Assessing Urbanization and Quality of Urban Environment Using Remote Sensing and GIS in East Delhi

The study aimed at evaluating the socio-economic impact of landcover / landuse changes in Owerri Municipal, Imo State, Nigeria using remote sensing and GIS with a view to improving the quality of life and social development through its objectives; to identify the changes of the landcover and landuse pattern of the study area for a period of 30 years i.e. 1987 to 2002 to 2017, to evaluate the socio-economic and environmental impact of these changes and to predict future impact of landcover / landuse changes in the study area. The methodology adopted in this study involved sourcing of satellite data from United States Geological Survey (USGS) for the period of years under study, social survey in Owerri Municipal Area for socio-economic impact monitoring. ArcGIS 10.5 and QGIS 2.9 software were used for data processing, analysis and presentation. The study observed a significant increase in built-up area from 1987 (34%) to 2017 (65%), on the contrary, there was a noticeable decrease in the class of bare land (26% to 12%) and vegetation. (37% to 15%) followed by water body which kept fluctuating as result of rainfall. Rapid population growth triggered by rural urban migration coupled with hasty socio-economic development were the main drivers of these changes. Results showed that remote sensing and GIS approach are recommended for further studies as the tools are efficient for landcover/landuse mapping.

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Applications of Remote Sensing and GIS in Hydrological and Hydrogeological Studies

10.4018/978-1-7998-8331-9.ch012 ◽

2022 ◽

pp. 237-251

Author(s):

Athar Hussain ◽

Richa Madan ◽

Vishal Kamboj

Keyword(s):

Remote Sensing ◽

Watershed Management ◽

Spatial Information ◽

Holistic Approach ◽

Remote Sensing And Gis ◽

Quality Of Water ◽

Integrated Watershed Management ◽

Vegetation Water ◽

Hydrogeological Studies

Integrated watershed management is a holistic approach aimed at linking upstream and downstream areas. It encompasses preventing the watershed from being polluted and maintaining the quality of water and prevention of soil erosion. Remote sensing and GIS techniques play a crucial role in assessing watershed conditions through modeling. These techniques are also very helpful in acquiring the spatial information of the land use, land cover, vegetation, water resources, etc. With the available data, characteristics of aquifers can easily be deciphered. Remote sensing aids in identifying and assessing the topographical features and alienates the unnecessary ones. Integration of GIS with remote sensing along with hydrological, hydrogeological, geomorphologic studies will make the practice of integrated watershed management more effective. The primary aim of this chapter is to encompass the remote sensing and GIS technologies currently in use for watershed monitoring and management.

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Land use/cover mapping and assessing the impact of solid waste on water quality of Dal Lake catchment using remote sensing and GIS (Srinagar, India)

SN Applied Sciences ◽

10.1007/s42452-018-0027-6 ◽

2018 ◽

Vol 1 (1) ◽

Cited By ~ 4

Author(s):

Junaid Qadir ◽

Perminder Singh

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Land Use ◽

Solid Waste ◽

Remote Sensing And Gis ◽

Dal Lake ◽

Lake Catchment ◽

The Impact

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Impact of land-use and land-cover change on groundwater quality in the Lower Shiwalik hills: a remote sensing and GIS based approach

Open Geosciences ◽

10.2478/v10085-010-0003-x ◽

2010 ◽

Vol 2 (2) ◽

Cited By ~ 25

Author(s):

