Top PDF Evaluation of course change detection of Ramganga river using remote sensing and GIS, India

Evaluation of course change detection of Ramganga river using remote sensing and GIS, India

Evaluation of course change detection of Ramganga river using remote sensing and GIS, India

a b s t r a c t Visibility of Ramganga river course change detection was made using Remote Sensing and GIS in study area among period of forty-one years from 1972 to 2013. Landsat MSS, TM, ETMþ, LISS-III satellite images from 1972, 1989, 2000, 2006, and 2013 respectively were used to delineate the historical changes of the river course. This study shows that for a long time this area has been suffering due to erosion problem and shifting characteristics of the Ramganga River. The Ramganga river course has been shifting and the overall shifting is towards the south-west direction in different places which leads to the village erosion. The area has remained undeveloped due to infrastructure damaged by flood, changing course. This study may be helpful for the overall river management and planning for future prevention of food, changing coursing, loss of properties.
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Evaluation of Urban Sprawl and Land use Land cover Change using Remote Sensing and GIS Techniques: A Case Study of Jaipur City, India

Evaluation of Urban Sprawl and Land use Land cover Change using Remote Sensing and GIS Techniques: A Case Study of Jaipur City, India

Abstract— Urban sprawl is a worldwide phenomenon but the rate of urbanization is very fast in developing country like India. It is mainly driven by unorganized growth, increased immigration, rapidly increasing population. The implementation of capitalistic economy has resulted in the internal reformation of agricultural land use from crop production to industries and commercial avenues. Urban expansion and pattern could be depicted by spatial and temporal remote sensing satellite data. In the present study, an attempt has been made to investigate the effect of Urban Sprawl on Land use / Land cover change of the year 1995, 2000, 2006 and 2010 for Jaipur city, one of the planned cities in India. The task comprises of steps: delineation of urban area for consecutive years, comparison between urban areas, identification of the urban sprawl pattern, recognition of magnitude and direction of changing sprawl and its effect on land use / land cover. The pattern of urban sprawl is identified using Remote Sensing technique. The investigation resembles noteworthy change in the spatio-temporal urban sprawl pattern, direction, magnitude and effects on Land use/ Land cover.
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Evaluation of coastal aquaculture ponds using remote sensing and GIS

Evaluation of coastal aquaculture ponds using remote sensing and GIS

Introduction Aquaculture is growing more rapidly than any other segment of the animal farming industry 1 . In India, the annual fisheries and aquaculture production increased from 0.75 million tonnes in 1950-1951 to 9.6 million tonnes in 2013-2014 2 . Expansion of aquaculture is due to potentially high commercial returns and the ability to produce export earnings. The coastal belt of Nagapattinam is one such locale where aquaculture activity is expanding owing to the availability of land and water resources which are conductive for aquaculture development. These locations cannot be utilized for agriculture. Remote sensing has emerged as the most suitable tool for quantitatively measuring land-cover changes at the landscape scale 3 . Satellite images provide a synoptic coverage of the earth’s surface in spatial and temporal scale and help us to understand how the changes have happened in various parts of the environment including coastal waters 4,5 . An integrated Geographical Information System (GIS) and remote sensing technique deals with the spatiotemporal information of land use and land cover (LULC) features that are well recognized for decision making in the scientific realm 6,7 . GIS and remote sensing combines the multiple spatial
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Course Change Detection of River AIE Using RS and GIS

Course Change Detection of River AIE Using RS and GIS

Das.J.D., et al (2007).,[1] has detected the changes in the Barak River channel, N.E. India using Remote Sensing and GIS application. The River flowing through alluvium having low gradient reduces the velocity which in turn leads to the creation of meanders and zigzag paths. The nature and the intensity of the channel is governed by the geological and tectonic condition of the River basin. Topographic data of two different years and four different years of satellite image were acquired and used in detecting the channel shift. The conclusion made after the analysis Subsequent change of channel position was seen. Abandoned loops due to cut-off can be seen on both sides of the channel. Vegetation pattern can be seen away from the banks of the channel. Strong northward shift at three places at the past and recently shifting can be seen towards southward.
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Land Use / Land Cover Change Detection in a part of Ramganga River Basin, at Bareilly District, Uttar Pradesh, India

Land Use / Land Cover Change Detection in a part of Ramganga River Basin, at Bareilly District, Uttar Pradesh, India

