Plastic litter floating in the ocean is a significant problem on a global scale. This study examines whether Sentinel-2 satellite images can be used to identify plastic litter on the sea surface for monitoring, collection and disposal. A pilot study was conducted to determine if plastic targets on the sea surface can be detected using remote sensing techniques with Sentinel-2 data. A target made up of plastic water bottles with a surface measuring 3 m × 10 m was created, which was subsequently placed in the sea near the Old Port in Limassol, Cyprus. An unmanned aerial vehicle (UAV) was used to acquire multispectral aerial images of the area of interest during the same time as the Sentinel-2 satellite overpass. Spectral signatures of the water and the plastic litter after it was placed in the water were taken with an SVC HR1024 spectroradiometer. The study found that the plastic litter target was easiest to detect in the NIR wavelengths. Seven established indices for satellite image processing were examined to determine whether they can identify plastic litter in the water. Further, the authors examined two new indices, the Plastics Index (PI) and the Reversed Normalized Difference Vegetation Index (RNDVI) to be used in the processing of the satellite image. The newly developed Plastic Index (PI) was able to identify plastic objects floating on the water surface and was the most effective index in identifying the plastic litter target in the sea.

This paper presents the findings of the impact of atmospheric effects when applied on satellite images intended for supporting archaeological research. The study used eleven multispectral Landsat TM/ETM+ images from 2009 until 2010, acquired over archaeological and agricultural areas. The modified Darkest Pixel (DP) atmospheric correction algorithm was applied, as it is considered one of the most simple and effective atmospheric corrections algorithm. The NDVI equation was applied and its values were evaluated before and after the application of atmospheric correction to satellite images, to estimate its possible effects. The results highlighted that atmospheric correction has a significant impact on the NDVI values. This was especially true in seasons where the vegetation has grown. Although the absolute impact on NDVI, after applying the DP, was small (0.06), it was considered important if multi-temporal time series images need to be evaluated and cross-compared. The NDVI differences, before and after atmospheric correction, were assessed using student's t-test and the statistical differences were found to be significant. It was shown that relative NDVI difference can be as much as 50%, if OPEN ACCESSRemote Sens. 2011, 3 2606 atmosphere effects are ignored. Finally, the results had proven that atmospheric corrections can enhance the interpretation of satellite images (especially in cases where optical thickness of water vapour is minimized ≈ 0). This fact can assist in the detection and identification of archaeological crop marks. Therefore, removal of atmospheric effects, for archaeological purposes, was found to be of great importance in improving the image enhancement and NDVI values.

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Abstract:The main goal of this study is the derivation of Carlson's Trophic State Index (TSI) through the remote sensing of four different Case-2 waters in the Mediterranean region such as Cyprus and Greece. TSI SD is derived through extensive field ground campaign of Secchi Disk Depth measurements for the Asprokremmos Dam, located in Paphos District in Cyprus; Alyki Salt Lake, located in Larnaca District in Cyprus; and in Karla Lake, located in Volos District in Greece; and finally to three coastal water areas in the Limassol coastal area. Several regression models have been applied in order to develop the best regression model between the TSI SD and in-band reflectance values for Landsat TM/ETM derived from spectroradiometric measurements using a GER-1500 field spectroradiometer over the main case study area in Asprokremmos Dam in Cyprus. Finally, we apply several regression models for Asprokremmos Dam for retrieving the suitable Landsat TM/ETM band or band combinations (obtained from field spectroradiometric measurements) in which TSI SD can be determined. Indeed, the best regression model has been obtained by correlating 'TSI Versus Band2/Band3', with R 2 =0.89. All field TSI SD and in-band reflectance values from the four different water bodies have been used to develop the best fitted model for the established T SI SD Versus Band2/Band3 model. We find that the exponential regression model provides the best fitted equation over the four different water bodies. Keywords

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Marine Spatial Planning (MSP), which is in concept similar to land-use planning, is a public process by which the relevant Member State’s authorities analyse and organise human activities in marine areas to achieve ecological, economic and social objectives. MSP aims to promote sustainable growth of maritime economies, sustainable development of marine areas and sustainable use of marine resources. This paper highlights the importance of MSP and provides basic outcomes of the main European marine development. The already successful MSP plans can provide useful feedback and guidelines for other countries that are in the process of implementation of an integrated MSP, such as Cyprus. This paper presents part of the MSP project, of which 80% funded by the European Regional Development Fund (ERDF) and 20% from national contribution. An overview of the project is presented, including data acquisition, methodology and preliminary results for the implementation of MSP in Cyprus

Technologies are becoming more and more important for water-quality management, due to the rapid development of computational problem-solving tools and the enhancement of scientific approaches for information support (Huang & Xia, 2001). The principal benefit of satellite remote sensing for inland water quality monitoring is the production of synoptic views without the need of costly in-situ sampling. Synoptic, multisensor satellite data products and imagery have become increasingly valuable tools for the assessment of water quality in inland and nears-shore coastal waters. Remote sensing of lakes using satellite images has the potential to produce a truly synoptic tool for the monitoring of water quality variables such as chlorophyll a (chl-a), total suspended sediment (TSS), suspended minerals (SM), turbidity, Secchi Disk Depth (SDD), particulate organic carbon and coloured dissolved organic matter (CDOM)