Innovations in Forest Fire Detection and Monitoring: Integrating GISs, Remote Sensing, and AI

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2417

Special Issue Editors


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Guest Editor
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China
Interests: remote sensing; GIS; forest management; wildfire; disaster risk management; machine/deep learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: remote sensing; resources and environment monitoring; fire detection; deep learning; geographic information system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the world faces an unprecedented surge in forest fire events, a renewed emphasis has been placed on fortifying our detection and monitoring capabilities. Within this global challenge lies a tapestry of complex variables: rapidly changing climate conditions, anthropogenic disturbances, and the innate unpredictability of wildfires. To address these intricacies, the integration of geospatial intelligence, advanced remote sensing, and burgeoning computational technologies has come to the forefront.

At the heart of this transformational shift is the fusion of Geographic Information Systems (GISs) and remote sensing technologies, creating a potent combination capable of providing real-time, high-resolution data of vast forested regions. Furthermore, with the integration of big data analytics, artificial intelligence (AI), and machine/deep learning algorithms, our capacity to predict, monitor, and respond to forest fires has been dramatically amplified.

This Special Issue seeks to consolidate recent advances, methodologies, and innovative applications at the nexus of GISs, remote sensing, AI, and forest fire management. I invite researchers, practitioners, and experts to contribute their insights, findings, and strategic visions to foster an interdisciplinary dialogue.

Topics of interest include, but are not limited to, the following:

  • Advanced GIS applications in forest fire detection and mitigation;
  • State-of-the-art remote sensing technologies in fire management;
  • Leveraging big data analytics in predicting wildfire patterns;
  • AI and machine/deep learning models for real-time fire monitoring;
  • Integration of disparate data sources for enhanced forest fire response;
  • Challenges and solutions in data fusion analysis for fire detection;
  • Proactive forest fire management strategies using AI;
  • Predictive analysis of forest fire susceptibility and risk;
  • Novel forest fire surveying methods harnessing GISs and remote sensing;
  • The role of advanced tech in post-fire recovery and landscape restoration.

Prof. Dr. Chao Ren
Dr. Maofang Gao
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. Forests 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 2600 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

  • GIS and remote sensing
  • forest surveying methods
  • wildfires/forest fires
  • big data analytics and artificial intelligence
  • machine/deep learning
  • data integration and fusion analysis
  • monitoring and prediction
  • susceptibility and risk mapping

Published Papers (2 papers)

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Research

18 pages, 5834 KiB  
Article
Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data
by Kun Yang, Bo-Hui Tang, Wei Fu, Wei Zhou, Zhitao Fu and Dong Fan
Forests 2024, 15(4), 614; https://doi.org/10.3390/f15040614 - 28 Mar 2024
Viewed by 535
Abstract
Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated [...] Read more.
Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings. Full article
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18 pages, 11133 KiB  
Article
A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale
by Xingyue You, Zhong Zheng, Kangquan Yang, Liang Yu, Jinbao Liu, Jun Chen, Xiaoning Lu and Shanyun Guo
Forests 2024, 15(1), 86; https://doi.org/10.3390/f15010086 - 31 Dec 2023
Viewed by 1271
Abstract
Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture [...] Read more.
Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture and parameters using the particle swarm optimization (PSO) algorithm, a convolutional neural network (CNN) deep learning model was proposed to predict forest fire risk on a national scale. Utilizing fire data and fire risk factors from 2001 to 2020 in China, the PSO-CNN-based deep learning model (PSO-CNN) was utilized and tested. Compared to logistic regression, random forest, support vector machine, k-nearest neighbors, and CNN models, the PSO-CNN model exhibited superior performance with an accuracy of 82.2% and an AUC value of 0.92. These results clearly highlighted the effectiveness of the PSO-CNN model in enhancing the accuracy of forest fire prediction. Furthermore, the forest fire risk prediction level estimated by the proposed model on a national scale for the entire country was mostly consistent with actual fire data distribution, indicating its potential to be used as an important direction for deep learning in forest fire prediction research. Full article
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