Remote Sensing of Wildfire: Regime Change and Disaster Response

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 May 2024 | Viewed by 13346

Special Issue Editors


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Guest Editor
GIS Training and Research Center (GIS TREC),Idaho State University, Pocatello, ID 83209, USA
Interests: spatial analysis; land cover change; wildfire, semiarid environments; multispectral satellite imagery; GIS
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Guest Editor
School of Biological Sciences, University of Tasmania, Private Bag 55, Hobart, TAS 7001, Australia
Interests: air quality and smoke management; GIS; remote sensing; fire ecology; landscape ecology; fire modelling; smoke transport modelling; forests; climate change; emission factors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfires continue to change the globe. Some of these changes may be considered advantageous while others are a detriment to ecosystem health, water and air quality, wildlife habitat, and the socioeconomic services provided by the affected landscape. Wildfire has become the primary driver of change across several continents. But this change has not occurred within a vacuum. Indeed, wildfire represents a complex problem that interacts with invasive plants, livestock grazing, and land management policy.

This special issue of Fire seeks papers describing research using remote sensing imagery that will lead to a better understanding of how fire is changing the landscape, how fire regimes are changing, how wildfire drives and is driven by invasive plants, livestock grazing, and land management policy, and how remote sensing can be used for wildfire disaster response.

I invite you to participate in this very special edition of Remote Sensing and share your land cover change research in savanna ecosystems.

Prof. Keith T. Weber
Dr. Grant Williamson
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wildfire regime
  • wildland urban interface
  • remote sensing
  • earth observing system
  • invasive plants
  • land management
  • livestock grazing

Published Papers (9 papers)

