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Agricultural growth and land use land cover change in peri-urban India

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Abstract

Varanasi district is comprised of eight administrative blocks. Owing to economic development, it has shown an increase in urban activities. Analysis of remotely sensed data for a period of two decades reveals that the built-up area increased by about 345% while vegetation decreased by 86% during 1993–2013. Contrary to other observations, land use changes, due to urban growth, increased not only the built area but also the agriculture class. Agricultural area increased by 39% in the two decades. Population density increased from 1217 to 1806 person/km2 and household density grew from 152 to 273 households/km2 during 1991–2011. Land absorption coefficient (LAC) and land consumption ratio (LCR) were calculated as demographic indices of land use land cover change (LULC). Vegetation delineation shows that sparse vegetation increased from 40.2 to 90.1 km2 while dense vegetation decreased from 28.4 to 1.7 km2 in 1993–2013. There was a distinct shift from agriculture, as a primary economic activity, towards non-agricultural pursuits. In order to frame better strategies for sustainable development and food security, this phenomenon of increasing urbanization around cities needs to be studied. This micro scale study can be helpful in formulating policy for urban areas in developing countries like India which heavily depend on agriculture to sustain their population and economy.

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References

  • Angel, S., Parent, J., Civco, D. L., & Blei, A. M. (2010). The persistent decline in urban densities: Global and historical evidence of ‘sprawl’. Lincoln Institute of Land Policy Working Paper. http://www.alnap.org/pool/files/1834-1085-angel-final-1.pdf. Accessed 5 Dec 2018

  • Anonymous (2003). “Accuracy Assessment.” http://www.yale.edu/ceo/OEFS/Accuracy_Assessment.pdf. Accessed 5 Feb 2016

  • Asif, K., & Rahman, H. (2013). Land use and socio-economic responses to urban encroachment on agricultural land–a study of an Indian urban-rural fringe. Geograficky Casopis/Geographical Journal, 65(4), 289–314.

    Google Scholar 

  • Bailly, H. (2007). Environmental baseline study of Margala and Margala north blocks. Islamabad: MOL Pakistan Oil and Gas Company BV.

    Google Scholar 

  • Behera, M. D., Tripathi, P., Das, P., Srivastava, S. K., Roy, P. S., Joshi, C., ... & Tripathi, O. P. (2018). Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985. Journal of environmental management, 206, 1192–1203. https://doi.org/10.1016/j.jenvman.2017.10.015.

  • Bettencourt, L., & West, G. (2010). A unified theory of urban living. Nature, 467(7318), 912. https://doi.org/10.1038/467912a.

    Article  CAS  Google Scholar 

  • Bhatla, R., Tabassum, S., & Tripathi, A. (2016). Trend analysis and extreme events of temperature during post monsoon and winter seasons over Varanasi, Uttar Pradesh, India. Journal Indian Geophysical Union, 20(1), 123–127.

    Google Scholar 

  • Biró, M., Czúcz, B., Horváth, F., Révész, A., Csatári, B., & Molnár, Z. (2013). Drivers of grassland loss in Hungary during the post-socialist transformation (1987–1999). Landscape ecology, 28(5), 789–803. https://doi.org/10.1007/s10980-012-9818-0.

    Article  Google Scholar 

  • Birthal, P. S., Negi, D. S., Khan, M. T., & Agarwal, S. (2015). Is Indian agriculture becoming resilient to droughts? Evidence from rice production systems. Food Policy, 56, 1–12.

    Article  Google Scholar 

  • Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change mapping and analysis using Remote Sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 251–259. https://doi.org/10.1016/j.jenvman.2017.10.015.

    Article  Google Scholar 

  • Changhong, S. U., Bojie, F. U., Yihe, L. U., Nan, L. U., Yuan, Z., Anna, H. E., & Lamparski, H. (2011). Land use change and anthropogenic driving forces: a case study in Yanhe 459 River Basin. Chinese Geographical Science, 21(40930528), 587–599.460. https://doi.org/10.1007/s11769-011-0495-8.

    Article  Google Scholar 

  • Chatterjee, R., Verma, P., Shaw, R., & Raghubanshi A.S. (2015). Climate and Disaster Resilience of Varanasi City, Zone and Ward Profile. Kyoto, Japan.

  • Chaturvedi, A., Kamble, R., Patil, N. G., & Chaturvedi, A. (2013). City–forest relationship in Nagpur: one of the greenest cities of India. Urban forestry & urban greening, 12(1), 79–87.

