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Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India

R. Siddi Raju, G Sudarsana Raju, M Rajasekhar

Abstract


Assessment, development, and management of watershed strategy require exact calculations reports of present and past land use/cover data and its change determine the ecological and hydrological process taking place in a watershed. In this study, we have to adopt supervised classification with maximum likelihood algorithm in ERDAS imagine to notice land use/cover changes (LU/LCC) analyzed in Mandavi river basin, Kadapa district, Andhra Pradesh, India using multispectral satellite data gained from Landsat satellite series for the years 2006 and 2018. These satellite data is intended for land use/cover through supervised classification in ERDAS 2014, software. In the result, we could identify six land use/land cover (LU/LC) classes, namely agricultural land, built-up land, fallow land, forest land, river and water bodies. The results shown that during the 2006 and 2018, built-up land fallow land have been increased about 0.84% (such as 12.30 km2) and 2.92% (42.82 km2), respectively, whereas the area under other land categories such as agricultural land, forest land, river and water bodies have decreased about 1.86% (27.30 km2), 1.34 (19.66 km2), 0.26 (3.87 km2) and 0.29 (4.28 km2), respectively. Finally, accuracy assessment has been carried out and their result shows that overall accuracy of classified images of the year 2006 and 2018 are 86.62% and 91.85% respectively. The overall Kappa coefficient values of classified images of the year 2006 and 2018 are 0.8343 and 0.8987. Hence, these values indicate that acceptable accuracy of the classified LU/LC features.

Keywords: Supervised classification, land use/cover, change detection, accuracy assessment, RS and GIS

Cite this Article

R. Siddi Raju, G. Sudarsana Raju, M. Rajasekhar. Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India. Journal of Remote Sensing & GIS. 2018; 9(3): 46–54p.


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REFERENCES

Mohsen A, Elshemy M, Zeidan B.A, Change detection for Lake Burullus , Egypt using remote sensing and GIS approaches, Environ. Sci. Pollut. Res. 2016; doi:10.1007/s11356-016-8167-y.

Adam E, Mutanga O, Odindi J, Abdel-Rahman E.M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 2014; 35, 3440-3458. http://dx.doi.org/10.1080/01431161.2014.903435.

Cassidy L, Southworth J, Gibbes C, Binford M. Beyond classifications: Combining continuous and discrete approaches to better understand land cover change within the lower Mekong River region. Applied Geography, 2013; 39, 26-45. http://dx.doi.org/10.1016/j.apgeog.2012.11.021.

Coppin P, Bauer M.E. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sens. Rev. 1996; 13, 207–234.

Lambin E.F, Turner B.L, Geist H.J, et al, The causes of land-use and land-cover change : moving beyond the myths,2001; 11, 261–269.

Eisavi, V, Homayouni, S, Yazdi,et al. Land cover mapping based on random forest classification of multitemporal spectral and thermal images. Environ- mental Monitoring and Assessment, 2015; 187, 1-14. http://dx.doi.org/10.1007/s10661-015-4489-3.

Foody G.M., Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002; 80 (1), 185–201.

Foody G.M. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sens. Environ. 2010; 114, 2271–2285.

Jiang H, Feng M, Zhu Y, et al. An Automated Method for Extracting Rivers and Lakes from Landsat Imagery, 2014; 5067–5089. doi:10.3390/rs6065067.

Kaliraj S, Chandrasekar N, Ramachandran K.K, et al. Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS Egypt. J. Remote Sensing Space Sci. 2017; 20, 169–185, http://dx.doi.org/10.1016/j.ejrs.2017.04.003

Islam K, Jashimuddin M, Nath B, et al. The Egyptian Journal of Remote Sensing and Space Sciences Land use classification and change detection by using multi-temporal remotely sensed imagery : The case of Chunati wildlife sanctuary , Egypt. J. Remote Sens. Sp. Sci. 2017;. doi:10.1016/j.ejrs.2016.12.005.

Rawat J.S, Biswas Y, Kumar M, Changes in land use / cover using geospatial techniques : A case study of Ramnagar town area , district Nainital , Uttarakhand , India, Egypt. J. Remote Sens. Sp. Sci 2013; 16, 111–117. doi:10.1016/j.ejrs.2013.04.002.

Rawat J.S, Monitoring land use / cover change using remote sensing and GIS techniques : A case study of Hawalbagh block , district Almora , Uttarakhand , India, Egypt. J. Remote Sens. Sp. Sci. 2015; 18, 77–84. doi:10.1016/j.ejrs.2015.02.002.

Kamrul Islam, Mohammed Jashimuddin, Biswajit Nath, et al, Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. 2018; 21,37–47, http://dx.doi.org/ 10.1016/j.ejrs.2016.12.005.

Kantakumar L.N, Neelamsetti P, Multi-temporal land use classification using hybrid approach. Egypt. J. Remote Sens. Space Sci. 2015; 18, 289–295. http://dx.doi.org/10.1016/j.ejrs.2015.09.003.

Li, B, Zhou Q. Accuracy assessment on multi-temporal land-cover change detection using a trajectory error matrix. Int. J. Remote Sens. 2009; 30, 1283–1296.

López E, Bocco G, Mendoza M, et al. Predicting land-cover and land use change in the urban fringe. Landscape Urban Plann. 2001; 55, 271–285. http://dx.doi.org/10.1016/S0169-2046(01)00160-8.

Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007; 28 (5), 823–870.

Staudinger M.D, Carter S.L, Cross M.S, et al. Biodiversity in a changing climate : a synthesis of current and projected trends in the US In a nutshell. 2013; doi:10.1890/120272.

Mohammady M, Moradi H.R, Zeinivand et al. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. Int. J. Environ. Sci. Technol. 2015; 12 (5), 1515–1526.

Richards J.A, Jia X. Remote Sensing Digital Image Analysis: an Introduction. Springer, Heidelberg, New York, 2006; pp. 247–268.

Szuster B.W, Chen Q, Borger M. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Appl.Geogr. 2011; 31 (2), 525–532.

Vescovi F.D, Park S.J, Vlek P.L. Detection of human-induced land cover changes in a savannah landscape in Ghana: I. Change detection and quantification. In 2nd Workshop of the EARSeL Special Interest Group on Remote Sensing for Developing Countries. 2002; Bonn, Germany.

Viera A.J, Garrett J.M. Understanding inter-observer agreement: the kappa statistic. Fam. Med. 2005; 37, 360–363.

Wang, G. X., Liu, J. Q., Kubota, J., Chen., L. 2007. Effect of land-use changes on hydrological processes in the middle basin of the Heihe River, northwest China. Hydrological Processes, 21, 1370–1382 DOI: 10D1002/hyp.6308.

Yang L, Stehman S.V, Smith J.H, et al. Short Communication: thematic accuracy of MRLC land-cover for the eastern United States. Remote Sens. Environ. 2001; 76, 418–422.

Siddiraju R, Sudarsanaraju G, Rajsekhar M, Estimation of Rainfall-Runoff using SCS-CN Method with RS and GIS Techniques for Mandavi Basin in YSR Kadapa District of Andhra Pradesh, India. Hydrospatial Analysis, 2018; 2(1), 1-15, http://dx.doi.org/10.21523/gcj3.18020101


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