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Remote Sensing of Wildland Fires, Emissions, and Impacts

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Dear Colleagues,

Biomass burning from wildland fires is one of the major sources of greenhouse gases and aerosols in many regions of the world. Advancements and applications in this domain are critical given the increasing intensity, range, and frequency of these fires in many parts of the world. All facets of fire and emissions monitoring could benefit from further development, including the following: more accurate burned area or active fire detections, biomass and fuel loading, and predictive fire models and fire behavior. Moreover, development of more complete emissions inventories is crucial. The combined use of data sources is encouraged due to the wide availability of coarse-resolution long-term fire information datasets, such as MODIS, VIIRS, Sentinel SLSTR, and AVHRR, as well as moderate-resolution civil sensors such as Landsat 8 and Sentinel-2, and SAR sensors of varying frequencies, such as ALOS-1, ALOS-2, Sentinel-1, TerraSAR-X.

This Special Issue welcomes the submission of original papers including applications, new algorithms, and review papers related to any aspect of fire mapping and monitoring and the associated emissions quantification. Advanced methods such as optical and SAR data fusion and machine-learning-based advancements relating to the remote sensing of wildfires are encouraged.

Some of the specific topics of interest include:

Active fire mapping and monitoring;
Burned area mapping;
Fire emissions modeling;
Relationship between vegetation phenology and fires;
Forecasting wildfire risk;
Fuel-load modeling;
Aerosol and smoke propagation;
Modeling and air quality effects from fires.

Dr. Kristofer Lasko
Dr. Krishna Vadrevu
Guest Editors

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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. 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

emissions
active fire
burned area
fuel load
aerosol
data fusion
VIIRS
air quality

Published Papers (2 papers)

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Research

19 pages, 18591 KiB

Open AccessArticle

Multi-Source Satellite and WRF-Chem Analyses of Atmospheric Pollution from Fires in Peninsular Southeast Asia

by Ailin Liang, Jingyuan Gu and Chengzhi Xiang

Remote Sens. 2023, 15(23), 5463; https://doi.org/10.3390/rs15235463 - 22 Nov 2023

Viewed by 626

Abstract

Atmospheric pollutant gases emitted from straw burning and forest fires can lead to air quality and human health problems. This work explored the evolutionary trends of atmospheric CO

_{2}

and other pollutant gases in five countries of Peninsular Southeast Asia (PSEA) over a [...] Read more.

Atmospheric pollutant gases emitted from straw burning and forest fires can lead to air quality and human health problems. This work explored the evolutionary trends of atmospheric CO

_{2}

and other pollutant gases in five countries of Peninsular Southeast Asia (PSEA) over a long time series using various satellite remote sensing data. The research results indicate that a considerable number of fires occur in the region every spring, which negatively affects air quality. The concentration of CO

_{2}

increased every year, indicating a correlation coefficient of 0.57 with the number of fire points. The concentration of CO and NO

_{2}

, respectively, showed a correlation coefficient of 0.87 and 0.95 with the number of fire points as well. Additionally, the AOD reflects the relationship between fire points and air quality. The study also used the meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) to simulate the fire season in March 2016. In this sensitivity study, we examined the impact of air pollutant gases on air quality in PSEA under a hypothetical scenario with and without fire emissions. The simulation results were also compared with satellite observations, which showed that the WRF-Chem model and the FINN (Fire INventory from NCAR) inventory could effectively simulate the spatial distribution and spatial–temporal variability characteristics of CO concentration in the fire, but the simulation result of NO

_{2}

was not satisfactory. This study suggests that spring wildfires affect not only air quality, but also short-term weather in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Wildland Fires, Emissions, and Impacts)

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24 pages, 11968 KiB

Open AccessArticle

Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia

by Arip Syaripudin Nur, Yong Je Kim, Joon Ho Lee and Chang-Wook Lee

Remote Sens. 2023, 15(3), 760; https://doi.org/10.3390/rs15030760 - 28 Jan 2023

Cited by 9 | Viewed by 3048

Abstract

Australia has suffered devastating wildfires recently, and is predisposed to them due to several factors, including topography, meteorology, vegetation, and ignition sources. This study utilized a geographic information system (GIS) technique to analyze and understand the factors that regulate the spatial distribution of wildfire incidents and machine learning to predict wildfire susceptibility in Sydney. Wildfire inventory data were constructed by combining the fire perimeter through field surveys and fire occurrence data gathered from the visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 and 2020 for the Sydney area. Sixteen wildfire-related factors were acquired to assess the potential of machine learning based on support vector regression (SVR) and various metaheuristic approaches (GWO and PSO) for wildfire susceptibility mapping in Sydney. In addition, the 2019–2020 “Black Summer” fire acted as a validation dataset to assess the predictive capability of the developed model. Furthermore, the information gain ratio (IGR) method showed that driving factors such as land use, forest type, and slope degree have a large impact on wildfire susceptibility in the study area, and the frequency ratio (FR) method represented how the factors influence wildfire occurrence. Model evaluation based on area under the curve (AUC) and root average square error (RMSE) were used, and the outputs showed that the hybrid-based SVR-PSO (AUC = 0.882, RMSE = 0.006) model performed better than the standalone SVR (AUC = 0.837, RMSE = 0.097) and SVR-GWO (AUC = 0.873, RMSE = 0.080) models. Thus, optimizing SVR with metaheuristics improved the accuracy of wildfire susceptibility modeling in the study area. The proposed framework can be an alternative to the modeling approach and can be adapted for any research related to the susceptibility of different disturbances. Full article

(This article belongs to the Special Issue Remote Sensing of Wildland Fires, Emissions, and Impacts)

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