    Article  Google Scholar 

  • Chen, R., Ye, C., Cai, Y., Xing, X., & Chen, Q. (2014). The impact of rural out-migration on land use transition in China: past, present and trend. Land Use Policy, 40, 101–110. https://doi.org/10.1016/j.landusepol.2013.10.003.

    Article  Google Scholar 

  • Comber, A., Fisher, P., Brunsdon, C., & Khmag, A. (2012). Spatial analysis of remote sensing image classification accuracy. Remote Sensing of Environment, 127, 237–246. https://doi.org/10.1016/j.rse.2012.09.005.

    Article  Google Scholar 

  • Coulter, L. L., Stow, D. A., Tsai, Y. H., Ibanez, N., Shih, H. C., Kerr, A., Benza, M., Weeks, J. R., & Mensah, F. (2016). Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery. Remote Sensing of Environment, 184, 396–409.

    Article  Google Scholar 

  • Csaplovics, E. (1998). High-resolution space imagery for regional environmental monitoring-status quo and future trends. International Archives of Photogrammetry and Remote Sensing, 32, 211–216.

    Google Scholar 

  • d’Amour, C. B., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B., Erb, K. H., ... & Seto, K. C. (2017). Future urban land expansion and implications for global croplands. Proceedings of the National Academy of Sciences, 114(34), 8939–8944.

  • DAC (2014). Department of Agriculture & Cooperation, District wise crop production statistics. Accessed on 5 December, 2018. Available at: https://aps.dac.gov.in/APY/Index.htm.

  • Deshpande, T. (2017). State of Agriculture in India. PRS Legislative Research, 6-7.

  • Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29(4), 1185–1206.

    Article  Google Scholar 

  • Dupont, V. (2007). Conflicting stakes and governance in the peripheries of large Indian metropolises–an introduction. Cities, 24(2), 89–94. https://doi.org/10.1016/j.cities.2006.11.002.

    Article  Google Scholar 

  • Fichera, C. R., Modica, G., & Pollino, M. (2012). Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing, 45(1), 1–18. https://doi.org/10.5721/EuJRS20124501.

    Article  Google Scholar 

  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1), 185–201.

    Article  Google Scholar 

  • Foody, G. M. (2003). Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development. International Journal of Remote Sensing, 24(20), 4035–4046. https://doi.org/10.1080/0143116031000103853.

    Article  Google Scholar 

  • Gašparović, M., & Jogun, T. (2018). The effect of fusing Sentinel-2 bands on land-cover classification. International journal of remote sensing, 39(3), 822–841.

    Article  Google Scholar 

  • Gašparović, M., Medak, D., Pilaš, I., Jurjević, L., & Balenović, I. (2018). Fusion of sentinel-2 and planetscope imagery for vegetation detection and monitoring. In Volumes ISPRS TC I Mid-term Symposium Innovative Sensing-From Sensors to Methods and Applications.

  • Gašparović, M., Zrinjski, M., & Gudelj, M. (2019). Automatic cost-effective method for land cover classification (ALCC). Computers, Environment and Urban Systems, 76, 1–10.

    Article  Google Scholar 

  • Gilbert, O. L. (1991). The ecology of urban habitats, 369 pp. New York: Chapman and Hall.

    Book  Google Scholar 

  • Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., ... & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science.

  • Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55–72.

    Article  Google Scholar 

  • Gumma, M., Mohammad, I., Nedumaran, S., Whitbread, A., & Lagerkvist, C. (2017). Urban sprawl and adverse impacts on agricultural land: a case study on Hyderabad, India. Remote sensing, 9(11), 1136.

    Article  Google Scholar 

  • Hassan, A. M., & Lee, H. (2015). Toward the sustainable development of urban areas: an overview of global trends in trials and policies. Land Use Policy, 48, 199–212. https://doi.org/10.1016/j.landusepol.2015.04.029.

    Article  Google Scholar 

  • Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725–749.

    Article  Google Scholar 

  • Imai, K. S., Gaiha, R., & Garbero, A. (2017). Poverty reduction during the rural–urban transformation: rural development is still more important than urbanisation. Journal of Policy Modeling, 39(6), 963–982. https://doi.org/10.1016/j.jpolmod.2017.10.002.

    Article  Google Scholar 

  • Jaiswal, J. K., & Verma, N. (2013). Land use change detection in Baragaon block, Varanasi District using remote sensing. International Journal of Engineering Science and Innovative, 2(7), 49–53.

