scholarly journals Land Use and Land Cover Spatial Distribution in Pondicherry Coastal Region Using Remote Sensing and GIS Techniques with Special Reference to Aquaculture Development

2020 ◽  
pp. 68-81
Author(s):  
Dr. I. Raja Rajasri Pramila Devi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Coastal Region ◽  
Policy Makers ◽  
Fallow Lands ◽  
Land Cover Map ◽  
Liss Iii ◽  
Gis Environment ◽  
Development And Management

The remote sensing with GIS plays a major role in land use land cover changes. The satellite imageries of IRS 1C LISS III (1998), IRS 1D LISS III (2000) and P6 (2008 & 2014) have been used for land use and land cover map preparation. The change detection study has been carried out in a GIS environment. The results observed through many research studies have showed agriculture land was reduced from 10,968 ha to 6214.24 ha due to rapid industrialization and settlements. Pondicherry having a coastal line of 45 km also has had significant potential in fisheries. Interestingly out of 800 ha of potential fisheries area, only 138 ha of aquaculture land is utilized. Hence, commercial aquaculture can be encouraged in this area, as it should not affect the other area. It is a good economic source for local people. Moreover, the socio-economic impacts on vegetation, with settlement, plantation, aquaculture, agriculture, fallow lands and marshy lands have also been identified. The present study will be useful for policy makers towards further development and management.

scholarly journals The Use of Spot-HRV Data for the Mapping of the Land Cover (applied upon East-Mono, Central Togo)

Afrika Focus ◽  
1991 ◽  
Vol 7 (1) ◽  
Author(s):  
Beata Maria De Vliegher
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Image Data ◽  
Visual Image ◽  
Data Set ◽  
Land Cover Map ◽  
Colour Composite

The mapping of the land use in a tropical wet and dry area (East-Mono, Central Togo) is made using remote sensing data, recorded by the satellite SPOT. The negative, multispectral image data set has been transferred into positives by photographical means and afterwards enhanced using the diazo technique. The combination of the different diazo coloured images resulted in a false colour composite, being the basic document for the visual image interpretation. The image analysis, based upon differences in colour and texture, resulted in a photomorphic unit map. The use of a decision tree including the various image characteristics allowed the conversion of the photomorphic unit map into a land cover map. For this, six main land cover types could be differentiated resulting in 16 different classes of the final map. KEY WORDS :Remote sensing, SPOT, Multispectral view, Visual image interpre- tation, Mapping, Vegetation, Land use, Togo. 

scholarly journals Analysis of Land Use and Land Cover Change in Gummidipoondi Using Remote Sensing and GIS Tools

2021 ◽  
Vol 9 (6) ◽  
pp. 675-682
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing And Gis ◽  
Rural Land ◽  
Anthropogenic Pressures ◽  
Rural Land Use ◽  
Gis Tools ◽  
Liss Iii ◽  
Surrounding Areas

The aim of the study was to evaluate the changes in land use and land cover (LULC) in Gummidipoondi and the surrounding areas in Thiruvallur district, Tamilnadu India.Spatio-temporal variation in the land use and land cover were analysed on a decadal basis for the period from 1990 to 2019 using remote sensing and GIS based tools. The Landsat 5 (TM) and Resource-Sat 2 (LISS-III) data was used for the LULC classificationin the study area. During the study period from 1990 to 2019, built-up area including industrial, urban and rural land use increased by about 147%. Predominant change was also noticed in the mudflat category where more than 95% of it was lost to various other land uses such as agriculture and marsh area. This observation calls for planning and conservation of sensitive ecosystems in the study area that may be lost due to anthropogenic pressures via pollution and undesirable conversion of LULC. The study revealed no significant changes in the extent of other LULC classes such as agriculture, forests, plantations, land with or without scrub, rivers and waterbodies in the study area

scholarly journals Land use Land Cover Change Detection using K-means Clustering and Maximum Likelihood Classification Method in the Javadi Hills, Tamil Nadu, India

2019 ◽  
Vol 9 (1S3) ◽  
pp. 51-56 ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Tamil Nadu ◽  
Hybrid Approach ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Change Analysis ◽  
Supervised Classifiers ◽  
Liss Iii

