sites could escape the fire trap. Previous studies have identified fire frequency, distance from forest patches, burn severity, soil type, species composition and several topographical variables as factors that affect rate of post‐fire recovery (Araújo et al., 2017; Bright et al., 2019; Goosem et al., 2016; Ireland & Petropoulos, 2015; Kurbanov et al., 2022; Marsh, Crockett, et al., 2022; Rochimi et al., 2021), but a systematic evaluation of the importance of these variables in the wet tropics is currently lacking. Crucially, many existing studies quantified post‐fire recovery by tracking the rebound of remotely‐sensed indices, such as the normalised difference vegetation index (NDVI) or normalised burn ratio (NBR), to pre‐disturbance values (Bright et al., 2019; Fernández‐García et al., 2018; Gouveia et al., 2010; Ireland & Petropoulos, 2015; Kurbanov et al., 2022; Pérez‐Cabello et al., 2021).…”

“…Burnt areas rapidly revegetate and return to pre‐fire conditions quickly (Chan et al., 2023; Idris et al., 2004; Melchiorre & Boschetti, 2018), but returning the system to the pre‐fire degraded condition is often not the goal of land managers. Instead, it is more appropriate to study recovery time to forests after fires, yet none of the existing studies have adopted such an approach to analyse post‐fire recovery over large scales (Kurbanov et al., 2022).…”

How Long Do Runoff‐Generated Debris‐Flow Hazards Persist After Wildfire?

“…Postfire recovery is often monitored using time series of remotely sensed vegetation indices (Thomas et al., 2021; Walker & Soulard, 2019), which provide data over larger areas than can practically be monitored with field measurements of vegetation and soil hydraulic recovery. Commonly used indices include Normalized Differenced Vegetation Index (Cuevas‐Gonzàles et al., 2009; Kurbanov et al., 2022; Walker & Soulard, 2019), Enhanced Vegetation Index (Jin et al., 2012) or National Aeronautical and Space Administration Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) (Ebel, 2020; Myneni et al., 2002). Recovery rates and trajectories are highly variable (Ebel et al., 2022), depending on factors including vegetation type, burn severity, aspect, and climatic conditions in the years after fire (Kinoshita & Hogue, 2011; Vanderhoof et al., 2018; Viana‐Soto et al., 2020).…”

Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study

“…Satellites with sufficient spatial, temporal, and radiometric resolution along with or without field sampling are common tools for mapping active fires, fire scars, and burn severity, especially in inaccessible areas [16][17][18][19]. Furthermore, satellites are useful for studying fire impacts on ecosystems and for mapping environmental characteristics such as fuel moisture content, vegetation type, slope, aspect, and anthropogenic variables (e.g., proximity to roads and settlements) that facilitate the mapping of fire risk [20][21][22][23].…”