Abstract:Worldwide, forest fires exert effects on natural ecosystems, contributing to economic/human losses, health effects, and climate change. Spectral indices are an essential tool for monitoring and analyzing forest fires. These indices make it possible to evaluate the affected areas and help mitigate possible future events and reduce damage. The case study addressed in this work corresponds to the Cerro of the Guadual community of La Carolina parish (Ibarra, Ecuador). This work aims to evaluate the degree of sever… Show more
“…For the dNBR, there are five levels of burn severity: high, medium–high, medium–low, low, and unburned (Key and Benson 2006 ). The dNDVI is divided into six different classes: unburned, very low severity, low severity, moderate severity, high severity, and very high severity (Morante-Carballo et al 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…(Morante-Carballo et al 2022 ) divided the dNDVI index into six classes to determine burn severity (Table 3 ). In this study, the dNDVI index was calculated with the NDVI differences calculated pre- and post-fire and reclassified into six classes.…”
This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m
2
in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m
2
after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m
2
. The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.
“…For the dNBR, there are five levels of burn severity: high, medium–high, medium–low, low, and unburned (Key and Benson 2006 ). The dNDVI is divided into six different classes: unburned, very low severity, low severity, moderate severity, high severity, and very high severity (Morante-Carballo et al 2022 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…(Morante-Carballo et al 2022 ) divided the dNDVI index into six classes to determine burn severity (Table 3 ). In this study, the dNDVI index was calculated with the NDVI differences calculated pre- and post-fire and reclassified into six classes.…”
This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m
2
in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m
2
after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m
2
. The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.
“…Logo, o estudo em questão trabalhou com dois períodos. O período chuvoso (PC) tem o seu início em outubro e se estende até março, com duração de 6 meses e o período seco (PS) com início em abril e fim em setembro, com período de duração de seis meses Aires, L. S. S., Angelini, L. P. (Nimer, 1989;Nascimento et al, 2020).…”
Section: Avaliação Dos Focos De Incêndiounclassified
The damage associated with fire events causes great loss every year to areas such as public health, economy and the environment. The present work aimed to quantify the hotspots within the municipality of Rio Verde - GO between January 2005 and December 2020 and to determine the areas with the highest incidences of fires. The land uses and occupations observed within the municipality were arable area in preparation (AEP), agricultural area (AAG), vegetation area (AVE) and area close to water bodies (APA). For all years of study, there was a greater area of coverage for AAG, followed by AEP, AVE and APA, respectively. Regarding the temporal behavior, the number of hotspots is increasing in the first years (2005 - 2011), then there is a decrease (2012 - 2016) and with a new increase in recent years (2017 - 2020). Regarding the seasons of the year, the dry period made up at least 75% of the total cases for each year. As for classes of land use and occupation, those that presented the highest numbers were AEP and AAG, respectively. From the present work, it was possible to conclude that the geotechnological tools proved to be highly efficient for spatio-temporal evaluation of fire events.
“…Forest fires, as a severe disaster, cause significant harm to the ecosystem and human society [6]. In order to promptly detect and respond to forest fires, scientists and rescue personnel are continuously exploring and developing various fire detection methods [7,8]. Common methods for forest fire detection include manual patrols, aerial remote sensing, and fire detectors.…”
To tackle the problem of missed detections in long-range detection scenarios caused by the small size of forest fire targets, initiatives have been undertaken to enhance the feature extraction and detection precision of models designed for forest fire imagery. In this study, two algorithms, DenseM-YOLOv5 and SimAM-YOLOv5, were proposed by modifying the backbone network of You Only Look Once version 5 (YOLOv5). From the perspective of lightweight models, compared to YOLOv5, SimAM-YOLOv5 reduced the parameter size by 28.57%. Additionally, although SimAM-YOLOv5 showed a slight decrease in recall rate, it achieved improvements in precision and average precision (AP) to varying degrees. The DenseM-YOLOv5 algorithm achieved a 2.24% increase in precision, as well as improvements of 1.2% in recall rate and 1.52% in AP compared to the YOLOv5 algorithm. Despite having a higher parameter size, the DenseM-YOLOv5 algorithm outperformed the SimAM-YOLOv5 algorithm in terms of precision and AP for forest fire detection.
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