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Land Use/Land Cover Change and Environmental Impact Analysis of Ramgarh-Naudiha Region in Uttar Pradesh, India through Geospatial Technology


Affiliations
1 CSIR-Recruitment and Assessment Board, CSIR Complex, Library Avenue, New Delhi 110 012, India
2 Amity Institute of Global Warming and Ecological Studies, Amity University, Noida 201 301, Uttar Pradesh, India
3 Amity Institute of Geo-Informatics and Remote Sensing, Amity University, Noida 201 301, Uttar Pradesh, India
4 CSIR-Central Glass & Ceramic Research Institute, Khurja Centre 203 131, Uttar Pradesh, India
5 Department of Chemistry, West Bengal State University, Berunanpukuria, Kolkata 700 126, West Bengal, India
 

Rapidly changing LULC scenario with growing population is of great concern in the modern world. The present study was undertaken to evaluate the changes in LULC pattern in Ramgarh-Naudiha region of Sonbhadra district, UP, over 20 years during 1998-2018 using datasets from the Landsat Thematic Mapper (TM) 5 and Landsat 8 (OLI/TIRS) satellites. LULC map for the chosen period has been generated by unsupervised classification with maximum likelihood algorithm. Results indicate that the study area is vulnerable to such LULC changes due to its sensitive geographic location. It is found that the major changes did happen in agriculture, forest, wasteland and water bodies. Agriculture and Forest areas have decreased by ~2 and 6.56% respectively in the study period. The wastelands had increased fast from 5.08% in 1998 to 18.87% in 2018 at the cost of the forest cover and agricultural land respectively. In 1998, water bodies were 7.49%, whereas, it has decreased to 2.04% in 2018. On the contrary, urban fringe area has grown from 0.33% in 1998 to 0.49% in 2018 especially due to population growth. The present study concludes that this LULC analysis will increase awareness and help in taking necessary action in appropriate land use planning and management.

Keywords

GIS, Land Use and Land Cover, NDVI, Remote Sensing, Unsupervised Classification.
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  • Amini S, Saber M, Rabiei-Dastjerdi H & Homayouni S, Urban land use and land cover change analysis using random forest classification of Landsat time series, Remote Sens, 14 (2022) 2654.

  • Mousivand A & Arsanjani J J, Insights on the historical and emerging global land cover changes: The case of ESA-CCI-LC datasets, Appl Geogr, 106 (2019) 82-92.

  • Harris N L, Brown S, Hagen, S C, Saatchi S S, Petrova S, Salas W, Hansen M C, Potapov P V & Lotsch A, Baseline map of carbon emissions from deforestation in tropical regions, Science, 336 (2012) 1573-1576.

  • Mahmood R, Pielke, R A & McAlpine C A, Climate-relevant land use and land cover change policies, Bull Am Meteorol Soc, 97 (2016) 195-202.

  • Mishra A & Pandey V K, Quantitative environmental impact assessment of the open cast mining in Sonbhadra district, Uttar Pradesh, India, J Appl Nat Sci, 5 (2013) 361-368.

  • Lin C, Tong X, Lu W, Yan L, Wu Y, Nie C, Chu C & Long J, Environmental impacts of surface mining on mined lands, affected streams and agricultural lands in the Dabaoshan mine region, Southern China, Land Degrad Dev, 16 (2005) 463-474.

  • Padmanaban R, Bhowmik A K & Cabral P, A remote sensing approach to environmental monitoring in a reclaimed mine area, ISPRS Int J Geo-Inf, 6 (2017) 401.

  • Firozjaei M K, Kiavarz M, Alavipanah S K, Lakes T & Qureshi S, Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-markov chain modelling: A case of Babol City, Iran, Ecol Indic, 91 (2018) 155-170.

  • Butt A, Shabbir R, Ahmad S S & Aziz N, Land use change mapping and analysis using remote sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan, Egypt J Remote Sens Space Sci, 18 (2015) 251-259.

  • Wang Z, Schaaf C B, Sun Q, Kim J H, Erb A M, Gao F, Román M O, Yang Y, Petroy S, Taylor J R, Masek J G, Morisette J T, Zhang X & Papuga S A, Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/Albedo product, Int J Appl Earth Obs Geoinf, 59 (2017) 104-117.

  • Weng Q, Lu D & Schubring J, Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies, Remote Sens Environ, 89 (2004) 467-483.

  • Li H, Zhou Y, Li X, Meng L, Wang X, Wu S & Sodoudi S, A New method to quantify surface urban heat island intensity, Sci Total Environ, 624 (2018) 262-272.

  • Karmakar D, Singh V, Singh R, Sharma L K & Ghosh S, Beneficiation of clays from Ramgarh-Naudiha region of Sonbhadra District, Uttar Pradesh, impart improved properties for ceramic industries, Ind J Eng Mater Sci, 30 (2023) 65-72.

  • Lillesand T, Kiefer R W & Chipman J, Remote sensing and image interpretation (John Wiley & Sons, 7th Edn) 2015.

  • Shibi C S & Gayathri R, An efficient hardware implementation of detecting targets from remotely sensed hyperspectral images, J Sci Ind Res, 81 (2022) 156-165.

  • Navalgund R R, Remote Sensing 1. Basics and applications, Resonance-J Sci Educ, 6 (2001) 51-60.

  • Kunkel M L, Flores A N, Smith T J, McNamara J P & Benner S G, A simplified approach for estimating soil carbon and nitrogen stocks in semi-arid complex terrain, Geoderma, 165 (2011) 1-11.

