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Evaluation of the resolution of pixel-based classification methods of Landsat 8 data for determining the type of land cover in mountainous areas, case study: Beshar Watershed

Document Type : Research Paper

Author

Assistant professor, Faculty of Agriculture and Natural Resources, Yasouj University

Abstract

The aim of this study was to investigate the ability of different supervised and unsupervised classification algorithms of remote sensing data for detecting and separating of land cover on Beshar River Basin using Landsat 8 data. For this purpose, after checking the geometric accuracy and radiometric-atmospheric corrections on satellite data, the data set was created to the combination of spectral bands (bands 2, 3, 4, 5, 6, 7 and 8) and thermal (band 10). Next, pixel-based classification using supervised algorithms including maximum likelihood, support vector machine, mahalanobis distance, minimum distance, neural network, parallelepiped, spectral angle mapping, spectral information divergence, binary coding, and unsupervised algorithms including K-Means and IsoData was done. The accuracy of the algorithms for identifying each land use /land cover based on the error matrix analysis was evaluated using the producer's accuracy, user accuracy and overall accuracy based on the omission and commission errors, and the kappa coefficient. The results showed that the most appropriate algorithm for separation and identification of land use/land cover including agriculture, construction, cliff, forest, orchard, rangeland, water body and fallow is maximum likelihood, mahalanobis distance, maximum likelihood, mahalanobis distance, neural network, support vector machine, support vector machine, and maximum likelihood, respectively. The percentage of overall accuracy and Kappa coefficient shows that the four algorithms including maximum likelihood, support vector machine, mahalanobis distance and neural network with overall accuracy 77.25, 75.9, 69.59, 68.26 and the Kappa coefficient 0.72, 0.69, 0.63, 0.58, respectively, is better than other algorithms. Generally, the integration of appropriate classification algorithms in mountainous areas increases classification accuracy and will have better results.

