Spatial distribution of ground water quality index using remote sensing and GIS techniques

Water is a vital and widely spread component required for sustaining the life. Groundwater is vastly valuable source and it is extremely beneficial for the human beings. But, the toxic elements release from sources such as industries, landfills as well as non-point causes of pollution such as pesticides and fertilizer from the past year showed high levels of pollution in ground water, hence this is very crucial for evaluating the water quality not only for it’s existing usage, but also a its capacity to develop as a sustainable source of water for human utilization. In rural areas of India, Groundwater is a significant water-drinking source. In Bhokardan area of Jalna District, quality of ground water is great significance, because it is a key alternative source of domestic supply along with drinking water and peoples residing there facing several water quality issues for drinking purpose. In this current research work, an endeavor has been developed to investigate the quality of groundwater as well as spatial distribution by utilizing Remote Sensing along with GIS approaches. Water quality analysis was performed for developing the quality index of water, by utilizing 12 quality parameters of water like Alkalinity, pH, Electrical Conductivity, Fluoride, Chloride, Nitrate, Sulphate, Potassium, Total Hardness, E. coli, Turbidity and Total Dissolved Solids measured at 35 different selected locations in this research work. Spatial distribution map showed that each region of research area falls under category of “Poor water” and “Very poor water” except some sites at Northern regions of the area which falls under “Good water” category during pre-monsoon, whereas most of the sites from Northern and Southern part of the region shifted category from poor to good water along with very poor to poor water during post-monsoon season. The quality index data of water of the current research disclosed that, the high WQI “Water Quality Index” values in the samples of groundwater were principally due to the occurrence of higher values of turbidity and E. Coli. The higher level of water quality parameters like TH, EC, alkalinity, potassium, TDS, chloride and fluoride were also accountable for high values of WQI in this research work. None of the location falls under excellent quality for water during pre-monsoon as well as post-monsoon season.

Considerations When Applying Large-Scale PIV and PTV for Determining River Flow Velocity

Large-scale image velocimetry is a novel approach for non-contact remote sensing of flow in rivers. Research within this topic has largely focussed on developing specific aspects of the image velocimetry work-flow, or alternatively, testing specific tools or software using case studies. This has resulted in the development of a multitude of techniques, with varying practice being employed between groups, and authorities. As such, for those new to image velocimetry, it may be hard to decipher which methods are suited for particular challenges. This research collates, synthesises, and presents current understanding related to the application of particle image velocimetry (PIV) and particle tracking velocimetry (PTV) approaches in a fluvial setting. The image velocimetry work-flow is compartmentalised into sub-systems of: capture optimisation, pre-processing, processing, and post-processing. The focus of each section is to provide examples from the wider literature for best practice, or where this is not possible, to provide an overview of the theoretical basis and provide examples to use as precedence and inform decision making. We present literature from a range of sources from across the hydrology and remote sensing literature to suggest circumstances in which specific approaches are best applied. For most sub-systems, there is clear research or precedence indicating how to best perform analysis. However, there are some stages in the process that are not conclusive with one set method and require user intuition or further research. For example, the role of external environmental conditions on the performance of image velocimetry being a key aspect that is currently lacking research. Further understanding in areas that are lacking, such as environmental challenges, is vital if image velocimetry is to be used as a method for the extraction of river flow information across the range of hydro-geomorphic conditions.

Climate Change Planning: Soil Carbon Regulating Ecosystem Services and Land Cover Change Analysis to Inform Disclosures for the State of Rhode Island, USA

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy.