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The Use of Remote Sensing Technology for Forest Fire

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A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Science Models, Remote Sensing, and Data".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 13340

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Prof. Dr. Fuquan Zhang

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Guest Editor

College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: intelligent forestry; forestry Internet of Things; wildland fire behavior; wildland fire management
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Prof. Dr. Ting Yun

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Guest Editor

School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence for forestry; forest digital twin; LiDAR data; remote sensing
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Dr. António Bento-Gonçalves

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Guest Editor

Communication and Society Research Centre, Department of Geography, Institute of Social Sciences, University of Minho, 4800-058 Guimarães, Portugal
Interests: physical geography; forest fires; soil erosion and land degradation; natural hazards
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Prof. Dr. Luis A. Ruiz

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Guest Editor

GeoEnvironmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: lidar for forest structure analysis; 3D fire behaviour models; object-based feature extraction and classification; land use/land cover change analysis
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Special Issue Information

Dear Colleagues,

As an important ecological factor in ecosystems, wildland fires and forest fires play a crucial role in the global ecosystem. However, uncontrolled fires can become a major threat to the environment and human lives, causing significant economic and ecological losses. Therefore, it is necessary to strengthen the research on forest fire management systems.

With the extensive application of modern information technology in the fire and smoke alarms, fire risk evaluation, fire behavior assessment, fire spreading analysis, and the exploratory appraisal of forest degeneration after fire disasters have become the primary strategies for forestland fire management.

The use of remote sensing and machine learning technology for forest fire prediction, deep-learning-based forest fire monitoring, and UAV-based forest fire severity classification have been gaining increasing attention in the field of fire management. The development of smart fire management needs to further promote the research, development, and application of more accurate and efficient methods for forest fire prediction and management, which can help reduce the risk of forest fires and provide timely and effective responses to forest fire emergencies. These technologies have the potential to greatly improve forest fire management and prevention efforts.

This Special Issue aims to cover the full range of applications in forest fire prediction and management. Possible topics include, but are not limited to:

Wildland fire or forest fire spreading, monitoring, or prediction;
Wildland fire or forest fire detection;
UAV-based forest fire severity classification;
Deep learning models for analyzing forest succession in chronological sequence;
Pattern recognition techniques for forest parameter retrieval;
Visible light smoke and fire recognition processing and intellectualization;
Early fire detection;
The accuracy of a fire protection system's positioning;
UAV-based forest fire spreading, monitoring, or prediction;
Forest aviation patrol.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Fuquan Zhang
Prof. Dr. Ting Yun
Dr. António Bento-Gonçalves
Prof. Dr. Luis A. Ruiz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

remote sensing

Published Papers (8 papers)

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Research

22 pages, 10600 KiB

Open AccessArticle

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

by Xingtong Ge, Ling Peng, Yi Yang, Yinda Wang, Deyue Chen, Lina Yang, Weichao Li and Jiahui Chen

Fire 2024, 7(4), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7040131 - 11 Apr 2024

Viewed by 382

Abstract

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from

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. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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21 pages, 2152 KiB

Open AccessArticle

Remote Sensing Active Fire Detection Tools Support Growth Reconstruction for Large Boreal Wildfires

by Tom J. Schiks, B. Mike Wotton and David L. Martell

Fire 2024, 7(1), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7010026 - 13 Jan 2024

