Cespe UnB

Editorial Assistants:
W. Abrahão
G. Oliveira
L. Salgueiro

Editorial Technical Support:
D. H. Diaz
M. A. Gomez
J. Barbosa

Editorial management and production:

95/105= 0.91


Virtual laboratory of remote sensing time series: visualization of MODIS EVI2 data set over South America

doi: 10.6062/jcis.2011.02.01.0032(Free PDF)


Ramon Morais de Freitas, Egidio Arai, Marcos Adami, Arley Souza Ferreira, Fernando Yuzo Sato, Yosio Edemir Shimabukuro, Reinaldo Roberto Rosa, Liana Oighenstein Anderson and Bernardo Friedrich Theodor Rudorff


Over the last ten years millions of gigabytes of MODIS (Moderate Resolution Imaging Spectroradiometer) data have been generated which is forcing the remote sensing users community to a new paradigm in data processing for image analysis and visualization of these time series. In this context this paper aims to present the development of a tool to integrate the 10 years time series of MODIS images into a virtual globe to support LULC change studies. Initially the development of a tool for instantaneous visualization of remote sensing time series within the concept of a virtual laboratory framework is described. The virtual laboratory is composed by a data set with more than 500 million EVI2 (Enhanced Vegetation Index 2) time series derived from MODIS 16-day composite data. The EVI2 time series were filtered with sensor ancillary data and Daubechies (Db8) orthogonal Discrete Wavelets Transform. Then EVI2 time series were integrated into the virtual globe using Google Maps and Google Visualization Application Programming Interface functionalities. The Land Use Land Cover changes for forestry and agricultural applications are presented using the proposed time series visualization tool. The tool demonstrated to be useful for rapid LULC change analysis, at the pixel level, over large regions. Next steps are to further develop the Virtual Laboratory of Remote Sensing Time Series Framework by extending this work for other geographical regions, incorporating new computational algorithms, testing data from other sensors and updating the MODIS time series.


MODIS, EVI2, wavelets transform, time series analysis, virtual globe, land use and land cover changes, forest, agriculture, South America, instantaneous visualization.


[1] LAMBIN EF & LINDERMAN M. 2006. Time series of remote sensing data for land change science. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1926-1928.

[2] DEFRIES RS, ASNER GP & HOUGHTON RA. 2004. Ecosystems and Land Use Change. American Geophysical Union, Washington, DC.

[3] JUSTICE CO, TOWNSHEND JRG, VERMOTE EF, MASUOKA E, WOLFE RE, SALEOUS, N, ROY DP & MORISETTE JT. 2002. An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83: 3-15.

[4] BUTLER D. 2006. Virtual globes: the web-wide world. Nature, 439(7078): 776-778.

[5] BALLAGH LM, RAUP BH, DUERR RE, KHALSA SJS, HELM C, FOWLER D & GUPTE A. 2011. Representing scientific data sets in KML: Methods and challenges, Computers & Geosciences, 37(1), Virtual Globes in Science, p. 57-64. ISSN 0098-3004, DOI: 10.1016/j.cageo.2010.05.004.

[6] CHIANG G, TOBY OH, DOVE MT, BOVOLO CI & EWEN J. 2011. Geo-visualization Fortran library, Computers & Geosciences, 37(1), Virtual Globes in Science, p. 65-74, ISSN 0098-3004, DOI: 10.1016/j.cageo.2010.04.012.

[7] NIELSON GM. 1991. Visualization in Scientific and Engineering Computation. IEEE Computer, 24(9): 58-66.

[8] EVA H, BELWARD A, EVARISTO M, DI BELLA C, GOND V, JONES S, SGRENZAROLI M & FRITZ S. 2004. A land cover map of South America, Global Change Biology, 10: 731-744.

[9] JIANG Z, HUETE AR, DIDAN K & MIURA T. 2008. Development of a two-band Enhanced Vegetation Index without a blue band. Remote Sensing of Environment, 112(10): 3833-3845.

[10] SAKAMOTO T, YOKOZAWA M, TORITANI H, SHIBAYAMA M, ISHITSUKA N & OHNO H. 2005. A crop phenology detection method using time series MODIS data. Remote Sensing of Environment, 96(3-4): 366-374.

[11] THAYN JB & PRICE KP. 2008. Julian dates and introduced temporal error in remote sensing vegetation phenology studies. International Journal of Remote Sensing, 29: 6045-6049.

