IJEP 43(3): 251-256 : Vol. 43 Issue. 3 (March 2023)
1. Dr. B.R. Ambedkar University, Department of Geosciences, Srikakulam, Andhra Pradesh – 532 410, India
2. Andhra University, Department of Geology, Visakhapatnam, Andhra Pradesh – 530 003, India
Natural landscapes have altered dramatically via anthropogenic activity, particularly in places that are heavily influenced by climate change and population increase, such as nations, like India. It is crucial for sustainable development, particularly effective water management methods, to know about the influence of landuse and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing (RS) were employed for monitoring landuse changes utilising Quantum ArcGIS and ERDAS Imagine. This research studied the variations in LULC in the Mahanadi river basin delta, Orissa for the years 2010, 2015 and 2020. Landsat satellite pictures were employed to track the landuse changes. For the categorization of Landsat images, maximum-likelihood supervised classification was applied. The broad categorization identifies four basic groups in the research region, including (i) water bodies, (ii) agriculture fields, (iii) forests, (iv) barren lands, (v) built-up areas and (vi) aquaculture. The findings indicated a big growth in forests from the year 2010 to 2020, but a substantial increase in barren lands had happened by the year 2020, while built-up landuse has witnessed a quick climb. The kappa coefficient was used to measure the validity of identified photos, with an overall kappa coefficient of 0.82, 0.84 and 0.90 for the years 2010, 2015 and 2020, respectively. However, a large drop will occur in agriculture fields in the predicted years. The study effectively demonstrates LULC alterations showing substantial patterns of landuse change in the Mahanadi delta. This information might be valuable for landuse management and future planning in the region.
Landuse/Land cover, Sustainable planning, Landsat image, Remote sensing, GIS
- Singh, A. 1989. Digital change detection techniques using remotely sensed data. Int. J. Remote Sens., 10:889-1003.
- Houghton, R.A. 1994. The worldwide extent of landuse change. Biosci., 44:305-313.
- Hathout, S. 2002. The use of GIS for monitoring and predicting urban growth in east and west St. Paul, Winnipeg, Manitoba, Canada. J. Env. Manage., 66:229-238.
- Fei, L., et al. 2018. Effects of landuse change on ecosystem services value in West Jilin since the reform and opening of China. Ecosyst. Serv., 31:12-20.
- Guzha, A. and M.C. Rufino. 2018. Impacts of landuse and land cover change on surface runoff, discharge and low flows : Evidence from East Africa. J. Hydrol. Reg. Stud., 15:49-67.
- Lopez, E., et al. 2001. Predicting land cover and landuse change in the urban fringe : A case in Morelia city, Mexico. Landsc. Urban Plan. 55:271-285.
- Jat, M.K., P. Garg and D. Khare. 2008. Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int. J. Appl. Earth Obs. Geoinf., 10:26-43.
- Guerra, F., H. Puig and R. Chauma. 1998. The forest-savanna dynamics from multi-date Landsat-TM data in Sierra Parima, Venezuela. Int. J. Remote Sens., 19:2061-2075.
- Roy, A. and A.B. Inamadar. 2019. Multi-temporal landuse land cover (LULC) change analysis of a dry semiarid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon. 5:e01478.
- Hussain, M., et al. 2013. Change detection from remotely sensed images : From pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens., 60:91-106.
- Singh, Y., P. Ferrazzoli and R. Rahmoune. 2013. Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin, India. Int. J. Remote Sens., 34:4967-4985.
- Alexakiis, D.D., et al. 2013. Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements : The case study of Paphosarea in Cyprus. Nat. Hazards. 72:119-141.
- Yatoo, S.A., et al. 2020. Monitoring landuse changes and its future prospect using cellular automata simulation and artificial neural network to Ahmedabad city, India. Geo. J., 85:1-22.
- Vibhute, A.D. and B.W. Gawali. 2013. Analysis and modelling of agricultural landuse using remote sensing and geographic information system : A review. Int. J. Eng. Res. Appl., 3:81-91.
- Rahman, A., et al. 2012. Assessment of landuse/land cover change in the north-east district of Delhi using remote sensing and GIS techniques. J. Indian Soc. Remote Sens., 40:689-697.
- Attri, P., S. Chaudhry and S. Sharma. 2015. Remote sensing and GIS based approaches for LULC change detection-A review. Int. J. Curr. Eng. Tech., 5:3126-3137.
