Fire risk assessment in the Brazilian Amazon using MODIS imagery and change vector analysis

Maeda, Eduardo Eiji; Arcoverde, Gustavo Felipe Balué; Pellikka, Petri; Shimabukuro, Yosio Edemir

doi:10.1016/j.apgeog.2010.02.004

Cited by 32 publications

(17 citation statements)

References 37 publications

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“…These spectral measures can change based on the observation time and fire factors, such as the severity of the fire, the time elapsed since the fire was extinguished, type of vegetation and soil exposure [14,30,31]. Thus, different methods were proposed to enhance and detect burnt areas and minimize the spectral confusion, for example, spectral indices [32][33][34], change detection algorithm using difference between pre-and post-fire [35,36]; spectral mixture analysis [37,38]; change vector analysis [39]; neural networks [40][41][42]; and object-based classification [43,44]. However, classification algorithms for burnt areas may lead to very different results [45].…”

Section: Burnt Area Classification and Fire Recurrence Analysismentioning

confidence: 99%

Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

et al. 2014

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Abstract:The Cerrado is the second largest biome in Brazil after the Amazon and is the savanna with the highest biodiversity in the world. Serra Tombador Natural Reserve (STNR) is the largest private reserve located in Goiás State, and the fourth largest in the Cerrado biome. The present study aimed to map the burnt areas and to describe the spatial patterns of fire recurrence and its interactions with the classes of land-cover that occurred in STNR and its surroundings in the period between 2001 and 2010. Several Landsat TM images acquired around the months of July, August and September, coinciding with the region's dry season when fire events intensify, were employed to monitor burnt areas. Fire scars were mapped using the supervised Mahalanobis-distance classifier and further refined using expert visual interpretation. Burnt area patterns were described by spatial landscape metrics. The effects of fire on landscape structure were obtained by comparing results among different land-cover classes, and results summarized in terms of fire history and frequencies. During the years covered by the study, 69% of the areas analyzed had fire events. The year with the largest burnt area was 2004, followed by 2001, 2007 and 2010. Thus, the largest fire events occurred in a 3-year cycle, which is compatible with other OPEN ACCESS Remote Sens. 2014, 6 9874 areas of the Brazilian savanna. The regions with higher annual probabilities of fire recurrence occur in the buffer zone around the park. The year 2004 also had the highest number of burnt area patches (831). In contrast, the burnt area in 2007 showed the most extensive fires with low number of patches (82). The physiognomies that suffered most fires were the native savanna formations. The study also identified areas where fires are frequently recurrent, highlighting priority areas requiring special attention. Thus, the methodology adopted in this study assists in monitoring and recovery of areas affected by fire over time.

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Section: Burnt Area Classification and Fire Recurrence Analysismentioning

confidence: 99%

Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

et al. 2014

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“…MODIS active fire hotspots have been used for validating the fire danger model in various studies as a proxy for the actual occurrence of fires (Chuvieco et al, 2008;Maeda et al, 2011;Adab et al, 2013;Eskandari and Chuvieco, 2015;Babu et al, 2016a, b). Similarly, in the present study, MODIS active fire product MCD14 has been used for the validation.…”

Section: Resultsmentioning

confidence: 99%

Automation of Forest Fire Danger Index from the Near Real Time Satellite Datasets

Babu

Roy²

2019

J ENVIRON INFORM LET

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Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the near real time data sets that can be available for download through Earthdata website after one hour of the satellite overpass and also automated the entire procedure. Static Fire Danger Index (SFDI) is a constant over the study area, computed from the MODIS Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER GDEM datasets. In this study, Dynamic Fire Danger Index (DFDI) has been calculated from the Near Real Time (NRT) Level 2 MODIS Terra Land Surface Temperature datasets (MOD11_L2) and MODIS TERRA NRT surface reflectance dataset MOD09. DFDI has been developed from three parameters i.e., Potential surface temperature, Perpendicular Moisture Index and Modified Normalized Difference Fire Index (MNDFI). Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was ranging from 86% to 95% and Area under Curve (AUC) values ranging from 0.81 to 0.91 during the major fire episode of 2018. Thus, the FFDI has been useful to assess the fire danger accurately over the study area and can be useful anywhere, where the meteorological stations are unavailable. The procedure of calculating the DFDI and FFDI has been automated in R studio environment in near real time and therefore, the fire danger maps can be disseminated to fire officials in near real time for the quick actions to suppress the fire activities.

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“…MODIS active fire hotspots have been used for validating the fire danger model in various studies as a proxy for the actual occurrence of fires [16][17][18][19]. In general, the accuracy of the developed fire danger index depends on the number of fires fell in very high and high fire danger classes and estimated accuracy was ranging from 72% to 91% and the overall accuracy was around 81.27%.…”

Section: = ( − )mentioning

confidence: 99%

Forest Fire Danger Index Based on Static and Dynamic Parameters Using Satellite Datasets

Kv¹,

Roy²,

P³

2018

Preprint

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Fire risk assessment in the Brazilian Amazon using MODIS imagery and change vector analysis

Cited by 32 publications

References 37 publications

Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil

Automation of Forest Fire Danger Index from the Near Real Time Satellite Datasets

Forest Fire Danger Index Based on Static and Dynamic Parameters Using Satellite Datasets

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