Volume XV |

Assessment of Nutritional Status and Deficiency Disease through Geographical Survey: A Case Study of Varanasi District in India

Abstract: Identifying the role of the diet at the start of many diseases and evaluating the nutritional status of an individual, family and community is significant for public health. The main objective of this paper is to geographically evaluate the nutritional status and deficiency disease in the development blocks of Varanasi district, India. Primary data are collected from 800 respondents of 16 selected villages (2 villages from each development block) in the rural part of Varanasi district and their results are analyzed with the help of SPSS statistical software. The study involved geographical survey based interviews using a questionnaire, measurement of nutrient intake and assessment of their general knowledge and awareness about nutrition and deficiency diseases on the basis of their occupational structure, house type and income status.
The findings of this paper also show that the socio-economic status of the respondents is closely related with nutritional status of people living in the rural areas of Varanasi district. It is interesting to note that about 15% of household’s intake is still less than 1500 calories per capita per day, which leads to malnutrition and deficiency diseases. More than half of the respondents of the study area are found to be suffering from nutritional deficiency diseases.

Volume XV |

Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images

Abstract: Timely and accurate land use/land cover (LULC) information is requisite for sustainable planning and management of natural resources. Remote sensing images are major information sources and they are widely used for mapping and monitoring various land features. Images from various sensors, with different spatial resolutions, are available; however, the selection of appropriate spatial resolution is an essential task to extract desired information from images. This paper presents the conclusions of the work related to LULC classification based on multi-resolution remote sensing images. Optical data collected by three different sensors (LISS IV with 5.8 m and Landsat 8-OLI with 30 m and AWiFS with 56 m spatial resolutions respectively) in 2013 are examined against the potential to correctly classify specific LULC classes. The classifications of images are performed using Maximum Likelihood Classifier (MLC). The results indicate that the overall accuracy and kappa coefficient of LISS IV with 5.8 m are higher than that of Landsat 8-OLI with 30 m and AWiFS with 56 m images. Understanding the role of spatial resolution in LULC classification accuracy will enable the appropriate interpretation of any classified images.

Volume XIV |

An object based building extraction method and classification using high resolution remote sensing data

Abstract: The increasing availability of the high spatial resolution satellite images has provided a new data source for building extraction. This paper proves the concept of object oriented classification using high-resolution satellite data (Cartosat-1 satellite data fused with IRS-1C, LISS IV data) for automatic building extraction using eCognition software. In this study, the overall accuracy of classified image is 0.94 and Kappa accuracy is 0.92. The producer accuracy for building, vegetation and shadow are 0.9745, 1.0 and 0.8999, respectively, whereas user accuracy for building, vegetation and shadow are 1.0, 0.9475 and 1.0 respectively. The classification overall accuracy is based on TTA mask (training and test area mask) and it is 0.98 and Kappa accuracy is 0.96. The producer accuracy for building, forest and shadow are 1.0, 1.0 and 0.7344, respectively and user accuracy for building, vegetation and shadow are 1.0, 0.9475 and 1.0, respectively.

Volume XIII |

Remote Sensing data & GIS for flood risk zonation mapping in Varanasi District, India

Abstract: Flood is one of the natural disasters causing colossal loss of life and property. It occurs with a strange regularity in different parts of India, thus devastating those particular areas. The most flood prone areas in the country are in the Ganga and Brahmaputra basins whilst the annual flood damages in the Ganga basin account for about 60 percent of the total. The extent of the damages shows a pattern of increase from west to east (downstream along the Ganga) and from south to north (towards the hills). The present study delineates the application of geographic information system (GIS) in mapping flood risk zones in Varanasi, one of the prominent districts of the Indo-Gangetic plain. The whole district witnesses havoc of flood in rainy season due to impounding of huge amount of water in the Ganga river. The damage and loss of life and cattle due to floods in the area under study are increasing year after year. It could be stated that high magnitude floods occurred in 2013 during the month of August, when the flood plains were mostly used for periodicity and gravity of food. Recognition of flooding parts and flood risk zones in the area under study are the primary steps in the flood control measures. GIS data base has been well utilized in delineating and tackling flood related problems in the area. An attempt has been made to prepare a map of an area measuring about 1526 sq. km. showing flood risk zone with the help of remote sensing data (IRS-P6, LISS III, 2008). A total of five flood risk zones are delineated in Varanasi district. With the help of remote sensing data, flood risk zones in the study area are categorized into low, middle, high, higher and highest level flood zones.

Volume XII |

GIS in Healthcare Planning: A Case Study of Varanasi, India

Abstract: The objective of this paper is to examine the relevance of Geographical information system (GIS) supporting health planners for a district level healthcare planning. For this purpose, an attempt has been made here to calculate the hospital requirement area to know the specific sector that needs to better develop health facilities. The weightage is assigned to the class of thematic layers respectively to produce weighted thematic maps, which have been overlaid and numerically added in order to produce a Hospital requirement index (HRI) and hospital requirement zone (HRZ) map. These maps are very useful to calculate the exact area having good health facilities and also those wherein healthcare facilities need to be improved in Varanasi district. The Hospital requirement index (HRI) values according to the weighting method are found to lie in the range from 11 to 23. After calculation by weighting method using selected indicators, it is found that the areas coming under very high and high requirement class is 46.62% and 7.55%, respectively, whereas 3.39% and 42.63% of the total areas comes under low and moderate requirement classes in Varanasi district. Primary data are also collected from 800 respondents of 16 selected villages (2 villages from each development block) in the rural parts of the district to know about the utilization of healthcare facilities and their results are analysed with the help of statistical SPSS software. It is interesting to note that only 25.38% respondents are satisfied with the available healthcare services of primary health centres (PHCs), while 60% of respondents remain partially satisfied. The remaining 14.62% (117) respondents are not satisfied with the services of PHCs.