Binary images in seasonal land-cover change identification: A comparative study in parts of New South Wales, Australia

International Journal of Remote Sensing

Volumn 34, Issue 6, 2013, Pages 2162-2186

  Sinha, Priyakant ^a   Kumar, Lalit ^a  

^a UNIVERSITY OF NEW ENGLAND   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY IMAGES; IMAGE ENHANCEMENT; VEGETATION;

BINARY CHANGE DETECTION METHODS; CHANGE VECTOR ANALYSIS; COMPARATIVE STUDIES; LANDSCAPE COMPLEXITY; NEW SOUTH WALES , AUSTRALIA; NORMALIZED DIFFERENCE VEGETATION INDEX; PRINCIPAL COMPONENTS; VEGETATION INDEX DIFFERENCING;

PRINCIPAL COMPONENT ANALYSIS;

COMPARATIVE STUDY; IMAGE ANALYSIS; LAND COVER; LANDSCAPE; NDVI; PRINCIPAL COMPONENT ANALYSIS; THEMATIC MAPPING;

AUSTRALIA; NEW SOUTH WALES;

EID: 84870533412     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2012.742214     Document Type: Article

References (57)

1. Agresti, A. 1996. An introduction to Categorical Data Analysis, New York, NY: Wiley.
2. Bureau of Meteorology (BOM). 2011. Australia. http://www.bom.gov.au/nsw/ (Accessed: 21 May 2011).
3. Bureau of Rural Sciences (BRS). 2006. Guidelines for Land Use Mapping in Australia: Principles, Procedures and Definition, Canberra: ACT 2601: BRS. Bureau of Rural Sciences (BRS)
4. Chander, G., and Markham, B. 2003. Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. Geoscience and Remote Sensing, IEEE Transactions on, 41: 2674-77.
5. Chavez, P. S. 1996. Image-Based Atmospheric Corrections-Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62: 1025-35.
6. Chavez, P. S., and Mackinnon, D. J. 1994. Automatic Detection of Vegetation Changes in the Southwestern United States Using Remotely Sensed Images. Photogrammetric Engineering and Remote Sensing, 60: 571-83.
7. Cohen, W. B., Fiorella, M, Gray, J., Helmer, E. and Anderson, K. 1998. An Efficient and Accurate Method for Mapping Forest Clearcuts in the Pacific Northwest Using Landsat Imagery. Photogrammetric Engineering and Remote Sensing, 64: 293-99.
8. Collins, J. B. and Woodcock, C. E. 1996. An Assessment of Several Linear Change Detection Techniques for Mapping Forest Mortality Using Multitemporal Landsat TM Data. Remote Sensing of Environment, 56: 66-77.
9. Congalton, R. G. 1991. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37: 35-46.
10. Congalton, R. G. and Green, K. 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed. Boca Raton, FL: CRC/Taylor & Francis.
11. Congalton, R. G., Oderwald, R. G. and Mead, R. A. 1983. Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques. Photogrammetric Engineering and Remote Sensing, 49: 1671-78.
12. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. and Lambin, E. 2004. Digital Change Detection Methods in Ecosystem Monitoring: A Review. International Journal of Remote Sensing, 25: 1565-96.
13. Dai, X. and Khorram, S. 1999. Remotely Sensed Change Detection Based on Artificial Neural Networks. Photogrammetric Engineering and Remote Sensing, 65: 1187-94.
14. Department of Environment and Climate change (DECC). 2008. The Bioregions of New South Wales-their Biodiversity, Conservation and History. NSW, Australia: Department of Environment and Climate Change. http://www.environment.nsw.gov.au/bioregions/BioregionOverviews.htm (Accessed: 21 November 2011).
15. De Sherbinin, A. 2002. "A CIESIN Thematic Guide to Land Land-Use and Land Land-Cover Change (LUCC)." Center for International Earth Science Information Network (CIESIN) Columbia University Palisades, NY, USApdf http://sedac.ciesin.columbia.edu/guides/lu/CIESIN_LUCC_TG
