Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Gitas, Ioannis Z.; Mitri, George; Ventura, Gemma

doi:10.1016/j.rse.2004.06.006

Cited by 110 publications

(42 citation statements)

References 15 publications

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“…Such a rule can have one single object feature or can consist of a combination of several features that have to be fulfilled for an object to be assigned to a class. In this case it was found that applying thresholds to each of the selected features was adequate; fuzzy logic was not applied as was seen in previous works [12,35,36].…”

Section: Development Of the Object-based Classification Proceduresmentioning

confidence: 94%

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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: Development Of the Object-based Classification Proceduresmentioning

confidence: 94%

“…Moreover, Polychronaki and Gitas [35], Gitas et al [36] and Mitri and Gitas [12] concluded that the combination of object features, such as spectral values together with contextual information, made it possible to avoid confusion in the classification between burned areas and other land cover types.…”

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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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“…A semi-automated GEOBIA procedure with Landsat TM data was proposed by Mitri and Gitas [37] for mapping burned areas in the Mediterranean region. GEOBIA of optical satellite data, ranging from low to very high spatial resolution, has been successfully used for burned area mapping, resulting in high classification accuracies [43][44][45]. The findings of these studies also demonstrated that confusion between burned areas and other land cover classes were significantly minimized.…”

Section: Introductionmentioning

confidence: 90%

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

2014

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Abstract:In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall) were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that -spatial misplacement‖ was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year) fire history in the Mediterranean, based on extended time series of Landsat or similar data.

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“…For a given triggering event over a given area, previous studies have found that the probability density of the occurred landslide areas fit a truncated inverse Gamma distribution (Malamud et al, 2004;Guzzetti, 2005). The distribution can be used to predict the size for the most abundant landslides in a watershed and more generally the probability of having landslides of a given size.…”

Section: Multi-scale Segmentation For Landslidesmentioning

confidence: 99%

“…Recently, Martha et al (2011) and Stumpf and Kerle (2011a) have also tried to determine scale thresholds for multi-scale analysis of landslides through different statistical approaches. In this study, we also adopted a multi-scale approach for landslide mapping, because landslide size statistics show that landslides occur in different sizes (Malamud et al, 2004;Guzzetti, 2005). We used a landslide size distribution to parameterise the scale parameters and performed a case study, in which a multi-scale OBIA methodology was developed to detect and classify landslides and to produce a complete landslide inventory map, i.e.…”

Section: Introductionmentioning

confidence: 99%

Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan

Lahousse

Chang

Lin³

2011

Nat. Hazards Earth Syst. Sci.

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Abstract. We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5 % with the training dataset and 86 % with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74 %. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m 2 . The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.

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Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Cited by 110 publications

References 15 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

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan

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