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Remote Sensing
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Vegetation Fires, Greenhouse Gas Emissions and Climate Change
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Vegetation Fires, Greenhouse Gas Emissions and Climate Change

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12243

Special Issue Editors

Dr. João Neves Silva

E-Mail Website
Guest Editor
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
Interests: vegetation fires; greenhouse gas emissions from biomass burning; land use/cover mapping; remote sensing; field spectroradiometry
Special Issues, Collections and Topics in MDPI journals
Dr. Duarte Oom

E-Mail Website
Guest Editor
Disaster Risk Management Unit (E1), Joint Research Centre, European Commission, I-21027 Ispra, Italy
Interests: vegetation fires; wilfire risk assessment; remote sensing; burned area algorithm develoment; fire regime
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation fires have a large impact on the global carbon cycle and climate, releasing a variety of trace gases, such as CO, CO2, and CH4, into the Earth’s atmosphere. Fire emissions are associated with different anthropogenic and natural processes, from fires in the humid tropics, largely associated with deforestation activities, to wildfires in the temperate and boreal forests. Fire is also used as a tool, in agricultural lands or in slash and burn activities, where it may escape to surrounding forests in drought periods. In most regions of the world, climate change will increase the extent and severity of wildfires.

Understanding the role of climatic factors (e.g., long-term droughts) and anthropogenic factors (e.g., use of fire in grasslands or tropical woodlands) is very important to define fire management policies, which should keep in consideration future complex interactions among climate, land use/land cover, and socioeconomic changes. Satellite remote sensing provides the of extracting long-term trends of these the relationships among vegetation dynamics, fire incidence, and environmental factors.

The aim of this Special Issue is to present current research on fire management practices that lead to a reduction in greenhouse gas emissions, taking into consideration the determinants and effects of fire in each region, and the projected impacts of climate change. In some ecosystems, such as the tropical savannas, reducing gas emissions can be obtained by carrying out controlled burning in the early part of the dry season to prevent more frequent and intense fires later in the dry season. In other ecosystems, such as the temperate forests, prescribed fires are a tool for the long-term reduction of large wildfires and greenhouse gas emissions.

The submission of articles regarding the following topics will be most appreciated: applications of remotely sensed data for fire and vegetation monitoring; estimation of greenhouse gas emissions from vegetation fires; fire dynamics and carbon cycle; spatiotemporal trend analysis of fire incidence at regional and global scales; prescribed fires; land use/land cover–fire relationships; impacts of climate change on fire regimes; drivers of land cover/land use change; fire management practices.

You may choose our Joint Special Issue in Fire.

Dr. João Neves Silva
Dr. Duarte Oom
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. 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

  • Vegetation fires
  • GHG emissions
  • Fire regimes
  • Land use/land cover
  • Fire management
  • Climate change
  • Remote sensing

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Published Papers (4 papers)

