Spatiotemporal dimensionality and Time-Space characterization of multitemporal imagery

Remote Sensing of Environment

Volumn 124, Issue , 2012, Pages 793-809

  Small, Christopher ^a  

^a Doherty Earth Observatory of Columbia University   (United States)

Author keywords

Deterministic; Dimensionality; Empirical orthogonal function analysis; EOF; PCA; Principal component; Spatial; Spatiotemporal; Spectral mixture model; Stochastic; Temporal; Temporal mixture model

Indexed keywords

DETERMINISTIC; DIMENSIONALITY; EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS; EOF; MIXTURE MODEL; PCA; PRINCIPAL COMPONENTS; SPATIAL; SPATIOTEMPORAL; SPECTRAL MIXTURE MODEL; STOCHASTIC; TEMPORAL;

CHARACTERIZATION; MIXTURES; PHENOLS; PRINCIPAL COMPONENT ANALYSIS; TIME SERIES; TOPOLOGY; VEGETATION;

STOCHASTIC MODELS;

EIGENVALUE; EMPIRICAL ANALYSIS; LANDSAT; MODIS; PHENOLOGY; PRINCIPAL COMPONENT ANALYSIS; SATELLITE IMAGERY; SPATIOTEMPORAL ANALYSIS; SPECTRAL ANALYSIS; STOCHASTICITY; TIME SERIES; TOPOLOGY;

GANGES DELTA;

EID: 84864060293     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2012.05.031     Document Type: Article

References (49)