  • Demirel H, Ozcinar C & Anbarjafari G, Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition, IEEE Geosci Remote Sens Lett, 7 (2009) 333-337.

  • Malo A R & Nicholson S E, A Study of Rainfall and Vegetation Dynamics in the African Sahel using Normalized Difference Vegetation Index, J Arid Environ, 19 (1990) 1-24.

  • Schmidt H & Karnieli A, Remote sensing of the seasonal variability of vegetation in a semi-arid environment, J Arid Environ, 45 (2000) 43-59.

  • Tucker C J, Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens Environ, 8 (1979) 127-150.

  • Karaburun A, Estimation of C Factor for Soil Erosion Modeling using NDVI in Buyukcekmece Watershed, Ozean J Appl Sci, 3 (2010) 77-85.

  • Ramachandra T & Kumar U, Geographic resources decision support system for land use, land cover dynamics analysis, in Proc FOSS/GRASS Users Conf, 2004 12-14.

  • Holben B N, Characteristics of maximum-value composite images from temporal AVHRR data, Int J Remote Sens, 7 (1986) 1417-1434.

  • Soufflet V, Tanré D, Begue A, Podaire A & Deschamps P, Atmospheric effects on NOAA AVHRR data over Sahelian regions, Int J Remote Sens, 12 (1991) 1189-1203.

  • Justice C O, Eck T, Tanre D & Holben B, The effect of water vapour on the normalized difference vegetation index derived for the Sahelian Region from NOAA AVHRR data, Int J Remote Sens, 12 (1991) 1165-1187.

  • Zha Y, Gao J & Ni S, Use of normalized difference built-up index in automatically mapping urban areas from tm imagery, Int J Remote Sens, 24 (2003) 583-594.

  • Tilahun A & Teferie B, Accuracy assessment of land use land cover classification using google earth, Am J Environ Prot, 4 (2015) 193-198.

  • Congalton R G, A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens Environ, 37 (1991) 35-46.

  • Jensen J R & Cowen D C, Remote sensing of urban/suburban infrastructure and socio-economic attributes, Photogramm Eng Remote Sens, 65 (1999) 611-622.

  • Fung T & LeDrew E, For change detection using various accuracy, Photogramm Eng Remote Sens, 54 (1988) 1449-1454.

  • Chakravarty S, Ghosh S, Suresh C, Dey A & Shukla G, Deforestation?: causes, effects and control strategies, in Global Perspectives on Sustainable Forest Management, edited by C A Okia (IntechOpen) 2012, 1-26.

  • Mallupattu P K & Reddy J R S, Analysis of land use/land cover changes using remote sensing data and GIS at an urban area, Tirupati, India, Sci World J, 2013, 268623.

  • Lambin E F, Turner B L, Geist H J, Agbola S B, Angelsen A, Bruce J W, Coomes O T, Dirzo R, Fischer G, Folke C, George P S, Homewood K, Imbernon J, Leemans R, Li X, Moran E F, Mortimore M, Ramakrishnan P S, Richards J F, Skanes H, Steffen W, Stone G D, Svedin U, Veldkamp T A, Vogel C & Xu J, The causes of land-use and land-cover change: moving beyond the myths, Glob Environ Change, 11 (2001) 261-269.

  • Rai R, Zhang Y, Paudel B, Li S & Khanal N R, A synthesis of studies on land use and land cover dynamics during 1930–2015 in Bangladesh, Sustainability, 9 (2017) 1866.


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  • Land Use/Land Cover Change and Environmental Impact Analysis of Ramgarh-Naudiha Region in Uttar Pradesh, India through Geospatial Technology

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Authors

Debiprasad Karmakar
CSIR-Recruitment and Assessment Board, CSIR Complex, Library Avenue, New Delhi 110 012, India
Vartika Singh
Amity Institute of Global Warming and Ecological Studies, Amity University, Noida 201 301, Uttar Pradesh, India
Raghvendra Singh
Amity Institute of Geo-Informatics and Remote Sensing, Amity University, Noida 201 301, Uttar Pradesh, India
Lalit Kumar Sharma
CSIR-Central Glass & Ceramic Research Institute, Khurja Centre 203 131, Uttar Pradesh, India
Swapankumar Ghosh
Department of Chemistry, West Bengal State University, Berunanpukuria, Kolkata 700 126, West Bengal, India

Abstract


Rapidly changing LULC scenario with growing population is of great concern in the modern world. The present study was undertaken to evaluate the changes in LULC pattern in Ramgarh-Naudiha region of Sonbhadra district, UP, over 20 years during 1998-2018 using datasets from the Landsat Thematic Mapper (TM) 5 and Landsat 8 (OLI/TIRS) satellites. LULC map for the chosen period has been generated by unsupervised classification with maximum likelihood algorithm. Results indicate that the study area is vulnerable to such LULC changes due to its sensitive geographic location. It is found that the major changes did happen in agriculture, forest, wasteland and water bodies. Agriculture and Forest areas have decreased by ~2 and 6.56% respectively in the study period. The wastelands had increased fast from 5.08% in 1998 to 18.87% in 2018 at the cost of the forest cover and agricultural land respectively. In 1998, water bodies were 7.49%, whereas, it has decreased to 2.04% in 2018. On the contrary, urban fringe area has grown from 0.33% in 1998 to 0.49% in 2018 especially due to population growth. The present study concludes that this LULC analysis will increase awareness and help in taking necessary action in appropriate land use planning and management.

Keywords


GIS, Land Use and Land Cover, NDVI, Remote Sensing, Unsupervised Classification.

References