- Jiang, X., et al. 2018. Examining impacts of the Belo Monte hydroelectric dam construction on land cover changes using multi-temporal landuse imagery. Appl. Geogr., 97:35-47.
- Hazarika, N., A.K. Das and S.B. Borah. 2015. Assessing landuse changes driven by river dynamics in chronically flood affected upper Brahmaputra plains, India, using RS-GIS techniques. Egyptian J. Remote Sens. Space Sci., 18:107-118.
- Lopez-Granados, E., M.E. Mendoza and D.I. Gonzalez. 2013. Linking geomorphologic knowledge, RS and GIS techniques for analyzing land cover and landuse change : A multi-temporal study in the Cointzio watershed, Mexico. Rev. Ambiente Aqua., 8:18-37.
- Serra, P., K. Pons and D. Sauri. 2008. Land cover and landuse charge in a Mediterranean landscape : A spatial analysis of driving forces integrating biophysical and human factors. Appl. Geogr., 28:189-209.
- Chowdhury, M., M.E. Hassan and M.M.A. Almamun. 2018. Landuse/land cover change assessment of Halda watershed using remote sensing and GIS. Egyptian J. Remote Sens. Space Sci., 23:63-75.
- Mohamed, M.A. 2017. Monitoring of temporal and spatial changes of landuse and land cover in metropolitan regions through remote sensing and GIS. Nat. Resour., 8:353-369.
- El Gammal, E.A., S.M. Salem and A.E.A. El Gammal. 2010. Change detection studies on the world’s biggest artificial lake (lake Nasser, Egypt). Egyptian J. Remote Sens. Space Sci., 13:89-99.
- Akinyemi, E.O. 2017. Land change in the central Albertine rift : Insights from analysis and mapping of landuse-land cover change in north-western Rwanda. Appl. Geogr., 87:127-138.
- Cheruto, M.C., et al. 2016. Assessment of landuse and land cover change using GIS and remote sensing techniques : A case study of Makueni Country, Kenya. J. Remote Sens. GIS. 5:1000175.
- Yusof, F.M., et al. 2016. Landuse change and soil loss risk assessment by using geographical information system (GIS) : A case study of lower part of Perak river. IOP Conf. Ser. Earth Env. Sci., 37:12065.
- Noh, N.S.M., et al. 2019. Erosion and sediment control best management practices in agricultural farms for effective reservoir sedimentation management at Camaron Highlands. Int. J. Recent Tech. Eng., 8:6198-6205.
- Hanif, M.F., et al. 2015. Spatio-temporal change analysis of Perak river basin using remote sensing and GIS. International Conference on Space science and communication. Lakawi, Malaysia.
- Kumar, T.L., et al. 2013. Studies on spatial pattern of NDVI over India and its relationship with rainfall, air temperature, soil moisture adequacy and ENSO. Geofizika. 30(1):1-18.
- US Geological Survey. 2011. Mineral commodity summaries, 2009. Government Printing Office.
- Srivastava, V.K., D.N. Giri and P. Bharadwaj. 2012. Study and mapping of groundwater prospect using remote sensing, GIS and geoelectrical resistivity techniques– A case study of Dhanabad district, Jharkhand, India. J. Ind. Geophys., 16(2):55–63.
- Churches, C. E., et al. 2014. Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data. Int. J. Appl. Earth Obs. Geoinf.,30:203-216.
- Rawat, J.S. and M. Kumar. 2015. Monitoring landuse/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egyptian J. Remote Sensing Space Sci.,18(1):77-84.
- Mubako, S., et al. 2018. Monitoring of landuse/land cover changes in the arid transboundary middle Rio Grande basin using remote sensing. Remote Sensing.10(12).
- Zhang, H.W., et al. 2022. ResNeSt: Split-attention networks. IEEE/CVF Conference on Computer vision and pattern recognition. Proceedings, pp 2736 -2746.
- Badapalli, P.K., et al. 2021. Land suitability analysis and water resources for agriculture in semi-arid regions of Andhra Pradesh, South India using remote sensing and GIS techniques. Int. J. Energy Water Resour., 1-16.
- Congalton, R. G. and K. Green. 2019. Assessing the accuracy of remotely sensed data: Principles and practices. CRC press.
- Congalton, R.G., R.G. Oderwald and R.A. Mead. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Eng. Remote Sensing.49(12):1671-1678.