Abstract
Multi-band remotely sensed image data contain information on landscape pattern and temporal changes that are greatly underutilized in this technological era when monitoring of disturbance and ecological dynamics is increasingly important to address questions regarding sustainability of ecosystem health and climate change. Among the reasons for this loss of analytical opportunity are the inadequacy of methods for systematic extraction of pattern elements, incongruity between information paradigms for remote sensing and geographic information systems (GIS), and the sheer volume of remotely sensed image data when acquired regularly over time. Long-term cooperative landscape ecological investigations concerning habitat and change detection in conjunction with remote sensing and GIS have yielded a pattern-based approach to progressively segmenting images (PSI) that culminates in a doubly segmented image representation by sets of approximating signal vectors that serve as parsimonious proxies for pixel vectors.
The coarser level of segmentation is entirely congruent with raster map structures for GIS, and yet mimics the appearance of an image display by colorization using information on typical spectral properties of segments contained in attribute tables. The components of the coarser representation as spatial segments constitute explicit elements of pattern at several levels. The explicit nature of these pattern elements enables spatial pattern matching for change detection that resolves difficulties with phenological variability and continuity of sensor configurations over time. Conversion to segmented representation can be applied to multi-temporal change indices so as to elicit longer-term patterns of change from temporal sequences of images. The finer level of segmentation for spectral detail enables restoration of image bands in the manner of a low-pass filter for analysis according to the usual paradigms of remote sensing. Mapping of the residuals for the finer detail of image approximation provides further information on exceptional features of landscape ecological pattern.
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Abbreviations
- GIS:
-
Geographic Information System
- PHASE:
-
Palette Homogeneity Among Segmentation Elements
- PSI:
-
Progressively Segmenting Images
References
Anderson, J., E. Hardy, J. Roach and R. Witmer. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U. S. Geological Survey Professional Paper 964. Resten, VA: U. S. Geological Survey. 28 p.
Ball, D. J., and G. H. Hall. 1965. ISODATA, a novel technique for data analysis and pattern classification. Technical Report, Stanford Research Institute, Menlo Park, California
Ball, D. J. and G. H. Hall. 1967. A clustering technique for summarizing multivariate data. Behavior Science 12: 153–155.
Baker, W. 1989. A review of models of landscape change. Landscape Ecology 2: 111–133.
Bruzzone, L. and D. Prieto. 2000. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing 38: 1171–1182.
Chen, J., P. Gong, C. He, R. Pu and P. Shi. 2003. Land-use/land-cover change detection using improved change vector analysis. Photogrammetric Engineering and Remote Sensing 69: 369–379.
Coppin, P. and M. Bauer. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews 13: 207–234.
Congalton, R. and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers/CRC Press, Boca Raton, FL.
Dai, X. and S. Khorram. 1998. The effects of image misregistration on accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing 36: 1566–1577.
Everitt, B. S., S. Landau and M. Leese. 2001. Cluster Analysis. Fourth edition. Arnold, London.
Forman, R. T. T. 1995. Land Mosaics: The Ecology of Landscapes and Regions. Cambridge Univ. Press, Cambridge, U.K.
Forman, R. T. T. and M. Godron. 1986. Landscape Ecology. John Wiley & Sons, New York.
Gibson, P. and C. Power. 2000. Introductory Remote Sensing: Digital Image Processing and Applications. Taylor and Francis, New York.
Gong, P., E. LeDrew and J. Miller. 1992. Registration noise reduction in difference images of change detection. International Journal of Remote Sensing 13: 773–779.
Gonzalez, R. C. and R. C. Woods. 1992. Digital Image Processing. Addison-Wesley Publishing Co., Reading, MA.
Gordon, A. D. 1999. Classification, 2nd edition. Chapman & Hall, Boca Raton, FL.
Groombridge, B. 1992. Global Biodiversity: Status of the Earth’s Living Resources. Chapman & Hall, New York.
Hall, F., D. Strebel, J. Nickeson and S. Goetz. 1991. Radiometric rectification: Toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment 35:11–27.
Hartigan, J. A. 1975. Clustering Algorithms. John Wiley & Sons, New York.
Hartigan, J. A. and M. A. Wong. 1979. A k-means clustering algorithm [Algorithm AS-136]. Applied Statistics 28: 100–108.
Hastie, T., R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning. Springer, New York.
Howarth, P. and G. Wickware. 1981. Procedures for Change Detection using Landsat Digital data. Internationaljournal of Remote Sensing 2: 277–291.
Jain, A. K., M. N. Murty and P. J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys, 31: 264–323.
James, M. 1985. Classification Algorithms. John Wiley & Sons, London, UK.
