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Márcio Pupin Mello  - - - 
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Marcos Adami

23 shared publications

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Article 0 Reads 2 Citations Structural characterization of canopies of Eucalyptus spp. using radiometric data from TM/Landsat 5 Ludmila Roque Ferraz Pacheco, Flávio Jorge Ponzoni, Sandra B... Published: 01 March 2012
CERNE, doi: 10.1590/s0104-77602012000100013
DOI See at publisher website
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Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capão Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to field data collection). Results indicate that stronger correlations were identified between crown dimensions and canopy height with near-infrared spectral band data (ρs4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a significant difference between models based on distinct data acquisition dates.
Article 0 Reads 0 Citations An R implementation for Bayesian networks applied to spatial data Marcos Adami, Daniel Alves Aguiar, Marcio Pupin Mello, Berna... Published: 01 January 2011
Procedia Environmental Sciences, doi: 10.1016/j.proenv.2011.07.048
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This work aimed to develop an R algorithm for land use classification based on the relationships among the land use and variables associated to its occurrence on remote sensing images. The algorithm was tested for soybean crop identification in the Brazilian Soy Moratorium context. Probability functions were modeled based on the number of pixels within discrete intervals. The result was encouraging with overall classification accuracy greater than 80%, indicating that the method is promising also to be applied for other land use classifications. The R algorithm is available at http://www.dsr.inpe.br/∼mello.