Meenu Rani

An Efficient Hybrid Classification Approach for Land Use/Land Cover Analysis in a Semi-Desert Area Using

Land-use and land-cover (LU/LC) studies help in assessing and monitoring the status of the natural resources, detecting the changes in spatial and temporal scale and predict them for the future. Due to changing environments and increasing anthropogenic pressures, the demand for a LU/LC database at the global level is increasing. Therefore, a comprehensive understanding of LU/LC at both local and regional scales is important since it plays a pivotal role in socioeconomic development and global environmental changes. There are many approaches for LU/LC analysis such as supervised classification, unsupervised classification and onscreen digitization but simplest and most popular approach on IRS LISS-III and

{\rm ETM}{+}

{\rm Landsat\hbox{-}ETM}{+}

and LISS-III Sensor

satellite data revealed a serious problem in some semidesert areas caused by spectral confusion because of the similar radiometric response like scrub land with harvested land, built-up with bare hills and many other. Present study suggests hybrid classification approach for LU/LC classification, which is found highly useful in achieving high accuracy for areas where spectral classes of images are inseparable.

Wetland Assessment and Monitoring Using Image Processing Techniques: A Case Study of Ranchi, India

Wetlands, the transitional zones that occupy an intermediate position between dry land and open water, regulate the flow of water and nutrients, thereby facilitating optimum functioning of the physical and biological cycles of nature. To conserve and manage wetland resources, it is important to invent and monitor wetlands and their adjacent uplands. Wetlands are most productive ecosystems besides being a rich repository of biodiversity and are known to play a significant role in carbon sequestration. Wetlands are halfway world between terrestrial and aquatic ecosystem and share properties of both. Wetlands exhibit enormous diversity according to their genesis, geographical location, water regime, chemistry, dominant plants and soil or sediment characteristic. Wetland vegetation provides a natural barrier to fast moving water and therefore aids in flood speed reduction. Remote sensing offers a cost effective means for identifying and monitoring wetlands over a large area and at different moments of time. The present paper describes the methodology and results of wetland area for the Ranchi city of the Jharkhand state for the year 1996-2004.The signatures of wetlands and associated land features are identified in unsupervised classification approach based on their DN value using Satellite data. There are drastic change in between 1996 and 2004. The spatial distributions of the NDVI values were evaluated to determine the cut-off points for the water bodies, and wetted area.

Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve

This study focuses on the estimation of soil organic carbon of Sariska Wildlife Reserve. The soil organic carbon is one of the most important issues in the research area of the global carbon cycle as it is the largest terrestrial carbon pool. Geospatial and various forest inventory approaches were used during study for statistical correlation between estimated and predicted value. Remote sensing plays a vital role in spatial data acquisition of the ecosystem carbon dynamics at local, regional, and global scale. The advantage of remote sensing is that it provides synoptic observation, periodical and continuous measurement, and availability of digital data for processing standardization. IRS P6 LISS III data (September 2012) were used to analyze the precise estimation of the percentage of the soil organic carbon associated with organic matter in soil. Statistical analysis was performed for finding the regression curve between the predicted and estimated value of soil organic carbon. The results illustrated that the determination of coefficient (r2) between the predicted and estimated SOC values is found to be 0.708.

Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data

Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993–2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r2 = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.

Vigorous Appraisal of Fluoride on Industrial Proponent in Thermal Power Plant over Anthropoid Biosphere Using F − Ion-Selective Electrode

This study was conducted to analyze the impact of fluoride in the anthropogenic condition in an industrial region promoting and affecting the health of the workers. Fluoride is toxic to humans in high concentrations, such as can occur in persons working in fluoride-containing mineral industries like aluminum industries. When workers are exposed to fluoride-containing minerals, they can suffer from a variety of health problems, such as dental disease. This paper presents the relationship of different clinical conditions correlated against the fluoride level. Contributing clinical aspects, such as morbidity, dysentery, overcrowding, and skin disease, are also studied to assess the consequences of fluoride upon consistent exposure. The relationship between pH and hardness of water with fluoride was measured, and then spatial maps were generated. The investigations resulted in a conclusion that hardness of water had a more pronounced impact on the level of fluoride concentration as compared with pH. Water with more hardness contains more fluoride concentration (25 mg/ml) as compared with soft water (4 mg/ml). This paper also revealed the concentration of fluoride content in the bodies of aluminum plant workers, which varied from 0.06 to 0.17 mg/L of blood serum in the case of pot room workers and 0.01 to 0.04 mg/L in the case of non-pot room workers. In fingernails, it varied from 0.09 to 3.77 mg/L and 0.39 to 1.15 mg/L in the case of pot room and non-pot room workers, respectively. In urine, it varied from 0.53 to 9.50 mg/L in pot room workers and 0.29 to 1.80 mg/L in non-pot room workers. This paper concluded that water was safe for drinking purposes if it has a low hardness (60-140 mg/ml) and pH (7.1-7.4).

Modelling spatial variation of fluoride pollutant using geospatial approach in the surrounding environment of an aluminium industries

Contamination by any substance takes place through air, water or soil and causes serious effects on flora and fauna of the regions. Air-transmitted pollutants spread in a faraway place from emission points of the pollutant source via wind (speed and direction as causing factors). Fluoride is one the pollutant generated in aluminium industries which is harmful to human and plants in excess concentration. This study focuses on the use of spatial interpolation methods for assessment of fluoride concentration around aluminium industries. The samples were collected from different test sites in the study area to investigate the fluoride concentration level. The test sites include several locations such as industrial unit, river site, residential and distant villages. Then, the collected samples were used to predict the overall fluoride concentration in the entire study area. The aim of the study was to evaluate the spatial variation and presence of fluoride concentration in the surroundings of the aluminium industries. Geostatistical interpolation modelling was applied to assess the prediction of fluoride contamination for other non-sampling points using the direction and distance method of empirical Bayesian kriging (EBK) modelling. Thus, geospatial modelling was used to predict the contamination of fluoride around the study area to create environmental awareness. In particular, this study assesses the fluoride pollutant concentration which might become dangerous with slowly increasing concentration against its standard concentration, which will severely impact the human health. In overall, EBK can provide valuable information regarding the fluoride concentration on possible level of fluoride in concern to public health.