Syllabus
1) Basic concepts of
spatiotemporal processes. Representation
and characterization of high dimensional processes. Measurement and sampling. Examples of spatiotemporal processes. Lab: Software tutorial; Manipulation and visualization of high dimensional
data.
2) Characterization of
spatiotemporal processes. Principal
Components and other useful high dimensional transformations. Basic concepts of statistical data analysis. Lab: Spatiotemporal characterization. Statistical analysis of
dimensionality and eigenstructure.
Alternative strategies for difficult problems.
3) Modeling of
spatiotemporal processes. Linear mixture
models and endmember selection. Model
evaluation and accuracy assessment. Lab:
Linear mixture modeling and model evaluation.
4) Validation of
spatiotemporal characterization and models in remote sensing. Multi-sensor characterization, analysis and
vicarious validation. Lab:
Multi-sensor fusion and vicarious validation.
Recommended Text: Eshel, G. (2012). Spatiotemporal Data
Analysis. Princeton, NJ, USA: Princeton University Press
Recommended Reading:
Byrne, G.F., Crapper, P.F., & Mayo,
K.K. (1980). Monitoring land-cover change by principal component analysis of
multitemporal landsat data. Remote
Sensing of Environment, 10, 175-184
Crist, E.P., & Cicone, R.C. (1984). A
physically-based transformation of thematic mapper data - The TM tasseled cap. I.E.E.E. Transactions on Geoscience and
Remote Sensing, GE-22, 256-263
Eastman, J.R., & Filk, M. (1993). Long
sequence time series evaluation using standardized principal components. Photogrammetric engineering and remote
sensing, 59, 991
Fung, T., & LeDrew, E. (1987).
Application of principal components analysis to change detection. Photogrammetric engineering and remote
sensing, 53, 1649
Pearson, K. (1901). LIII. The London, Edinburgh and Dublin
philosophical magazine and journal of science, 2, 559-572
Preisendorfer, R.W. (1988). Principal component analysis in meteorology
and oceanography. Amsterdam: Elsevier
Press, W.H., Teukolsky, S.A., Vetterling,
W.T., & Flannery, B.P. (2007). Numerical
Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge
University Press
Quarmby, N.A., Townshend, J.R.G., Settle,
J.J., White, K.H., Milnes, M., Hindle, T.L., & Silleos, N. (1992). Linear
mixture modelling applied to AVHRR data for crop area estimation. International Journal of Remote Sensing, 13,
415-425
Richards, J.A. (1984). Thematic mapping
from multitemporal image data using the principal components transformation. Remote Sensing of Environment, 16, 35-46
Settle, J.J., & Drake, N.A. (1993).
Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing, 14,
1159-1177
Singh, A., & Harrison, A. (1985).
Standardized principal components. International
Journal of Remote Sensing, 6, 883-896
Small, C. (2004). The Landsat ETM+ Spectral
Mixing Space. Remote Sensing of
Environment, 93, 1 –17
Small, C. (2012). Spatiotemporal
dimensionality and Time-Space characterization of multitemporal imagery. Remote Sensing of Environment, 124,
793-809
Smith, M.O., Johnson, P.E., & Adams,
J.B. (1985). Quantitative determination of mineral types and abundances from
reflectance spectra using principal component analysis. Journal of Geophysical Research, 90, 792-804
Smith, M.O., Ustin, S.L., Adams, J.B.,
& Gillespie, A.R. (1990). Vegetation in deserts: I. A regional measure of
abundance from multispectral images. Remote
Sensing of Environment, 31, 1-26