The Greek National Observatory of Forest Fires (NOFFi)

Tompoulidou, Maria; Stefanidou, Alexandra; Grigoriadis, Dionysios; Dragozi, Eleni; Stavrakoudis, Dimitris G.; Gitas, Ioannis Z.

doi:10.1117/12.2240560

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…NOFFi-OBAM's algorithm is designed to map fire perimeters and follows a supervised learning approach using a postfire Sentinel-2 (Level-1C) image, although a prefire image is also used for photo-interpretation purposes. The methodology applied to retrieve the fire perimeters is fully described in Tompoulidou et al (2016).…”

Section: Noffi Greece (2016-2018)mentioning

confidence: 99%

Development of a standard database of reference sites for validating global burned area products

Franquesa¹,

Vanderhoof²,

Libonati³

et al. 2020

Preprint

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Abstract. Over the past two decades, several global burned area products have been produced and released to the public. However, the accuracy assessment of such products largely depends on the availability of reliable reference data that currently does not exist on a global scale or whose production requires high level dedication of project resources. The important lack of reference data for the validation of burned area products is addressed in this paper. We provide the first Burned Area Reference Database (BARD) that was created by compiling existing reference burned area datasets from different international projects. The Database contains a total of 2769 reference burned area files derived from Landsat or Sentinel-2 imagery. All reference files have been checked for internal quality and are freely provided by the authors. To ensure database consistency, all files were transformed to a common format and were properly documented by following metadata standards. This should help future users of this database to read and convert the files to their own preferred formats or projections. The database is freely available at: https://doi.org/10.21950/BBQQU7 (Franquesa et al., 2020).

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Section: Noffi Greece (2016-2018)mentioning

confidence: 99%

Development of a standard database of reference sites for validating global burned area products

Franquesa¹,

Vanderhoof²,

Libonati³

et al. 2020

Preprint

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“…The midterm prediction of fire danger also necessitates spatial information regarding the type and distribution of fuels, which constitutes one of the most important parameters that directly affect fire ignition and propagation danger. A national fuel type map (with 30 m spatial resolution) was developed in the framework of the NOFFi project (http://epadap.web.auth.gr/?lang=en), using the Landsat 8 OLI satellite imagery and ancillary spatial layers, such as the official national vegetation layer provided by the Hellenic Ministry of Environment and Energy [36]. The mapping process was performed through the development of an object-based Image Analysis (OBIA ) model [55] based on fuzzy logic rules, which was applied in all regional units to derive a country-wide map.…”

Section: Datasetsmentioning

confidence: 99%

“…The mapping process was performed through the development of an object-based Image Analysis (OBIA ) model [55] based on fuzzy logic rules, which was applied in all regional units to derive a country-wide map. The validity of the map was assessed using control points from the land use and land cover area frame survey (LUCAS) provided by Eurostat and the overall accuracy reached 92.59% [36].…”

Section: Datasetsmentioning

confidence: 99%

“…Satellite remote sensing technologies have the advantage of capturing the spatial-spectral variability on a regular basis and over extended and/or remote areas [32][33][34]. In the last decade, satellite remote sensing has been investigated for its applicability in reliable forecasting of fire danger conditions, either by employing the remotely-sensed data alone or combining them with meteorological variables [33,35,36].…”

Section: Introductionmentioning

confidence: 99%

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Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

et al. 2019

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Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index’s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (≥3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation.

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“…Figure 10 depicts the potential of this approach showing how this method is feasible to process satellite imagery using minimal resources automatically focusing on the fire extents. Also, some other techniques for fire mapping could be added to increase the accuracy of the fire mapping like the semiautomatic method proposed by National Observatory of Forest Fires (NOFFI) [18]. For instance, normalized vegetation index (NDVI) could be computed to carry out an auto segmentation at low resolution, using a single image or the difference of the NDVI.…”

Section: Exploiting the Fire Detection For Burnt Area Mappingmentioning

confidence: 99%

Non-supervised method for early forest fire detection and rapid mapping

Artés¹,

Roberto²,

Giorgio³

et al. 2017

Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017)

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Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

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The Greek National Observatory of Forest Fires (NOFFi)

Cited by 8 publications

References 15 publications

Development of a standard database of reference sites for validating global burned area products

Development of a standard database of reference sites for validating global burned area products

Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

Non-supervised method for early forest fire detection and rapid mapping

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