Remote Sensing

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24 pages, 5696 KiB

Open AccessArticle

Translating Fire Impacts in Southwestern Amazonia into Economic Costs

by Wesley A. Campanharo, Aline P. Lopes, Liana O. Anderson, Thiago F. M. R. da Silva and Luiz E. O. C. Aragão

Remote Sens. 2019, 11(7), 764; https://doi.org/10.3390/rs11070764 - 29 Mar 2019

Cited by 41 | Viewed by 8676

Abstract

Between 1998 and 2017, climate-related disasters represented 91% of all occurrences worldwide, causing approximately US$ 2.245 billion of direct economic losses. In the Amazon region, fire is used as a widely spread technique for land clearing, agricultural management, hunting, and religious rituals. However, [...] Read more.

Between 1998 and 2017, climate-related disasters represented 91% of all occurrences worldwide, causing approximately US$ 2.245 billion of direct economic losses. In the Amazon region, fire is used as a widely spread technique for land clearing, agricultural management, hunting, and religious rituals. However, over the past 20 years, severe droughts caused a major amplification of fire occurrences, leading to several socioeconomic and environmental impacts. Particularly in Acre state, located in the southwestern Brazilian Amazon, the occurrence of extensive fires, associated with extreme climatic events, has been reported since 2005. However, fire dynamics, land tenure relationships, and associated impacts are poorly quantified. In this study, we aim to investigate the following: (1) The spatiotemporal variability of fire dynamics during anomalously dry and regular climate conditions; (2) the attribution of fire occurrence and land tenure relationship, and (3) the environmental, social, and economic impacts caused by fires and its consequences for Acre’s economy. We analyzed information on the spatial patterns of fire, its direct impacts on land use and land cover, carbon stocks, CO₂ emissions, the indirect impact on human illness, and finally the costs of these impacts from 2008 to 2012. During the studied period, burned areas were concentrated around the major cities and roads, forming polygons up to 0.6 km². However, in 2010, an extremely dry year, fires spread to remote areas, impacting protected private areas and sustainable-use conservation areas. In 2010, the total area affected by forest fires was approximately 16 times greater than in meteorologically normal years. The total economic loss estimated in 2010 was around US$ 243.36 ± 85.05 million and for the entire period, US$ 307.46 ± 85.41 million. These values represent 7.03 ± 2.45% and 9.07 ± 2.46% of Acre’s gross domestic product (GDP), respectively. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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19 pages, 2413 KiB

Open AccessArticle

Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

by Federico Filipponi

Remote Sens. 2019, 11(6), 622; https://doi.org/10.3390/rs11060622 - 14 Mar 2019

Cited by 101 | Viewed by 8707

Abstract

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has [...] Read more.

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km², with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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17 pages, 3180 KiB

Open AccessArticle

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

by Katherine A. Hess, Cheila Cullen, Jeanette Cobian-Iñiguez, Jacob S. Ramthun, Victor Lenske, Dawn R. Magness, John D. Bolten, Adrianna C. Foster and Joseph Spruce

Remote Sens. 2019, 11(3), 283; https://doi.org/10.3390/rs11030283 - 1 Feb 2019

Cited by 11 | Viewed by 5726

Abstract

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US [...] Read more.

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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20 pages, 5004 KiB

Open AccessArticle

Fire on the Water Towers: Mapping Burn Scars on Mount Kenya Using Satellite Data to Reconstruct Recent Fire History

by Mary C. Henry, John K. Maingi and Jessica McCarty

Remote Sens. 2019, 11(2), 104; https://doi.org/10.3390/rs11020104 - 9 Jan 2019

Cited by 15 | Viewed by 7776

Abstract

Mount Kenya is one of Kenya’s ‘water towers’, the headwaters for the country’s major rivers including the Tana River and Ewaso Nyiro River, which provide water and hydroelectric power to the semiarid region. Fires affect water downstream, but are difficult to monitor given [...] Read more.

Mount Kenya is one of Kenya’s ‘water towers’, the headwaters for the country’s major rivers including the Tana River and Ewaso Nyiro River, which provide water and hydroelectric power to the semiarid region. Fires affect water downstream, but are difficult to monitor given limited resources of local land management agencies. Satellite-based remote sensing has the potential to provide long term coverage of large remote areas on Mount Kenya, especially using the free Landsat data archive and moderate resolution imaging spectroradiometer (MODIS) fire products. In this study, we mapped burn scars on Mount Kenya using 30 m Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) derived dNBR (change in normalized burn ratio) and MODIS active fire detection and burned area data for fires occurring from 2004 to 2015. We also analyzed topographic position (elevation, slope, aspect) of these fires using an ASTER global digital elevation model (GDEM v2) satellite-derived 30 m digital elevation model (DEM). Results indicate that dNBR images calculated from data acquired about one year apart were able to identify large fires on Mount Kenya that match locations (and timing) of MODIS active fire points and burned areas from the same time period, but we were unable to detect smaller and/or older fires. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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24 pages, 3514 KiB

Open AccessArticle

A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

by Zixi Xie, Weiguo Song, Rui Ba, Xiaolian Li and Long Xia

Remote Sens. 2018, 10(12), 1992; https://doi.org/10.3390/rs10121992 - 8 Dec 2018

Cited by 48 | Viewed by 7510

Abstract

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest [...] Read more.

