Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

Syifa, Mutiara; Panahi, Mahdi; Lee, Chang-Wook

doi:10.3390/rs12040623

Cited by 42 publications

(21 citation statements)

References 54 publications

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“…According to such authors, Landsat-8 owns better spectral bands than Sentinel-2. Other works integrate both Landsat-8 and Sentinel-2 products to improve BA estimates [85][86][87]. In this study, however, the comparison with further products, such as the series of "MODIS Combined Data" (MCD45, MCD64) and the "Fire Climate Change Initiative" (FIRE CCI), has not been made in the face of their unfeasible application in the Amazon region [4], due to large differences in spatial resolution [60].…”

Section: Discussionmentioning

confidence: 98%

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Castillo

Cayo

Almeida

et al. 2020

IJGI

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During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

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Section: Discussionmentioning

confidence: 98%

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Castillo

Cayo

Almeida

et al. 2020

IJGI

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“…Thinning forests may decrease fire intensity but also increase rates of spread during critical phases of initial attack under certain conditions [14][15][16][17][18] . Under extreme wind events such as those experienced in Paradise, California during the Camp Fire in 2018 19 or the south eastern Australian fires in 2019 20 , fire behaviour in fuel treatments can be affected by fire-atmosphere interactions, affecting fire suppression outcomes.…”

Section: Effects Of Canopy Midstory Management and Fuel Moisture On Wmentioning

confidence: 99%

Effects of canopy midstory management and fuel moisture on wildfire behavior

Banerjee

Heilman

Goodrick

et al. 2020

Sci Rep

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Increasing trends in wildfire severity can partly be attributed to fire exclusion in the past century which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk, while restoring fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a simple fire line and propagating through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a dense midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

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“…In this case, and to cope with such uncertainity, hybrid methods have been suggested as effective, to integrate the advantages of quantitative and qualitative analysis. These techniques have recently become the latest research subject, with considerable attention from researchers, aiming to optimize, create, and introduce efficient methods based on GIS‐MCDA (Abedi Gheshlaghi, Feizizadeh, & Blaschke, 2020; Bui et al, 2017; Busico, Giuditta, Kazakis, & Colombani, 2019; Jaafari et al, 2019; Syifa, Panahi, & Lee, 2020). Therefore, on the basis of research background and literature review, it is understood that many techniques have been used for mapping forest fires.…”

Section: Introductionmentioning

confidence: 99%

“…In various scientific fields, the successful designing of hybrid techniques for forest fires (e.g., Abedi Gheshlaghi & Feizizadeh, 2017; Chen, Xie, et al, 2018; Li, Siwabessy, Huang, & Nichol, 2019; Liu & Sasaki, 2019; Sakizadeh, 2020; Takahashi & Yao, 2020; Talal, Attiya, Metwalli, El‐Samie, & Dessouky, 2019) is the next logical step in spatially evident forest fire modeling. This helps hybrid techniques to evaluate the probability of forest fires with minimum error, both during training and particularly during the validation phase (Abedi Gheshlaghi et al, 2020; Bui et al, 2017; Busico et al, 2019; Jaafari et al, 2019; Syifa et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Forest fire susceptibility modeling using hybrid approaches

Gheshlaghi

Feizizadeh

Blaschke

et al. 2020

Transactions in GIS

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Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.

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Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

Cited by 42 publications

References 54 publications

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

Effects of canopy midstory management and fuel moisture on wildfire behavior

Forest fire susceptibility modeling using hybrid approaches

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