scholarly journals Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

2020 ◽  
Vol 12 (12) ◽  
pp. 2061 ◽  
Author(s):  
Carlos Ivan Briones-Herrera ◽  
Daniel José Vega-Nieva ◽  
Norma Angélica Monjarás-Vega ◽  
Jaime Briseño-Reyes ◽  
Pablito Marcelo López-Serrano ◽  
...  
Keyword(s):  
Real Time ◽  
Forest Fires ◽  
Large Scale ◽  
Infrared Imaging ◽  
Activity Patterns ◽  
Burned Area ◽  
Management Decision ◽  
Coarse Scale ◽  
Early Evaluation ◽  
Active Fire

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.

scholarly journals Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors

2018 ◽  
Vol 27 (4) ◽  
pp. 241 ◽  
Author(s):  
M. M. Valero ◽  
O. Rios ◽  
E. Pastor ◽  
E. Planas
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Infrared Imaging ◽  
Weather Conditions ◽  
Automated System ◽  
Rate Of Spread ◽  
Active Fire ◽  
Edge Detectors ◽  
Spatio Temporal ◽  
Manual Tasks

A variety of remote sensing techniques have been applied to forest fires. However, there is at present no system capable of monitoring an active fire precisely in a totally automated manner. Spaceborne sensors show too coarse spatio-temporal resolutions and all previous studies that extracted fire properties from infrared aerial imagery incorporated manual tasks within the image processing workflow. As a contribution to this topic, this paper presents an algorithm to automatically locate the fuel burning interface of an active wildfire in georeferenced aerial thermal infrared (TIR) imagery. An unsupervised edge detector, built upon the Canny method, was accompanied by the necessary modules for the extraction of line coordinates and the location of the total burned perimeter. The system was validated in different scenarios ranging from laboratory tests to large-scale experimental burns performed under extreme weather conditions. Output accuracy was computed through three common similarity indices and proved acceptable. Computing times were below 1 s per image on average. The produced information was used to measure the temporal evolution of the fire perimeter and automatically generate rate of spread (ROS) fields. Information products were easily exported to standard Geographic Information Systems (GIS), such as GoogleEarth and QGIS. Therefore, this work contributes towards the development of an affordable and totally automated system for operational wildfire surveillance.

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Spread ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Fractal Properties ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

Integration of AWiFS and MODIS active fire data for burn mapping at regional level using the Burned Area Synergic Algorithm (BASA)

2009 ◽  
Vol 18 (4) ◽  
pp. 404 ◽  
Author(s):  
Federico González-Alonso ◽  
Silvia Merino-de-Miguel
Keyword(s):  
Forest Fires ◽  
Burned Area ◽  
Wide Field ◽  
Imaging Spectrometer ◽  
Area Index ◽  
North West ◽  
Active Fire ◽  
Moderate Resolution ◽  
Field Sensor ◽  
Speed Accuracy

The present paper presents an algorithm that synergistically combines data from four different parts of the spectrum (near-, shortwave, middle- and thermal infrared) to produce a reliable burned-area map. It is based on the use of a modified version of the BAIM (MODIS – Moderate Resolution Imaging Spectrometer – Burned Area Index) together with active fire information. The following study focusses in particular on an image from the AWiFS (Advanced Wide Field Sensor) sensor dated 21 August 2006 and MODIS active fires detected during the first 20 days of August as well as ancillary maps and information. The methodology was tested in Galicia (north-west Spain) where hundreds of forest fires occurred during the first 20 days of August 2006. Burned area data collected from the present work was compared with official fire statistics from both the Spanish Ministry of the Environment and the Galician Forestry Service. The speed, accuracy and cost-effectiveness of this method suggest that it would be of great interest for use at both regional and national levels.

Where’s the fire? Using in-situ observations from the NOAA/NASA FIREX-AQ campaign to validate small fire in the central and southern U.S.

