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Forests
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Application of Remote Sensing Technology in Forest Fires
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Application of Remote Sensing Technology in Forest Fires

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: closed (25 March 2024) | Viewed by 4029

Special Issue Editor

Dr. Víctor Fernández-García

E-Mail Website
Guest Editor
Institute of Geography and Environment, University of Lausanne, Lausanne, Switzerland
Interests: remote sensing; soil science; vegetation science; fire ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has emerged as a rapidly growing field with diverse applications in forest systems. These applications encompass vital aspects such as forest fire risk prediction, forest fire detection, assessment of fire damage in forests, and the monitoring of post-fire trajectories. Optimizing these applications has become crucial not only to accomplish scientific objectives but also to foster more sustainable forest management. This involves adopting proactive measures in areas at risk, optimizing resources, and focusing emergency and management actions in critical regions.

The advancement of sensors has widened the scope of available spatial, temporal, spectral, and radiometric resolutions, unveiling new research gaps, needs, and opportunities. Additionally, the development of innovative analysis techniques and improved computational capacity are playing significant roles in driving the field forward.

Considering these developments, we are pleased to announce the launch of a Special Issue entitled "Application of Remote Sensing Technology in Forest Fires". We aim to gather scientific publications that contribute to the progress of remote sensing and forest sciences, focusing on the fire-related aspects mentioned earlier. This includes research on fire and fire damage prediction, fire detection, fire damage analysis, and the analysis of post-fire recovery in both forest and forestry ecosystems.

We encourage submissions that leverage the potential of cutting-edge remote sensing tools, such as optical, thermal, RADAR, or LiDAR information. Furthermore, we welcome studies that employ state-of-the-art data processing and analysis methods, such as exploiting cloud computing facilities or utilizing artificial intelligence techniques.

Dr. Víctor Fernández-García
Guest Editor

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. 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

  • remote sensing
  • satellite
  • UAV
  • LiDAR
  • wildfire
  • fire risk
  • fire detection
  • fire severity
  • post-fire dynamics
  • Google Earth engine

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Published Papers (2 papers)

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19 pages, 3685 KiB  
Article
AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data
by Xuexue Chen, Ye Tian, Change Zheng and Xiaodong Liu
Forests 2024, 15(4), 705; https://doi.org/10.3390/f15040705 - 17 Apr 2024
Cited by 4 | Viewed by 1869
Abstract
Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of [...] Read more.
Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Forest Fires)
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20 pages, 2976 KiB  
Article
Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index
by Daniel José Vega-Nieva, Jaime Briseño-Reyes, Pablito-Marcelo López-Serrano, José Javier Corral-Rivas, Marín Pompa-García, María Isabel Cruz-López, Martin Cuahutle, Rainer Ressl, Ernesto Alvarado-Celestino and Robert E. Burgan
Forests 2024, 15(1), 42; https://doi.org/10.3390/f15010042 - 24 Dec 2023
Cited by 2 | Viewed by 1668
Abstract
There is a need to convert fire danger indices into operational estimates of fire activity to support strategic fire management, particularly under climate change. Few studies have evaluated multiple accumulation times for indices that combine both dead and remotely sensed estimates of live [...] Read more.
There is a need to convert fire danger indices into operational estimates of fire activity to support strategic fire management, particularly under climate change. Few studies have evaluated multiple accumulation times for indices that combine both dead and remotely sensed estimates of live fuel moisture, and relatively few studies have aimed at predicting fire activity from both such fuel moisture estimates and autoregressive terms of previous fires. The current study aimed at developing models to forecast the 10-day number of fires by state in Mexico, from an accumulated Fuel Dryness Index (FDI) and an autoregressive term from the previous 10-day observed number of fires. A period of 50 days of accumulated FDI (FDI50) provided the best results to forecast the 10-day number of fires from each state. The best predictions (R2 > 0.6–0.75) were obtained in the largest states, with higher fire activity, and the lower correlations were found in small or very dry states. Autoregressive models showed good skill (R2 of 0.99–0.81) to forecast FDI50 for the next 10 days based on previous fuel dryness observations. Maps of the expected number of fires showed potential to reproduce fire activity. Fire predictions might be enhanced with gridded weather forecasts in future studies. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology in Forest Fires)
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