Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China

Geocarto International

Volumn 29, Issue 8, 2014, Pages 941-951

  Jia, Kun ^a   Wei, Xiangqin ^b   Gu, Xingfa ^b   Yao, Yunjun ^a   Xie, Xianhong ^a   Li, Bin ^b  

^a BEIJING NORMAL UNIVERSITY   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

classification; land cover; Landsat 8; Operational Land Imager; texture

Indexed keywords

EID: 84905730401     PISSN: 10106049     EISSN: None     Source Type: Journal    
DOI: 10.1080/10106049.2014.894586     Document Type: Article

References (35)

1. Bounoua L, DeFries R, Collatz GJ, Sellers P, Khan H. 2002. Effects of land cover conversion on surface climate. Climatic Change. 52: 29-64.10.1023/A:1013051420309
2. Burges CJC. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 2: 121-167.10.1023/A:1009715923555
3. Chen J, Zhu XL, Vogelmann JE, Gao F, Jin SM. 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment. 115: 1053-1064.10.1016/j.rse.2010.12.010
4. Congalton RG, Green K. 1999. Assessing the accuracy of remotely sensed data: principles and practices. Florida: Lewis Publishers.
5. Cooley T, Anderson GP, Felde GW, Hoke ML, Ratkowski AJ, Chetwynd JH, Gardner JA, Adler-Golden SM, Matthew MW, Berk A, Bernstein LS, Acharya PK, Miller D, Lewis P. 2002. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium; Toronto, 1414-1418.
6. Duda RO, Hart PE. 1973. Pattern classification and scene analysis. New York, NY: Wiley.
7. Foody GM. 2009. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment. 113: 1658-1663.10.1016/j.rse.2009.03.014
8. Foody GM. 2013. Ground reference data error and the mis-estimation of the area of land cover change as a function of its abundance. Remote Sensing Letters. 4: 783-792.10.1080/2150704X.2013.798708
9. Foody GM, Mathur A. 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing. 42: 1335-1343.10.1109/TGRS.2004.827257
10. Foody GM, Campbell NA, Trodd NM, Wood TF. 1992. Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric Engineering and Remote Sensing. 58: 1335-1341.
11. Gadkari D. 2004. Image quality analysis using GLCM. Orlando (FL): University of Central Florida.
12. Gong P, Wang J, Yu L, Zhao YC, Zhao YY, Liang L, Niu ZG, Huang XM, Fu HH, Liu S, Li CC, Li XY, Fu W, Liu CX, Xu Y, Wang XY, Cheng Q, Hu LY, Yao WB, Zhang H, Zhu P, Zhao ZY, Zhang HY, Zheng YM, Ji LY, Zhang YW, Chen H, Yan A, Guo JH, Wang L, Liu XJ, Shi TT, Zhu MH, Chen YL, Yang GW, Tang P, Xu B, Giri C, Clinton N, Zhu ZL, Chen J, Chen J. 2013. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing. 34: 2607-2654.10.1080/01431161.2012.748992
13. Gumma MK, Thenkabail PS, Hideto F, Nelson A, Dheeravath V, Busia D, Rala A. 2011. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sensing. 3: 816-835.10.3390/rs3040816
14. Hansen MC, Defries RS, Townshend JRG, Sohlberg R. 2000. Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing. 21: 1331-1364.10.1080/014311600210209
15. Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 3: 610-621.10.1109/TSMC.1973.4309314
16. Irons JR, Dwyer JL, Barsi JA. 2012. The next landsat satellite: the landsat data continuity mission. Remote Sensing of Environment. 122: 11-21.10.1016/j.rse.2011.08.026
17. Jia K, Wu BF, Tian YC, Zeng Y, Li QZ. 2011. Vegetation classification method with biochemical composition estimated from remote sensing data. International Journal of Remote Sensing. 32: 9307-9325.10.1080/01431161.2011.554454
18. Jia K, Li QZ, Tian YC, Wu BF, Zhang FF, Meng JH. 2012. Crop classification using multi-configuration SAR data in the North China Plain. International Journal of Remote Sensing. 33: 170-183.10.1080/01431161.2011.587844
19. Jia K, Wu BF, Li QZ. 2013. Crop classification using HJ satellite multispectral data in the North China Plain. Journal of Applied Remote Sensing. 7: 073576.10.1117/1.JRS.7.073576
20. Jiang B, Liang SL, Townshend JR, Dodson ZM. 2013. Assessment of the radiometric performance of Chinese HJ-1 satellite CCD instruments. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 840-850.10.1109/JSTARS.2012.2212236
21. Jung M, Henkel K, Herold M, Churkina G. 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment. 101: 534-553.10.1016/j.rse.2006.01.020
22. Liang S. 2008. Advances in land remote sensing. Dordrecht: Springer.10.1007/978-1-4020-6450-0
23. Liu JY, Zhuang DF, Luo D, Xiao X. 2003. Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data. International Journal of Remote Sensing. 24: 2485-2500.10.1080/01431160110115582
24. Lulla K, Duane Nellis M, Rundquist B. 2013. The Landsat 8 is ready for geospatial science and technology researchers and practitioners. Geocarto International. 28: 191-191.10.1080/10106049.2013.812346
25. Miller SN, Phillip Guertin D, Goodrich DC. 2007. Hydrologic modeling uncertainty resulting from land cover misclassification. Journal of the American Water Resources Association. 43: 1065-1075.10.1111/jawr.2007.43.issue-4
26. Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing. 66: 247-259.10.1016/j.isprsjprs.2010.11.001
27. Pahlevan N, Schott JR. 2013. Leveraging EO-1 to evaluate capability of new generation of landsat sensors for Coastal/Inland water Studies. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6: 360-374.10.1109/JSTARS.2012.2235174
28. Pal M, Foody GM. 2012. Evaluation of SVM, RVM and SMLR for accurate image classification with limited ground data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 5: 1344-1355.10.1109/JSTARS.2012.2215310
29. Pal M, Mather PM. 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing. 26: 1007-1011.10.1080/01431160512331314083
30. Running SW. 2008. Climate change: ecosystem disturbance, carbon, and climate. Science. 321: 652-653.10.1126/science.1159607
31. Thenkabail PS, Biradar CM, Noojipady P, Dheeravath V, Li YJ, Velpuri M, Gumma M, Gangalakunta ORP, Turral H, Cai XL, Vithanage J, Schull MA, Dutta R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing. 30: 3679-3733.10.1080/01431160802698919
32. Tollefson J. 2013. Landsat 8 to the rescue. Nature. 494: 13-14.
33. Townshend JR, Masek JG, Huang CQ, Vermote EF, Gao F, Channan S, Sexton JO, Feng M, Narasimhan R, Kim D, Song K, Song DX, Song XP, Noojipady P, Tan B, Hansen MC, Li MX, Wolfe RE. 2012. Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. International Journal of Digital Earth. 5: 373-397.10.1080/17538947.2012.713190
34. Tso B, Mather PM. 2001. Classification methods for remotely sensed data. London: Taylor and Francis.10.4324/9780203303566
35. Wu Q, Li H, Wang R, Paulussen J, He Y, Wang M, Wang B, Wang Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning. 78: 322-333.10.1016/j.landurbplan.2005.10.002