Sudhir Singh ◽

Chander Singh ◽

Saumitra Mukherjee

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Groundwater Quality ◽

Large Scale ◽

Hydrological Cycle ◽

Groundwater Resources ◽

Local Level ◽

Remote Sensing And Gis

AbstractHuman activities have exerted small to large scale changes on the hydrological cycle. The current scenario regarding groundwater resources suggests that globally there is a water crisis in terms of quantity (availability) and quality. Therefore there is a great need for the assessment and monitoring of quality and quantity of groundwater resources at local level. This paper presents a case study of the lower Shiwalik hills, in Rupnagar, Punjab, India, to trace land-use and land-cover changes during the past 17 years, with an emphasis on groundwater quality and quantity. This study was performed in alluvial and hilly terrain. The results show that the quantity of groundwater increased with the help of natural and artificial recharge due to change in land-use and land-cover pattern (increased area of fallow land). The quality of groundwater deteriorated due to input of fertilizers for enhancing the short-term soil fertility. Using a Remote Sensing and GIS based approach, we show the final results in map form. In particular we highlight a potential groundwater exploration site, which could be useful for district level planning. Our research shows that the change in land-use and land-cover affects the quantity and quality of groundwater.

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Detecting changes and health status of mangrove forest in Achara estuary, Maraharashtra using remote sensing and GIS

Sustainability Agri Food and Environmental Research ◽

10.7770/safer-v0n0-art2093 ◽

2020 ◽

Vol 8 (3) ◽

Author(s):

Vidya Ajit Chougule ◽

Jagdish Bhagwat Sapkale

Keyword(s):

Remote Sensing ◽

Mangrove Forest ◽

Horizontal Distribution ◽

Remote Sensing And Gis ◽

Western Coast ◽

Mangrove Forests ◽

Hydraulic Characteristics ◽

Mangrove Area ◽

Spatio Temporal

Mangrove forests protect the Western coast of Maharashtra from natural disasters striking from seaward and riverside. The quantity and quality of mangroves were influenced by the morphological and hydraulic characteristics of estuaries. The study aims to identify the Spatio-temporal changes that took place in the mangrove forest of Achara estuary in the last four decades since 1989. It also highlights the zonal horizontal distribution of mangrove land from its mouths to the head of the estuary. Most of the mangroves concentrate in the zone that comprises the area of 1-3 km from the mouth. A gain of 39.17% of the mangrove area shows the growth of mangroves in the study area during this period. NDVI value, which ranges from 0.123 to 0.560, acknowledged the dense and healthy status of mangroves in Achara estuary.

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Monitoring the quality of life in urban area using TDVI- Case study of Kalaburagi city

International Journal of Interdisciplinary and Multidisciplinary Research ◽

10.54121/2021/09/1496 ◽

2021 ◽

Vol 06 (09) ◽

pp. 68-75

Author(s):

Abhilasha Kumari ◽

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Urban Environment ◽

Vegetation Cover ◽

Vegetation Index ◽

Ground Truth ◽

Urban Vegetation ◽

Urban Image ◽

Better Than

Many vegetation indices have been proposed over last decades made specialists search for the most suitable vegetation index for a given remote sensing application. Measuring the Quality of Place (QOP) is a hard task since it involves both physical and socio-economic dimensions. Being one of the major land use categories, urban vegetation plays a significant role in one‟s judgment for QOP in a neighborhood. Both quantity and quality of the community parks and recreation areas are major determinants of neighborhood attraction. For these reasons, detection of urban vegetation cover has been one of the important implication areas of urban image classification techniques. “Transformed Difference Vegetation Index (TDVI) developed by Bannari et al. (2002), is tested in a previous work where the index has performed better than NDVI and SAVI. In that work, a comparative study between TDVI, SAVI and NDVI for estimating vegetation cover in urban environment from the Indian Remote Sensing Satellite (IRS-1D) imagery has been conducted. The validation of the obtained results according to the ground truth showed that the TDVI is an excellent tool for vegetation cover monitoring in urban environment. It does not saturate like NDVI or SAVI, it shows an excellent linearity as a function of the rate of vegetation cover. This paper adds on the previous work by analyzing the performance of TDVI in urban image classification. Results indicate that, the performance of TDVI in urban image classification is better than NDVI and SAVI. The new index not only differentiates the urban vegetation cover better but also helps to minimize the error in classifying other unclassified pixels of urban categories.

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