Viewed by 1473

Abstract

Spatial and temporal estimates of burned areas are often used to model greenhouse gas and air pollutant emissions from fire events that occur in a region of interest and over specified time frames. However, fire behaviour, fuel consumption, fire severity, and ecological effects vary over both time and space when a fire grows across varying fuels and topography under different environmental conditions. We developed a method for estimating the progression of individual wildfires (i.e., day-of-burn) employing ordinary kriging of a combination of different satellite-based active fire detection data sources. We compared kriging results obtained using active fire detection products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and combined MODIS and VIIRS data to study how inferences about a wildfire’s evolution vary among data sources. A quasi-validation procedure using combined MODIS and VIIRS active fire detection products that we applied to an independent data set of 37 wildfires that occurred in the boreal forest region of the province of Ontario, Canada, resulted in nearly half of each fire’s burned area being accurately estimated to within one day of when it actually burned. Our results demonstrate the strengths and limitations of this geospatial interpolation approach to mapping the progression of individual wildfires in the boreal forest region of Canada. Our study findings highlight the need for future validations to account for the presence of spatial autocorrelation, a pervasive issue in ecology that is often neglected in day-of-burn analyses. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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19 pages, 4428 KiB

Open AccessArticle

Enhancing Fire Monitoring Method over Peatlands and Non-Peatlands in Indonesia Using Visible Infrared Imaging Radiometer Suite Data

by Andy Indradjad, Muhammad Dimyati, Yenni Vetrita, Erna Sri Adiningsih and Rokhmatuloh Rokhmatuloh

Fire 2024, 7(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/fire7010009 - 23 Dec 2023

Viewed by 1618

Abstract

Indonesia needs a daily monitoring system due to its frequent fires and, more importantly, to assist stakeholders in the field in taking action to mitigate disasters. Our method simplified the number of hotspots for field-based purposes and was verified by comparing the point-based (point-HS) VIIRS (Visible Infrared Imaging Radiometer Suite) 375m-derived temperature anomalies (hotspots) and clustered-based hotspots (cluster-HS, our suggested method). Using Euclidean clustering, we calculated the distance between hotspot points and applied specific criteria to reduce the number of hotspots while aligning them closely with fire incidents. We evaluated accuracy at different fire sizes, burned areas, peatlands, and distances from the reported burn center. We found that the accuracy increases at 1.5 km from the center of the fire for both point- and cluster-HS at 52% and 53%, respectively. For areas larger than 14 ha, both types of hotspots yielded superior results of 83%. Cluster-HS performs better on peatlands than non-peatlands (62% vs. 57%). Without diminishing the precision of the hotspot observation, this study indicates that our method is reliable for assisting field stakeholders in the field in taking actions. Therefore, this product could be implemented into Indonesia’s daily hotspot monitoring. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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18 pages, 1891 KiB

Open AccessArticle

The Effects of Fire Severity on Vegetation Structural Complexity Assessed Using SAR Data Are Modulated by Plant Community Types in Mediterranean Fire-Prone Ecosystems

by Laura Jimeno-Llorente, Elena Marcos and José Manuel Fernández-Guisuraga

Fire 2023, 6(12), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6120450 - 24 Nov 2023

Viewed by 1601

Abstract

Vegetation structural complexity (VSC) plays an essential role in the functioning and the stability of fire-prone Mediterranean ecosystems. However, we currently lack knowledge about the effects of increasing fire severity on the VSC spatial variability, as modulated by the plant community type in complex post-fire landscapes. Accordingly, this study explored, for the first time, the effect of fire severity on the VSC of different Mediterranean plant communities one year after fire by leveraging field inventory and Sentinel-1 C-band synthetic aperture radar (SAR) data. The field-evaluated VSC retrieved in post-fire scenarios from Sentinel-1 γ⁰ VV and VH backscatter data featured high fit (R² = 0.878) and low predictive error (RMSE = 0.112). Wall-to-wall VSC estimates showed that plant community types strongly modulated the VSC response to increasing fire severity, with this response strongly linked to the regenerative strategies of the dominant species in the community. Moderate and high fire severities had a strong impact, one year after fire, on the VSC of broom shrublands and Scots pine forests, dominated by facultative and obligate seeder species, respectively. In contrast, the fire-induced impacts on VSC were not significantly different between low and moderate fire-severity scenarios in communities dominated by resprouter species, i.e., heathlands and Pyrenean oak forests. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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19 pages, 4544 KiB

