Environmental hazard assessment and metal contamination in coastal sediments.

In order to reduce contamination levels from diverse sources, it is important to understand the factors affecting the natural ecosystems that are impacted by coastal and marine pollution. In this study, we used GIS and remote sensing techniques to investigate and evaluate the distribution of heavy metals (Fe, Mn, Zn, Cr, Pb, Co, and Cu) in surface sediments along Tamil Nadu's East Coast (from Besant Nagar to Sathurangapattinam). The CF and Igeo of metals indicate that sediments contain no evidence of Fe, Mn, or Zn metal pollution in the sediments, with only mild contamination from Co, Cu, and Pb. In contrast, the sediment samples were found to be significantly contaminated with Cr. Heavy metal contamination occurs in the following order, according to our research: Cr > Pb > Cu > Co > Mn > Zn > Fe. Except for sites 8, 10, 11, and 13, where PLI>1 implies that there is no pollution in this area, the PLI values show that most of the locations are contaminated. The ecological risk index (ERI) values for five metals in the study areas are as follows: Cr > Pb > Cu > Mn > Zn. The sediment samples fall into the low-risk and highly polluted to dangerous sediment categories for SPI, according to the Risk index (RI). Based on the Mean Effect Range-Median Quotient (M-ERM-Q), Cu, Pb, Zn, and Cr metals in the research region have a 9-21% probability of being harmful. Statistical approaches show that the majority of heavy metals in sediments are of natural origin. The spatial distribution of heavy metals in surface sediments provides the conceptual framework for practical strategies to protect coastal areas. Many shreds of evidence indicate that anthropogenic inputs from the surrounding land area are primarily responsible for the deposition of these heavy metals in the coastal zone.

Towards a Combined Use of Geophysics and Remote Sensing Techniques for the Characterization of a Singular Building: "El Torreón" (the Tower) at Ulaca Oppidum (Solosancho, Ávila, Spain).

This research focuses on the study of the ruins of a large building known as "El Torreón" (the Tower), belonging to the Ulaca oppidum (Solosancho, Province of Ávila, Spain). Different remote sensing and geophysical approaches have been used to fulfil this objective, providing a better understanding of the building's functionality in this town, which belongs to the Late Iron Age (ca. 300-50 BCE). In this sense, the outer limits of the ruins have been identified using photogrammetry and convergent drone flights. An additional drone flight was conducted in the surrounding area to find additional data that could be used for more global interpretations. Magnetometry was used to analyze the underground bedrock structure and ground penetrating radar (GPR) was employed to evaluate the internal layout of the ruins. The combination of these digital methodologies (surface and underground) has provided a new perspective for the improved interpretation of "El Torreón" and its characteristics. Research of this type presents additional guidelines for better understanding of the role of this structure with regards to other buildings in the Ulaca oppidum. The results of these studies will additionally allow archaeologists to better plan future interventions while presenting new data that can be used for the interpretation of this archaeological complex on a larger scale.

Land subsidence phenomena investigated by spatiotemporal analysis of groundwater resources, remote sensing techniques, and random forest method: the case of Western Thessaly, Greece.

The main objective of the present study was to investigate land subsidence phenomena and the spatiotemporal pattern of groundwater resources in an area located in western Thessaly, Greece, by using remote sensing techniques and data mining methods. Specifically, the nonparametric Mann-Kendall test and the Sen's slope estimator were used to estimate the trend concerning the groundwater table, whereas a set of Synthetic Aperture Radar images, processed with the Persistent Scatterer Interferometry technique, were used investigate the spatial and temporal patterns of ground deformation. Random forest (RF) method was utilized to predict the subsidence deformation rate based on three related variables, namely: thickness of loose deposits, the Sen's slope value of groundwater-level trend, and the Compression Index of the formation covering the area of interest. The outcomes of the study suggest a strong correlation among the thickness of the loose deposits and the deformation rate and also show that a clear trend between the deformation rate and the fluctuation of the groundwater table exists. For the RF model and based on the validation dataset, the r square value was calculated to be 0.7503. In the present study, the potential deformation rate assuming different water pumping scenarios was also estimated. It was observed that with a mean decrease in the Sen's slope value of groundwater-level trend of 20%, there would be a mean decrease of 9.01% in the deformation rate, while with a mean increase in the Sen's slope value of groundwater-level trend of 20%, there would be a mean increase of 12.12% in the deformation rate. The ability of identifying surface deformations allows the local authorities and government agencies to take measures before the evolution of severe subsidence phenomena and to prepare for timely protection of the affected areas.

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales.

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process.

Environmental hazards and distribution of radioactive black sand along the Rosetta coastal zone in Egypt using airborne spectrometric and remote sensing data.

High-resolution airborne gamma ray spectrometry, conducted in 2003, was used to estimate radioactive elements spatial abundance along the Rosetta coastal zone area. It was noticed that both Uranium and Thorium are concentrated in the black sand deposits along the beach. In contrary, Potassium was observed in high level abundance at the cultivated Nile Delta lands due to the accumulated usage of fertilizers. Exposure Rate (ER), Absorbed Dose Rate (ADR) and Annual Effective Dose Rate (AEDR) were calculated to evaluate the radiation background influence in human. Results indicated that the human body in the study sites is subjected to radiation hazards exceeds the accepted limit for long duration exposure. In addition, the areas covered by the highest concentration of Uranium and Thorium show the highest level of radiogenic heat production. Detection the environmental hazards of the radioactive black sands in the study site encouraged this research to monitor the spatial and temporal distribution of these sediments. The Landsat Thematic Mapper images acquired in 1990, 2003 and 2013 were analyzed using remote sensing image processing techniques. Image enhancements, classification and changes detection indicated a positive significant relationship between the patterns of coastline changes and distribution of the radioactive black sand in the study sites. The radioactive black sands are usually concentrated in the eroded areas. Therefore, in 1990 high concentration of the radioactive black sands were observed along the eastern and western flanks of the Rosetta promontory. Distribution of these sediments decreased due to the construction of the protective sea walls. Most of the radioactive black sands are transported toward the east in Abu Khashaba bay under the effect of the longshore currents and toward the west in Alexandria and Abu Quir bay under the action of the seasonal reverse currents.

Study on relationship between vegetation and spatial distribution of Oncomlania snails in Poyang Lake region / 中国血吸虫病防治杂志

Objective To explore the relationship between the vegetation and space distribution of Oncomelania snails in Poyang Lake region,so as to get better understanding of the habitats of Oncomelania snails by using remote sensing technology.Methods The marshlands close to schistosomiasis endemic villages in Poyang Lake region were selected,and the traditional methods were used to investigate the situation of vegetation and Oncomelania snails.The longitude and latitude of investigation spots were recorded by GPS,and the field survey data were vectored by using ArcGIS software.The geometric calibration and unsupervised classification were made on TM satellite images of low-flow period of spring by using PCI 9.0.And then,the relationship between space distribution of snail habitats and vegetation was analyzed through superposing the filed data and satellite images.Results The vegetation in marshlands of Poyang Lake region can be differentiated as three types,in which the single dominant vegetation area was mainly breeding place of Oncomelania snails,and wet vegetation type and hybrid weed type were snail seldom breeding areas.Conclusions The distribution roles of vegetation in marshland are closely related to the habitats of snails,presenting a flaky and bandy distribution.The habitats of Oncomelania snails can be quickly understood by remote sensing technology.