    Google Scholar 

  • Jiang, L., & Zhang, Y. (2016). Modeling urban expansion and agricultural land conversion in Henan Province, China: an integration of land use and socioeconomic data. Sustainability, 8(9), 920.

    Article  Google Scholar 

  • Jiao, L., Mao, L., & Liu, Y. (2015). Multi-order landscape expansion index: characterizing urban expansion dynamics. Landscape and Urban Planning, 137, 30–39. https://doi.org/10.1016/j.landurbplan.2014.10.023.

    Article  Google Scholar 

  • Kamini, J., Jayanthi, S. C., & Raghavswamy, V. (2006). Spatio-temporal analysis of land use in urban Mumbai-using multi-sensor satellite data and gis techniques. Journal of the Indian Society of Remote Sensing, 34(4), 385. https://doi.org/10.1007/bf02990923.

    Article  Google Scholar 

  • Kantakumar, L. N., Kumar, S., & Schneider, K. (2016). Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat International, 51, 11–22.

    Article  Google Scholar 

  • Kaul, H. A., & Sopan, I. (2012). Land use land cover classification and change detection using high resolution temporal satellite data. Journal of Environment, 1(4), 146–152.

    Google Scholar 

  • Kumar, M., Mukherjee, N., Sharma, G. P., & Raghubanshi, A. S. (2010). Land use patterns and urbanization in the holy city of Varanasi, India: a scenario. Environmental monitoring and assessment, 167(1-4), 417–422. https://doi.org/10.1007/s10661-009-1060-0.

    Article  Google Scholar 

  • Landsat, U. S. G. S. (2015). 8 (l8) data users handbook. LSDS-1574 Version, 3.

  • Liu, M., Hu, Y., Chang, Y., He, X., & Zhang, W. (2009). Land use and land cover change analysis and prediction in the upper reaches of the Minjiang River, China. Environmental management, 43(5), 899–907. https://doi.org/10.1007/s00267-008-9263-7.

    Article  Google Scholar 

  • Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863.

    Article  Google Scholar 

  • MacGregor-Fors, I. (2011). Misconceptions or misunderstandings? On the standardization of basic terms and definitions in urban ecology. Landscape and Urban Planning, 100(4), 347–349.

    Article  Google Scholar 

  • Meyfroidt, P., & Lambin, E. F. (2011). Global forest transition: prospects for an end to deforestation. Annual Review of Environment And Resources, 36, 343–371. https://doi.org/10.1146/annurev-environ-090710-143732.

    Article  Google Scholar 

  • Mishra, V. N., Prasad, R., Rai, P. K., Vishwakarma, A. K., & Arora, A. (2018). Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Science Informatics, 1–16.

  • Mondal, A., Kundu, S., Chandniha, S. K., Shukla, R., & Mishra, P. K. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116–123.

    Google Scholar 

  • Mutoko, M. C., Hein, L., & Bartholomeus, H. (2014). Integrated analysis of land use changes and their impacts on agrarian livelihoods in the western highlands of Kenya. Agricultural Systems, 128, 1–12. https://doi.org/10.1016/j.agsy.2014.04.001.

    Article  Google Scholar 

  • Najmuddin, O., Deng, X., & Siqi, J. (2017). Scenario analysis of land use change in Kabul River Basin–a river basin with rapid socio-economic changes in Afghanistan. Physics and Chemistry of the Earth, Parts A/B/C, 101, 121–136. https://doi.org/10.1016/j.pce.2017.06.002.

    Article  Google Scholar 

  • Nkonya, E., Mirzabaev, A., & Von Braun, J. (Eds.). (2016). Economics of land degradation and improvement: a global assessment for sustainable development. Cham: Springer Open.

    Google Scholar 

  • Ouedraogo, I., Tigabu, M., Savadogo, P., Compaoré, H., Odén, P. C., & Ouadba, J. M. (2010). Land cover change and its relation with population dynamics in Burkina Faso, West Africa. Land Degradation & Development, 21(5), 453–462. https://doi.org/10.1002/ldr.981.

    Article  Google Scholar 

  • Pandey, B., & Seto, K. C. (2015). Urbanization and agricultural land loss in India: comparing satellite estimates with census data. Journal of environmental management, 148, 53–66.

    Article  Google Scholar 

  • Pandey, V. L., & Suganthi, D. (2015). Fueling agricultural growth in India: some reflections. Land Use Policy, 42, 227–232.

    Article  Google Scholar 

  • Pham, V. C., Pham, T. T. H., Tong, T. H. A., Nguyen, T. T. H., & Pham, N. H. (2015). The conversion of agricultural land in the peri-urban areas of Hanoi (Vietnam): patterns in space and time. Journal of Land Use Science, 10(2), 224–242.