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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15 pages, 3437 KiB

Open AccessArticle

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

by Xiangzhuo Liu, Binbin He, Xingwen Quan, Marta Yebra, Shi Qiu, Changming Yin, Zhanmang Liao and Hongguo Zhang

Remote Sens. 2018, 10(10), 1654; https://doi.org/10.3390/rs10101654 - 17 Oct 2018

Cited by 47 | Viewed by 9488

Abstract

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area [...] Read more.

Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R²) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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23 pages, 1417 KiB

Open AccessArticle

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

by Stéfano Arellano-Pérez, Fernando Castedo-Dorado, Carlos Antonio López-Sánchez, Eduardo González-Ferreiro, Zhiqiang Yang, Ramón Alberto Díaz-Varela, Juan Gabriel Álvarez-González, José Antonio Vega and Ana Daría Ruiz-González

Remote Sens. 2018, 10(10), 1645; https://doi.org/10.3390/rs10101645 - 16 Oct 2018

Cited by 39 | Viewed by 6369

Abstract

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions [...] Read more.

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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23 pages, 11290 KiB

Open AccessArticle

Economic Assessment of Fire Damage to Urban Forest in the Wildland–Urban Interface Using Planet Satellites Constellation Images

by Yaron Michael, Itamar M. Lensky, Steve Brenner, Anat Tchetchik, Naama Tessler and David Helman

Remote Sens. 2018, 10(9), 1479; https://doi.org/10.3390/rs10091479 - 16 Sep 2018

Cited by 35 | Viewed by 9671

Abstract

The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it [...] Read more.

The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it is unsuitable for WUI fire monitoring due to the low spatial resolution of the images from satellites that provide frequent information which is relevant for timely fire monitoring in WUI. Here, we take advantage of frequent (i.e., ca. daily), high-spatial-resolution (3 m) imagery acquired from a constellation of nano-satellites operated by Planet Labs (“Planet”) to assess fire damage to urban trees in the WUI of a Mediterranean city in Israel (Haifa). The fire occurred at the end of 2016, consuming ca. 17,000 of the trees (152 trees ha⁻¹) within the near-by wildland and urban parts of the city. Three vegetation indices (GNDVI, NDVI and GCC) from Planet satellite images were used to derive a burn severity map for the WUI area after applying a subpixel discrimination method to distinguish between woody and herbaceous vegetation. The produced burn severity map was successfully validated with information acquired from an extensive field survey in the WUI burnt area (overall accuracy and kappa: 87% and 0.75, respectively). Planet’s vegetation indices were calibrated using in-field tree measurements to obtain high spatial resolution maps of burned trees and consumed woody biomass in the WUI. These were used in conjunction with an ecosystem services valuation model (i-Tree) to estimate spatially-distributed and total economic loss due to damage to urban trees caused by the fire. Results show that nearly half of the urban trees were moderately and severely burned (26% and 22%, respectively). The total damage to the urban forest was estimated at ca. 41 ± 10 M USD. We conclude that using the method developed in this study with high-spatial-resolution Planet images has a great potential for WUI fire economic assessment. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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19 pages, 2617 KiB

Open AccessArticle

Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica

by Papia F. Rozario, Buddhika D. Madurapperuma and Yijun Wang

Remote Sens. 2018, 10(9), 1427; https://doi.org/10.3390/rs10091427 - 7 Sep 2018

Cited by 26 | Viewed by 7382

Abstract

This study develops a site specific burn severity modelling using remote sensing techniques to develop severity patterns on vegetation and soil in the fire prone region of the Palo Verde National Park in Guanacaste, Costa Rica. Terrain physical features, soil cover, and scorched [...] Read more.

