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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)

Authors

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

Abstract

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.

Keywords

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

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