02529nam a2200253 a 450000100080000000500110000800800410001910000180006024500690007826001460014730000180029350000170031152017390032865000160206765000130208365000250209665300290212165300280215070000140217870000170219270000220220970000170223170000270224819022302020-01-24       2010    bl uuuu     u00u1 u    #d1 aAGUIAR, D. A.  aMODIS time series to assess pasture land.h[electronic resource]  aIn: INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, Honolulu. Remote sensing: global vision for local action. [S.l.]: IEEEc2010  ap. 2123-2126.  aIGARSS 2010.  aLand use conversion is a key factor in the mitigation of GHG emission. Maximum mitigation can be achieved when degraded pasture land is converted to biofuel crops. Remote sensing images, and in particular the MODIS time series data, have a great potential to asses degraded pasture land. This work has the objective to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil. MODIS time series were used to obtain vegetation indices and fraction images. The wavelet technique was applied at various levels of decomposition to extract the input parameters in the WEKA J48 classifier. Pasture land was well distinguished from Cerrado. The distinction among different pasture land presented lower performance with best results for pasture with invasive plants followed by good pasture. Pasture land with bare soil patches and termite mounds were not distinguished from other classes of pasture. made it possible to reduce pasture land without herd reduction. Consequently more land became available for sugarcane. Considering that land use change is a key factor for the benefit of biofuel production to mitigate carbon emission, this benefit can be even higher if sugarcane expansion occurs on degraded pasture land. Remote sensing images have a great potential to evaluate degraded pasture land although is not a trivial task and requires intensive fieldwork. MODIS time series data transformed into vegetation indices or linear spectral mixing model are suitable to represent different pasture land conditions. Under these considerations this work has the objective to use MODIS time series to identify pasture land and its different levels of degradation in Mato Grosso do Sul state, Brazil.  aDegradation  aPastures  aTime series analysis  aDegradação de pastagem  aSéries temporais MODIS1 aADAMI, M.1 aSILVA, W. F.1 aRUDORFF, B. F. T.1 aMELLO, M. P.1 aSILVA, J. dos S. V. da