1. Adams J.B., Smith M.O., Johnson P.E. Spectral mixture modeling; a new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research 1986, 91:8098-8122.
2. Asner G.P., Lobell D.B. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sensing of Environment 2000, 74:99-112.
3. Bellman R.E. Dynamic programming 1957, Princeton University Press.
4. Boardman J.W. Inversion of imaging spectrometry data using singular value decomposition. IGARSS'89 12th Canadian Symposium on Remote Sensing 1989, 2069-2072. Vancouver, B.C.: IEEE.
5. Boardman J. Inversion of high spectral resolution data. SPIE - Imaging Spectroscopy of the Terrestrial Environment 1990, 1298:222-233.
6. Boardman J.W. Geometric mixture analysis of imaging spectrometery data. Proc. Int. Geoscience and Remote Sensing Symp. 1994, 4:2369-2371.
7. Boardman J.W., Kruse F.A. Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine mountains, Nevada. Tenth Thematic Conference on Geologic Remote Sensing 1994, I407-I418. Environmental Research Institute of Michigan, Ann Arbor, MI.
8. Bretherton C.S., Smith C., Wallace J.M. An intercomparison of methods for finding coupled patterns in climate data. Journal of Climate 1992, 5:541-560.
9. Byrne G.F., Crapper P.F., Mayo K.K. Monitoring land-cover change by principal component analysis of mulittemporal Landsat data. Remote Sensing of Environment 1980, 10:175-184.
10. Coppin P.R., Bauer M.E. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews 1996, 13:207-234.
11. Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E. Review ArticleDigital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 2004, 25:1565-1596.
12. Crist E.P., Cicone R.C. A physically-based transformation of thematic mapper data - The TM tasseled cap. I.E.E.E. Transactions on Geoscience and Remote Sensing 1984, GE-22:256-263.
13. deBeurs k.m, Henebry G.M. Phenological Mixture Models: Using MODIS to identify key Phenological End Members and their spatial distribution in the northern Eurasian Semi-Arid Grain Belt. Eos AGU Fall Meeting Supplement 2006, 87.
14. Eastman J.R., Fulk M. Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing 1993, 59:1307-1312.
15. Elvidge C.D., Ziskin D., Baugh K.E., Tuttle B.T., Ghosh T. A fifteen year record of global natural gas flaring derived from satellite data. Energies 2009, 2:595-622.
16. Fisher J.I., Mustard J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sensing of Environment 2007, 109:261-273.
17. Fisher J.I., Mustard J.F., Vadeboncoeur M.A. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sensing of Environment 2006, 100:265-279.
18. Fung T., LeDrew E. Application of principal components analysis to change detection. Photogrammetric Engineering and Remote Sensing 1987, 53:1649-1658.
19. Galford G.L., Mustard J.F., Melillo J., Gendrin A., Cerri C.C., Cerri C.E.P. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sensing of Environment 2008, 112:576-587.
20. Gershenfeld N. The nature of mathematical modeling 1999, Cambridge University Press, Cambridge England.
21. Gillespie A.R., Smith M.O., Adams J.B., Willis S.C., Fischer A.F., Sabol D.E. Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California. Proceedings of the 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop 1990, 243-270. NASA Jet Propulsion Laboratory, Pasadena, CA.
22. Green A.A., Berman M., Switzer P., Craig M.D. A transformation for ordering mutispecral data in terms of image quality with implications for noise removal. I.E.E.E. Transactions on Geoscience and Remote Sensing 1988, 26:65-74.
23. Huete A., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 2002, 83:195-213.
24. Johnson P.E., Smith M.O., Adams J.B. Quantitative analysis of planetary reflectance spectra with principal components analysis. Journal of Geophysical Research 1985, 90:C805-C810.
25. Kauth R.J., Thomas G.S. The Tasseled Cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Porcessing of Remotely Sensed Data 1976, 4B41-44B51. Purdue University, West Lafayette, Indiana.
26. Lee J.B., Woodyatt A.S., Berman M. Enhancement of high specral resoltuion remote sensing data by a noise-adjusted principal components tranform. I.E.E.E. Transactions on Geoscience and Remote Sensing 1990, 28:295-304.
27. Lobell D.B., Asner G.P. Cropland distributions from temporal unmixing of MODIS data. Remote Sensing of Environment 2004, 93:412-422.
28. Lu D., Mausel P., Brondizio E., Moran E. Change detection techniques. International Journal of Remote Sensing 2004, 25:2365-2401.
29. Ozdogan M. The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis. Remote Sensing of Environment 2010, 114:1190-1204.
30. Parker R.L. Geophysical Inverse Theory 1994, Princeton University Press, Princeton, New Jersey.
31. Percival D.B., Walden A.T. Spectral analysis for physical applications 1993, Cambridge University Press, Cambridge, UK.
32. Piwowar J.M., Peddle D.R., LeDrew E.F. Temporal Mixture Analysis of Arctic sea ice imagery; A new approach for monitoring enviornmental change. Remote Sensing of Environment 1998, 63:195-207.
33. Piwowar J.M., Peddle D.R., Sauchyn D.J. Identifying ecological variability in vegetation dynamics through temporal mixture analysis. Geoscience and Remote Sensing (IGARSS), IEEE International Symposium 2006, 3766-3770. IEEE.
34. Preisendorfer R.W. Principal component analysis in meteorology and oceanography 1988, Elsevier, Amsterdam.
35. Quarmby N.A., Townshend J.R.G., Settle J.J., White K.H., Milnes M., Hindle T.L., et al. Linear mixture modeling applied to AVHRR data for crop area estimation. International Journal of Remote Sensing 1992, 13:415-425.
36. Richards J.A. Thematic mapping from multitemporal image data using the principal components transformation. Remote Sensing of Environment 1984, 16:35-46.
37. Settle J.J., Drake N.A. Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing 1993, 14:1159-1177.
38. Singh A., Harrison A. Standardized Principal Components. International Journal of Remote Sensing 1985, 6:883-896.
39. Small C. The landsat ETM plus spectral mixing space. Remote Sensing of Environment 2004, 93:1-17.
40. Small, C., & Elvidge, C.D. (in press). Night on Earth: Mapping decadal changes of night light in Asia. International Journal of Applied Earth Observation and Geoinformation.
41. Small C., Lu J.W.T. Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sensing of Environment 2006, 100:441-456.
42. Smith M.O., Johnson P.E., Adams J.B. Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research 1985, 90:792-804.
43. Smith M.O., Ustin S.L., Adams J.B., Gillespie A.R. Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment 1990, 31:1-26.
44. Tanre D., Herman M., Deschamps P.Y., Leffe A.D. Atmospheric Modeling For Space Measurements Of Ground Reflectances, Including Bidirectional Properties. Applied Optics 1979, 18:3587-3594.
45. Townshend J.R.G., Goff T.E., Tucker C.J. Multitemporal dimensionality of images of normalized difference vegetation index at continental scales. I.E.E.E. Transactions on Geoscience and Remote Sensing 1985, GE-23.
46. Tukey J.W. Exploratory Data Analysis 1977, Addison-Wesley.
47. Verbesselt J., Hyndman R., Newnham G., Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 2010, 114:106-115.
48. von Storch H., Zwiers F.W. Statistical Analysis in Climate Research 1999, Cambridge University Press, Cambridge UK.
49. Zhang X., Friedl M.A., Schaaf C.B. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research 2006, 111:1-14.