Abstract –The paper deals with the status of the land use/land cover change taken place in a part of Ramganga River Basin, at Bareilly district, Uttar Pradesh, India, by using remote sensing satellite data. The present study area is confined to latitude 28°10 ′ to 28 0 54 ʹ North and longitude 78°58 ′ to 78 o 58 ʹ East, covering an area of 4120 km 2 . The satellite images of 1979 and 2009 have been obtained from global land cover facility (GLCF) and examined by unsupervised classification method. The general classification level has been adopted. The identified classes include Settlements, Croplands, Vegetation/Plantations, Water Bodies, and Waste Land. The result shows increasing trend of crop land and built up area and decreasing trend of vegetation and Plantation land.
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LAND USE AND LAND COVER CHANGE DETECTION STUDY AT INDIAN SUNDARBAN DELTA USING REMOTE SENSING AND GIS

LAND USE AND LAND COVER CHANGE DETECTION STUDY AT INDIAN SUNDARBAN DELTA USING REMOTE SENSING AND GIS

Sundarban delta is the part of Ganga, Brahmaputra and Meghna. The sundarban is a stretch of largely impenetrable mangrove forest laying the southern part of west Bengal, India. Extending about 350 km along with the Bay of Bengal from the Hooghly river estuary. The Indian Sundarban lies between 21 ̊40'04”N and 22 ̊09'21”N latitude, and 88 ̊01'56”E and 89 ̊06'01”E longitude. The average altitude of the park is 7.5 m above sea level. The present researchworks have selected total part of Indian Sundarban area. The Indian Sundarban Delta is bounded by the IchamatiRaimangal River in the east, by the Hooghly River in the west, by the Bay of Bengal in the south, and the Dampier Hodges line drawn in 1829-1830 in the north (Fig-1).
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Morphometric Analysis of River Catchments Using Remote Sensing and GIS (A Case Study of the Sukri (River, Rajasthan)

Morphometric Analysis of River Catchments Using Remote Sensing and GIS (A Case Study of the Sukri (River, Rajasthan)

were incorporated from LANDSAT ETM+, October, 22, 2000 (www.landsat.org). The ordering of drainage reached to the fifth order as highest order in both sub-catchments (Fig. 3). The software package which was used is ArcGIS, GIS software. The symbols of the parameters used are of standard usages. The parameters computed include stream orders and stream numbers, cumulative length and stream orders, mean cumulative length and stream orders, stream length ratio, bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, elongation ratio, constant of channel maintenance, relative relief, percentage of slope, and relief ratio. The statistical methods were also applied to validate data and to obtain further precise results. The various aspects were studied for their inter-relationship which helps to depict the nature of the sub-catchments.
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APPLICATION OF REMOTE SENSING AND GIS IN LAND USE/ LAND COVER CHANGE DETECTION ANALYSIS

APPLICATION OF REMOTE SENSING AND GIS IN LAND USE/ LAND COVER CHANGE DETECTION ANALYSIS

 Consequently, it is more suitable to give the sensor characteristics of LISS III and LISS IV MX. Map of 1:25,000 scale obtained from Survey of India covering the whole study area is used to extract the (GCPs) Ground Control Points and to separate the boundary of the given study area.

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Land Use Land Cover Change Detection by Using Remote Sensing Data in Akaki River Basin

Land Use Land Cover Change Detection by Using Remote Sensing Data in Akaki River Basin

In the above table the negative sign in waterbody has shown us the decrement of water body in the period between 2011 and 2015. This reduction of water body storage may be associated with the climate change effect such as the prominent ElNino related drought in the country. Dynamic and abrupt change observed on the agricultural land and its conversion rate very higher in comparing with others. It indicates the more displacement of rural dwellers in the past 30 years and that requires careful analysis on the horizontal expansion of the cities and towns in the basin. According to the Leuliseged et al (2011), the peri-urban people face with the problem due to rapid expansion of Addis Ababa city. Industrial, commercial, service rendering centers and other facility construction booming in the Addis Ababa and that consumes more fertile agricultural land. Society dependant on agricultural products not secured by having the other alternative income generating sources which designed as livelihood copping strategy to solve their problem (Lueliseged et al., 2011).
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GEOMORPHOMETRIC ANALYSIS OF BILDI RIVER SUB BASIN IN BULDHANA DISTRICT OF MAHARASHTRA STATE, INDIA USING GIS AND REMOTE SENSING

GEOMORPHOMETRIC ANALYSIS OF BILDI RIVER SUB BASIN IN BULDHANA DISTRICT OF MAHARASHTRA STATE, INDIA USING GIS AND REMOTE SENSING

