Browse > Article
http://dx.doi.org/10.7780/kjrs.2007.23.3.153

Object-oriented Classification and QuickBird Multi-spectral Imagery in Forest Density Mapping  

Jayakumar, S. (School of Civil and Environmental Engineering, Yonsei University)
Ramachandran, A. (Tamil nadu Forest Department)
Lee, Jung-Bin (School of Civil and Environmental Engineering, Yonsei University)
Heo, Joon (School of Civil and Environmental Engineering, Yonsei University)
Publication Information
Korean Journal of Remote Sensing / v.23, no.3, 2007 , pp. 153-160 More about this Journal
Abstract
Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.
Keywords
Object oriented classification; NDVI; Forest density; Eastern Ghats; High-resolution data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Baatz, M., Benz, U., Dehghani, S., Heynen, M., Holtje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., and Willhauck, G., 2004. eCognition Professional: User guide 4.; Munich: Definiens-Imaging
2 Caylor, K. K., Dowty, P. R., Shugart, H. H., and Ringrose, S., 2004. Relationship between small-scale structural variability and simulated vegetation productivity across a regional moisture gradient in southern Africa. Global Change Biology, 10: 374-382   DOI   ScienceOn
3 Cross, A. M., Mason, D. C., and Drury, S. J., 1988. Segmentation of remotely sensed images by a split-and-merge process. International Journal of Remote Sensing, 9: 1329-1345   DOI   ScienceOn
4 Jensen, J. R., 1996. Introductory digital image processing: a remote sensing perspective. 2nd ed.; Upper Saddle River, New Jersey: Prentice Hall
5 Johnsson, K., 1994. Segment-based land-use classification from SPOT satellite data. Photogrammetric Engineering and Remote Sensing, 60: 47-53
6 Lillesand, T. M. and Kiefer, R. W., 2000. Remote sensing and image interpretation. 4th ed.; New York ; Chichester: Wiley
7 Privette, J. L., Tian, Y., Roberts, G., Scholes, R. J.,Wang, Y., and Caylor, K. K., et al., 2004. Vegetation structure characteristics and relationships of Kalahari woodlands and savannas. Global Change Biology, 10: 281-291   DOI   ScienceOn
8 Scholes, R. J., Frost, P. G. H., and Tian, Y. H., 2004. Canopy structure in savannas along a moisture gradient on Kalahari sands. Global Change Biology, 10: 292-302   DOI   ScienceOn
9 Stein, A., Van der Meer, F., and Gorte, B. G. H. (eds), 1999. Spatial Statistics for Remote Sensing (Dordrecht: Kluwer Academic)
10 Tso, B. and Mather, P. M., 2001. Classification Methods for Remotely sensed data, Taylor and Francis Inc, ISBN: 0-415-25909-6
11 Willhauck, G., Schneider, T., De Kok, R., and Ammer, U., 2000. Comparison of objectoriented classification techniques and standard image analysis for the use of change detection betweeen SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS Congress, 16-22 July, Amsterdam
12 Okin, G. S. and Painter, T. H., 2004. Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces. Remote Sensing of Environment, 89: 272-280   DOI   ScienceOn
13 Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M., 2004. 'Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information'. ISPRS Journal of Photogrammetry and Remote Sensing, 58: 239-258   DOI   ScienceOn
14 Definiens, 2006. Definiens professional user guide, Definiens AG Trappentreustr 1, Germany
15 Whiteside, T., 2000. Multi temporal land cover change in the Humpty Doo region NT, 1990-1997, Master of Natural Resources Management thesis, University of Adelaide, Adelaide
16 Congalton, R. G., 1991. 'A review of assessing the accuracy of classifications of remotely sensed data'. Remote Sensing of Environment, 37: 35-46   DOI   ScienceOn
17 Fisher, P., 1997. The pixel: a snare and a delusion. International Journal of Remote Sensing, 18: 679-685   DOI
18 Phinn, S., Franklin, J., Hope, A., Stow, D., and Huenneke, L., 1996. Biomass distribution mapping using airborne digital video imagery and spatial statistics in a semi-arid environment. Journal of Environmental Management, 47: 139-164   DOI   ScienceOn
19 OruC, M., Marangoz, A. M., and Büyüksalih, G., 2004. Comparison of Pixel-based and Objectoriented Classification Approaches Using LANDSAT-7 ETM Spectral Bands, ISPRS XXth Congress, Istanbul
20 Mansor, S., Hong, W. T., and Shariff, A. R. M., 2002. Object oriented classification for land cover mapping. Proceedings of Map Asia 2002, 7-9 August, Bangkok: GISDevelopment
21 Okin, G. S., Roberts, D. A., Murray, B., and Okin,W. J., 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment, 77: 212-225   DOI   ScienceOn
22 Oruc, M., Marangoz, A. M., and Buyuksalih, G., 2004. Comparison of pixel-based and objectoriented classification approaches using Landsat-7 ETM spectral bands. Proceedings of ISPRS Conference, 19-23 July, Istanbul
23 Manakos, I., Schneider, T., and Ammer, U., 2000. A comparison betwen the ISODATA and the eCognition classification on basis of field data. Proceedings of XIX ISPRS Congress, 16-22 July, Amsterdam
24 Franklin, S. E. and Wilson, B. A., 1991. Spatial and spectral classification for remote-sensing imagery. Computers and Geosciences, 17: 1151-1172   DOI   ScienceOn
25 Congalton, R. G. and Green, J., 1999. Assessing the accuracy of remotely sensed data: principles and practice. New York: Lewis Publishers
26 Weeks, R. J., Smith, M., Pak, K., Li, W. H., Gillespie, A., and Gustafson, B., 1996. Surface roughness, radar backscatter, and visible and near-infrared reflectance in Death Valley, California. Journal of Geophysical Research, [Planets], 101: 23077-23090   DOI
27 Price, J. C., 1994. How unique are spectral signature? Remote Sensing of Environment, 49: 181-186   DOI   ScienceOn
28 Okin, G. S. and Gillette, D. A., 2001. Distribution of vegetation in winddominated landscapes: Implications for wind erosion modeling and landscape processes. Journal of Geophysical Research, [Atmospheres], 106: 9673-9683   DOI
29 Rawat, J. K., Saxena, A., and Gupta, S., 2003. Remote sensing satellite based forest cover mapping: Some recent developments. Indian Cartographer 195-198
30 Niemeyer, I. and Canty, M. J., 2003. Pixel-Based and Object-Oriented Change Detection Analysis Using High-Resolution Imagery. Proceedings of 25th Symposium on Safeguards and Nuclear Material Managment, 13-15 May, Stockholm
31 Schlesinger, W. H. and Gramenopoulos, N., 1996. Archival photographs show no climateinduced changes in woody vegetation in the Sudan, 1943-1994. Global Change Biology, 2: 137-141
32 Bradley, B. A. and Mustard, J. F., 2005. Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin. Remote Sensing of Environment, 94: 204-213   DOI   ScienceOn