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Volume XIX |
GIS and Remote Sensing

Assessment of spatial changes of land use/land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India

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Abstract: The present work aims at presenting certain important ob-servations on food and water security of the peri-urban environment, by considering field data and satellite image classification to understand the spatial change pattern of natural resources and its impact on agriculture and water resources. Gautam Buddh Nagar is considered one of the important urban areas of the National Capital Region (NCR), being associated with multi-functional activity; it continues to grow in terms of infrastructure and other urban activities and the numerous infrastructural projects and other anthro-pogenic actions in the area cause a rising pressure on water, agriculture, and human health. The Landsat satellite images from 2000, 2005, 2010, and 2016 were classified and used to obtain the Land use / Land cover maps of the area, in order to estimate and to understand the rate of change during the last 16 years. There are mapped the important land use classes, such as the agricultural land, the vegetation surfac-es, the built-up areas, the open land, and the water bodies. The results indicate the fact that during the 16 years taken into study, vegetation (2.26%), water bodies (1.65%), and agriculture (3.5%) undergone a major decline, while the built-up land displayed values increased around four times (from 3.39% to 12.26%). The results of the present work clearly showed that the large-scale changes in natural land cover affected the agriculture, as well as the surface and groundwater resources of the area.

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Full article 10.5775/fg.2020.063.iDates:
Received: 02-02-2020 Accepted: 17-06-2020 Printed: 30-06-2020 Online: 16-10-2020
Affiliations:
  • 1 Amity Institute of Geoinformatics and Remote Sensing, Amity University, Sector-125, Noida, India
  • 2 Census Commissioner of India (Map Division), New Delhi, India
Corresponding author:
  • pks.jiwaji@gmail.com
Citation:
APA 6 | Harvard 2008 | Turabian 6 | MLA 7
RIS | BibTeX
Acknowledgements: The authors are grateful to the Amity Institute of Geoinformatics and Remote Sensing, Noida, as well as to their colleagues for providing facility and con-stant encouragement to carry out this research work.
Copyright: © 2020 The Author(s). Published by Forum geografic.
Provided under the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Open Access article.
Volume XVII |
Environment

Estimation of drinking water supply and its future trends in Varanasi city, India

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Abstract: The current study shows the estimation of drinking water supply, future trends and related issues in Varanasi City, India. Varanasi (from 25013’N to 25024’N latitudes and from 820 54’E to 830 04’E longitude) is one of the most important and historic city located almost in the Middle Ganga valley in the northern plain of India. For the convenience of civic administration, the city is presently divided into 90 wards and for the purpose of extending adequate and safe water supply facilities to the entire area, the city has been divided presently into 16 water supply zones of which 5 zones lie in the trans-Varuna area and the remaining 11 zones in the cis-Varuna area. The present work is based on the secondary sources of data which are obtained from Water Supply Department (Jal Sansthan), Nagar Nigam (Municipal Corporation) of Varanasi. In the first phase of the study, data pertaining to ward wise generation of water supply is collected from Water Supply Department, NNV (Nagar Nigam Varanasi). Informal focused group discussion, PRA (Participatory Action Research), and observation technique were applied to get the first hand information about the water supply scenario in the city. In the second phase of the study, Arc GIS 10.1 software was used to create maps for estimation of water supply management. The population growth in Varanasi from 2011 to 2041 is estimated to have a growth of 21% in 30 years at a rate of 2.25 % per year. This growth rate is considered in the model from 2011 to 2041 to predict the water demand in the city. The data was collected from various municipalities as per the office records. It is estimated that the amount of drinking water requirement forecasted for 2021, 2031 and 2041 is 0.49, 0.76 and 1.17 Billion liters per day respectively in Varanasi City. These data show that with an increase in population, the water demand is also increasing decade after decade. Increasing population growth rate, decreasing surface water resources, overexploitation of groundwater, deterioration of ground water quality and poor sewage treatment are the major water supply related issues in Varanasi city, India.

Keywords: , , ,
Full article 10.5775/fg.2018.100.iDates:
Received: 01-05-2018 Accepted: 25-08-2018 Printed: 30-06-2018 Online: 09-12-2018
Affiliations:
  • 1 Department of Geography, Institute of Science, Banaras Hindu University, Varanasi-221005, U.P., India
Corresponding author:
  • rai.vns82@gmail.com
Citation:
APA 6 | Harvard 2008 | Turabian 6 | MLA 7
RIS | BibTeX
Copyright: © 2018 The Author(s). Published by Forum geografic.
Provided under the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Open Access article.
Volume XV |
Regional geography

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

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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.

Keywords: , , ,
Full article 10.5775/fg.2067-4635.2016.361.dDates:
Received: 26-11-2016 Accepted: 30-12-2016 Printed: 30-12-2016 Online: 23-04-2017
Affiliations:
  • 1 Department of Geography, Institute of Science, Banaras Hindu University, Varanasi-221005, U.P., India
Corresponding author:
  • rai.vns82@gmail.com
Citation:
APA 6 | Harvard 2008 | Turabian 6 | MLA 7
RIS | BibTeX
Copyright: © 2017 The Author(s). Published by Forum geografic.
Provided under the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Open Access article.
Volume XV |
Geomorphology

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

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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.

Keywords: , , , ,
Full article 10.5775/fg.2016.137.iDates:
Received: 18-04-2016 Accepted: 30-05-2016 Printed: 30-06-2016 Online: 16-11-2016
Affiliations:
  • 1 Department of Physics, Indian Institute of Technology (BHU), Varanasi, India
  • 2 Department of Geography, Banaras Hindu University, Varanasi, India
Corresponding author:
  • rprassad.app@itbhu.ac.in
Citation:
APA 6 | Harvard 2008 | Turabian 6 | MLA 7
RIS | BibTeX
Copyright: © 2016 The Author(s). Published by Forum geografic.
Provided under the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Open Access article.
Volume XIV |
Various

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

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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.

Keywords: , , , ,
Full article 10.5775/fg.2067-4635.2015.045.iDates:
Received: 04-02-2015 Accepted: 30-05-2015 Printed: 30-06-2015 Online: 15-09-2015
Affiliations:
  • 1 Indian Institute of Remote Sensing, Dehradun, Uttrakhand, India
  • 2 Department of Geography, Banaras Hindu University, Varanasi-221005, Uttar Pradesh, India
Corresponding author:
  • neeti.iirs@gmail.com
Citation:
APA 6 | Harvard 2008 | Turabian 6 | MLA 7
RIS | BibTeX
Acknowledgements: The first author expresses her heartily thanks to Mrs. Minakshi Kumar, Scientist-F and Mrs. Shefali Agrawal, Scientist-F, IIRS (ISRO), for their useful suggestions.
Copyright: © 2015 The Author(s). Published by Forum geografic.
Provided under the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Open Access article.
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