Organized by C.O.E.T, Akola & IWWA, Amravati Center. Available Online at www.ijpret.com 337 The drainage density less than 2 indicates very coarse, between 2.4 is related to coarse, between 4 and 6 is moderate, between 6 and 8 is fine and greater than 8 is very fine drainage texture. The observed, drainage texture is 5.7 indicates the very coarse drainage texture. The high value of drainage texture and lower value of drainage density indicates that the basin has porous nature and coarse grained texture and the region has highly permeable subsoil materials, homogenous rock type, low runoff zone, loose soil with high infiltration capacity. The entire basin elongation ratio (0.60) indicates that the basin is elongated shape and less prone to overflowing. The values of circularity ratio and the elongated ratio suggest that the basins are more elongated. Low form factor and high circulatory ratio suggest that the basin is prone for high floods. Higher the ratio lesser will be flood peak. The basin Bifurcation ratio is 4.14 which indicate dendritic to sub dendretic drainage type. Kolar Sub basin drainage basin is having good ground water prospects. Drainage morphometric analysis gives overall view of the terrain information, like hydrological, lithological, slope, relief, variations in the watershed, ground water recharge, porosity, soil characteristics, flood peak, rock resistant, permeability and runoff intensity. This information is useful for all geological, hydrological, ground water studies. The Lg values of in the study area is 0.20 indicating high relief of the area. It is noted that the Rf values of the study area suggesting that Bildi River basin elongated in shape. Arc GIS 10.1 software tools like Hydrology, Data management, Geo statistical, Analysis tools etc. are very useful for mapping, analysis and representation for fast and accurate results in less time.
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Land use and land cover change detection study at Sukinda valley using remote sensing and GIS

Land use and land cover change detection study at Sukinda valley using remote sensing and GIS

A detailed understanding of the impact of mining on changes in land use/land cover pattern has become necessary for the Sukinda valley. Therefore, present study was undertaken to analyze the extent of human-induced landscape transformation in the mining affected areas of Sukinda valley by interpreting temporal remote sensing data using geographic information system (GIS). Land cover types (dense forest, quarry, water body and open forest) were delineated in order to achieve the above objective. The areas under settlement/non-forest were also taken into consideration to know the trend due to the impact of mining activities in different time periods.
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Forest Cover Change Detection Using Remote Sensing and GIS in Banja District, Amhara Region, Ethiopia

Forest Cover Change Detection Using Remote Sensing and GIS in Banja District, Amhara Region, Ethiopia

In relation to the available information of forest cover in the country, EFAP [2] documented that there is no adequate information on the location, extent of the remaining forest cover of the country and the rate at which this resource is depleted. However, there were some works done to offer information on forest cover of the country. Due to massive exploitation, the forest resource of the country has confined itself to small remnants on the highlands particularly, almost all located at unreachable areas [3]. In the study area the Kathasa Priority State Forest is one of the few remnant forests which are found in Banja district. Furthermore, like in many other parts of the country, the problem of forest cover change is a very serious environmental problem in the study area and extensive areas of forest cover including shrub lands have been deforested. However, the rate and areal extent of the forest cover change is not well studied till date. Thus, for a sustainable forest resource management, it is necessary to estimate forest cover change on large spatial and temporal scales. The General Objectives of this research was to detect the magnitude of forest cover change. The Specific objectives include identifying forest cover and its spatial distribution, to analyze the spatio-temporal change, to create forest cover map of the study area for the years 1973, 1986 and 2003 and to assess the cause of forest cover change.
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Urban Sprawl Prediction and Change Detection Analysis in and around Thiruvannamalai Town Using Remote Sensing and GIS

Urban Sprawl Prediction and Change Detection Analysis in and around Thiruvannamalai Town Using Remote Sensing and GIS

The Thiruvannamalai is located to the east of Eastern Ghats. The topography of Thiruvannamalai is almost plain sloping from west to east. Thiruvannamalai experiences hot and dry weather throughout the year. The temperature ranges from a maximum of 40 °C (104 °F) to a minimum of 20 °C (68 °F). Like the rest of the state, April to June is the hottest months and December to January are the coldest. The area of investigation is about 30.13 sq.km. It is one of the most famous temple towns of India. It receives large number of piligrims throughout the year. It has high potential for growth in industries around the town. To create better urban solutions for this growing city. Urban growth is one of the
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Change Detection in Land use / Land cover in Akola Taluka Using Remote Sensing and GIS Technique

