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Case study for estimation of the amount of contaminants stored in soil in an industrial area
1-11Views:372One of the main sources of contaminants in the soil is industrial activity which has become one of the major environmental problems of the last few decades. The development of geoinformatics as well as the introduction of standards and regulations has led to a decreased risk of soil contamination and the cost-effective optimization of remediation activities. Based on the above, the aim of our study is to demonstrate the geoinformation processing of the remediation performed in an industrial area located in the Great Hungarian Plain, with special regard to the estimation of the amount and spread of the contaminants accumulated in the soil. In order to reveal the lithological and hydrogeological properties of the investigated area and the environmental status of the underground areas, we performed a large number of shallow land drillings (115). During the field sampling, 1000–1500 grams of samples were collected from the drill bit and were processed in an accredited laboratory. Based on the concentration and volume models created it can be concluded that with the estimations performed via modeling, we were able to locate the most critical areas from the standpoint of contamination. It was revealed that the focal point of the contaminants accumulated in the soil was in the central part of the investigated area. Furthermore, the model demonstrated the effect of lithological factors, since contaminants tend to accumulate more heavily in cohesive soils compared to porous rocks. The extent of contaminant concentration in the aquifer increased with decreasing depth; however, after reaching the floor clay the extent of contaminant concentration began to decrease. The lithological layer closest to the surface contained the most contaminants.
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Coastal landuse land cover change and transformations in-between Cuddalore and Nagore, south east coast of India using remote sensing and GIS
11-24Views:146This study was conducted to assess the Land use and land cover (LULC) changes in a dynamic coastal zone; this is also an essential factor of studying the relationships between the human activity and coastal environment. The study region has been suffered from various natural hazards such as cyclone impacts, coastal erosion and rarely tsunamis. LULC changes was studied and reported for the period of 4 decades from 1980 to 2020. The overall accuracy assessment and Kappa coefficient values shows the substantial results of LULC maps. In the study area LULC changes has been classified in the six classes. The result shows reduction in plantations, coastal wetland and fallow land. Whereas improvement found in barren land, built-up land and water body of the study area from 1980 to 2020. Immediate attention is required to the increase the mangrove forest to be as a natural protection from the calamities in coastal wetlands. The information resulting from this study can be used in forthcoming management plans for urbanization and towards the sustainable development of the region. This study can be adapted to the world’s any coastal region to establish a strategic plan of action to protect the coastal communities and the environment.
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Sentinel-2 satellite-based analysis of bark beetle damage in Sopron Mountains, Hungary
33-40Views:140Sopron mountains were affected by bark beetle (Ips typographus) damage between 2017 and 2020, which was surveyed on high-resolution ESA Sentinel-2 satellite images for the period 2017 and 2020 using Mosaic Hub, Anaconda, and Jupyter Notebook web-based computing environments. Biotic forest damage was detected based on vegetation (NDVI) and moisture (MSI, NDWI) indices derived from satellite images. The spatial and temporal change of damage was observed in the image series, resulting in information about the level of degradation and regeneration. In pursuance of GIS processing, 84 forest compartments were compared, which showed in most of the cases (97%) negative interannual change in the index mean values (MSI = - 0.14, NDWI = - 0.2, NDVI= - 0.19) when years compared to each other. The remote sensing-based survey was marked out and validated based on the forest database of the Hungarian Division of Forest of National Land Centre and forest protection damage reports of the Hungarian National Forest Damage Registration System.