Biogenic oil film is a natural surface slick that is mainly derived by sea flora and fauna. The often observation of the film near aquaculture facilities has raised awareness on the possibility of the linkage between the development of the film and the anthropogenic activities taking place on site (i.e. artificial feeding and liquid waste). This study aims to investigate the possibility of the detection of biogenic oil film on optical satellite images and discriminate it from other oceanographic phenomena. For the purposes of the study we have used a Sentinel-2 (S2) dataset consisted of 73 images for the year 2019 to detect the film on three aquaculture areas. An automatic procedure was developed on a Python based algorithm which included the following stages: (a) downloading images, (b) preprocessing the input data, (c) identifying dark formations in the adjacent fish farming area, (d) extracting attribute tables with the statistical characteristics of the formations (shape, area, etc.), (e) classification of formations as biogenic film or other (lookalike) and (f) extraction of biogenic film vectors. The developed algorithm was able to detect biogenic oil film successfully however some misleading results regarding the decision of true or false positive (biogenic oil film or lookalike) was evidenced. The efficiency of the algorithm was tested against manual classification with overall accuracy 82,6%. As further step the results of this study should be validated with in-situ measurements and further work is required to verify the results obtained by testing the methods in other sites.
Forest fires are regarded as one of the most threatening sources of disturbance for the property, infrastructure as well as ecosystems. The present study aimed at analyzing spectral information products derived from the Landsat–8 OLI sensor together with spectral indices to evaluate their ability to map burn scars and burn severity. In particular the study objectives were: (1) to identify the capability of OLI to burnt area mapping and burn severity, (2) to evaluate the contribution of several spectral indices to the overall accuracy (3) to assess post-fire effects such as flood risk and, (4) to investigate the vegetation re-growth in relation to the burn severity. As a case study, Chios Island was selected due to the recent fire event in the south-western part of the island (25/07/2016). Three multispectral Landsat-8 OLI images, acquired on 13/07/2016 (pre-fire), 15/09/2016 (post-fire) and 27/03/2017 (six months after the fire), were utilized. Several spectral indices were implemented to detect the burnt areas and assess the burn severity (Burn Area Index – BAI, Normalized Burn Ratio - NBR, Normalized Burn Ration + Thermal - NBRT), as well as to evaluate the vegetation conditions and re-growth six months after the fire event (Normalized Difference Vegetation Index - NDVI and the Normalized Difference Water Index - NDWI). Additionally, NBR index of pre- and post-fire images was calculated in a difference change detection procedure which estimates the Differenced Normalized Burn Ratio dNBR. Overall, a total burned area of 45,9 km2 was delineated, and both burned severity map and vegetation recovery map were created and evaluated.
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