Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Abstract

Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data for monitoring large oil slicks with the Fluorescence/Emissivity Index (FEI) and Object Based Image Analysis (OBIA). Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors

Oil Spill Detection and Mapping Using Sentinel 2 Imagery

“…Maianti et al [18] applied OBIA on Moderate Resolution Imaging Spectroradiometer (MODIS) bands and MODIS-based indexes for monitoring oil spill dynamics. Kolokoussis et al [19] developed an object-based method for oil spill detection using very high multispectral images such as Ikonos, QuickBird, RapidEye, and WorldView2, as well as high-resolution satellite images such as Landsat TM.…”

Using an OBCD Approach and Landsat TM Data to Detect Harvesting on Nonindustrial Private Property in Upper Michigan

“…The scale parameter was representative of the maximum heterogeneity allowed in the segments and, therefore, controlled the size of the objects. The shape and compactness parameters controlled the outline and the dependency of objects to spectral and geometrical features [43]. In the first step, fixed parameters (i.e., scale: 15; shape: 0.1; compactness: 0.5) performed well in detecting atmospheric clouds in our dataset, these values are highly region-specific and may not work in other areas.…”

Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

“…Object-based image analysis (OBIA) aims at grouping adjacent image pixels into self-existent objects (or segments) with spectral and geometric similarities, so that textural and contextual/relational characteristics among objects can be exploited as well in thematic classification [18], [19]. Better results of OBIA approaches in LC/LU mapping compared to PB analyses have been widely demonstrated, especially with VHR optical data [16], [20].…”