Abstract:The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through OPEN ACCESS Remote Sens. 2014, 6 12006 higher-order features is added to the classification process of VHR imagery for burned area mapping.

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral's spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

Abstract:Monitoring post-fire vegetation response using remotely-sensed images is a top priority for post-fire management. This study investigated the potential of very-high-resolution (VHR) GeoEye images on detecting the field-measured burn severity of a forest fire that occurred in Evros (Greece) during summer 2011. To do so, we analysed the role of topographic conditions and burn severity, as measured in the field immediately after the fire (2011) and one year after (2012) using the Composite Burn Index (CBI) for explaining the post-fire vegetation response, which is measured using VHR satellite imagery. To determine this relationship, we applied redundancy analysis (RDA), which allowed us to identify which satellite variables among VHR spectral bands and Normalized Difference Vegetation Index (NDVI) can better express the post-fire vegetation response. Results demonstrated that in the first year after the fire event, variations in the post-fire vegetation dynamics can be properly detected using the GeoEye VHR data. Furthermore, results showed that remotely-sensed NDVI-based variables are able to encapsulate burn severity variability over time. Our analysis showed that, in this specific case, burn severity variations are mildly affected by the topography, while the NDVI index, as inferred from VHR data, can be successfully used to monitor the short-term post-fire dynamics of the vegetation recovery.

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The 2021 fire season in Greece was the worst of the past 13 years, resulting in more than 130,000 ha of burnt area, with about 70% consumed by five wildfires that ignited and spread in early August. Common to these wildfires was the occurrence of violent pyroconvection. This work presents a meteorological analysis of this outbreak of extreme pyroconvective wildfires. Our analysis shows that dry and warm antecedent weather preconditioned fuels in the fire-affected areas, creating a fire environment that alone could effectively support intense wildfire activity. Analysis of surface conditions revealed that the ignition and the most active spread of all wildfires coincided with the most adverse fire weather since the beginning of the fire season. Further, the atmospheric environment was conducive to violent pyroconvection, as atmospheric instability gradually increased amid the breakdown of an upper-air ridge ahead of an approaching long-wave trough. In summary, we highlight that the severity and extent of the 2021 Greek wildfires were not surprising considering the fire weather potential for the period when they ignited. Continuous monitoring of the large- and local-scale conditions that promote extreme fire behavior is imperative for improving Greece’s capacity for managing extreme wildfires.