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Research

28 pages, 16344 KiB  
Article
Operational Forest-Fire Spread Forecasting Using the WRF-SFIRE Model
by Manish P. Kale, Sri Sai Meher, Manoj Chavan, Vikas Kumar, Md. Asif Sultan, Priyanka Dongre, Karan Narkhede, Jitendra Mhatre, Narpati Sharma, Bayvesh Luitel, Ningwa Limboo, Mahendra Baingne, Satish Pardeshi, Mohan Labade, Aritra Mukherjee, Utkarsh Joshi, Neelesh Kharkar, Sahidul Islam, Sagar Pokale, Gokul Thakare, Shravani Talekar, Mukunda-Dev Behera, D. Sreshtha, Manoj Khare, Akshara Kaginalkar, Naveen Kumar and Parth Sarathi Royadd Show full author list remove Hide full author list
Remote Sens. 2024, 16(13), 2480; https://doi.org/10.3390/rs16132480 - 6 Jul 2024
Viewed by 2189
Abstract
In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for [...] Read more.
In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting. Full article
(This article belongs to the Special Issue Vegetation Fires, Greenhouse Gas Emissions and Climate Change)
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21 pages, 10097 KiB  
Article
Regional Spatiotemporal Patterns of Fire in the Eurasian Subarctic Based on Satellite Imagery
by Yikang Zhou, Shunping Ji and Timothy A. Warner
Remote Sens. 2022, 14(24), 6200; https://doi.org/10.3390/rs14246200 - 7 Dec 2022
Viewed by 1723
Abstract
The fire risks in the vast Eurasian Subarctic are increasing, raising concerns for both local and global climate systems. Although some studies have addressed this problem, their conclusions only draw from relatively lower resolution data, and the sub-regional analysis of fire patterns in [...] Read more.
The fire risks in the vast Eurasian Subarctic are increasing, raising concerns for both local and global climate systems. Although some studies have addressed this problem, their conclusions only draw from relatively lower resolution data, and the sub-regional analysis of fire patterns in this area is lacking. In this paper, using a huge amount of multi-temporal and multi-resolution remotely sensed data, derived products, and weather data between the period 2001 and 2021, we reveal several novel and recent findings concerning regional and overall fire patterns in the Eurasian Subarctic. First, we discovered that fire occurrence over the period 2001 and 2021 varied by sub-region within the Eurasian Subarctic, with perennial low fire incidence in the East European and West Siberian Plain, increasing fire incidence in the Central Siberian Plateau, and marked periodicity of fire in the East Siberian Highlands. Second, we reveal the larger scale of individual fires in the Eurasian Subarctic compared to the adjacent region to the south, with fires of longer duration (13 vs. 8 days), larger daily expansion area (7.5 vs. 3.0 km2/d), and faster propagation (442 vs. 280 m/d). Third, the northern limit of fire has extended poleward approximately 1.5° during the study period. Fourth, the start dates of fire seasons in Eurasian Subarctic, dominated by the Central Siberian Plateau, has advanced at a rate of 1.4 days per year. We also analyzed the factors resulting in the regional patterns of fire incidence including weather, human activity, land cover, and landscape structure. Our findings not only increase the knowledge of regional fire patterns and trends in Eurasian Subarctic but also will benefit the design of special fire management policies. Full article
(This article belongs to the Special Issue Vegetation Fires, Greenhouse Gas Emissions and Climate Change)
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17 pages, 4678 KiB  
Article
Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model
by Jinxiu Liu, Du Wang, Eduardo Eiji Maeda, Petri K. E. Pellikka and Janne Heiskanen
Remote Sens. 2021, 13(24), 5131; https://doi.org/10.3390/rs13245131 - 17 Dec 2021
Cited by 7 | Viewed by 3578
Abstract
Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, [...] Read more.
Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated. Full article
(This article belongs to the Special Issue Vegetation Fires, Greenhouse Gas Emissions and Climate Change)
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12 pages, 13861 KiB  
Communication
Interannual and Seasonal Variability of Greenhouse Gases and Aerosol Emissions from Biomass Burning in Northeastern China Constrained by Satellite Observations
by Hongmei Zhao, Guangyi Yang, Daniel Q. Tong, Xuelei Zhang, Aijun Xiu and Shichun Zhang
Remote Sens. 2021, 13(5), 1005; https://doi.org/10.3390/rs13051005 - 6 Mar 2021
Cited by 13 | Viewed by 3316
Abstract
Biomass burning is a major source of greenhouse gases (GHGs) and particulate matter (PM) emissions in China. Despite increasing efforts of fire monitoring, it remains challenging to quantify the variability in interannual and seasonal emissions of GHGs and PM from biomass burning. In [...] Read more.
Biomass burning is a major source of greenhouse gases (GHGs) and particulate matter (PM) emissions in China. Despite increasing efforts of fire monitoring, it remains challenging to quantify the variability in interannual and seasonal emissions of GHGs and PM from biomass burning. In this study, we investigated the biomass burning emissions in Northeastern China based on fire radiative power (FRP) obtained from the Visible Infrared Imaging Radiometer Suites (VIIRS) active fires datasets during the period 2012 to 2019. Our results showed that the average annual emissions from biomass burning in Northeastern China during 2012–2019 were: 81.6 Tg for CO2, 260.2 Gg for CH4, 5.5 Gg for N2O, 543.2 Gg for PM2.5 and 573.6 Gg for PM10, respectively. Higher levels of GHGs and PM emissions were concentrated in the Songnen Plain and Sanjiang Plain, the main grain producing areas in this region, and were associated with dense fire points. There were two emission peaks observed each year: after harvesting (October to November) and before planting (March to April). During this study period, the total fire emissions initially increased and then decreased in a fluctuating pattern, with emissions peaking in 2015, the year when more emission regulations were introduced. Crop straw was the major source of GHGs and PM among all kinds of biomass burning. Following more stringent controls on burning and the utilization of crop straw, the main burning season changed from autumn to spring. The proportion from spring burning increased from 20.5% in 2013 to 77.1% in 2019, with an annual growth rate of 20%. The results of this study demonstrate the effectiveness of regulatory control in reducing GHGs and PM emissions, as well as satellite fire observations as a powerful means to assess such outcomes. Full article
(This article belongs to the Special Issue Vegetation Fires, Greenhouse Gas Emissions and Climate Change)
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