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(a) Location of forest fire outbreak, F22-U, and (b) the Sentinel-2 RGB image captured the beginning phase of the fire event (5 March 2022). The ignition location is marked on the map.
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Analysis of the progression of forest fires is critical in understanding fire regimes and managing the risk of active fires. Major fire events in Korea mostly occur in the eastern mountainous areas (Gangwon Province), where the wind and moisture conditions are prone to fire in the late winter season. Despite the significance of the fire events in t...
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Context 1
... ignition of F22-U was at Buk-myeon, Uljin, Republic of Korea (129°18′ E, 37°2′ N) at approximately 11:00 am on 4 March 2022 ( Figure 1a). The effective area for F-22U is in the eastern part of the Taebaek Mountains, which is bounded approximately by 129°12′ E-129°30′ E, 36°7′ N-37°10′ N (Figure 1b). ...
Context 2
... ignition of F22-U was at Buk-myeon, Uljin, Republic of Korea (129°18′ E, 37°2′ N) at approximately 11:00 am on 4 March 2022 ( Figure 1a). The effective area for F-22U is in the eastern part of the Taebaek Mountains, which is bounded approximately by 129°12′ E-129°30′ E, 36°7′ N-37°10′ N (Figure 1b). The average elevation of the area is 463 m and the maximum elevation is 1258 m. ...
Context 3
... removal and normalization: The major source of anomalous pixels in the used images was fire smoke occurring in the beginning phase of events, as shown in Fig- ure 1. Smoke typically increases the severity estimates and makes retrieval of surface properties extremely difficult with the visible bands. ...
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Large forest fires can cause significant damage to forest ecosystems and threaten human life and property. Heilongjiang Province is a major forested area in China with the highest number and concentration of lightning-caused forest fires in the country. This study examined the spatial and temporal distribution patterns of forest fires in Heilongjia...
Citations
... Organic matter reduces soil erodibility because it reduces soil detachment and increases infiltration. The nomograph for soil erodibility [46] consists of soil profile parameters, as shown in Equation (12). ...
The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate.
... The expansion and severity of the burned area were accentuated by the slope of the region, cleared areas, the availability of fuel, and the induction of drought in pastures where practices were adopted to prevent flooding, such as blocking water flow channels. These factors allowed the fire to spread easily and quickly, while some of them made it difficult to control and fight the fire 13,40 . ...
... Although in July 2020 the national government decreed a fire moratorium, which banned the use of fire for four months during the dry season, especially in the Amazon and Pantanal biomes, it did not guarantee that in 2020 the Pantanal biome would face its worst fire in decades 59 . Lack of consolidated environmental legislation covering the entire biome 60 , the low budgets of agencies such as IBAMA and the lack of inspection and monitoring due to the limited number of inspectors reinforce the concept of impunity and responsibility of offenders 13 . ...
The 2020 environmental catastrophe in Pantanal has highlighted the fragility of environmental policies and practices for managing and fighting fires in this biome. Therefore, it is essential to know the causes and circumstances that potentiate these fires. This study aimed to: (I) assess the relationship between fire foci and carbon absorption (GPP), precipitation, and carbon dioxide (CO 2 ) flux; (ii) analyze vegetation recovery using the differenced normalized burn ratio (ΔNBR) in Brazilian Pantanal between 2001 and 2022; and (iii) identify priority areas, where the highest intensities of fire foci have occurred, in order to guide public policies in Brazil to maintain local conservation. To this purpose, fire foci were detected using data from the MODIS MOD14/MYD14 algorithm, annual precipitation with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), and CO 2 flux using the MODIS/MODO9A1 product, and Gross Primary Production (GPP) with the MODIS/MOD17A2 product. The severity of the burned area was also assessed using the ΔNBR index and the risk areas were determined using the averages of these images. During the time series studied, a total of 300,127 fire foci were detected throughout the Pantanal, where 2020 had the highest number of foci and the lowest accumulated precipitation. The years with the highest precipitation were 2014 and 2018. The year 2018 was also the second year with the highest GPP value. The Pettit test showed a trend for 2008 and 2011 as the points of change in the CO 2 flux and GPP variables. Principal component analysis clustered fire foci and precipitation on opposite sides, as well as GPP and CO 2 flux, while ΔNBR clustered HS, MHS and MLS classes with the years 2020, 2019, 2002 and 2021. There was a high negative correlation between fire foci × rainfall and GPP × CO 2 flux. The years with the largest areas of High severity (HS), Moderate-high severity (MHS) and Moderate-low severity (MLS) classes were 2020 and 2019, respectively. The most vulnerable areas for severe fires were the municipalities of Cáceres, Poconé, and Corumbá. The major fire catastrophe in 2020 is correlated with the low precipitation in 2019, the high precipitation in 2018, and the increased GPP, as well government policies unfavorable to the environment.
... However, lately, with climate change and the increase in human activities, the intensity of forest fires is causing more damage than benefits [1,3]. The main causes of forest fires are human, such as smoking, the use of explosives, and sparks in power lines; and natural, such as lightning [4], dry seasons, or temperature conditions [5]. ...
... The spread of forest fires is influenced by various forest characteristics, including the density and height of trees, the amount of dead wood and debris, and the amount of moisture present in the vegetation. To effectively manage forest fires, it is crucial to identify burned areas accurately and monitor their temporal progression [5]. In this context, RS and ML techniques have proven to be effective for monitoring burned areas, providing accurate and cost-effective data over large areas with high temporal resolution [5][6][7][8]. ...
... To effectively manage forest fires, it is crucial to identify burned areas accurately and monitor their temporal progression [5]. In this context, RS and ML techniques have proven to be effective for monitoring burned areas, providing accurate and cost-effective data over large areas with high temporal resolution [5][6][7][8]. ...
Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M’diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M’diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M’diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region.