Johnson, R. and E. Kasischke. 1998. Change vector analysis: A technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing 19: 411–426.
Kelly, P. and J. White. 1993. Preprocessing Remotely-Sensed Data for Efficient Analysis and Classification. Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, Proceedings SPIE 1993. pp. 24–30.
Lambin, E. and A. Strahler. 1994a. Indicators of land-cover change for change vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing 15:2099–2119.
Lambin, E. F. and A. H. Strahler. 1994b. Change-vector analysis in multi-temporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environment 48:231–244.
Li, J. and R. Gray. 2000. Image Segmentation and Compression Using Hidden Markov Models. Kluwer Academic Publishers, Norwell, MA.
Lunetta, R. and C. Elvidge (eds.). 1998. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Ann Arbor Press, Ann Arbor, MI.
Lunetta, R., J. Lyon, B. Guindon and C. Elvidge. 1998. North American landscape characterization dataset development and data fusion issues. Photogrammetric Engineering and Remote Sensing 64: 821–829.
Mas, J. 1999. Monitoring land-cover changes: A comparison of change detection techniques. International Journal of Remote Sensing 20: 139–152.
McGarigal, K. and B. J. Marks. 1995. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. General Technical Report PNW 351, U. S. Forest Service, Pacific Northwest Research Station. 122 pp.
Michalek, J., T. Wagner, J. Luczkovich and R. Stoffle. 1993. Multi-spectral change vector analysis for monitoring coastal marine environments. Photogrammetric Engineering and Remote Sensing 59: 381–384.
Miller, R., ed. 1994. Mapping the Diversity of Nature. Chapman & Hall, New York.
Myers, W. 2003. Doubly segmented images for pattern-based approach to change detection. Final report on NASA Research Project NAG5-1054. Research Report PSIE 2003-6, Penn State Institutes of Environment, The Pennsylvania State University, Univ. Park, PA 16802 USA. 90 pp. + CD-ROM.
Myers, W., G. P. Patil and C. Taillie. 1999. Conceptualizing pattern analysis of spectral change relative to ecosystem status. Ecosystem Health 5: 285–293.
Patil, G. P., R. Brooks, W. Myers, D. Rapport and C. Taillie. 2001. Ecosystem health and its measurement at landscape scale: Toward the next generation of quantitative assessments. Ecosystem Health 7: 307–316.
Patil, G. P. and W. Myers. 1999. Environmental and ecological health assessment of landscapes and watersheds with remote sensing data. Ecosystem Health 5:221–224.
Pratt, W. 1991. Digital Image Processing. John Wiley & Sons, New York.
Richards, J. A. and X. llia. 1999. Remote Sensing Digital Image Analysis, 3rd edition. Springer-Verlag, Berlin.
Rogan, J., J. Franklin and D. Roberts. 2003. A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sensing of Environment 80: 143–156.
Rogan, J., J. Miller, D. Stow, J. Franklin, L. Levien and C. Fischer. 2003. Land-cover change monitoring with classification trees using landsat tm and ancillary data. Photogrammetric Engineering and Remote Sensing 69: 793–804.
Singh, A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10: 989–1003.
Sohl, T. and J. Dwyer. 1998. North American landscape characterization project: the production of a continental scale three-decade landsat data set. Geocarto International 13: 43–51.
Song, C., C. Woodcock, K. Seto, M. Lenney and S. Macomber. 2001. Classification and change detection using landsat tm data: when and how to correct atmospheric effects. Remote Sensing of Environment 75: 230–244.
Tso, B. and P. Mather. 2001. Classification Methods for Remotely Sensed Data. Taylor and Francis, New York.
Turner, M. R. Gardner and R. O’Neill. 2001. Landscape Ecology in Theory and Practice: Pattern and Process. Springer-Verlag, Inc., New York.
Wilson, J. and J. Gallant (eds.) 2000. Terrain Analysis: Principles and Applications. John Wiley and Sons, Inc., New York.
Acknowledgements
Prepared with partial support from NASA Biospheric Sciences Branch, Goddard Space Flight Center and the NSF Digital Government Program, Division of Experimental and Integrative Activities, Directorate for Computer and Information Science and Engineering. The contents have not been subjected to Agency review and therefore do not necessarily reflect the views of the Agencies and no official endorsement should be inferred.
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Myers, W.L., Patil, G.P. & Taillie, C. Doubly segmented proxy images for multi-scale landscape ecology and ecosystem health. COMMUNITY ECOLOGY 4, 163–183 (2003). https://doi.org/10.1556/ComEc.4.2003.2.5
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DOI: https://doi.org/10.1556/ComEc.4.2003.2.5