Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece

Gitas, Ioannis Z.; Polychronaki, Anastasia; Katagis, Thomas; Mallinis, Giorgos

doi:10.1080/01431160701874553

Cited by 49 publications

(27 citation statements)

References 10 publications

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“…During the summer of 2007, Greece faced the worst natural disaster recorded in recent decades in terms of human losses, the number of fire outbreaks, and the extent of the estimated burned area (more than 12% of the total forested areas in Greece) [41]. The first serious fire occurred at Mount Parnitha, where significant forested parts of Parnitha National Park were damaged.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis

Polychronaki

Gitas

2012

Remote Sensing

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Abstract:The devastating series of fire events that occurred during the summers of 2007 and 2009 in Greece made evident the need for an operational mechanism to map burned areas in an accurate and timely fashion to be developed. In this work, Système pour l'Observation de la Terre (SPOT)-4 HRVIR images are introduced in an object-based classification environment in order to develop a classification procedure for burned area mapping. The development of the procedure was based on two images and then tested for its transferability to other burned areas. Results from the SPOT-4 HRVIR burned area mapping showed very high classification accuracies (~0.86 kappa coefficient), while the object-based classification procedure that was developed proved to be transferable when applied to other study areas.

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Section: Study Area and Datasetsmentioning

confidence: 99%

Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis

Polychronaki

Gitas

2012

Remote Sensing

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“…During the summer of 2007, Greece faced the worst natural disaster recorded in recent decades in terms of human losses, the number of fire outbreaks, and the extent of the estimated burned area [32], mainly affecting large areas of Western and Southern Peloponnese. In this work, we investigate the fire-affected area located near the town of Megalopoli in the Peloponnese (Figure 1b).…”

Section: Study Area and Dataset Descriptionmentioning

confidence: 99%

Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification

et al. 2013

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Abstract:In this work, the potential of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) imagery to map burned areas was evaluated in two study areas in Greece. For this purpose, we developed an object-based classification scheme to map the fire-disturbed areas using the PALSAR imagery acquired before and shortly after fire events. The advantage of employing an object-based approach was not only the use of the temporal variation of the backscatter coefficient, but also the incorporation in the classification of topological features, such as neighbor objects, and class related features, such as objects classified as burned. The classification scheme resulted in mapping the burned areas with satisfactory results: 0.71 and 0.82 probabilities of detection for the two study areas. Our investigation revealed that the pre-fire vegetation conditions and fire severity should be taken in consideration when mapping burned areas using PALSAR in Mediterranean regions. Overall, findings suggest that the developed scheme could be applied for rapid burned area assessment, especially to areas where cloud cover and fire smoke inhibit accurate mapping of burned areas when optical data are used.

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“…Accurate information of natural and human influences about fire regimes are critical to prevent future events and restore areas already affected. Remote sensing is one of the main techniques for assessing the damage effects from fire events because of its synoptic nature, cost-efficiency, rapid wildfire damage assessments, and acquisition of long-term information about ecosystem dynamics [1][2][3]. Several remote sensing techniques have been proposed to assess burned areas [4,5].…”

Section: Introductionmentioning

confidence: 99%

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

et al. 2015

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Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference OPEN ACCESS Remote Sens. 2015, 7 6951 method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series.

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Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece

Cited by 49 publications

References 10 publications

Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis

Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis

Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

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