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Wildfire Monitoring Using Remote Sensing Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 7355

Special Issue Editor


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Guest Editor
NASA-Ames Research Center, Moffett Field, CA 94035, USA
Interests: wildland fire; post-fire burn assessment; sensor systems; TIR; UAS platforms; decision support systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Globally, wildland fires have been increasing in numbers, intensity, and size and have resulted in significant landscape change, property and infrastructure loss, and loss of lives. The fire management communities and the science community need to understand wildland fire propagation and to have access to timely and relevant information to mitigate losses and reduce damage.  Remote sensing observation systems (in situ, airborne, or satellite) can provide the necessary measurement capabilities to enable effective pre-fire planning, active-fire management, and post-fire recovery operations.  Remote sensing systems are effective tools in fire-related studies and operational applications at local, regional and global scales. With expanded remote sensing systems available from the international Earth Observations (EO) community and the growing private sector satellite observations community, there are ample opportunities to inform improved management of wildland fire, thus facilitating the systematic monitoring of fire disturbance at various spatio-temporal scales.

This Special Issue entitled: “Wildfire Monitoring Using Remote Sensing Data” solicits scientific contributions focused on remote sensing observation systems and services for monitoring wildfires through all stages of fire (pre-fire predictive analysis, active-fire monitoring, and post-fire remediation / recovery).  In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following topics:

  • Contribution of the current and upcoming satellite observation systems, including those available from traditional EO sources (Landsat, MODIS, VIIRS, Sentinel, etc.) and from non-traditional source systems, such as from the growing community of SmallSat / CubeSat providers, and private companies.
  • Contributions from airborne systems, including both manned and remotely piloted aerial vehicles (RPAS) aircraft operations and new sensor capabilities.
  • New methods for combining in situ, orbital, and airborne data (sensor web concepts) for improved wildland fire decision support systems.
  • Methods for provision of real-time / near-real-time wildfire observation information services from these sources.
  • Improved methods of modelling post-fire vegetation trends and ecological recovery of affected environments.

Dr. Vincent G. Ambrosia
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
  • Thermal Sensing
  • Earth Observations (EO)
  • Fire prediction
  • Post-fire assessment
  • RPAS (UAS)

Published Papers (2 papers)

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Research

27 pages, 5765 KiB  
Article
Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia
by Saroj Kumar Sharma, Jagannath Aryal and Abbas Rajabifard
Remote Sens. 2022, 14(7), 1645; https://doi.org/10.3390/rs14071645 - 29 Mar 2022
Cited by 7 | Viewed by 4007
Abstract
The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence [...] Read more.
The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts. Full article
(This article belongs to the Special Issue Wildfire Monitoring Using Remote Sensing Data)
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19 pages, 5325 KiB  
Article
Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China
by Changming Yin, Minfeng Xing, Marta Yebra and Xiangzhuo Liu
Remote Sens. 2021, 13(24), 5127; https://doi.org/10.3390/rs13245127 - 17 Dec 2021
Cited by 4 | Viewed by 2762
Abstract
Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The [...] Read more.
Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China. Full article
(This article belongs to the Special Issue Wildfire Monitoring Using Remote Sensing Data)
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