Land use land cover change detection in the lower Bhavani basin, Tamil Nadu, using geospatial techniques
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Abstract
Land use land cover (LULC) change detection is essential for sustainable development, planning and management. This study was an attempt to evaluate the LULC change in the lower bhavani basin from 2014 to 2019, using Landsat 8 data integrating Google Earth Engine (GEE) as a web-based platform and Geographic Information System. The CART and Random Forest classifiers in GEE were used for performing supervised classification. The classified map accuracy was assessed using high resolution imagery and evaluated using a confusion matrix implemented in GEE. Five major LULC classes, viz., agriculture, built up, current fallow, forest and waterbody, were identified, and the dominant land use in the study area was agriculture and current fallow, followed by dominant land use of forest. During the study period (2014–2019) the change inbuilt-up area 7.37% in 2019 and 5.45% in 2014, was noted due to urban sprawl. GEE showed significant versatility and proved to be an effective platform for LULC detection.
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Keywords
Google Earth Engine, GIS, Land use land cover, Landsat, Remote sensing
References
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Lu, D., Mausel, P., Brondizio, E. & Moran, E. (2004). Change detection techniques. International journal of remote sensing, 25(12), 2365-2401.
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Shalaby, A. & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28-41.
Sinha, S., Sharma, L. K. & Nathawat, M. S. (2013). Integrated Geospatial Techniques for Land-use/Land-cover and Forest Mapping of Deciduous Munger Forests (India). Universal Journal of Environmental Research & Technology, 3(2).
Tekle, K. & Hedlund, L. (2000). Land cover changes between 1958 and 1986 in Kalu District, southern Wello, Ethiopia. Mountain Research and Development, 20(1), 42-51.
Tewabe, D. & Fentahun, T. (2020). Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6(1), 1778998.
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S. & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002.
Zhang, T., Zhang, X., Xia, D. & Liu, Y. (2014). An analysis of land use change dynamics and its impacts on hydrological processes in the Jialing River Basin. Water, 6(12), 3758–3782. https://doi.org/10.3390/w6123758
Anand, B. & Karunanidhi, D. (2020). Long term spatial and temporal rainfall trend analysis using GIS and statistical methods in Lower Bhavani basin, Tamil Nadu, India.
Anandakumar, S., Subramani, T. & Elango, L. (2008). Spatial variation and seasonal behaviour of rainfall pattern in Lower Bhavani River basin, Tamil Nadu, India. The Ecoscan, 2(1), 17-24.
Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
Brahmabhatt, V. S., Dalwadi, G. B., Chhabra, S. B., Ray, S. S. & Dadhwal, V. K. (2000). Land use/land cover change mapping in Mahi canal command area, Gujarat, using multi-temporal satellite data. Journal of the Indian Society of Remote Sensing, 28(4), 221-232.
Campbell, M., Congalton, R. G., Hartter, J. & Ducey, M. (2015). Optimal land cover mapping and change analysis in northeastern Oregon using Landsat imagery. Photogrammetric Engineering & Remote Sensing, 81(1), 37-47.
Czaplewski, R.L. 2003. Accuracy assessment of maps of forest condition: statistical design and methodological considerations. In: Wulder, M.A., Franklin, S.E. (Eds.), Remote Sensing of Forest Environments: Concepts and Case Studies, Kluwer Academic Publishers, The Netherlands, pp. 115–140
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.
Geremew, A. A. (2013). Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gigel-Abbay Watershed, Lake Tana Basin, Ethiopia (Doctoral dissertation).
Ghimire, B., Rogan, J., Galiano, V. R., Panday, P. & Neeti, N. (2012). An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing, 49(5), 623-643.
Gislason, P. O., Benediktsson, J. A. & Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern recognition letters, 27(4), 294-300.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
Griffiths, P., van der Linden, S., Kuemmerle, T. & Hostert, P. (2013). A pixel-based Landsat compositing algorithm for large area land cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5), 2088-2101.
Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C. & Hobart, G. W. (2018). Disturbance-informed annual land cover classification maps of Canada's forested ecosystems for a 29-year landsat time series. Canadian Journal of Remote Sensing, 44(1), 67-87.
Jansen, L. J. & Di Gregorio, A. (2004). Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon. International Journal of Applied Earth Observation and Geoinformation, 5(2), 141-157.
Jin, Y., Liu, X., Chen, Y. & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong. International journal of remote sensing, 39(23), 8703-8723.
Lu, D., Mausel, P., Brondizio, E. & Moran, E. (2004). Change detection techniques. International journal of remote sensing, 25(12), 2365-2401.
Mather, P. & Tso, B. (2016). Classification methods for remotely sensed data. CRC press
Mohamed, M. A., Anders, J. & Schneider, C. (2020). Monitoring of changes in land use/land cover in Syria from 2010 to 2018 using multitemporal landsat imagery and GIS. Land, 9(7), 226.
Nery, T., Sadler, R., Solis-Aulestia, M., White, B., Polyakov, M. & Chalak, M. (2016). Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5165-5168). IEEE.
Shalaby, A. & Tateishi, R. (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied Geography, 27(1), 28-41.
Sinha, S., Sharma, L. K. & Nathawat, M. S. (2013). Integrated Geospatial Techniques for Land-use/Land-cover and Forest Mapping of Deciduous Munger Forests (India). Universal Journal of Environmental Research & Technology, 3(2).
Tekle, K. & Hedlund, L. (2000). Land cover changes between 1958 and 1986 in Kalu District, southern Wello, Ethiopia. Mountain Research and Development, 20(1), 42-51.
Tewabe, D. & Fentahun, T. (2020). Assessing land use and land cover change detection using remote sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6(1), 1778998.
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S. & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, 112002.
Zhang, T., Zhang, X., Xia, D. & Liu, Y. (2014). An analysis of land use change dynamics and its impacts on hydrological processes in the Jialing River Basin. Water, 6(12), 3758–3782. https://doi.org/10.3390/w6123758
Section
Research Articles
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How to Cite
Land use land cover change detection in the lower Bhavani basin, Tamil Nadu, using geospatial techniques . (2022). Journal of Applied and Natural Science, 14(SI), 58-64. https://doi.org/10.31018/jans.v14iSI.3566
