Special Issue "Detecting, Mapping, and Characterizing Wildfires 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: 31 July 2022 | Viewed by 3311

Special Issue Editors

Dr. Fangjun Li
E-Mail Website
Guest Editor
Geospatial Sciences of Excellences, Department of Geography & Geospatial Sciences, South Dakota State University, 1021 Medary Ave, Wecota Hall 115, Brookings, SD 57007, USA
Interests: remote sensing; biomass burning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoyang Zhang
E-Mail Website
Guest Editor
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: biomass burning emissions; burned area; fire seasonality; climate change; real-time monitoring; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfires have a profound influence on ecosystem structure and function, energy feedbacks to the climate system, regional socioeconomic conditions, and future land use planning. Quantifying wildfires remains challenging, with large uncertainties, although considerable efforts have been devoted to detecting fire occurrences, mapping burned areas, and characterizing fire behaviors during the last several decades. Therefore, this Special Issue aims to collect articles concerning the quantification of wildfires using observations from satellite (including PlantScope, Landsat, Sentinel-2, MODIS, VIIRS, and geostationary satellites), airborne sensors, and unmanned aerial vehicles.  The specific topics include:

  • New algorithms of detecting actively burning fires and mapping burned areas, particularly in areas dominated by small and/or cool fires (e.g., agriculture burnings) and frequently obscured by clouds (e.g., tropical deforestation fires).
  • Evaluation and validation of existing and emerging fire products using fine resolution fire observations and ground-based fire measurements.
  • Characterization of fire behaviors (intensity, spread rate, progression, etc.) at landscape scale.
  • Characterization of diurnal cycles of fire activity and long-term fire regimes at regional and global scales.
  • Examination of long-term variations of regional and global fire activities.

You may choose our Joint Special Issue in Fire.

Dr. Fangjun Li
Dr. Xiaoyang Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • active fire
  • burned area
  • fire behavior
  • fire regimes
  • diurnal cycles
  • validation

Published Papers (4 papers)

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Research

Article
How Much of a Pixel Needs to Burn to Be Detected by Satellites? A Spectral Modeling Experiment Based on Ecosystem Data from Yellowstone National Park, USA
Remote Sens. 2022, 14(9), 2075; https://doi.org/10.3390/rs14092075 - 26 Apr 2022
Viewed by 308
Abstract
We present a simple modeling technique based on linear spectral mixture analysis to assess satellite detectability of sub-pixel burned area. Pixel observations are modeled using a linear combination of pure land covers, called endmembers. We executed an experiment using spectral data from Yellowstone [...] Read more.
We present a simple modeling technique based on linear spectral mixture analysis to assess satellite detectability of sub-pixel burned area. Pixel observations are modeled using a linear combination of pure land covers, called endmembers. We executed an experiment using spectral data from Yellowstone National Park, USA. Using endmember samples from spectral libraries, pixel samples were assessed on burn detectability using the widely used differenced Normalized Burn Ratio (dNBR). While individual samples yielded differing results for Landsat 8, Sentinel-2, and the Moderate Resolution Imaging Spectroradiometer (MODIS), the average park-wide detectability of burned area was consistent across satellites. For the commonly used dNBR threshold of 0.15, the results indicated that detectability is reached when around a quarter of a pixel’s area is burned. However, a significant percentage of the modeled burned pixels remained undetectable, especially those with low pre-fire vegetation cover. This has consequences for burned area estimates, as smaller fires in sparsely vegetated terrain may remain undetected in moderate resolution burned area products. Full article
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Article
Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning
Remote Sens. 2022, 14(4), 992; https://doi.org/10.3390/rs14040992 - 17 Feb 2022
Cited by 1 | Viewed by 915
Abstract
Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years [...] Read more.
Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples. Full article
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Article
Comparing the Ability of Burned Area Products to Detect Crop Residue Burning in China
Remote Sens. 2022, 14(3), 693; https://doi.org/10.3390/rs14030693 - 01 Feb 2022
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Abstract
Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three [...] Read more.
Burning crop residues is a common way to remove them during the final stages of crop ripening in China. To conduct research effectively, it is critical to reliably and quantitatively estimate the extent and location of a burned area. Here, we investigated three publicly available burned area products—MCD64A1, FireCCI 5.1, and the Copernicus Burnt Area—and evaluated their relative performance at estimating total burned areas for cropland regions in China between 2015 and 2019. We compared these burned area products at a fine spatial and temporal scale using a grid system comprised of three-dimensional cells spanning both space and time. In general, the Copernicus Burnt Area product detected the largest annual average burned area (37,095.1 km2), followed by MCD64A1 (21,631.4 km2) and FireCCI 5.1 (12,547.99 km2). The Copernicus Burnt Area product showed a consistent pattern of monthly burned areas during the study period, whereas MCD64A1 and FireCCI 5.1 showed frequent changes in monthly burned area peaks. The greatest spatial differences between all three products occurred in Northeast and North China, where cultivated land is concentrated. The burned area detected by Copernicus in Xinjiang Province was larger than that detected by the other two products. In conclusion, we found that all three products underestimated the amount of crop residues present in a burned area. This limits the ability of end users to understand fire-related impacts and burned area characteristics, and hinders them in making an informed choice of which product is most appropriate for their application. Full article
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Article
A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine
Remote Sens. 2021, 13(21), 4298; https://doi.org/10.3390/rs13214298 - 26 Oct 2021
Cited by 2 | Viewed by 822
Abstract
A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes [...] Read more.
A preliminary version of a global automatic burned-area (BA) algorithm at medium spatial resolution was developed in Google Earth Engine (GEE), based on Landsat or Sentinel-2 reflectance images. The algorithm involves two main steps: initial burned candidates are identified by analyzing spectral changes around MODIS hotspots, and those candidates are then used to estimate the burn probability for each scene. The burning dates are identified by analyzing the temporal evolution of burn probabilities. The algorithm was processed, and its quality assessed globally using reference data from 2019 derived from Sentinel-2 data at 10 m, which involved 369 pairs of consecutive images in total located in 50 20 × 20 km2 areas selected by stratified random sampling. Commissions were around 10% with both satellites, although omissions ranged between 27 (Sentinel-2) and 35% (Landsat), depending on the selected resolution and dataset, with highest omissions being in croplands and forests; for their part, BA from Sentinel-2 data at 20 m were the most accurate and fastest to process. In addition, three 5 × 5 degree regions were randomly selected from the biomes where most fires occur, and BA were detected from Sentinel-2 images at 20 m. Comparison with global products at coarse resolution FireCCI51 and MCD64A1 would seem to show to a reliable extent that the algorithm is procuring spatially and temporally coherent results, improving detection of smaller fires as a consequence of higher-spatial-resolution data. The proposed automatic algorithm has shown the potential to map BA globally using medium-spatial-resolution data (Sentinel-2 and Landsat) from 2000 onwards, when MODIS satellites were launched. Full article
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