Land use/Land cover (LU/LC) change analysis is the present-day challenging task for the researchers in defining the environmental change across the world in the field of remote sensing and GIS (Geographic Information System). This paper analyzes the LU/LC changes between the years 2009 and 2019 in the region of Javadi Hills located in Tamil Nadu, India. Images from the Indian remote sensing satellite Resourcesat-1 LISS III and American earth observation satellite Landsat-8 were used for analyzing the LU/LC change for the study area. In this work, the classification was performed by using the hybrid approach of unsupervised and supervised classifiers. The classified LU/LC map for the study area defines forest and non-forest covered region. The key objective of this work was to identify the percentage of LU/LC change occurred in our study area for the years 2009 to 2014 and 2014 to 2019. Observing and examining the changes occurred in the study area provides a clear view to the land resources management to take effective measures in protecting the environment.

scholarly journals Monitoring Land Use/Land Cover Changes in a River Basin due to Urbanization using Remote Sensing and GIS Approach

2014 ◽  
Vol XL-8 ◽  
pp. 949-953
Author(s):  
S. Shukla ◽  
M. V. Khire ◽  
S. S. Gedam
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Agricultural Lands ◽  
Sensing Technology ◽  
Liss Iii ◽  
Spatio Temporal

Faster pace of urbanization, industrialization, unplanned infrastructure developments and extensive agriculture result in the rapid changes in the Land Use/Land Cover (LU/LC) of the sub-tropical river basins. Study of LU/LC transformations in a river basin is crucial for vulnerability assessment and proper management of the natural resources of a river basin. Remote sensing technology is very promising in mapping the LU/LC distribution of a large region on different spatio-temporal scales. The present study is intended to understand the LU/LC changes in the Upper Bhima river basin due to urbanization using modern geospatial techniques such as remote sensing and GIS. In this study, the Upper Bhima river basin is divided into three adjacent sub-basins: Mula-Mutha sub-basin (ubanized), Bhima sub-basin (semi-urbanized) and Ghod sub-basin (unurbanized). Time series LU/LC maps were prepared for the study area for a period of 1980, 2002 and 2009 using satellite datasets viz. Landsat MSS (October, 1980), Landsat ETM+ (October, 2002) and IRS LISS III (October 2008 and November 2009). All the satellite images were classified into five LU/LC classes viz. built-up lands, agricultural lands, waterbodies, forests and wastelands using supervised classification approach. Post classification change detection method was used to understand the LU/LC changes in the study area. Results reveal that built up lands, waterbodies and agricultural lands are increasing in all the three sub-basins of the study area at the cost of decreasing forests and wastelands. But the change is more drastic in urbanized Mula-Mutha sub-basin compared to the other two sub-basins.

scholarly journals Urban Growth Assessment of Chennai District by Remote Sensing and Gis Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5119-5125
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Satellite Images ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Gis Techniques ◽  
Land Cover Map ◽  
Landsat Satellite

Urban growth of Chennai district is exponential and heading towards extreme urbanisation. Hence this necessitates the study of urban growth in Chennai district. The recent advancement in Remote sensing and GIS has an excellent ability to derive various data from the satellite images obtained .This helps us to map, monitor and picturise various aspects of development with respect to their demands. The basic principle of remote sensing is followed as the methodology. By following the methodology correctly and by proper processing of the data acquired from the satellite images, the exact requirements of information can be obtained. The Change in the urban growth of the Chennai district for three decades from 1989 to 2019 have been found by using remote sensing and GIS techniques. The satellite images of various years are obtained from Landsat satellite from the USGS Earth Explorer .The Land use characteristics of Chennai district of each year can be obtained by preparing the land use land cover map of Chennai district by the use of landsat satellite images. The two software namely ArcGIS and ERDAS Imagine are used to create the Land use land cover map. From the Land use land cover map of Chennai district, the change detection and statistical analysis of three decades are done and these analysis clearly shows that the urban growth of Chennai district is constantly increasing and there is a huge decrease in other natural features such as vegetation, water body and barren land. By performing urban trend analysis the urban growth of Chennai district for the upcoming years are predicted to prove the urban agglomeration in Chennai district.

scholarly journals CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1585-1591
Author(s):  
M. S. Mondal ◽  
N. Sharma ◽  
M. Kappas ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Land Cover Map ◽  
Liss Iii

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

Sign in / Sign up

Export Citation Format

Share Document