an overview of the current state of the art in the use of field spectroradiometry in
examining the spectral discrimination between different land cover targets. In this
framework, techniques employed today and linked with the most important scale
factors are critically reviewed and examples of recent related studies and spectral
libraries are provided. Then, it focuses on the use of hyperspectral remote sensing for
obtaining land use/cover mapping from space. An overview of the different satellite
sensors and techniques employed is furnished, providing examples taken from recent
studies. The chapter closes by highlighting the main challenges that need to be
addressed in the future towards a more precise estimation of land cover from spectral
information acquired from hyperspectral sensing systems at variant spatial scales.
REFERENCES
Agam, N., Kustas, W. P., Anderson, M. C., Li, F., and Neale, C. M. U. 2007. A vegetation index
based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment
107(4):545–558.
Allen, T. F. H., O’Neill, R. V., and Hoekstra, T. W. 1987. Interlevel relations in ecological
research and management: Some working principles from hierarchy. Journal of Applied
Systems Analysis 14:63–79.
Aplin P. 2006. On scales and dynamics in observing the environment. International Journal of
Remote Sensing 27(11):2123–2140.
Aplin, P., and Atkinson, P. M. 2004. Predicting missing field boundaries to increase perfield classification accuracy. Photogrammetric Engineering and Remote Sensing
70:141–149.
Asner, G. P., Bustamante, M. M. C., and Townsend, A. R. 2003. Scale dependence of
biophysical structure in deforested areas bordering the Tapajos National Forest, Central
Amazon. Remote Sensing of Environment 87(4):507–520.
Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information.
ISPRS Journal of Photogrammetry & Remote Sensing 58:239–258.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65(1):2–16.
Buyantuyev, A., and Wu, J. 2007. Effects of thematic resolution on landscape pattern analysis.
Landscape Ecology 22(1):7–13.
Clarke, K. C. 1986. Computation of the fractal dimension of topographic surfaces using the
triangular prism surface area method. Computers and Geosciences 12:713–722.
De Jong, S. M., and Burrough, P. A. 1995. A fractal approach to the classification of
Mediterranean vegetation types in remotely sensed images. Photogrammetric Engineering
and Remote Sensing 61:1041–1053.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 1999. Multiscale fractal analysis of image
texture and pattern. Photogrammetric Engineering and Remote Sensing 65:51–61.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 2005. A comparison of local variance,
fractal dimension, and Moran’s I as aids to multispectral image classification. International
Journal of Remote Sensing 26:1575–1588.
REFERENCES
9
examining the spectral discrimination between different land cover targets. In this
framework, techniques employed today and linked with the most important scale
factors are critically reviewed and examples of recent related studies and spectral
libraries are provided. Then, it focuses on the use of hyperspectral remote sensing for
obtaining land use/cover mapping from space. An overview of the different satellite
sensors and techniques employed is furnished, providing examples taken from recent
studies. The chapter closes by highlighting the main challenges that need to be
addressed in the future towards a more precise estimation of land cover from spectral
information acquired from hyperspectral sensing systems at variant spatial scales.
REFERENCES
Agam, N., Kustas, W. P., Anderson, M. C., Li, F., and Neale, C. M. U. 2007. A vegetation index
based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment
107(4):545–558.
Allen, T. F. H., O’Neill, R. V., and Hoekstra, T. W. 1987. Interlevel relations in ecological
research and management: Some working principles from hierarchy. Journal of Applied
Systems Analysis 14:63–79.
Aplin P. 2006. On scales and dynamics in observing the environment. International Journal of
Remote Sensing 27(11):2123–2140.
Aplin, P., and Atkinson, P. M. 2004. Predicting missing field boundaries to increase perfield classification accuracy. Photogrammetric Engineering and Remote Sensing
70:141–149.
Asner, G. P., Bustamante, M. M. C., and Townsend, A. R. 2003. Scale dependence of
biophysical structure in deforested areas bordering the Tapajos National Forest, Central
Amazon. Remote Sensing of Environment 87(4):507–520.
Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information.
ISPRS Journal of Photogrammetry & Remote Sensing 58:239–258.
Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of
Photogrammetry and Remote Sensing 65(1):2–16.
Buyantuyev, A., and Wu, J. 2007. Effects of thematic resolution on landscape pattern analysis.
Landscape Ecology 22(1):7–13.
Clarke, K. C. 1986. Computation of the fractal dimension of topographic surfaces using the
triangular prism surface area method. Computers and Geosciences 12:713–722.
De Jong, S. M., and Burrough, P. A. 1995. A fractal approach to the classification of
Mediterranean vegetation types in remotely sensed images. Photogrammetric Engineering
and Remote Sensing 61:1041–1053.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 1999. Multiscale fractal analysis of image
texture and pattern. Photogrammetric Engineering and Remote Sensing 65:51–61.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 2005. A comparison of local variance,
fractal dimension, and Moran’s I as aids to multispectral image classification. International
Journal of Remote Sensing 26:1575–1588.
REFERENCES
9