Change Detection in Land use / Land cover in Akola Taluka Using Remote Sensing and GIS Technique

change has become a central component in current strategies for managing natural resources and monitoring environmental changes. This involves development of spatial and temporal database and analysis techniques. The advancement in the concept of vegetation mapping has greatly increased research on land use/ land cover change thus providing an accurate evaluation of the spread and health of the world’s forest, grassland, and agricultural resources has become an important priority. Viewing the Earth from space is now crucial to the understanding of the influence of man’s activities on his natural resource base over time. In situations of rapid and often unrecorded land use change, observations of the earth from space provide objective information of human utilization of the landscape. Over the past years, data from Earth sensing satellites has become vital in mapping the Earth’s features and infrastructures, managing natural resources and studying environmental change. Further, satellite remote sensing data have been successfully used to estimate Leaf Area Index (LAI), based on the relationship between LAI and the Normalized Difference Vegetation Index (NDVI) (Kale et al., 2005). An accurate forest cover-type and/or land-classification system is essential to providing
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Analyzing Land Cover Change Using Remote Sensing and GIS: A Case Study of Gilgit River Basin, North Pakistan

Analyzing Land Cover Change Using Remote Sensing and GIS: A Case Study of Gilgit River Basin, North Pakistan

The findings show that over the last three periods, rangelands and barren lands transferred into agricultural class. Around the main water bodies (rivers, streams, lakes) land has shifted into agriculture/residential cover. In the first period agricultural/residential class was in the lower areas of the basin, but in the other two periods, it has shifted into the upper areas of the basin or towards the rangeland area. Another important finding was observed in the agricultural class during three periods that rivers and stream areas are transferred into agricultural and residential land. The proportions of rangelands transferring into other land during 1976, 1999, 2008, 2015 were 6118km2 (45.155%), 2761km2 (20.37%), 2002km2 (14.77%) and 1727km2 (12.74%) respectively (Fig. 5). The findings show that during the first two periods, rangelands were very rapidly transferred into barren land and agricultural/residential land. In the Gilgit river basin, the rangelands provide an extensive amount of fuel wood to meet domestic energy needs, fodder for livestock, and medicinal plants for traditional uses and sale purpose. The results show that rangelands are declining very disturbingly and its conservation is now becoming a key issue and challenge in the area.
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Change detection in rice area using remote sensing data

Change detection in rice area using remote sensing data

selecting suitable algorithm and accuracy analysis (Lillesand and Kiefer, 2000). Maximum likelihood algorithm was used for this study in supervised classification. It is assumed that data histograms band is normally distributed in the rule of maximum likelihood. Maximum likelihood decision rule is based on the probability that a pixel belongs to a certain class (Ayhan et al., 2003). Numerous studies were carried out using Landsat satellite images for determination of land use and land cover until today. Wood and Macomber (2001) conducted a study about determantion of changing forest cover in wide area by Landsat Image.Reis and Yomralioglu (2002) were attempted to determine land cover of Trabzon by Landsat UTM Image. Esetlili and Kurucu (2003) used supervised classification method to determine cotton cultivation area. Frokling et al. (2003) determined Rice cultivation area by using of Landsat Image. Rice (Oryza sativa L.) is one of the most important staple food crops, feeding over half of the world’s population. In 2010, the global Rice production was approximately 672 million tons from a cultivation area of around 154 million ha (Zhao et al., 2015). Rice is a very important plant in Turkey. Turkey contributed to Rice production was approximately in 99.400 ha and 900,000 tons in 2011. 55% of this production is realized from Thrace region. Satellite imagery from optical sensors has been widely utilized to discriminate and map the rice cropping systems (Peng et al., 2010; Sakamoto et al., 2006; Sakamoto et al., 2009; Xiao et al., 2002a, b; Xiao et al., 2002). In this study, Rice fields tried to determine in Edirne Province which is the most production area in Turkey. The changing of Rice production areas was monitored by 2001, 2006, and 2011 years of Landsat Images.
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Change Detection in Land Use and Land Cover Using Remote Sensing Data and GIS in Renuka Forest Division

Change Detection in Land Use and Land Cover Using Remote Sensing Data and GIS in Renuka Forest Division

