Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data

Expert Systems with Applications

Volumn 38, Issue 5, 2011, Pages 5577-5583

  Punia, Milap ^a   Joshi, P K ^b   Porwal, M C ^c  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

^b TERI UNIVERSITY   (India)

^c INDIAN INSTITUTE OF REMOTE SENSING   (India)

Author keywords

AWiFS; Decision tree approach; LULC; Mutlitemporal; Resourcesat

Indexed keywords

ANCILLARY DATA; AWIFS; BIOGEOCHEMICAL MODELS; CLASSIFICATION ACCURACY; CROPPING PATTERNS; DECISION TREE CLASSIFICATION; DELHI , INDIA; DIVERSE RANGE; LAND COVER; LULC; MULTI-TEMPORAL; MUTLITEMPORAL; RESOURCESAT; SPATIAL RESOLUTION; TEMPORAL DATA; WIDE FIELD;

CLASSIFICATION (OF INFORMATION); LAND USE;

DECISION TREES;

EID: 79151481003     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.10.078     Document Type: Article

References (27)

1. Anonymous (2006). National land use and land cover mapping using multi-temporal AwiFS data. National Remote Sensing Agency, Hyderabad.
2. H. Carräa, P. Goncalves, and M. Caetano Contribution of multispectral and multitemporal information from MODIS images to land cover classification Remote Sensing of Environment 112 2008 986 997
3. Cole, C.; Wentz.; E. and Christense, P. (2005). Expert system approach for classifying land cover in New Delhi using ASTER Imagery. In Proceedings third international symposium remote sensing and data fusion over urban areas (URBAN 2005) and fifth international symposium remote sensing of urban areas (URS 2005), March 14-16, 2005.
4. R. DeFries, M. Hanson, J. Townshend, and R. Sohlberg Global land cover classifications at 8 km spatial resolutions: The use of training data derived from landsat imagery in decision tree classifiers International Journal of Remote Sensing 19 6 1998 3141 3168
5. R.Q. Feitosa, G.A.O.P. Costa, G.L.A. Mota, K. Pakzad, and M.C.O. Costa Cascade multitemporal classification based on fuzzy Markov changes ISPRS Journal of Photogrammetry and Remote Sensing 64 2009 159 170
6. M.A. Friedl, C.E. Brodley, and A.H. Strahler Maximizing land cover classification accuracies produced by decision trees at continental to global scales IEEE Trans. Geosci. Remote Sensing 37 2 1999 969 977
7. R. Gamanya, P.D. Maeyer, and M.D. Dapper An Automated satellite image classification design using object oriented segmentation algorithms: A move towards standardization Expert Systems with Applications 32 2007 616 624
8. R. Gamanya, P.D. Maeyer, and M.D. Dapper Object oriented change detection for the city of Harare, Zimbabwe Expert Systems with Applications 36 2009 571 588
9. GoI (2005). Annual Report 2004-2005. Ministry of Agriculture, Government of India. Available at http://agricoop.nic.in/Annual-Rep04-05/Annual-Report.pdf. Accessed 15.05.09.
10. M.C. Hansen, R.S. DeFries, J.R.G. Townshend, and R. Sohlberg Global land cover classification at 1 km spatial resolution using a classification tree approach International Journal of Remote Sensing 21 2000 1331 1364
11. P.K. Joshi, P.S. Roy, S. Singh, S. Agrawal, and Deepshikha Yadav Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data Remote Sensing of Environment 103 2 2006 190 202
12. S. Kandrika, and P.S. Roy Land use land cover classification of Orissa using multi-temporal IRS-P6 AWiFS data: A decision tree approach International Journal of Applied Earth Observation and Geoinformation 10 2 2008 186 193
13. Netzband, M.; Wentz, E.L. & Rahman, A. Urban land cover and spatial variation observation using satellite image data - the urban environmental monitoring project. (2005). Accessed 15.05.09.
14. M. Pal, and P.M. Mather An assessment of the effectiveness of decision tree methods for land cover classification Remote Sensing of Environment 86 2003 554 565
15. R. Pu, P. Gong, R. Michishta, and R. Sasgawa Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data Remote Sensing of Environment 112 2008 939 954
16. J.R. Quiland C4.5: programs for machine learning 1993 Morgan Kauffmann Publishers San Mateo, California
17. G.H. Rosenfield, and K. Fitzpatrick-Lins A coefficient of agreement on a measure of thematic classification accuracy Photogrammetric Engineering and Remote Sensing 52 1986 223 227
18. S.E. Sesnie, P.E. Gessler, B. Finegan, and S. Thessler Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments Remote Sensing of Environment 112 2008 2145 2159
19. State of Forest Report (SFR) (2005). Forest survey of India. MoEF, Dehradun, p. 214.
20. C. Small High spatial resolution spectral mixture analysis of urban reflectance Remote Sensing of Environment 88 2003 170 186
21. B.S. Sokhi, N.D. Sharma, and P.S. Uttarwar Satellite remote sensing in urban sprawl mapping and monitoring - a case study of Delhi Journal of the Indian Society of Remote Sensing 17 3 1989 57 69
22. W.L. Stefanov, M.S. Ramsey, and P.R. Christensen Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers Remote Sensing of Environment 77 2001 173 185
23. S.V. Stehman Estimation of Kappa coefficient and its variance using stratified random sampling Photogrammetric Engineering and Remote Sensing 26 1996 401 407
24. R.B. Thapa, and Y. Murayama Urban mapping, accuracy and image classification: A comparison of multiple approaches in Tsukuba city, Japan Applied geography 29 2009 135 144
25. T.R. Tooke, N.C. Coops, N.R. Goodwin, and J.A. Voogt Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications Remote Sensing of Environment 113 2009 398 407
26. E.A. Wentz, D. Nelson, A. Rahman, W.L. Stefanov, and S.S. Roy Expert system classification of urban land use/cover for Delhi, India International Journal of Remote Sensing 29 15-16 2008 4405 4427
27. C.E. Woodcock, and A.H. Strahler The factor of scale in remote sensing Remote Sensing of Environment 21 1987 311 332