Abstract
One of the challenges for urban and regional planners and other users of remotely sensed imagery is how to select the appropriate data for a particular monitoring or mapping problem. In the past, the dearth of available imagery meant that the problem itself usually had to be adapted to fit the data, which was typically limited to either high spatial resolution film-based aerial imagery, or coarse-spatial resolution digital satellite imagery. Today, a vast range of aerial and satellite imagery is available (Kramer, 2002), opening a new range of potential scales of problems that can be investigated. However, these new options also place additional burdens on the remote sensing user, who, in selecting data, has to consider differences in spectral, temporal, radiometric, and spatial characteristics of the imagery. Spatial properties are particularly important, and the pixel size of current sensors varies over more than three orders of magnitude (from 0.6 m to 1 km and larger) (Kramer, 2002).
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References
Anselin, L., 1995. Local Indicators of Spatial Autocorrelation — LISA. Geographical Analysis 27(2):93–15.
Bannari, A., K. Omari, P. Tellet, and G. Fedosejevs, 2005. Radiometric Uniformity and Stability of Test Sites Used for the Calibration of Earth Observation Sensors. IEEE Transactions on Geosciences and Remote Sensing 43(12):2918–2926.
Cohen, W. B., T. A. Spies, and G. A. Bradshaw, 1990. Semivariograms of Digital Imagery for Analysis of Conifer Canopy Structure. Remote Sensing of Environment 34:167–178.
Curran, P. J., 1988. The Semivariogram in Remote Sensing: An Introduction. Remote Sensing of Environment 24: 493–507.
Geary, R., 1954. The contiguity ratio and statistical mapping. The Incorporated Statistician 5:115–145.
Getis, A., and J. K. Ord, 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis 24(3):189–206.
Goodchild, M. F., 1986. Spatial Autocorrelation. Geo, Norwich, United Kingdom, 56 pp.
Hyppänen, H., 1996. Spatial Autocorrelation and Optimal Spatial Resolution of Optical Remote Sensing Data in Boreal Forest Environment. International Journal of Remote Sensing 17(17):3441–3452.
Jupp, D. L. B., A. H. Strahler, and C. E. Woodcock, 1988. Autocorrelation and Regularization in Digital Images I. Basic Theory. IEEE Transactions on Geoscience and Remote Sensing 26(4):463–473.
Jupp, D. L. B., A. H. Strahler, and C. E. Woodcock, 1989. Autocorrelation and Regularization in Digital Images II. Simple Image Models. IEEE Transactions on Geoscience and Remote Sensing 27(3):247–258.
Kramer, H. J., 2002. Observation of the Earth and its Environment. Springer, Berlin, Germany, 1510pp.
LeDrew, E. F., H. Holden, M. A. Wulder, C. Derksen, C. Newman, 2004. A spatial statistical operator applied to multidate satellite imagery for identification of coral reef stress. Remote Sensing of Environment 91:271–279.
Marceau, D., 1999. The scale issue in social and natural sciences. Canadian Journal of Remote Sensing 25(4): 347–356.
Matheron, G., 1971. The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphologie Mathematiques de Fontainebleau No. 5, Fontainebleau, France.
Moran, P. A. P., 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society, series B:246–251.
Ord, J., and A. Getis, 1995. Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis 27:286–306.
Sawada, M. 2004. Global Spatial Autocorrelation Indices-Moran’s I, Geary’s C and the General Cross-Product Statistic. Research paper from the Laboratory for Paleoclimatology and Climatology at the University of Ottawa. (as posted online at: http://www.lpc.uottawa.ca/publications/moransi/moran.htm)
St-Onge, B. A. and F. Cavayas, 1997. Automated Forest Structure Mapping from high Resolution Imagery Based on Directional Semivariogram Estimates. Remote Sensing of Environment 61:82–95.
Switzer, P., and S. E. Ingebritsen, 1986. Ordering of Time-Difference Data from Multispectral Imagery. Remote Sensing of Environment 20(1): 85–94.
Warner, T. A., 1999. Analysis of spatial patterns in remotely sensed data using multivariate spatial correlation. Geocarto International 14(1):59–65.
Warner, T. A. and M. C. Shank, 1997. Spatial Autocorrelation Analysis of Hyperspectral Imagery for Feature Selection. Remote Sensing of Environment 60:58–70.
Warner, T. and K. Steinmaus, 2005. Classification of orchards and vineyards with high spatial resolution panchromatic imagery. Photogrammetric Engineering and Remote Sensing 71(2):179–187.
Woodcock, C. E., A. H. Strahler, and D. L.B. Jupp, 1988. The Use of Variograms in Remote Sensing: I. Scene Models and Simulated Images. Remote Sensing of Environment 25:323–348.
Wu, S-S., B. Xu, and L. Wang, 2006. Urban Land-use Classification Using Variogram-based Analysis with an Aerial Photograph. Photogrammetric Engineering & Remote Sensing 72(7): 813–822.
Wulder, M. and B. Boots, 1998. Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic. International Journal of Remote Sensing 19(11):2223–2231.
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Spiker, J.S., Warner, T.A. (2007). Scale and Spatial Autocorrelation From A Remote Sensing Perspective. In: Jensen, R.R., Gatrell, J.D., McLean, D. (eds) Geo-Spatial Technologies in Urban Environments. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69417-5_10
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DOI: https://doi.org/10.1007/978-3-540-69417-5_10
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