This study develops a site specific burn severity modelling using remote sensing techniques to develop severity patterns on vegetation and soil in the fire prone region of the Palo Verde National Park in Guanacaste, Costa Rica. Terrain physical features, soil cover, and scorched vegetation characteristics were examined to develop a fire risk model and to quantify probable burned areas. Spectral signatures of affected areas were captured through multi-spectral analysis; i.e., Normalized Burn Ratio (NBR), Landsat derived differenced Normalized Burn Ratio (dNBR) and relativized dNBR (RdNBR). A partial unmixing algorithm, Mixture Tuned Matched Filtering (MTMF) was used to isolate endmembers for scorched vegetation and soil. The performance of dNBR and RdNBR for predicting ground cover components was acceptable with an overall accuracy of 84.4% and Cohen’s Kappa 0.82 for dNBR and an overall accuracy of 89.4% and Cohen’s Kappa 0.82 for RdNBR. Landsat derived RdNBR showed a strong correlation with scorched vegetation (r² = 0.76) and moderate correlation with soil cover (r² = 0.53), which outperformed dNBR. The ecologically diverse and unique park area is threatened by wetland fires, which pose a potential threat to various species. Human induced fires by poachers are a common occurrence in such areas to gain access to these species. This paper aims to prioritize areas that are at a higher risk from fire and model spatial adaptations in relation to the direction of fire within the affected wetlands. This assessment will help wildlife personnel in managing disturbed wetland ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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19 pages, 5815 KiB

Open AccessArticle

Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing

by Xuedong Li, Hongyan Zhang, Guangbin Yang, Yanling Ding and Jianjun Zhao

Remote Sens. 2018, 10(7), 1000; https://doi.org/10.3390/rs10071000 - 23 Jun 2018

Cited by 24 | Viewed by 5134

Abstract

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow [...] Read more.

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m⁻²·month⁻¹ (30–80%) and 0.13–0.35 kg C·m⁻²·year⁻¹ (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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17 pages, 7181 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape—Implications for Air Pollution Research

by Krishna Vadrevu and Kristofer Lasko

Remote Sens. 2018, 10(7), 978; https://doi.org/10.3390/rs10070978 - 21 Jun 2018

Cited by 58 | Viewed by 7650

Abstract

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) [...] Read more.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r² = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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14 pages, 2777 KiB

Open AccessFeature PaperArticle

An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data

by Masoud Abdollahi, Tanvir Islam, Anil Gupta and Quazi K. Hassan

Remote Sens. 2018, 10(6), 923; https://doi.org/10.3390/rs10060923 - 12 Jun 2018

Cited by 38 | Viewed by 7199

Abstract

Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern [...] Read more.

Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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21 pages, 1962 KiB

Open AccessArticle

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

by Francisco Guindos-Rojas, Manuel Arbelo, José R. García-Lázaro, José A. Moreno-Ruiz and Pedro A. Hernández-Leal

Remote Sens. 2018, 10(5), 789; https://doi.org/10.3390/rs10050789 - 19 May 2018

Cited by 10 | Viewed by 4596

Abstract

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from [...] Read more.

Burned Area (BA) is deemed as a primary variable to understand the Earth’s climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands’ dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R²) of 0.82. The Bayesian algorithm—originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record—can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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19 pages, 1987 KiB

Open AccessArticle

Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm

by Valeria Di Biase and Giovanni Laneve

Remote Sens. 2018, 10(5), 741; https://doi.org/10.3390/rs10050741 - 11 May 2018

Cited by 42 | Viewed by 5989

Abstract

The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the [...] Read more.

The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI, Spinning Enhanced Visible and InfraRed Imager, on board of MSG, Meteosat Second Generation, satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4 × 4 km² at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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17 pages, 2175 KiB

Open AccessArticle

Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data

by Anne C. Axel

Remote Sens. 2018, 10(3), 371; https://doi.org/10.3390/rs10030371 - 27 Feb 2018

Cited by 24 | Viewed by 8713

Abstract

A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry [...] Read more.

A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry forests, yet remotely sensed mapping studies of fire-impacted tropical dry forests lag behind fire research of other forest types. Methods used to map burns in temperature forests may not perform as well in tropical dry forests where it can be difficult to distinguish between multiple-age burn scars and between bare soil and burns. In this study, the extent of forest lost to stand-replacing fire in Kirindy Mitea National Park was quantified using both spectral and textural information derived from multi-date satellite imagery. The total area of the burn was 18,034 ha. It is estimated that 6% (4761 ha) of the Park’s primary tropical dry forest burned over the period 23 June to 27 September. Half of the forest burned (2333 ha) was lost in the large conflagration adjacent to the Research Station. The best model for burn scar mapping in this highly-seasonal tropical forest and pastoral landscape included the differenced Normalized Burn Ratio (dNBR) and both uni- and multi-temporal measures of greenness. Lessons for burn mapping of tropical dry forest are much the same as for tropical dry forest mapping—highly seasonal vegetation combined with a mixture of background spectral information make for a complicated analysis and may require multi-temporal imagery and site specific techniques. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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9532 KiB

Open AccessArticle

A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR

by Jeanine Engelbrecht, Andre Theron, Lufuno Vhengani and Jaco Kemp

Remote Sens. 2017, 9(8), 764; https://doi.org/10.3390/rs9080764 - 31 Jul 2017

Cited by 57 | Viewed by 7997

Abstract

In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle [...] Read more.

In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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