2020 ◽  
Author(s):  
Eileen Rintsch ◽  
Jessica L. McCarty
Keyword(s):  
United States ◽  
Land Use ◽  
Infrared Imaging ◽  
The United States ◽  
Climate Impacts ◽  
Pollution Monitoring ◽  
Burned Area ◽  
Small Scale ◽  
Active Fire ◽  
Emissions Inventories

<p>Crop residue and rangeland burning is a common practice in the United States but verified ground-based estimates for the frequency of these fires is sparse. We present a comparison between known fire locations collected during the summer 2019 NOAA/NASA FIREX-AQ field campaign with several satellite-based active fire detections to estimate the occurrence of small-scale fires in agroecosystems. Many emissions inventories at the state-, country-, and global-level are driven by active fire detections and not burned area estimates for small fires in agroecosystems. The study area is focused on the southern Great Plains and Mississippi Delta of the United States. We combined fire occurrence data from 375 m Visible Infrared Imaging Spectrometer (VIIRS), 1 km Moderate Resolution Imaging Spectroradiometer (MODIS), and 2 km Geostationary Operational Environmental Satellite (GOES) active fires with 30 m land use data from U.S. Department of Agriculture Cropland Data Layer (CDL). The detections were compared to fires and land use validated in the field during the NOAA/NASA FIREX-AQ mission. GOES detected these fires at a higher frequency than MODIS or VIIRS. For example, MODIS detected 873 active fires and VIIRS detected 2,859, while GOES detected 13,634 active fires. Additionally, a large amount of the fires documented in the field, approximately 41%, were not detected by any satellite instrument used in the study. If GOES detections are excluded, approximately 5% of the documented fires were detected. This suggests that a large amount of cropland and rangeland burning are not detected by current active fire products from polar orbiting satellites like MODIS and VIIRS, with implications for regional air pollution monitoring, emissions inventories, and climate impacts of open burning.  </p>

Integration of operational Multi-Hazard Early Warning Systems and COVID-19 data for the humanitarian community

2021 ◽  
Author(s):  
Chiara Proietti ◽  
Alessandro Annunziato ◽  
Pamela Probst ◽  
Stefano Paris ◽  
Thomas Peter
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Research Centre ◽  
Time Data ◽  
Joint Research ◽  
Coordination System

<p>To improve preparedness and response in case of large-scale disasters, the international humanitarian community needs to understand the anticipated impact of an event as soon as possible in order to take informed operational decisions. The European Commission’s Joint Research Centre (JRC), DG ECHO, and the United Nations’ OCHA and UNOSAT launched the Global Disaster Alert and Coordination System (www.GDACS.org) in 2002-03 as cooperation platform to provide early disaster warning and coordination services to humanitarian actors. After more than 15 years, GDACS has around 30k registered users among humanitarian organisations at global level.</p><p>At the beginning, one of GDACS’s main tasks was the dissemination of automatic alerts for earthquakes, tsunamis and tropical cyclones; today, the system has been augmented to include also floods, droughts and volcanoes, and it will soon include forest fires.  Alerts are sent to the international humanitarian community to ensure timely warning in severe events that are expected to require international assistance. Alert levels are determined by automated algorithms without, or with very limited, human intervention, using automatic real-time data-feeds from various scientific institutes or the JRC’s own systems.</p><p>From 2020, because of the potential impact of the COVID-19 emergency on international preparedness and response activities, the COVID-19 situation in affected countries is now also monitored by the system, providing real time information updates on the website. This new feature allows to consider in the planning of the emergency response, the severity of the outbreak in the affected countries.</p><p>This contribution presents the challenges and outcomes of combining science-based information from different independent systems into a single Multi-Hazard Early Warning System and introduces new functionalities that were recently developed to address the new challenges related to the COVID-19 emergency.</p>

scholarly journals A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

2020 ◽  
Vol 12 (5) ◽  
pp. 858 ◽  
Author(s):  
Alfonso Fernández-Manso ◽  
Carmen Quintano
Keyword(s):  
Maximum Entropy ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Hyperspectral Data ◽  
Burned Area ◽  
Burned Areas ◽  
Mediterranean Countries ◽  
Burned Area Mapping

Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

scholarly journals MAPPING OF FOREST FIRE BURNED SEVERITY USING THE SENTINEL DATASETS

2018 ◽  
Vol XLII-5 ◽  
pp. 469-474 ◽  
Author(s):  
K. V. Suresh Babu ◽  
A. Roy ◽  
R. Aggarwal
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Products ◽  
Mitigation Measures ◽  
Fire Season ◽  
Burned Area ◽  
Active Fire ◽  
Major Loss ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