Keywords

References
  1. Adepoju, K.A. and S.A. Adelabu. 2019. Improving accuracy evaluation of Landsat-8 OLI using image composite and multisource data with Google Earth Engine. Remote Sensing Letters, 11(2): 107–116.
  2. Akar, A., E. Gokalp, Ö. Akar and V. Yilmaz. 2017. Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images. Geocarto International, 32(9): 1-14.
  3. Bharathidason, H. and C. Jothi Venkataeswaran. 2014. Improving classification accuracy based on random forest model with uncorrelated high performing trees. International Journal of Computer Applications, 101(13): 26-30.
  4. Borak, J.S. 1999. Feature selection and land cover classification of a MODIS-like data set for a semi-arid environment. International Journal of Remote Sensing, 20: 919-938.
  5. Boschetti, L., S.P. Flasse and P.A. Brivio. 2004. Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: the Pareto Boundary. Remote Sensing of Environment, 91: 280–292.
  6. Bradley, B.A. 2009. Accuracy assessment of mixed land covers using a GIS-designed sampling scheme. International Journal of Remote Sensing, 30(13): 3515–3529.
  7. Carlotto, M.J. 2009. Effect of errors in ground truth on classification accuracy. International Journal of Remote Sensing, 30: 4831–4849.
  8. Chen, B., B. Huang and B. Xu. 2017. Multi-source remotely sensed data fusion for improving land cover classification. Journal of Photogrammetry and Remote Sensing, 124: 27-39.
  9. Chowdhury, M., M. Emran Hasan and M.M. bdullah-Al-Mamun. 2020. Land use/land cover change assessment of Halda Watershed using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Sciences, 23: 63-75.
  10. Coppin, P. and M.E. Bauer. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13: 207–234.
  11. Foody, G.M. 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1): 185–201.
  12. Foody, G.M. 2010. Assessing the accuracy of land cover change with imperfect ground reference data. Remote Sensing of Environment, 114: 2271–2285.
  13. Gallego, F.J. 2004. Remote sensing and land cover area estimation. International Journal of Remote Sensing, 25: 3019–3047.
  14. Gohari, Z., H. Ara and H. Memarian. 2019. Comparison of performance in image classification algorithms of satellite in detection of Sarakhs sandy zones. Journal of Environmental Erosion Research, 9(2): 19-36 (in Persian).
  15. Gomariz-Castillo, F., F. Alonso-Sarría and F. Cánovas-García. 2017. Improving classification accuracy of multi-temporal Landsat images by assessing the use of different algorithms, textural and ancillary information for a Mediterranean semi-arid area from 2000 to 2015. Remote Sensing, 9: 1-23.
  16. Huang, S.W. and H.I. Hsieh. 2012. The study of the land-use change factors in coastal land subsidence area in Taiwan. Conference on Environment, Energy and Biotechnology (IPCBEE), Vol. 3, IACSIT Press, Singapore, 70–74.
  17. Kaliraj, S., N. Chandrasekar, K.K. Ramachandran, Y. Srinivas and S. Saravanan. 2017. Coastal land use and land cover change and transformations of Kanyakumari Coast, India using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Sciences, 20: 169–185.
  18. Khazaei, M., M. Zare, M.H. Mokhtari, A. Rashtian and F. Arabi Aliabad. 2019. Comparison of different classification methods in terms of accuracy for land use mapping: a case study of the city of Yazd. Journal of Geographical Research on Desert Areas, 7(1): 165-178 (in Persian).
  19. Li, B. and Q. Zhou. 2009. Accuracy assessment on multi-temporal land-cover change detection using a trajectory error matrix. International Journal of Remote Sensing, 30(5): 1283–1296.
  20. Li, W., R. Dong, H. Fu, J. Wang, L. Yu and P. Gong. 2020. Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping. Remote Sensing of Environment, 237: 1-16.
  21. Lossou, E., N. Owusu-Prempeh and G. Agyemang. 2019. Monitoring land cover changes in the tropical high forests using multi-temporal remote sensing and spatial analysis techniques. Remote Sensing Applications: Society and Environment, 16: 1-14.
  22. Lu, D. and Q. Weng. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5): 823–870.
  23. Luo, Y., M. Liao, J. Yan and C. Zhang. 2012. A multi-features fusion support vector machine method (MF-SVM) for classification of mangrove remote sensing image. Journal of Computational Information Systems, 8: 323–334.
  24. Islam, K., M. Jashimuddin, B. Nath and T.K. Nath. 2018. Land use classification and change detection by using multi-temporal remotely sensed imagery: the case of Chunati wildlife sanctuary, Bangladesh. Egyptian Journal of Remote Sensing and Space Sciences, 21: 37-47.
  25. Masria, A., K. Nadaoka, A. Negm and M. Iskandar. 2015. Detection of shoreline and land cover changes around rosetta promontory, Egypt, based on remote sensing analysis. Land, 4: 216-230.
  26. Mathur, A. and G.M. Foody. 2008. Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29: 2227-2240.
  27. Mishra, V.N., R. Prasad, P. Kumar Rai, A.K. Vishwakarma and A. Arora. 2019. 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, 12: 71–86.
  28. Mohammady, M., H.R. Moradi, H. Zeinivand and A. Temme. 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environmental Science and Technology, 12(5): 1515–1526.
  29. Mujabar, P.S. and N. Chandrasekar. 2013. Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arabian Journal of Geoscience, 6: 647–664.
  30. Onur, I., M. Derya, S. Mustafa and N.K. Sönmez. 2009. Change detection of land cover and land use using remote sensing and GIS, a case study in Kemer, Turkey. International Journal of Remote Sensing, 30(7): 1749–1757.
  31. Otukei, J.R. and T. Blaschke. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12: 27-31.
  32. Phinn, S.R., C. Menges, G.J.E. Hill and M. Stanford. 2000. Optimizing remotely sensed solutions for monitoring, modeling and managing coastal environments. Remote Sensing of Environment, 73: 117–132.
  33. Rajeesh, R. and G.S. Dwarakish. 2015. Satellite oceanography, a review. Aquatic Procedia, 4: 165-172.
  34. 2011. Supervised/unsupervised land use land cover classification using ERDAS imagine. Summer Course Computational Geoecology. Retrieved from http://horizon.science.uva. (Accessed 17th April 2020).
  35. Shen, H., Y. Lin, Q. Tian, K. Xu and J. Jiao. 2018. A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification. International Journal of Remote Sensing, 39(11): 3705–3722.
  36. Smith, M.R. and T. Martinez 2011. Improving classification accuracy by identifying and removing instances that should be misclassified. The International Joint Conference on Neural Networks, 31 July 5 Aug., San Jose, CA, USA, 2690-2697.
  37. Tong, X.Y., G.S. Xia, Q. Lu, H. Shen, S. Li, S. You and L. Zhang. 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237: 1-20.
  38. Twisa, S. and M.F. Buchroithner. 2019. Land-Use and Land-Cover (LULC) change detection in Wami River Basin, Tanzania. Land, 8(136): 1-15.
  39. 2019. Landsat 8 (L8) data user’s handbook. Version 5.0, March 29, LSDS-1574, 106 pages.
  40. Venkateswaran, K., N. Kasthuri and N. Kousika. 2017. Performance comparison of multiwavelet and multicontourlet frame based features for improving classification accuracy in remote sensing images. Journal of the Indian Society of Remote Sensing, 45: 903–911.
  41. Zhang, S., S. Zhang and J. Zhang. 2000. A study on wetland classification model of remote sensing in the Sangjiang Plain. Chinese Geographical Science, 10: 68–73.
  42. Zhang, C., Y. Chen, X. Yang, S. Gao, F. Li, A. Kong, D. Zu and L. Sun. 2020. Improved remote sensing image classification based on multi-scale feature fusion. Remote Sensing, 12: 1-19.
Watershed Engineering and Management
Volume 13, Issue 2
July 2021
Pages 405-416
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