Open AccessArticle

Simulation of Soil Organic Carbon Dynamics in Postfire Boreal Forests of China by Incorporating High-Resolution Remote Sensing Data and Field Measurement

by Tongxin Hu, Cheng Yu, Xu Dou, Yujing Zhang, Guangxin Li and Long Sun

Fire 2023, 6(11), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6110414 - 26 Oct 2023

Viewed by 1359

Abstract

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO₂ emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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19 pages, 11641 KiB

Open AccessArticle

Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia

by Andrea Carbone, Dario Spiller and Giovanni Laneve

Fire 2023, 6(10), 395; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6100395 - 13 Oct 2023

Viewed by 1577

Abstract

Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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27 pages, 9511 KiB

Open AccessArticle

Comparing Forest Understory Fuel Classification in Portugal Using Discrete Airborne Laser Scanning Data and Satellite Multi-Source Remote Sensing Data

by Bojan Mihajlovski, Paulo M. Fernandes, José M. C. Pereira and Juan Guerra-Hernández

Fire 2023, 6(9), 327; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6090327 - 22 Aug 2023

Viewed by 2183

Abstract

Wildfires burn millions of hectares of forest worldwide every year, and this trend is expected to continue growing under current and future climate scenarios. As a result, accurate knowledge of fuel conditions and fuel type mapping are important for assessing fire hazards and predicting fire behavior. In this study, 499 plots in six different areas in Portugal were surveyed by ALS and multisource RS, and the data thus obtained were used to evaluate a nationwide fuel classification. Random Forest (RF) and CART models were used to evaluate fuel models based on ALS (5 and 10 pulse/m²), Sentinel Imagery (Multispectral Sentinel 2 (S2) and SAR (Synthetic Aperture RaDaR) data (C-band (Sentinel 1 (S1)) and Phased Array L-band data (PALSAR-2/ALOS-2 Satellite) metrics. The specific goals of the study were as follows: (1) to develop simple CART and RF models to classify the four main fuel types in Portugal in terms of horizontal and vertical structure based on field-acquired ALS data; (2) to analyze the effect of canopy cover on fuel type classification; (3) to investigate the use of different ALS pulse densities to classify the fuel types; (4) to map a more complex classification of fuel using a multi-sensor approach and the RF method. The results indicate that use of ALS metrics (only) was a powerful way of accurately classifying the main four fuel types, with OA = 0.68. In terms of canopy cover, the best results were estimated in sparse forest, with an OA = 0.84. The effect of ALS pulse density on fuel classification indicates that 10 points m⁻² data yielded better results than 5 points m⁻² data, with OA = 0.78 and 0.71, respectively. Finally, the multi-sensor approach with RF successfully classified 13 fuel models in Portugal, with moderate OA = 0.44. Fuel mapping studies could be improved by generating more homogenous fuel models (in terms of structure and composition), increasing the number of sample plots and also by increasing the representativeness of each fuel model. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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14 pages, 20516 KiB

Open AccessArticle

An Efficient Forest Fire Target Detection Model Based on Improved YOLOv5

by Long Zhang, Jiaming Li and Fuquan Zhang

Fire 2023, 6(8), 291; https://0-doi-org.brum.beds.ac.uk/10.3390/fire6080291 - 31 Jul 2023

Cited by 3 | Viewed by 2096

Abstract

To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall rate, it achieved improvements in precision and average precision (AP) to varying degrees. The DenseM-YOLOv5 algorithm achieved a 2.24% increase in precision, as well as improvements of 1.2% in recall rate and 1.52% in AP compared to the YOLOv5 algorithm. Despite having a higher parameter size, the DenseM-YOLOv5 algorithm outperformed the SimAM-YOLOv5 algorithm in terms of precision and AP for forest fire detection. Full article

(This article belongs to the Special Issue The Use of Remote Sensing Technology for Forest Fire)

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