Automatic fire perimeter determination using MODIS hotspots information

Chiaraviglio, N; Artés, Tomàs; Bocca, R; López, Jon; Gentile, Alessandro; Ayanz, Jesús San-Miguel; Cortés, Ana; Margalef, Tomás

doi:10.1109/escience.2016.7870928

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…Based on a certain threshold on the temperature of a pixel, hotspots are identified [87]. Chiaraviglio, et al [88] proposed an alpha algorithm to estimate the fire perimeter in situations when cloud or smoke blocks the fire front position. The algorithm implemented in the European Forest Fire Information System (EFFIS) to estimate perimeters of fire from MODIS hotspots on fire data occurred in 2014 in Sweden.…”

Section: Satellite-based Monitoring Of Forest Firementioning

confidence: 99%

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

Arif¹,

K²,

A³

et al. 2021

J Robotics Autom

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Forest fire disasters are recently getting lots of attention due to climate change globally. Globally, climate changes are rapidly changing the fire patterns on Earth. Effective fire management requires accurate information about the fire occurrence, its spread, and impact on the environment. Prediction of fire activities in the forest guides the authorities to make optimal, efficient, and sound decisions in fire management. This paper aims to summarize recent trends in the forest fire events prediction, detection, spread rate, and mapping of the burned areas. Furthermore, fire emissions in terms of smoke also put the Earth's public health and ecological system at greater risk. Hence, future policymaking can be more accurate in saving billions of dollars, improving the healthy environment and ecological cycle for the inhabitants of this Earth. This paper provides a comprehensive review of the usage of different machine learning algorithms in forest fire or wildfire management. Furthermore, we have identified some potential areas where new technologies and data can help better fire management decision making.

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Section: Satellite-based Monitoring Of Forest Firementioning

confidence: 99%

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

Arif¹,

K²,

A³

et al. 2021

J Robotics Autom

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“…Following, a potential burnt area is computed applying the alpha shape algorithm [15] obtaining the convex hull of each set of points. The alpha shape algorithm is applied in a loop and stops when a given set of points creates two different polygons for the convex hull.…”

Section: Algorithmmentioning

confidence: 99%

Non-supervised method for early forest fire detection and rapid mapping

Artés¹,

Roberto²,

Giorgio³

et al. 2017

Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)

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Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

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“…Instead of using the number of active fire pixels, other studies, mainly at local scales, have proposed to directly utilize the aggregation of active fire detections to delineate approximate fire perimeters. Some studies have even tested such interpolations of conglomerates of active fires to visualize the approximate advance of the perimeter of large fires [13,14,[30][31][32][33][34][35] and to calibrate fire propagation models [36][37][38][39][40][41][42][43][44]. Several techniques are being investigated for this aggregation approach, including the direct aggregation or buffering of active fires (e.g., [30,31]), the inverse distance weighted or the weighted mean and distance methods (e.g., [13]), kriging analysis (e.g., [14]), or the use of convex hull algorithms applied to active fire clusters (e.g., [15,43]).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, the majority of the literature relating active fire detections to burned areas has been conducted with coarse-resolution sensors, mainly with 1 km resolution, such as Advanced very-highresolution radiometer (AVHRR) [19,[25][26][27] or MODIS [3,14,30,32,33]. While those studies showed that first-order burned area estimates can be obtained from 1 km satellite active fire data, it is widely recognized that higher spatial and temporal resolutions are needed to explore the possibility of direct fire perimeter mapping from active fires [31].…”

Section: Introductionmentioning

confidence: 99%

“…The approach tested in this study was the direct aggregation of spatially contiguous pixels [30,31,47]. Other active fire cluster aggregation techniques, such as the alpha shape algorithm, a generalization of the convex hull envelope developed by Chiaraviglio et al [32], have been recently tested, with promising results for individual fire perimeter rapid delineation. The assessment of this algorithm for total burned area prediction based on MODIS and VIIRS active fires [15], and the algorithm parameter optimization, are still ongoing tasks (Artés, personal communication, 2019), for which a long-term evaluation against burned area records is still required.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Automatic fire perimeter determination using MODIS hotspots information

Cited by 9 publications

References 17 publications

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

Non-supervised method for early forest fire detection and rapid mapping

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

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