skip to main content

Monitoring Land Use and Land Cover Changes Prospects Using Remote Sensing and GIS for Mahanadi River Delta, Orissa, India

*Asha Vaggela  -  Dept of Geology, Andhra University, Visakhapatnam, India, India
Harikrishna Sanapala  -  Dept of Geology, Andhra University, Visakhapatnam, India, India
Jagannadha Rao Mokka  -  Dept of Geology, Andhra University, Visakhapatnam, India, India

Citation Format:
Abstract
Natural landscapes have altered dramatically via anthropogenic activity, particularly in places that are heavily influenced by climate change and population increase, such as nation like India. It is crucial for sustainable development, particularly effective water management methods, to know about the influence of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing (RS) were employed for monitoring land use changes utilising quantum ArcGIS and ERDAS Imagine were done for prediction of LULC changes. 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 land use 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) waterbodies, (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 lands use 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 pattern of land use change in the Mahanadi delta. This information might be valuable for land use management and future planning in the region
Fulltext View|Download
Keywords: Landuse/Landcover, Sustainable planning, Landsat image, Remote Sensing, GIS.

Article Metrics:

  1. Akinyemi, F. O. (2017). Land change in the central Albertine rift: Insights from analysis and mapping of land use-land cover change in north-western Rwanda. Applied Geography, 87, 127–138. [https://doi.org/10.1016/j.apgeog.2017.07.016">Crossref]

  2. Alexakis, D. D., Agapiou, A., Tzouvaras, M., Themistocleous, K., Neocleous, K., Michaelides, S., & Hadjimitsis, D. G. (2013). Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Natural Hazards, 72(1), 119–141. [https://doi.org/10.1007/s11069-013-0770-3">Crossref]

  3. Amici, V., Marcantonio, M., La Porta, N., & Rocchini, D. (2017). A multi-temporal approach in MaxEnt modelling: A new frontier for land use/land cover change detection. Ecological Informatics, 40, 40–49.

  4. Cheruto, M. C., Kauti, M. K., Kisangau, D. P., & Kariuki, P. C. (2016). Assessment of land use and land cover change using GIS and remote sensing techniques: A case study of Makueni County, Kenya.

  5. Chowdhury, M., Hasan, M. E., & Abdullah-Al-Mamun, M. M. (2020). Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 63–75.

  6. Churches, C. E., Wampler, P. J., Sun, W., & Smith, A. J. (2014). Evaluation of forest cover estimates for Haiti using the supervised classification of Landsat data. International Journal of Applied Earth Observation and Geoinformation, 30, 203–216.

  7. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of Remotely Sensed Data: Principles and Practices (second). CRC press. [https://doi.org/https:/doi.org/10.1201/9781420055139">Crossref]

  8. Congalton, R. G., Oderwald, R. G., & Mead, R. A. (1983). Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49(12), 1671–1678.

  9. Fei, L., Shuwen, Z., Jiuchun, Y., Liping, C., Haijuan, Y., & Kun, B. (2018). Effects of land use change on ecosystem services value in West Jilin since the reform and opening of China. Ecosystem Services, 31, 12–20.

  10. Gammal, E. A. El, Salem, S. M., & El Gammal, A. E. A. (2010). Change detection studies on the world’s biggest artificial lake (Lake Nasser, Egypt). The Egyptian Journal of Remote Sensing and Space Science, 13(2), 89–99.

  11. Guerra, F., Puig, H., & Chaume, R. (1998). The forest-savanna dynamics from multi-date Landsat-TM data in Sierra Parima, Venezuela. International Journal of Remote Sensing, 19(11), 2061–2075.

  12. Guzha, A. C., Rufino, M. C., Okoth, S., Jacobs, S., & Nóbrega, R. L. B. (2018). Impacts of land use and land cover change on surface runoff, discharge and low flows: Evidence from East Africa. Journal of Hydrology: Regional Studies, 15, 49–67.

  13. Hanif, M. F., ul Mustafa, M. R., Hashim, A. M., & Yusof, K. W. (2015). Spatio-temporal change analysis of Perak river basin using remote sensing and GIS. 2015 International Conference on Space Science and Communication (IconSpace), 225–230.

  14. Hathout, S. (2002). The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal of Environmental Management, 66(3), 229–238.

  15. Hazarika, N., Das, A. K., & Borah, S. B. (2015). Assessing land-use changes driven by river dynamics in chronically flood-affected Upper Brahmaputra plains, India, using RS-GIS techniques. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 107–118.

  16. Houghton, R. A. (1994). The worldwide extent of land-use change. BioScience, 44(5), 305–313.

  17. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106.

  18. Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43.

  19. Jiang, X., Lu, D., Moran, E., Calvi, M. F., Dutra, L. V., & Li, G. (2018). Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery. Applied Geography, 97, 35–47.

