Correcting the Fire Scar Perimeter of a 1983 Wildfire Using USGS-Archived Landsat Satellite Data

Nioti, Foula; Dimopoulos, Panayotis; Koutsias, Nikos

doi:10.2747/1548-1603.48.4.600

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…This information can be summarized for the development of thematic layers containing information on recently burned areas, including patchiness of burn severity (Nioti, Dimopoulos, & Koutsias, 2011), on areas that have suffered from intense and frequent fires (Sherriff & Veblen, 2007), or on unburned areas with high fuel accumulation (Morgan, Hardy, Swetnam, Rollins, & Long, 2001). Long-term fire history also provides the necessary information to reconstruct the fire regime of an area/ecosystem, allowing the comparison of recent fire characteristics with the expected natural fire regime; therefore, management measures can be obtained accordingly (Hardy, 2005;Morgan et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

et al. 2012

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Maps depicting the spatially explicit fire history of an area, including variables such as fire frequency and fire return interval, are important tools promoting the understanding of processes associated with wildfires (fire ignition and spread), the assessment of the impacts of wildland fires on landscape dynamics, and decisions on appropriate management practices. Remote sensing is a cost-and time-effective alternative to automatically assess a vast amount of spatial information and produce various thematic maps. The aim of this study was to reconstruct the recent fire history of Attica region (Greece), in a spatially explicit mode by means of remote sensing techniques using a series of Landsat images acquired from 1984 to 2011. The results show that the fire scar perimeters were captured with high accuracy. Regression modelling shows that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained (86.3% of the variance) by the number of satellite images used (standardized coefficient 0.752) followed by the date of the first image (standardized coefficient 0.705). The use of satellite data as the basic source of information alongside automated classification methods should be promoted for the creation of fire history maps. The latter is further supported when considering the long history of data capture from Landsat satellites, which provide a huge, global historical archive of repeat images of the Earth's surface.

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Section: Introductionmentioning

confidence: 99%

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

et al. 2012

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“…Images were obtained atmospherically corrected, from the United States Geological Survey (USGS) archive (http://glovis.usgs.gov/). All images were also geometrically corrected and resampled with elevation correction (Nioti et al 2011;Singh et al 2014).…”

Section: Datasetsmentioning

confidence: 99%

Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining

Srivastava

Petropoulos

Gupta

et al. 2018

Model. Earth Syst. Environ.

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Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre-and post-fire conditions. The GIS-based modeling was based on a Multi Criterion Evaluation (MCE) technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.

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“…Instead, only a general sketch of the burn perimeter was available and this lacked detailed spatial information, especially for unburned patches within the fire scar perimeter. The spatially explicit mapping of the fire scar including the unburned patches was addressed in the study of [30] using 1984 LANDSAT satellite images. In that study, we corrected the fire scar perimeter using USGS archived LANDSAT multi-temporal satellite data.…”

Section: Burned Land Mappingmentioning

confidence: 99%

A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece

et al. 2015

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Management strategies and silvicultural treatments of fire-prone ecosystems often rely on knowledge of the regeneration potential and long-term recovery ability of vegetation types. Remote sensing and GIS applications are valuable tools providing cost-efficient information on vegetation recovery patterns and their associated environmental factors. In this study we used an ordinal classification scheme to describe the land cover changes induced by a wildfire that occurred in 1983 in Pinus brutia woodlands on Karpathos Aegean Island, south-eastern Greece. As a proxy variable that indicates ecosystem recovery, we also estimated the difference between the NDVI and NBR indices a few months (1984) and almost 30 years after the fire (2012). Environmental explanatory variables were selected using a digital elevation model and various thematic maps. To identify the most influential environmental factors contributing to woodland recovery, binary logistic regression and linear regression techniques were applied. The analyses showed that although a large proportion of the P. brutia woodland has recovered 26 years after the fire event, a considerable amount of woodland had turned into scrub vegetation. Altitude, slope inclination, solar radiation, and pre-fire woodland physiognomy were identified as dominant factors influencing the vegetation's recovery probability. Additionally, altitude and inclination are the variables that explain changes in the satellite remote sensing vegetation indices reflecting the recovery potential. Pinus brutia showed a good post-fire recovery potential, especially in parts of the study area with increased moisture availability.

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Correcting the Fire Scar Perimeter of a 1983 Wildfire Using USGS-Archived Landsat Satellite Data

Cited by 8 publications

References 38 publications

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining

A Remote Sensing and GIS Approach to Study the Long-Term Vegetation Recovery of a Fire-Affected Pine Forest in Southern Greece

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