[12] FREITAS RM & SHIMABUKURO YE. 2008. Combining wavelets and linear spectral mixture model for MODIS satellite sensor time series analysis. JCIS - Journal of Computational Interdisciplinary Sciences, 1: 51-56.

[13] DAUBECHIES I. 1992. Ten lectures on wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics 61, Philadelphia, PA. Soc. Ind. Appl. Math, 377 pp.

[14] MEYER Y. 1992. Wavelets and operators, Cambridge Studies in Advanced Math., vol. 37, Cambridge Univ. Press, Cambridge, 223 p.

[15] MALLAT S. 1989. A theory for multi resolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 674-693.

[16] LAPOLA DM, SCHALDACH R, ALCAMO J, BONDEAU A, KOCH J, KOELKING C & PRIESS JA. 2010. Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proceedings of the National Academy of Sciences, 107(8): 3388-3393.

[17] RUDORFF BFT, AGUIAR DA, SILVA WF, SUGAWARA LM, ADAMI M & MOREIRA MA. 2010. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in Sao Paulo State (Brazil) Using ˜ Landsat Data. Remote Sensing, 2: 1057-1076.

[18] MALLAT S. 1999. A wavelet tour of signal processing, 2 nd Edition, Academic Press.

[19] LE PENNEC E & MALLAT S. 2005. Sparse Geometric Image Representation with Bandelets, IEEE Trans. on Image Processing, 14(4): 423-438.

[20] BOASHASH B. 2003. Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Oxford: Elsevier Science.


[22] ASSIREU AT, ROSA RR, VIJAYKUMAR NL & LORENZZETTI JA. 2002. Gradient pattern analysis of short nonstationary time series: an application to Lagrangian data from satellite tracked drifters. Physica D, Elsevier, 169c: 397-403.

[23] FREITAS RM, ROSA RR & SHIMABUKURO YE. 2010. Using Gradient Pattern Analysis for land use and land cover change detection. In: International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Honolulu, 1: 3648-3651.

[24] PENG CK et al. 1994. Mosaic organization of DNA nucleotides. Phys Rev E, 49(2): 1685-1689.

[25] KANTELHARDT JW et al. 2001. Detecting long-range correlations with detrended fluctuation analysis. Phys A, 295(3-4): 441-454.

[26] DUDA RO, HART PE. 1973. Pattern Classification and Scene, Analysis, New York: John Wiley & Sons, Inc.

[27] HARTIGAN JA. 1985. Statistical Theory in Clustering. Journal of Classification, 2: 63-76.

[28] THEODORIDIS S, KOUTROUMBAS K. 2009. Pattern Recognition, 4 th Edition, Academic Press.

[29] YANG T. 2006. Computational Verb Decision Trees. International Journal of Computational Cognition (Yang's Scientific Press), 4(4): 34-46.

[30] YUAN Y, SHAW MJ. 1995. Induction of fuzzy decision trees. Fuzzy Sets and Systems, 69: 125-139.

[31] LONGLEY P, GOODCHILD MF, MAGUIRE DJ, RHIND DW. 2005. Geographical information systems and science: John Wiley & Sons Inc.

[32] LONGLEY P. 2008. To what extent are the fundamental spatial concepts that lie behind GIS relevant in design? In Spatial Concepts in GIS and Design. Santa Barbara, CA: UCSB.

[33] BRETHERTON FP, SINGLEY PT. 1994. Metadata: A User's View, Proceedings of the International Conference on Very Large Data Bases (VLDB). pp. 1091-1094.


Combining wavelets and linear spectral mixture model for MODIS satellite sensor time-series analysis
doi: 10.6062/jcis.2008.01.01.0005
Freitas and Shimabukuro(Free PDF)

Riddled basins in complex physical and biological systems
doi: 10.6062/jcis.2009.01.02.0009
Viana et al.(Free PDF)

Use of ordinary Kriging algorithm and wavelet analysis to understanding the turbidity behavior in an Amazon floodplain
doi: 10.6062/jcis.2008.01.01.0006
Alcantara.(Free PDF)

A new multi-particle collision algorithm for optimization in a high performance environment
doi: 10.6062/jcis.2008.01.01.0001
Luz et al.((Free PDF)

Reviewer Guidelines
(Under Construction)
Advertises Media Information