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This paper presents the utilization of Synthetic Aperture Radar (SAR) data for monitoring and detection of oil spills. In this work, a case study of an oil spill has been investigated using C-band Sentinel-1A SAR data to detect the oil spill that occurred on 28 January 2017, near Ennore port, Chennai, India. Oil spill damages marine ecosystems causing serious environmental effects. Quite often, oil spills on the sea/ocean surface are seen nowadays, mainly in major shipping routes. They are caused due to tanker collisions, illegal discharge from the ships, etc. An oil spill can be monitored and detected using various platforms such as vessel-based, airborne-based and satellite-based. Vessel based and airborne methods are expensive with less area coverage. This process also consumes more time. For ocean applications such as oil spill and Ship detection, optical sensors cannot image during bad weather. As SAR is an active sensor, weather independent, and has cloud penetrating capability, the images can be acquired during the day as well as at night. Radar Remote Sensing (RRS) has rapidly gained popularity for monitoring and detection of oil spills and ships for more than a decade. With the availability of the satellite images, detection of oil spill has improved due to its wide coverage and less revisit time. The present paper gives an overview of the methodologies used to detect oil spills on the SAR images using dual-pol Sentinel-1A Level 1 SLC data. This work clearly demonstrates the preprocessing steps of the Sentinel 1A data for oil spill detection. The oil spill was only visible in the VV channel, therefore, for ocean application VV channel image is preferred. SEASAT was the first space-borne SAR mission launched in 1978 by NASA to observe sea surface. The preprocessing was carried out at the European Space Agency (ESA), the Sentinel Application Platform (SNAP) toolbox and Envi 5.1 toolbox. Based on the Sigma naught values, oil spill can be discriminated with the ocean surface. The results obtained with the VV channel are satisfactory and one could map out the oil spill very well. Supervised classifiers SVM and NN were applied on the boxcar filtered 3 × 3 VV channel image to delineate the oil spill. The result of oil spill detection mapping is validated with Supervised SVM and Neural Network classifiers. The results show there is a good agreement between oil spill mapping and classified image using SVM and NN classified images. The Overall Accuracy (OA) obtained using SVM classifier is 98.13% with kappa coefficient as 0.95 and using NN classifier is 98.11% with kappa coefficients 0.95. This technique is considered to be a potential proxy for the detection and monitoring of Oil spills on water bodies. Application of SAR data for oil spill detection is considered to be first of its kind from Indian coasts. This study aims to detect the oil spill occurred due to collision of two LPG tankers with Sentinel-1A SLC data in Chennai coast area.

Landsat 8 OLI/TIRS data of Tupilipalem coast demonstrations the periodic closure of lagoon mouth over a brief period affecting the ecological setting and the faunal diversity and distribution. The coastal ecosystem includes a significant metal pollutant due to anthropogenic activities. This study documents forty-nine numbers of benthic foraminiferal species and establishes its relationship with heavy metal concentrations (Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd). The factor loading matrix generated using factor analysis grades the positive and negative correlations among the heavy metals species. The results of geoaccumulation index, enrichment factor, and pollution load index, construes the seasonal differences in metal concentrations and prevalence of higher concentrations of metals in the sediments. Besides, this paper discusses the impact of coastal morphology on heavy metal concentrations coupled with ecology and distribution of benthic foraminifera.

The Tungabhadra River is one of the sacred rivers in south India. Industrial and mining operations on its banks generate enormous amounts of effluent that have led to substantial metal and nutrient contamination of the aquatic system. In the present investigation an attempt has been made to assess variation in the physico-chemical characteristics and speciation of zinc in the water and sediment of the Tungabhadra River. Most of the metal ions in the collected samples were in high concentrations compared to the applicable standards. The Individual Contamination Factor (ICF) of Zn was found to be more at Site-B compared to sites A and C. Zinc-bearing particles were characterized by the different morphologies measuring from large colloidal to small particles which were associated with biological structures or may be present in amorphous nature which were observed by Transition electron microscopy (TEM) with X-EDS (Energy dispersive spectrometry). In conclusion, the level of Zn contamination in the Tungabhadra River is increasing at an alarming rate due to industrial activities.

Sediment from twelve stations was sampled from the Tupilipalem Coast, southeast coast of India, and the presence of a set of heavy metals was established including iron (Fe), manganese (Mn), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), zinc (Zn) and cadmium (Cd). The heavy metals were assessed by factor analysis, the results of which showed positive and/or negative correlations among Fe, Mn, Cr, Cu, Ni, Pb, Zn, and Cd. Factor analysis also indicated that heavy metals in the sediments of the study area have different natural and anthropogenic sources. Similarly, a sediment pollution assessment was done using the Geoaccumulation Index (Igeo), Enrichment Factor (EF), and Pollution Load Index (PLI). The Geoaccumulation Index indicated that the surface sediment of the Tupilipalem Coast was extremely contaminated with Fe, Mn, Cr, Cu, Ni, Pb, and Zn. The calculation of enrichment factors showed a significant enrichment with respect to Pb, Zn, and Cd and a moderate enrichment with Cr, Cu, and Ni. The falling trend of average contents’ enrichment factors is Cd> Pb> Zn> Cu> Cr> Ni> Mn> Fe. The PLI values of the Cd show higher (>1) values due to the influence of distinct external sources like agricultural runoff, industrial activities, and other anthropogenic inputs. Ninety two percent of heavy metals under study showed the highest concentrations at station TP-5 where the Buckingham Canal and other agricultural and aquacultural effluents connect with the Bay of Bengal. This location is the second inlet which is periodically closed and it seemed that these parts of the study area are heavily affected by anthropogenic pollution.

Bottom sediment samples from six stations were sampled in pre monsoon 2016, from the Govindampalli – Durgarajupatnam (GP-DP) coast. Heavy metals viz., Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd analysis was carried out by using ICP-OES, and the average concentrations are as follows Fe > Mn > Zn > Cr > Pb > Ni > Cu > Cd. Various environmental indices like Factor Analysis (FA), Geo-accumulation Index (Igeo), Enrichment Factor (EF) and Pollution Load Index (PLI) were applied to the chemical data in order to know the levels of contamination and factors contributing to the pollution. Correlation coefficient results exhibits significant positive and negative relationships among Fe, Mn, Pb, Zn, Cd. All the environmental indices suggest that heavy metals were present at higher concentrations and the impacts of anthropogenic activities are crucial that serves as source of heavy metals in the zone. Relatively, maximum number of heavy metals viz., Fe, Ni and Pb were accumulated at the brackish environment i.e., at confluence of Swarnamukhi river (GP-S Station).