01998nam a2200289 a 450000100080000000500110000800800410001910000250006024501160008526001850020130000180038650000170040452010150042165000240143665000250146065000210148565300110150665300270151765300120154465300220155665300310157870000220160970000180163170000180164970000200166770000210168720016362020-01-08       2014    bl uuuu     u00u1 u    #d1 aGONÇALVES, R. R. V.  aLand use temporal analysis through clustering techniques on satellite image time series.h[electronic resource]  aIn: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; CANADIAN SYMPOSIUM ON REMOTE SENSING, 35., 2014, Québec. Energy and our changing planet: proceedings. [S.l.]: IEEEc2014  ap. 2173-2176.  aIGARSS 2014.  aSatellite images time series have been used to study land surface, such as identification of forest, water, urban areas, as well as for meteorological applications. However, for knowledge discovery in large remote sensing databases can be use clustering techniques in multivariate time series. The clustering technique on three-dimensional time series of NDVI, albedo and surface temperature from AVHRR/NOAA satellite images was used, in this study, to map the variability of land use. This approach was suitable to accomplish the temporal analysis of land use. Additionally, this technique can be used to identify and analyze dynamics of land use and cover being useful to support researches in agriculture, even considering low spatial resolution satellite images. The possibility of extracting time series from satellite images, analyzing them through data mining techniques, such as clustering, and visualizing results in geospatial way is an important advance and support to agricultural monitoring tasks.  asurface temperature  aTime series analysis  aVegetation index  aAlbedo  aÍndice de vegetação  aK-means  aSéries temporais  aTemperatura da superfície1 aZULLO JÚNIOR, J.1 aAMARAL, B. F.1 aCOLTRI, P. P.1 aSOUSA, E. P. M.1 aROMANI, L. A. S.