<p><strong>Abstract.</strong> Forest fires are frequent phenomena in Uttarakhand Himalayas especially in the months of April to May, causing major loss of valuable forest products and impact on humans through the emissions and therefore effects the climate change. The major forest fire was started on May 19, 2018 and spread in 10 districts out of 13 districts of Uttarakhand state till the fire was suppressed after May 30, 2018. The burned area mapping is essential for the forest officials to plan for mitigation measures and restoration activities after the fire season. In this study, sentinel 2A &amp;amp; 2B satellite datasets were used to map burned severity over Uttarakhand districts. Differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) were calculated and compared with the active fire points. Results shows that both the dNBR and RBR are in good agreement with the actual occurence of forest fires.</p>

A quantitative comparison of forest fires in central and northern California under early (1911–1924) and contemporary (2002–2015) fire suppression

2019 ◽  
Vol 28 (2) ◽  
pp. 138 ◽  
Author(s):  
Brandon M. Collins ◽  
Jay D. Miller ◽  
Eric E. Knapp ◽  
David B. Sapsis
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Regime ◽  
Fire Suppression ◽  
Major Change ◽  
Timber Harvesting ◽  
Burned Area ◽  
Annual Number ◽  
Regime Changes ◽  
Major Shift

Most studies of fire-regime changes in western North American forests rely on a reference period that pre-dates Euro-American settlement. Less is known about fire-regime changes relative to the early onset of major change agents, i.e. fire suppression and timber harvesting. We digitised ledgers that contained over 18000 individual fire records from 1911 through 1924 (early suppression period). We performed analyses comparing a subset of these fire records, largely in mixed-conifer forests, to similar records from 2002 through 2015 (contemporary period). Mapped ignition frequencies indicated similar geographic patterns for lightning-caused fires between periods, but notable shifts in certain areas for human-caused fires. There was no statistical difference in annual number of human-caused fires between the early suppression and contemporary time periods. However, there was a major shift in the distribution of burned area across fire size classes. Fires &gt;12145ha accounted for 0–6% of total burned area in the early suppression period, and 53–73% in the contemporary period. Also, both the total number and percentage of fires &gt;2024ha occurred significantly earlier in the year in the contemporary period. These shifts are likely driven by large-scale changes in fuel loads and continuity, and possibly exacerbated by climatic warming.

scholarly journals Dimensions of the 2020 wildfire catastrophe in the Pantanal wetland: the case of the municipality of Poconé, Mato Grosso, Brazil

2021 ◽  
Vol 10 (15) ◽  
pp. e08101522619
Author(s):  
Vinícius de Freitas Silgueiro ◽  
Carolina Ortiz Costa Franco de Souza ◽  
Eriberto Oliveira Muller ◽  
Carolina Joana da Silva
Keyword(s):  
Water Surface ◽  
Infrared Imaging ◽  
Burned Area ◽  
Mato Grosso ◽  
Fire Emissions ◽  
Active Fire ◽  
Preventive Actions ◽  
Rivers And Lakes ◽  
Climate Crisis ◽  
Pantanal Wetland

In 2020, a total of 3.9 million hectares were burned in the Pantanal biome, which represents approximately 30% of its total area. Of the three existing biomes in the state of Mato Grosso, the Pantanal was the most impacted and, among all the municipalities in Mato Grosso, Poconé had the largest burned area. We aimed to characterize the areas affected by fires in the municipality of Poconé in 2020 to support prevention and adaptation actions in future scenarios. For this, we used the mapping of areas affected by fires made from the detections of active fire collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor and available by the Global Fire Emissions Database (GFED). The results showed that a total of 869,170 hectares were burned in Poconé in 2020. Of this total, 97.3% were in natural areas, viz. forest formations (37%), savanna (2.8%), grassland formations (23.4%), wetlands (29.7%), and vegetation in dried-up rivers and lakes (4.4%). Concerning land categories, almost half of the fires occurred in private rural properties registered in the Rural Environmental Registry (CAR). In this scenario, we highlighted the importance of monitoring fires and holding those responsible for them accountable. It is also important to implement preventive actions in synergy with managers and local communities as a way of adapting to the climate crisis, intense drought, and less water surface available in the region, which increases the risk and damage of fires.

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