16. Eckhardt, D., Verdin, J. and G., Lyford. 1990. Automated Update of an Irrigated Lands GIS Using SPOT HRV Imagery. Photogrammetric Engineering and Remote Sensing, 56: 1515-22.
17. Falcucci, A., Maiorano, L. and Boitani, L. 2007. Changes in Land-Use/Land-Cover Patterns in Italy and Their Implications for Biodiversity Conservation. Landscape Ecology, 22: 617-31.
18. Foody, G. M. 2004. Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 70: 627-34.
19. Foody, G. M. 2008. Harshness in Image Classification Accuracy Assessment. International Journal of Remote Sensing, 29: 3137-58.
20. Foody, G. M. 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: 1658-63.
21. Fung, T. 1990. An Assessment of TM Imagery for Land-Cover Change Detection. Geoscience and Remote Sensing, IEEE Transactions on, 28: 681-84.
22. Fung, T. and Ledrew, E. 1987. Application of Principal Components Analysis to Change Detection. Photogrammetric Engineering and Remote Sensing, 53: 1649-58.
23. Fung, T. and Ledrew, E. 1988. The Determination of Optimal Threshold Levels for Change Detection Using Various Accuracy Indices. Photogrammetric Engineering and Remote sensing, 54: 1449-54.
24. Green, K., Kempka, D. and Lackey, L. 1994. Using Remote Sensing to Detect and Monitor Land-Cover and Land-Use Change. Photogrammetric Engineering and Remote Sensing, 60: 331-37.
25. Hall, F. G., Strebel, D, Nickeson, J and Goetz, S. 1991. Radiometric Rectification: Toward a Common Radiometric Response among Multidate, Multisensor Images. Remote Sensing of Environment, 35: 11-27.
26. Hayes, D. J. and Sader, S. A. 2001. Comparison of Change-Detection Techniques for Monitoring Tropical Forest Clearing and Vegetation Regrowth in a Time Series. Photogrammetric Engineering and Remote Sensing, 67: 1067-75.
27. Hobbs, R. J. 1993. Effects of Landscape Fragmentation on Ecosystem Processes in the Western Australian Wheatbelt. Biological Conservation, 64: 193-201.
28. Janssen, L. L. F. and Van Der Wel, F. J. M. 1994. Accuracy Assessment of Satellite Derived Land-Cover Data: A Review. Photogrammetric Engineering and Remote Sensing, 60: 419-26.
29. Jensen, J. R. and Toll, D. L. 1982. Detecting Residential Land Use Development at the Urban Fringe. Photogrammteric Engineering and Remote Sensing, 48: 629-43.
30. Jha, C. S. and Unni, N. V. W. 1994. Digital Change Detection of Forest Conversion of a Dry Tropical Indian Forest Region. International Journal of Remote Sensing, 15: 2543-52.
31. Johnson, R. D. and Kasischke, E. S. 1998. Change Vector Analysis: A Technique for the Multitemporal Monitoring of Land Cover and Condition. International Journal of Remote Sensing, 19: 411-26.
32. Lu, D., Mausel, P, Batistella, M. and Moran, E. 2005. Land-Cover Binary Change Detection Methods for Use in the Moist Tropical Region of the Amazon: A Comparative Study. International Journal of Remote Sensing, 26: 101-14.
33. Lu, D., Mausel, P, Brondizio, E. and Moran, E. 2002. Assessment of Atmospheric Correction Methods for Landsat TM Data Applicable to Amazon Basin LBA Research. International Journal of Remote Sensing, 23: 2651-71.
34. Lu, D., Mausel, P, Brondizio, E. and Moran, E. 2004. Change Detection Techniques. International Journal of Remote Sensing, 25: 2365-401.
35. Markham, B. and Barker, J. 1987. Thematic Mapper Bandpass Solar Exoatmospheric Irradiances. International Journal of Remote Sensing, 8: 517-23.
36. Mas, J. 1999. Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques. International Journal of Remote Sensing, 20: 139-52.
37. Ode, D. J., Tieszen, L. L. and Lerman, J. C. 1980. The Seasonal Contribution of C"3 and C"4 Plant Species to Primary Production in a Mixed Prairie. Ecology,: 61: 1304-11.
