Monitoring environment changes has become a necessity as a result of current environment deteriorating due to human activities like mining activities. In most developing countries like Kosovo (Mitrovica), acquiring information concerning the current condition and the dynamic changes of the environment for a rapid monitoring is not easy. The present study provides The Normalized Difference vegetation Index (NDVI) of the study area, Mitrovica. The main aim of the study is to monitor and evaluate changes in vegetation over the years, using NDVI time series outputs. As well as this technique of monitoring vegetation will be used to evaluate drying vegetation as e result of the mining tailings in this area, exactly because of the presence of the heavy metals that come by industrial activities in this region. The NDVI technique involves the use of remote sensed data in different time series extracted from Landsat 5 and Landsat 8. The Landsat satellite images give us reliable information for monitoring vegetation change according in NDVI time series. According to the present study, the NDVI data generated gives us valuable informations about vegetation. However, the study demonstrated that in Mitrovica was changed the vegetation over the years as result of human mining activities and climate change as well. The NDVI technique can be employed for monitoring vegetation cover and it's values range from-1 to 1. The present study shows monitoring vegetation changes for 2000, 2010 and 2018. The NDVI time series Mitrovica maps computed in the study, range from-0.30 to 0.26 in 2000, from-0.09 to 0.47 in 2010 and-0.14 to 0.60 in 2018. This technique can be applied in different areas in the country and will be effective tool for environmental monitoring and evaluating vegetation change and vegetation monitoring as well. Furthermore, the study will answer to the many questions for vegetation change in Mitrovica and its change ratio.
Continuous monitoring of surface water is essential in terms of heavy metals investigation. Therefore, surface water quality is an environmental aspect which should be analyzed and monitored depending on its spatial distribution. The aim of this study is to provide an overview for evaluation of surface water pollution in the Mitrovica area by applying spatial distribution using Geographic Information System (GIS), geostatistical and non-geostatistical techniques. Nowadays, GIS with the geostatistics and non-geostatistics are very frequently used techniques in environmental monitoring studies. By providing the spatial distribution, there is possibility to place the pollution values in space. The surface water pollution caused by heavy metals (As, Cr, Cu, Ni, Pb, Zn and Cd) were sampled and analyzed from six monitoring stations in Sitnica river on different time series within three months countineously. The monitoring stations (samples) in Sitnica river were been distributed randomly. Pollution maps were produced using geostatistical and non-geostatistical (Spline and Kriging) approach. There were produced different pollution values in Sitnica river during the period of monitoring. Mainly the north part of Sitnica river has been poluted mostly with Heavy Metal Pollution Index (HPI) from 50 to 85 in the month of May, from 125 to 265 in the month of June and from 320 to 535 in the month of July. As well as the Metal Index (MI) from 0.60 to 2.05 in the month of May, June and July. The different statistical models were tested for geostatistical and non-geostatistical techniques in order to identify the best fitted technique for the pollution indices and the best interpolation techniques were selected on the basis of Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). These statistical tested model have shown that the best fitted interpolation technique is Kriging because of the lowest values of MSE, MAD, RMSE, MAE and MAPE. In the study were involved statistical models such as correlation and regression, for showing the relation between time series datasets and interpolated pollution indices as well. The cartographic output derived from the study were raster maps (15m spatial resolution) which represent the spatial distribution of surface water pollution as a result of monitoring process on time series. It is our believe that the present study will be used as a reference study for further environmental investigation and monitoring in Mitrovica since.
The aim of the paper is developing the Digital Terrain Model (DTM in the further text) through QGIS software. In order to accomplish intention of the paper will test some of the methods and techniques that are widely known in nowadays and those are supported by QGIS software – an open source software. And those methods named TIN and GRID. For showing complexity on the study area will analyse some features or characteristics of terrain in the created DTM. All of these methods and techniques will be applied in QGIS. In general, the QGIS software has rich methodology for creation, intepretation, visualization and analysing the geo-spatial data and the DTM in particular.
The authors propose to derive the formulas given in [1, 2] for determining the height and latitude based on the Cartesian rectangular coordinates X, Y, Z, giving an accuracy for the geodetic height H of 1 mm for heights up to 50 km and for geodetic latitude B of 0,0001 arc seconds for H < 10 km. The formulas proposed in [1, 2] apply to all values of latitude and longitude (B and L). In [3], we propose two new formulas for H and B. In this paper, it is shown that the formulas proposed in [3] apply to points of ellipsoid surface and points with geodetic latitude of 0° and 90°. For the same formulas proposed in [3], the corrections are derived to ensure an accuracy of H of 1 mm at H ≤ 10 km, which apply to all values of B and L. Basing on the presented geometric conclusions, calculations and analyzes, a new solution for H and B respectively is proposed for given X, Y, Z, which provides an accuracy for H less than 1 mm for H ≤ 100 km and for B of 0,0001 arc seconds for H ≤ 50 km.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.