Human beings have been altering the face of the earth for the last few centuries but with the introduction of machines, the land cover of the earth has changed drastically in the last three centuries. The debate about the relationship between human population dynamics and the availability of natural resources dates back to more than 200 years when Malthus (1798) put forward his argument that population growth would eventually outstrip the production capacity of the land. It was only in the second half of the 20th century when the probability of the Malthusian projection seemed to be a reality, that sincere efforts to study the human population–environment relation were undertaken. The scientific study and analysis of land use and land cover change involves a quantitative estimation of land use and land cover at a particular location and time. In this regard, remote sensing plays a major role in giving a synoptic view of the spatial extent of land use and land cover at a particular point of time. The Human use of land resources gives rise to land use which varies with the purpose it serves, whether it be food production, provision of shelter, recreation, extraction and processing of materials, and the biophysical characteristics of the land itself. In the developing countries, due to population pressure and in a bid to extract the maximum output from the available sources, the impact of degradation can be worse than in other countries and adversely affect the land cover of the region.
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Land Use Land Cover Change Detection on Kanchinegalur sub watershed using GIS and Remote Sensing Technique

Land Use Land Cover Change Detection on Kanchinegalur sub watershed using GIS and Remote Sensing Technique

Geographical information systems (GIS) and remote sensing are well-established information technologies, the value of which for applications in land and natural resources management are now widely recognized. Current technologies such as geographical information systems (GIS) and remote sensing provide a cost effective and accurate alternative to understand landscape dynamics. Digital change detection techniques based on multi-temporal and multi- spectral remotely sensed data have demonstrated an enormous potential to understand landscape dynamics- detect, identify, map, and monitor differences in land use and land cover patterns over time.
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Preparation of Flood Model and Hazard Estimation on Yamuna River (using GIS and Remote Sensing)

Preparation of Flood Model and Hazard Estimation on Yamuna River (using GIS and Remote Sensing)

Flooding is a process that has always played an important role in shaping the landscape. Floods that occur also generally have beneficial impacts for the ecosystems and soil fertility, and help to recharge underground stores of water. It is important to note that climate change occurring and flood hazard mapping typically provide a snapshot of flood risk at a given point in time. Due to climate change in relative sea level flood hazard maps will require periodic updates in order to reflect the changing risk at flooding. Flood hazard map can be used by developers to determine flood insurance premium in area where flood insurance exists. GIS are frequently used to produce flood hazard map and they provide an effective way of gathering information from different map and digital elevation model. Potential of GIS technology is that it integrate all data with transparency and also provide one platform for surveying, mapping, storing, analysing and present all types of spatial or geographical data and Non-Spatial data or related information.As GIS is fast, transparent and accurate so it can act as a decision support tool. GIS data can be utilized for evaluation of flood impact and also can be used for planning and management before execution of policies. GIS is useful in flood hazard zone mapping as it manage things geographically and also represent knowledge in visual format so during emergency mitigation of people can be possible. Real time geographic data can improve the allocation of resources for response.
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Land Cover Change Analysis with Special Reference to Forests and Paddy Wetlands of Neyyar and Karamana River Basins, Kerala, SW India Using GIS and Remote Sensing

Land Cover Change Analysis with Special Reference to Forests and Paddy Wetlands of Neyyar and Karamana River Basins, Kerala, SW India Using GIS and Remote Sensing

The land use change is significant due to various physical and socio-economic factors and the land use pattern of an area is directly related with the level of techno-economic advancement, nature and degree of civilization of its inhabitants (Whyte, 1961).Land use changes in any region are mainly related either with the external forces and the pressure built up within the system. The spatio-temporal changes in land use in the river basin have a direct influence on its hydrological realm and affect in many ways such as change in total run off, flooding in low lying areas etc. (Wilk and Hughes, 2002). The National Forest Policy (1988) envisage bringing one-third of the geographical area of the country under forest cover to maintain ecological balance and environmental stability. The forest cover of India has been estimated to be 637 Km 2 and 19.39% of the geographic area of the country (Mahajan and Panwar, 2005). Followed by forests, agriculture is the most dominant land use in India. Deforestation and conversion of water logged wetlands into built-up area affect the water retention capacity of soils of the river basins (Nikhil and Azeez, 2010). High rates of wetland conversions were mostly occurred by the push of population growth resulted in the reclamation of these ecosystems for various developmental activities (Turner et al, 1993).The present study deals with land use mapping with special reference to degradation of forest and paddy wetlands in Neyyar and Karamana river basins of Kerala State using remote sensing data, topographical maps, and ground truth studies applying GIS and Remote Sensing techniques .It is an attempt to derive useful information for the river basin management by analyzing land use/land cover in the study area spatially and temporally.
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