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Remote Sensing
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Satellite Remote Sensing Applications for Fire Management
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Satellite Remote Sensing Applications for Fire Management

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 38795

Special Issue Editor

Dr. Giovanni Laneve

E-Mail Website
Guest Editor
Earth Observation and Satellite Image Applications Laboratory (EOSIAL), School of Aerospace Engineering (SIA), Sapienza University of Rome, Via Salaria, Roma, Italy
Interests: land degradation; vegetation mapping; satellite image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, there has been significant growth in the development of satellite-based applications capable of supporting all phases (prevention/prevision, fighting/contrast, and recovery/damage assessment) of the wildfire management. However, in the panorama of scientific publications devoted to this sector of remote sensing, there is a lack of description of applications that were effectively adopted or will be adopted in the near future to support the wildfire management in the real world. Therefore, this Special Issue will cover the following topics:

  • Description of satellite remote sensing techniques producing products/services adopted in wildfire management procedures of any fire-fighting organization;
  • Description of novel methodologies that are capable, in principle, of overcoming the present satellite-based product limitations, mainly the limitation that prevents their adoption by any institution responsible for forest fire management;
  • Applications of multi-sensor data fusion capable of improving the present methodology performances;
  • Use of data from next-generation satellite sensors (with improved spatial, spectral, and temporal resolution) that, when applied to well-established methodologies, could enhance the uptake of satellite remote sensing fire information in the operational forest fire management activity;
  • Impact analysis of satellite remote sensing information in the management of forest fires.

Prof. Giovanni Laneve
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.

Published Papers (6 papers)

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Research

18 pages, 5303 KiB  
Article
Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon
by Claudia Arantes Silva, Giancarlo Santilli, Edson Eyji Sano and Giovanni Laneve
Remote Sens. 2021, 13(3), 376; https://doi.org/10.3390/rs13030376 - 22 Jan 2021
Cited by 32 | Viewed by 8256
Abstract
This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the [...] Read more.
This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006–2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, which is 48% higher than the previous ten years, with an average of 6760 km2. The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO2, which is equivalent to 16.4% of the combined emissions of CO2 and CH4 emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots’ location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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23 pages, 16707 KiB  
Article
Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine
by Andrea Sulova and Jamal Jokar Arsanjani
Remote Sens. 2021, 13(1), 10; https://doi.org/10.3390/rs13010010 - 22 Dec 2020
Cited by 45 | Viewed by 8878
Abstract
Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the [...] Read more.
Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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23 pages, 6894 KiB  
Article
A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data
by Lisa Knopp, Marc Wieland, Michaela Rättich and Sandro Martinis
Remote Sens. 2020, 12(15), 2422; https://doi.org/10.3390/rs12152422 - 28 Jul 2020
Cited by 65 | Viewed by 8411
Abstract
Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with [...] Read more.
Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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17 pages, 689 KiB  
Article
The Daily Fire Hazard Index: A Fire Danger Rating Method for Mediterranean Areas
by Giovanni Laneve, Valerio Pampanoni and Riyaaz Uddien Shaik
Remote Sens. 2020, 12(15), 2356; https://doi.org/10.3390/rs12152356 - 22 Jul 2020
Cited by 14 | Viewed by 5616
Abstract
Mediterranean forests are gravely affected by wildfires, and despite the increased prevention effort of competent authorities in the past few decades, the yearly number of fires and the consequent damage has not decreased significantly. To this end, a number of dynamical methods have [...] Read more.
Mediterranean forests are gravely affected by wildfires, and despite the increased prevention effort of competent authorities in the past few decades, the yearly number of fires and the consequent damage has not decreased significantly. To this end, a number of dynamical methods have been developed in order to produce short-term hazard indices, such as the Fire Probability Index and the Fire Weather Index. The possibility to estimate the fire hazard is based on the observation that there is a relationship between the characteristics of the vegetation (i.e., the fuel), in terms of abundance and moisture content, and the probability of fire insurgence. The density, type, and moisture content of the vegetation are modeled using custom fuel maps, developed using the latest Corine Land Cover, and using a number of indices such as the NDVI (Normalized Difference Vegetation Index), Global Vegetation Moisture Index (GVMI), and the evapotranspiration, derived from daily satellite imagery. This paper shows how the algorithm for the calculation of the Fire Potential Index (FPI) was improved by taking into account the effect of wind speed, topography, and local solar illumination through a simple temperature correction, preserving the straightforward structure of the FPI algorithm. The results were validated on the Italian region of Sardinia using official wildfire records provided by the regional administration. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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19 pages, 7444 KiB  
Article
A Spatio-Temporal Analysis of Active Fires over China during 2003–2016
by Xikun Wei, Guojie Wang, Tiexi Chen, Daniel Fiifi Tawia Hagan and Waheed Ullah
Remote Sens. 2020, 12(11), 1787; https://doi.org/10.3390/rs12111787 - 1 Jun 2020
Cited by 26 | Viewed by 2882
Abstract
Fire is a common circumstance in the world. It causes direct casualties and economic losses, and also brings severe negative influences on the atmospheric environment. In the background of climate warming and rising population, it is important to understand the fire responses regarding [...] Read more.
Fire is a common circumstance in the world. It causes direct casualties and economic losses, and also brings severe negative influences on the atmospheric environment. In the background of climate warming and rising population, it is important to understand the fire responses regarding the spatio-temporal changes. Thus, a long-term change analysis of fires is needed in China. We use the remote sensed MOD14A1/MYD14A1 fire products to analyze the seasonal variations and long-term trends, based on five main land cover types (forest, cropland, grassland, savannas and urban areas). The fires are found to have clear seasonal variations; there are more fires in spring and autumn in vegetated lands, which are related to the amount of dry biomass and temperature. The fire numbers have significantly increased during the study period, especially from spring to autumn, and those have decreased in winter. The long-term fire trends are different when delineated into different land cover types. There are significant increasing fire trends in grasslands and croplands in North, East and Northeast China during the study period. The urban fires also show increasing trends. On the contrary, there are significant decreasing fire trends in forests and savannas in South China where it is most densely vegetated. This study provides an overall analysis of the spatio-temporal fire changes from satellite products, and it may help to understand the fire risk in the changing climate for a better risk management. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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19 pages, 1884 KiB  
Article
Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts
by Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie and Xiaojuan Lin
Remote Sens. 2020, 12(1), 156; https://doi.org/10.3390/rs12010156 - 2 Jan 2020
Cited by 16 | Viewed by 3907
Abstract
Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research [...] Read more.
Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applications for Fire Management)
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