    Article  Google Scholar 

  • Pontius, R. G., Jr., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429.

    Article  Google Scholar 

  • Pontius, R. G., Jr., & Santacruz, A. (2014). Quantity, exchange, and shift components of difference in a square contingency table. International Journal of Remote Sensing, 35(21), 7543–7554.

    Article  Google Scholar 

  • Poursanidis, D., Chrysoulakis, N., & Mitraka, Z. (2015). Landsat 8 vs. Landsat 5: a comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation, 35, 259–269.

    Article  Google Scholar 

  • Rahman, A., Kumar, S., Fazal, S., & Siddiqui, M. A. (2012). Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing, 40(4), 689–697. https://doi.org/10.1007/s12524-011-0165-4.

    Article  Google Scholar 

  • Ramankutty, N., Mehrabi, Z., Waha, K., Jarvis, L., Kremen, C., Herrero, M., & Rieseberg, L. H. (2018). Trends in global agricultural land use: implications for environmental health and food security. Annual Review of Plant Biology, 69, 789–815.

    Article  CAS  Google Scholar 

  • Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002.

    Article  Google Scholar 

  • Riebsame, W. E., Meyer, W. B., & Turner, B. L. (1994). Modeling land use and cover as part of global environmental change. Climatic Change, 28(1-2), 45–64. https://doi.org/10.1016/j.ejrs.2015.02.002.

    Article  Google Scholar 

  • Samra, J. S., Singh, G., & Ramakrishna, Y. S. (2003). Cold wave of 2002-2003, Impact on Agriculture. Accessed on 5 December, 2018. Available at: http://www.un-spider.org/sites/default/files/6-Cold%20Wave_Impacts_India_Indian%20Council%20of%20Agricultural%20Research.pdf.

  • Sharma, A., & Chandrasekhar, S. (2014). Growth of the urban shadow, spatial distribution of economic activities, and commuting by workers in rural and urban India. World Development, 61, 154–166. https://doi.org/10.1016/j.worlddev.2014.04.003.

    Article  Google Scholar 

  • Sharma, L., Pandey, P. C., & Nathawat, M. S. (2012). Assessment of land consumption rate with urban dynamics change using geospatial techniques. Journal of Land Use Science, 7(2), 135–148. https://doi.org/10.1080/1747423X.2010.537790.

    Article  CAS  Google Scholar 

  • Shukla, A. K., Ojha, C. S. P., Mijic, A., Buytaert, W., Pathak, S., Garg, R. D., & Shukla, S. (2018). Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin. Hydrology and Earth System Sciences, 22(9), 4745–4770.

    Article  Google Scholar 

  • Siddiqui, A., Siddiqui, A., Maithani, S., Jha, A. K., Kumar, P., & Srivastav, S. K. (2018). Urban growth dynamics of an Indian metropolitan using CA Markov and Logistic Regression. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 229–236.

    Article  Google Scholar 

  • Singh, S., & Rai, P. K. (2018). Application of earth observation data for estimation of changes in land trajectories in Varanasi District, India. Journal of Landscape Ecology, 11(1), 5–18.

    Article  Google Scholar 

  • Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61–78.

    Article  Google Scholar 

  • Singh, R., Srivastava, P., Singh, P., Upadhyay, S., & Raghubanshi, A. S. (2019). Human overpopulation and food security: challenges for the agriculture sustainability. In Urban agriculture and food systems: breakthroughs in research and practice (pp. 439–467). IGI Global.

  • Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote sensing of Environment, 75(2), 230–244.

    Article  Google Scholar 

  • Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250–1265. https://doi.org/10.1016/j.asr.2012.06.032.

    Article  Google Scholar 

  • Srivastava, P., Singh, R., Tripathi, S., & Raghubanshi, A. S. (2016). An urgent need for sustainable thinking in agriculture–an Indian scenario. Ecological Indicators, 67, 611–622.

    Article  Google Scholar 

  • Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29–39. https://doi.org/10.1016/j.jag.2003.08.002.

    Article  Google Scholar 

  • Suribabu, C. R., Bhaskar, J., & Neelakantan, T. R. (2012). Land use/cover change detection of Tiruchirapalli City, India, using integrated remote sensing and GIS tools. Journal of the Indian Society of Remote Sensing, 40(4), 699–708. https://doi.org/10.1007/s12524-011-0196-x.

