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Object-based image analysis supported by data mining to discriminate large areas of soybean

International Journal of Digital Earth 2019年第3期12卷 270-292页

作者： Carlos Antonio da Silva Junior Marcos Rafael Nanni José Francisco de Oliveira-Júnior Everson Cezar Paulo Eduardo Teodoro Rafael Coll Delgado luciano shozo shiratsuchi Muhammad Shakir Marcelo Luiz Chicati Geotechnology Applied in Agriculture and Forest(GAAF) State University of Mato Grosso(UNEMAT)Alta FlorestaMato GrossoBrazil Department of Agronomy(DAG/PGA) State University of Maringá(UEM)MaringáParanáBrazil Institute of Atmospheric Sciences(ICAT) Federal University of Alagoas(UFAL)MaceióAlagoasBrazil Federal University of Mato Grosso do Sul(UFMS) Chapadão do SulMato Grosso do SulBrazil Department of Environmental Sciences(DCA) Forestry Institute(IF)Federal Rural University of Rio de Janeiro(UFRRJ)SeropédicaRio de JaneiroBrazil AgCenter Louisiana State University(LSU)Baton RougeLAUSA Institute of Space Technology Islamabad Pakistan IslamabadPakistan

This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS(Moderate-Resolution Imaging Spectroradiometer)*** major techniques were used:GEOgraphic-Object-Based Image Analysis(GEOBIA)and Data Mining(DM).In order to obtain the images,the segmentation algorithm implemented by Definiens Developer was used.A decision tree(DT)was created from a training set previously ***-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop *** time-series data were used only for the CEI ***,to compare the results obtained from GEOBIA,the slicing technique was used at the CEI *** the training,the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of *** accordance with the error matrix and kappa parameter analysis,tests for statistical significance were *** indicate that the classification achieved by Kappa coefficients is *** short,the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.

关键词： GEOBIA time-series vegetation indices MODIS vegetation dynamics

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19-year remotely sensed data in the forecast of spectral models of the environment

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International Journal of Digital Earth 2021年第8期14卷 1040-1066页

作者： Fernando Saragosa Rossi Carlos Antonio da Silva Junior JoséFrancisco de Oliveira-Júnior Paulo Eduardo Teodoro luciano shozo shiratsuchi Mendelson Lima Larissa Pereira Ribeiro Teodoro Auana Vicente Tiago Guilherme Fernando Capristo-Silva State University of Mato Grosso(UNEMAT) Alta FlorestaMato GrossoBrazil Department of Geography State University of Mato Grosso(UNEMAT)SinopMato GrossoBrazil cFederal University of Alagoas(UFAL) MaceióAlagoasBrazil dFederal University of Mato Grosso do Sul(UFMS) Chapadão do SulMato Grosso do SulBrazil eAgCenter School of PlantEnvironmentaland Soil SciencesLouisiana State University(LSU)Baton RougeLouisianaUSA fPostgraduate Program in Agronomy Federal University of Mato Grosso(UFMT)SinopMato GrossoBrazil

The aims of this study were:i)to compare no-till areas in two municipalities located in different regions of Brazil,along with the influence on CO_(2)Flux and GPP,and ii)to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios(ARIMA time-series model number).The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS,both in Brazil.A time series of 19 years was performed with data acquired by remote sensing from the following satellites:i)Landsat-8(OLI and TIRS),and ii)TERRA/AQUA(MODIS).The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time,with a satisfactory correlation in future predictions of the variables for the next ten years,in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality,and CO_(2)Flux tends to decrease in its seasonality,indicating a higher CO_(2) absorption.

关键词： ARIMA numerical modeling remote sensing orbital products tillage

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