  20. Kumar, C. H. M. S., Valarmathi, R. S., & Aswath, S. (2021). An Empirical Review on Image Dehazing Techniques for Change Detection of Land Cover. 2021 Asian Conference on Innovation in Technology (ASIANCON), 1–9.

  21. Liang, H., Li, W., Lai, S., Zhu, L., Jiang, W., & Zhang, Q. (2018). The integration of terrestrial laser scanning and terrestrial and unmanned aerial vehicle digital photogrammetry for the documentation of Chinese classical gardens : A case study of Huanxiu Shanzhuang, Suzhou, China. Journal of Cultural Heritage, 33, 222–230. [https://doi.org/10.1016/j.culher.2018.03.004">Crossref]

  22. López-Granados, E., Mendoza, M. E., & González, D. I. (2013). Linking geomorphologic knowledge, RS and GIS techniques for analyzing land cover and land use change: a multitemporal study in the Cointzio watershed, Mexico. Revista Ambiente & Água, 8, 18–37.

  23. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico. Landscape and Urban Planning, 55(4), 271–285.

  24. Mohamed, M. A. (2017). Monitoring of temporal and spatial changes of land use and land cover in metropolitan regions through remote sensing and GIS.

  25. Mubako, S., Belhaj, O., Heyman, J., Hargrove, W., & Reyes, C. (2018). Monitoring of land use/land-cover changes in the arid transboundary middle Rio grande basin using remote sensing. Remote Sensing, 10(12), 2005.

  26. Noh, N. S. M., Sidek, L. M., Wayayok, A., Abdullah, A. F., Basri, H., Farhan, S. A., Sulaiman, T., & Md Ariffin, A. B. (2019). Erosion and sediment control best management practices in agricultural farms for effective reservoir sedimentation management at Cameron Highlands. Int. J. Recent Technol. Eng, 8, 6198–6205.

  27. 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.

  28.  

  29.  

  30. 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.

  31. Rout, S. P., Palanivel, K., & Kathiravan, R. (2018). Delineation of subsurface features of geological importance using GPR along coastal tract of puri-balasore districts of Odisha, India. Journal of Applied Science and Computations, 5(11), 1081–1090.

  32. Roy, P. S., & Roy, A. (2010). Land use and land cover change in India: Aremote sensing & GIS prespective. Journal of the Indian Institute of Science, 90(4), 489–502.

  33. Sejati, A. W., Buchori, I., Kurniawati, S., Brana, Y. C., & Fariha, T. I. (2020). Quantifying the impact of industrialization on blue carbon storage in the coastal area of Metropolitan Semarang, Indonesia. Applied Geography, 124, 102319.

  34. Serra, P., Pons, X., & Sauri, D. (2008). Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Applied Geography, 28(3), 189–209.

  35. Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.

  36. Singh, Y., Ferrazzoli, P., & Rahmoune, R. (2013). Flood monitoring using microwave passive remote sensing (AMSR-E) in part of the Brahmaputra basin, India. International Journal of Remote Sensing, 34(14), 4967–4985.

  37. 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.

  38. Vibhute, A. D., & Gawali, B. W. (2013). Analysis and modeling of agricultural land use using remote sensing and geographic information system: a review. International Journal of Engineering Research and Applications, 3(3), 81–91.

  39. Yatoo, S. A., Sahu, P., Kalubarme, M. H., & Kansara, B. B. (2020). Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India. GeoJournal, 1–22.

  40. Yusof, F. M., Jamil, N. R., Aini, N., Abd Manaf, L., & others. (2016). Land use change and soil loss risk assessment by using geographical information system (GIS): A case study of lower part of Perak River. IOP Conference Series: Earth and Environmental Science, 37(1), 12065.

  41. Zhang, K., Lv, X., Chai, H., & Yao, J. (2022). Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–19.


Last update:

  1. Modifying the Contact Perimeter Approach for Measuring Urban Compactness Gradients in the Joglosemar Urban Region, Indonesia

    Dimas Danar Dewa, Imam Buchori, Iwan Rudiarto, Anang Wahyu Sejati. Journal of Geovisualization and Spatial Analysis, 7 (1), 2023. doi: 10.1007/s41651-023-00135-3
  2. Flood hazard risk assessment based on multi-criteria spatial analysis GIS as input for spatial planning policies in Tegal Regency, Indonesia

    Sejati Wahyu, Savira Putri, Sri Rahayu, Imam Buchori, Kristantri Rahayu, Wiratmaja Andika, Ahmad Muzaki, Yudi Basuki. Geographica Pannonica, 27 (1), 2023. doi: 10.5937/gp27-40927

Last update: 2024-11-05 09:21:53

No citation recorded.