    Article  Google Scholar 

  • Sutton, P. C., Anderson, S. J., Costanza, R., & Kubiszewski, I. (2016). The ecological economics of land degradation: impacts on ecosystem service values. Ecological Economics, 129, 182–192.

    Article  Google Scholar 

  • Tyagi, S., Singh, N., Sonkar, G., & Mall, R. K. (2018). Sensitivity of evapotranspiration to climate change using DSSAT model in sub humid climate region of Eastern Uttar Pradesh. Modeling Earth Systems and Environment, 1–11.

  • Ustaoglu, E., & Williams, B. (2017). Determinants of urban expansion and agricultural land conversion in 25 EU countries. Environmental management, 60(4), 717–746.

    Article  Google Scholar 

  • Verburg, P. H., Veldkamp, A., Willemen, L., Overmars, K. P., & Castella, J. C. (2004). Landscape level analysis of the spatial and temporal complexity of land-use change. Ecosystems and land use. Geographical Monograph Series, 153, 217–230.

    Google Scholar 

  • Verma, P., & Raghubanshi, A. S. (2018). Urban sustainability indicators: challenges and opportunities. Ecological Indicators, 93, 282–291.

    Article  Google Scholar 

  • Verma, P., & Raghubanshi, A. S. (2019). Rural development and land use land cover change in a rapidly developing agrarian South Asian landscape. Remote Sensing Applications: Society and Environment.

  • Vij, S., & Narain, V. (2016). Land, water & power: the demise of common property resources in periurban Gurgaon, India. Land Use Policy, 50, 59–66. https://doi.org/10.1016/j.landusepol.2015.08.030.

    Article  Google Scholar 

  • Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of Earth’s ecosystems. Science, 277(5325), 494–499. https://doi.org/10.1126/science.277.5325.494.

    Article  CAS  Google Scholar 

  • Wang, X., Zheng, D., & Shen, Y. (2008). Land use change and its driving forces on the Tibetan Plateau during 1990–2000. Catena, 72(1), 56–66. https://doi.org/10.1016/j.catena.2007.04.003.

    Article  CAS  Google Scholar 

  • Wang, J., Chen, Y., Shao, X., Zhang, Y., & Cao, Y. (2012). Land-use changes and policy dimension driving forces in China: present, trend and future. Land use policy, 29(4), 737–749. https://doi.org/10.1016/j.landusepol.2011.11.010.

    Article  CAS  Google Scholar 

  • Weng, Q. (2010). Remote sensing and GIS integration.

  • Wilson, E. H., Hurd, J. D., Civco, D. L., Prisloe, M. P., & Arnold, C. (2003). Development of a geospatial model to quantify, describe and map urban growth. Remote Sensing of Environment, 86(3), 275–285. https://doi.org/10.1016/S0034-4257(03)00074-9.

    Article  Google Scholar 

  • Yeates, M., & Garner, B. J. (1976). The North American city. HarperCollins Publishers.

  • Yin, Z. Y., Stewart, D. J., Bullard, S., & MacLachlan, J. T. (2005). Changes in urban built-up surface and population distribution patterns during 1986–1999: a case study of Cairo, Egypt. Computers, Environment and Urban Systems, 5, 595–616. https://doi.org/10.1016/j.compenvurbsys.2005.01.008.

    Article  Google Scholar 

  • Yu, W., Zhou, W., Qian, Y., & Yan, J. (2016). A new approach for land cover classification and change analysis: integrating backdating and an object-based method. Remote Sensing of Environment, 177, 37–47.

    Article  Google Scholar 

  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote sensing of Environment, 98(2-3), 317–328. https://doi.org/10.1016/j.rse.2005.08.006.

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to Prof. A. S. Raghubanshi, Director, Institute of Environment and Sustainable Development, BHU, Varanasi, for his critical inputs in this work; Pradiip K. Verma for editing the English language of the article; and the University Grants Commission (UGC), New Delhi, India, for providing all necessary facilities.

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  1. Institute of Environment & Sustainable Development (IESD), Banaras Hindu University (BHU), Varanasi, India

    Sanoj Kumar Patel & Gopal Shankar Singh

  2. Integrative Ecology Laboratory (IEL), Institute of Environment & Sustainable Development (IESD), Banaras Hindu University (BHU), Varanasi, India

    Pramit Verma

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Patel, S.K., Verma, P. & Shankar Singh, G. Agricultural growth and land use land cover change in peri-urban India. Environ Monit Assess 191, 600 (2019). https://doi.org/10.1007/s10661-019-7736-1

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