19 June 2015 Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA), and Composite Burn Index (CBI) measurements
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Proceedings Volume 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015); 953515 (2015) https://doi.org/10.1117/12.2193149
Event: Third International Conference on Remote Sensing and Geoinformation of the Environment, 2015, Paphos, Cyprus
Abstract
Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), Objectbased image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees’ ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.
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E. Dragozi, E. Dragozi, Ioannis Z. Gitas, Ioannis Z. Gitas, Dimitris G. Stavrakoudis, Dimitris G. Stavrakoudis, C. Minakou, C. Minakou, } "Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA), and Composite Burn Index (CBI) measurements", Proc. SPIE 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 953515 (19 June 2015); doi: 10.1117/12.2193149; https://doi.org/10.1117/12.2193149
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