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Research

18 pages, 6131 KiB  
Article
An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework
by Xinyu Hu, Feng Jiang, Xianlin Qin, Shuisheng Huang, Xinyuan Yang and Fangxin Meng
Fire 2024, 7(3), 68; https://doi.org/10.3390/fire7030068 - 26 Feb 2024
Viewed by 824
Abstract
Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and [...] Read more.
Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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23 pages, 16402 KiB  
Article
Regional-Scale Assessment of Burn Scar Mapping in Southwestern Amazonia Using Burned Area Products and CBERS/WFI Data Cubes
by Poliana Domingos Ferro, Guilherme Mataveli, Jeferson de Souza Arcanjo, Débora Joana Dutra, Thaís Pereira de Medeiros, Yosio Edemir Shimabukuro, Ana Carolina Moreira Pessôa, Gabriel de Oliveira and Liana Oighenstein Anderson
Fire 2024, 7(3), 67; https://doi.org/10.3390/fire7030067 - 25 Feb 2024
Viewed by 969
Abstract
Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned [...] Read more.
Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km2) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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31 pages, 4651 KiB  
Article
An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques
by Olga D. Mofokeng, Samuel A. Adelabu and Colbert M. Jackson
Fire 2024, 7(2), 61; https://doi.org/10.3390/fire7020061 - 19 Feb 2024
Viewed by 865
Abstract
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in [...] Read more.
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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21 pages, 14031 KiB  
Article
The Spatially Adaptable Filter for Error Reduction (SAFER) Process: Remote Sensing-Based LANDFIRE Disturbance Mapping Updates
by Sanath Sathyachandran Kumar, Brian Tolk, Ray Dittmeier, Joshua J. Picotte, Inga La Puma, Birgit Peterson and Timothy D. Hatten
Fire 2024, 7(2), 51; https://doi.org/10.3390/fire7020051 - 08 Feb 2024
Viewed by 1539
Abstract
LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel [...] Read more.
LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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21 pages, 19858 KiB  
Article
Assessment of the Analytic Burned Area Index for Forest Fire Severity Detection Using Sentinel and Landsat Data
by Rentao Guo, Jilin Yan, He Zheng and Bo Wu
Fire 2024, 7(1), 19; https://doi.org/10.3390/fire7010019 - 05 Jan 2024
Viewed by 1863
Abstract
The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned [...] Read more.
The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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15 pages, 2417 KiB  
Article
Modeling the Monthly Distribution of MODIS Active Fire Detections from a Satellite-Derived Fuel Dryness Index by Vegetation Type and Ecoregion in Mexico
by Daniel José Vega-Nieva, María Guadalupe Nava-Miranda, Jaime Briseño-Reyes, Pablito Marcelo López-Serrano, José Javier Corral-Rivas, María Isabel Cruz-López, Martin Cuahutle, Rainer Ressl, Ernesto Alvarado-Celestino and Robert E. Burgan
Fire 2024, 7(1), 11; https://doi.org/10.3390/fire7010011 - 26 Dec 2023
Viewed by 1365
Abstract
The knowledge of the effects of fuel dryness on fire occurrence is critical for sound forest fire management planning, particularly in a changing climate. This study aimed to analyze the monthly distributions of MODIS active fire (AF) detections and their relationships [...] Read more.
The knowledge of the effects of fuel dryness on fire occurrence is critical for sound forest fire management planning, particularly in a changing climate. This study aimed to analyze the monthly distributions of MODIS active fire (AF) detections and their relationships with a fuel dryness index (FDI) based on satellite-derived weather and vegetation greenness. Monthly AF distributions showed unimodal distributions against FDI, which were described using generalized Weibull equations, fitting a total of 19 vegetation types and ecoregions analyzed in Mexico. Monthly peaks of fire activity occurred at lower FDI values (wetter fuels) in more hygrophytic ecosystems and ecoregions, such as wet tropical forests, compared to higher fire activity in higher FDI values (drier fuels) for the more arid ecosystems, such as desert shrublands. In addition, the range of fuel dryness at which most monthly fire activity occurred was wider for wetter vegetation types and regions compared to a narrower range of fuel dryness for higher monthly fire occurrence in the more arid vegetation types and ecoregions. The results from the current study contribute towards improving our understanding of the relationships between fuel dryness and fire occurrence in a variety of vegetation types and regions in Mexico. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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28 pages, 28050 KiB  
Article
Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine
by Bohdan Yailymov, Andrii Shelestov, Hanna Yailymova and Leonid Shumilo
Fire 2023, 6(11), 411; https://doi.org/10.3390/fire6110411 - 26 Oct 2023
Viewed by 1710
Abstract
Wildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important [...] Read more.
Wildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important in Ukraine to assess the losses caused by the military actions. This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine. The methodology enables efficient national-scale burned area detection and automated identification of anthropogenic fires in regions with low fire risk. Implemented over Ukraine, 104.229 ha were mapped as burned during July 2022, with fires inconsistently corresponding to high FPI risk, indicating predominantly anthropogenic causes. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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27 pages, 8801 KiB  
Article
Wildfires Risk Assessment Using Hotspot Analysis and Results Application to Wildfires Strategic Response in the Region of Tangier-Tetouan-Al Hoceima, Morocco
by Hamid Boubekraoui, Yazid Maouni, Abdelilah Ghallab, Mohamed Draoui and Abdelfettah Maouni
Fire 2023, 6(8), 314; https://doi.org/10.3390/fire6080314 - 13 Aug 2023
Viewed by 1626
Abstract
In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic [...] Read more.
In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic responses to wildfires in the Tangier-Tetouan-Al Hoceima (TTA) region, widely reputed to exhibit the most significant incidences of wildfires in Morocco. We adopted a combined approach, using burned area products (Fire_CCI51: 2002–2020) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and active fires from the Fire Information for Resource Management System (FIRMS: 2001–2022) and processing them with spatiotemporal statistical methods: optimized hotspot analysis (OHA) and emerging hotspot analysis (EHA). The main findings indicate that the TTA region recorded an average of 39.78 km2/year of burned areas, mostly located in forests (74%), mainly cork oak and matorral stands (50%). The OHA detected hotspots covering 2081 km2, with 63% concentrated in the provinces of Chefchaouen and Larache. Meanwhile, clusters of EHA extended over 740 km2 and were composed of the oscillating coldspot (OCS) and oscillating hotspot (OHS) patterns at 50% and 30%, respectively. Additionally, an average of 149 fires/year occurred, located mostly in forests (75%), mainly cork oak and matorral stands (61%). The OHA detected active fire hotspots covering 3904 km2, with 60% located in the provinces of Chefchaouen and Larache. Clusters of EHA over 941 km2 were composed of the oscillating hotspot (OHS) and new hotspot (NHS) patterns at 57% and 25%, respectively. The prevalence of the oscillating and new models mirrors, respectively, the substantial fluctuations in wildfires within the region alternating between periods of high and low wildfire activities and the marked increase in fires in recent times, which has occasioned the emergence of novel hotspots. Additionally, we identified six homogeneous wildfire zones to which we assigned three strategic responses: “maintain” (73% of the territory), “monitor and raise awareness” (14% of the territory), and “reinforce” (13% of the territory). These strategies address current wildfire management measures, which include prevention, risk analysis, preparation, intervention, and rehabilitation. To better allocate firefighting resources, strategic responses were classified into four priorities (very high, high, medium, and low). Last, the wildfire zoning and strategic responses were validated using burned areas from 2021 to 2023, and a global scheme was suggested to assess the effectiveness of future wildfire measures. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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19 pages, 9871 KiB  
Article
Wetland Fire Assessment and Monitoring in the Paraná River Delta, Using Radar and Optical Data for Burnt Area Mapping
by Héctor Del Valle, Walter Fabián Sione and Pablo Gilberto Aceñolaza
Fire 2022, 5(6), 190; https://doi.org/10.3390/fire5060190 - 12 Nov 2022
Cited by 1 | Viewed by 1535
Abstract
In the past decades, important research has been carried out to map the natural disturbances in the Paraná River Delta. The benefits of the combined use optical and radar data are also known. The main objective of this paper is to assess the [...] Read more.
In the past decades, important research has been carried out to map the natural disturbances in the Paraná River Delta. The benefits of the combined use optical and radar data are also known. The main objective of this paper is to assess the wetland fire cartography through a synergetic use of radar and optical data. We focus on integrating radar (SAOCOM) and Sentinel 1, as well as Sentinel 2 optical data, concerning the fires impact analyses in the wetland areas. The generation of water masks through the radar images can contribute to improve the burned wetland area estimations. The relationship between landforms, vegetation cover, and the spatial/temporal resolution imposed by the flood pulse, play a vital role in the results. Burnt areas represent a total of 2439.57 sq km, which is more than 85% of the wetland, during the winter and spring (Q3 and Q4) periods. Understanding the wetland heterogeneity and its recovery pattern after a fire, is crucial to improve the cartography of the burned areas; for this, biweekly or monthly image compositions periodicity are of crucial importance. The inclusion of different indexes, for optical and radar images, improve the precision for the final classification. The results obtained here are promising for post-flood and post-fire evaluation, even applying radar and optical data integration into the evaluation and the monitoring of wetland fires is far from being a uniform standardized process. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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