38. Pax-Lenney, M. and Woodcock, C. E. 1997. Monitoring Agricultural Lands in Egypt with Multitemporal Landsat TM Imagery: How Many Images Are Needed?. Remote Sensing of Environment, 59: 522-29.
39. Pilon, P., Howarth, P, Bullock, R. and Adeniyi, P. 1988. An Enhanced Classification Approach to Change Detection in Semi-Arid Environments. Photogrammetric Engineering and Remote Sensing, 54: 1709-16.
40. Pontius, R. G. Jr and Millones, M. 2011. Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment. International Journal of Remote Sensing, 32: 4407-29.
41. Prakash, A. and Gupta, R. P. 1998. Land-Use Mapping and Change Detection in a Coal Mining Area-A Case Study in the Jharia Coalfield, India. International Journal of Remote Sensing, 19: 391-410.
42. Price, J. C. 1987. Calibration of Satellite Radiometers and the Comparison of Vegetation Indices. Remote Sensing of Environment, 21: 15-27.
43. Pu, R., Gong, P, Tian, Y., Miao, X., Carruthers, R. and Anderson, G. 2008. Using Classification and NDVI Differencing Methods for Monitoring Sparse Vegetation Coverage: A Case Study of Saltcedar in Nevada, USA. International Journal of Remote Sensing, 29: 3987-4011.
44. Reed, B. C., Brown, J. F, Vanderzee, D., Loveland, T. R., Merchant, J. W. and Ohlen, D. O. 1994. Measuring Phenological Variability from Satellite Imagery. Journal of Vegetation Science,: 5: 703-14.
45. Reid, R. S., Kruska, R. L, Muthui, N., Taye, A., Wotton, S., Wilson, C. J. and Mulatu, W. 2000. Land-Use and Land-Cover Dynamics in Response to Changes in Climatic, Biological and Socio-Political Forces: The Case of Southwestern Ethiopia. Landscape Ecology, 15: 339-55.
46. Ridd, M. K. and Liu, J. 1998. A Comparison of Four Algorithms for Change Detection in an Urban Environment. Remote Sensing of Environment, 63: 95-100.
47. Sader, S. and Winne, J. 1992. RGB-NDVI Colour Composites for Visualizing Forest Change Dynamics. International Journal of Remote Sensing, 13: 3055-67.
48. Schriever, J. R. and Congalton, R. G. 1995. Evaluating Seasonal Variability as an Aid to Cover-Type Mapping from Landsat Thematic Mapper Data in the Northeast. Photogrammetric Engineering and Remote Sensing, 61: 321-27.
49. Singh, A. 1989. Review Article: Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing, 10: 989-1003.
50. Singh, A. and Harrison, A. 1985. Standardized Principal Components. International Journal of Remote Sensing, 6: 883-96.
51. Smits, P., Dellepiane, S. and Schowengerdt, R. 1999. Quality Assessment of Image Classification Algorithms for Land-Cover Mapping: A Review and a Proposal for a Cost-Based Approach. International Journal of Remote Sensing, 20: 1461-86.
52. Song, C., C. E, Woodcock, K. C., Seto, M. P., Lenney, S. A. and Macomber. 2001. Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?. Remote Sensing of Environment, 75: 230-44.
53. Stow, D. 1999. Reducing the Effects of Misregistration on Pixel-Level Change Detection. International Journal of Remote Sensing, 20: 2477-83.
54. Townshend, J. R. G., Justice, C. O, Gurney, C. and McManus, J. 1992. The Impact of Misregistration on Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 30: 1054-60.
55. Verbyla, D. and Boles, S. 2000. Bias in Land Cover Change Estimates Due to Misregistration. International Journal of Remote Sensing, 21: 3553-60.
56. Wolter, P. T., Mladenoff, D. J, Host, G. E. and Crow, T. R. 1995. Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat Imagery. Photogrammetric Engineering and Remote Sensing, 61: 1129-44.
57. Xiuwan, C. 2002. Using Remote Sensing and GIS to Analyse Land Cover Change and Its Impacts on Regional Sustainable Development. International Journal of Remote Sensing, 23: 107-24.