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J. R. Ducati  - - - 
Top co-authors See all
Maurício Roberto Veronez

50 shared publications

Advanced Visualization & Geoinformatics Laboratory, Unisinos University, São Leopoldo, Brazil

M. G. Pastoriza

46 shared publications

Departamento de Astronomia, Universidade Federal do Rio Grande do Sul, Brasil

Rodrigo Turcati

15 shared publications

Departamento de AstronomiaUniversidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9 500 91 501-970 Porto Alegre, Brazil

Damien Arvor

12 shared publications

UMR LETG CNRS 6554, University of Rennes, Rennes, France

M. Rubio

10 shared publications

Departamento de Astronomía, Universidad de Chile, Casilla 36-D Santiago, Chile

Publication Record
Distribution of Articles published per year 
(1986 - 2017)
Total number of journals
published in
Publications See all
Article 0 Reads 1 Citation Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers Jorge Ricardo Ducati, Virindiana Colet Bortolotto, Anibal Gu... Published: 01 December 2017
Acta Amazonica, doi: 10.1590/1809-4392201700543
DOI See at publisher website ABS Show/hide abstract
The agricultural dynamics of soybean expansion have long been recognized as a major driver of excessive land cover change on the southwestern border of the Brazilian Amazon. The hypothesis that the soybean market exerts an influence on land use was investigated by the association between economic indicators and soybean crop dynamics in the state of Mato Grosso (western Brazil). We integrated a historical series of satellite data of soybean cropland expansion and the two main economic variables (selling prices and production costs) for soybean in Mato Grosso. We focused on the relation between profit (the difference between the average soybean price and production costs) and land-use transition to soybean from 2001 to 2013. The spatially explicit analysis showed that the overall accuracy between the resulting first-time use and the most recent soybean crop area in 2013 was 96.75%, with a Kappa index of 0.63. However, dissimilar values found between Omission and Commission indicators suggest that most of the expanded areas prior to 2013 (5.57 million ha) were under a high dynamical range of land uses. Although there is no direct relation between either the deforestation rate or expansion trends (first-time-use rate) and profit, the results strongly suggest (R2=0.81) that profit exerts a direct and non-negligible influence on the evolution of consolidated land use for soybean in Mato Grosso State.
Article 0 Reads 3 Citations Model for soybean production forecast based on prevailing physical conditions Anibal Gusso, Damien Arvor, Jorge Ricardo Ducati Published: 01 February 2017
Pesquisa Agropecuária Brasileira, doi: 10.1590/s0100-204x2017000200003
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The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R 2 =0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R 2 =0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast. Index terms: agriculture; EVI; Modis; remote sensing; satellite
Revista Árvore, doi: 10.1590/0100-67622015000600004
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RESUMO A conversão de áreas naturais para a produção agrícola e exploração imobiliária levou a uma redução considerável das áreas florestais do bioma Mata Atlântica no último século. Dados oficiais apontam redução de 71% das florestas contidas nesse bioma, sendo um indicativo preocupante, uma vez que é reconhecida mundialmente como o quinto dos 34 hot spots do planeta, abrigando alta biodiversidade e elevado grau de endemismo. Nesse contexto, o objetivo deste estudo foi avaliar a evolução da fragmentação florestal do bioma Mata Atlântica no Município de Caxias do Sul, RS, calibrando um modelo dinâmico espacial desse processo e simulando um cenário futuro para o ano 2021. Para atingir o objetivo proposto, foi utilizada uma série temporal de imagens do satélite Landsat 5 referentes aos anos 1985, 2004 e 2011, dados do relevo e também informações pedológicas. Os resultados indicaram aumento da cobertura florestal nativa de 1985 a 2011 (incremento de 36%) e também do cenário simulado (2021) de 20% de áreas florestais. Esse aumento da cobertura florestal na área avaliada está possivelmente associado ao êxodo rural, maior rigor na aplicação da legislação ambiental e fiscalização rígida por parte do órgão ambiental. Essa informação apresenta relevância na tomada de decisão no que tange à gestão e fiscalização dos recursos florestais. Palavras-Chave: Sensoriamento remoto; Autômatos celulares; Fragmentação
Article 0 Reads 1 Citation Application of remote sensing techniques to discriminate between conventional and organic vineyards in the Loire Valley,... Rafael E. Sarate, Jandyra M. G. Fachel, Jorge R. Ducati Published: 30 September 2014
OENO One, doi: 10.20870/oeno-one.2014.48.3.1574
DOI See at publisher website ABS Show/hide abstract
Aim: To test the use of Remote Sensing imagery and techniques to differentiate between conventional and organic vineyards.Methods and results: Conventional and organic vineyards were identified on three satellite images acquired by the ASTER sensor of the Loire Valley. A sample of 46 conventional and 12 organic plots was used; grape varieties were Chenin Blanc (33 plots) and Cabernet Franc (25 plots). Mean reflectances were extracted from pixels inside each plot for the nine spectral bands (visible and infrared) of ASTER. A statistical discriminant analysis was performed. The vegetation index NDVI was also analysed. Results showed that all 12 organic plots, and 41 out of 46 conventional plots were correctly separated, a 91.4% success rate. Also, 23 out of 25 Cabernet, and 30 out of 33 Chenin plots were also correctly identified, also a 91.4% success rate. Regarding NDVI, there are no differences between conventional and organic vineyards within a 5% significant level. Analyses focused on the influences of chemical treatments on vineyard colors and on the effects of light reflected by inter-row spaces, suggested that both processes introduce spectral changes in conventional vineyards, mainly in short-wave infrared. Results also indicate that infrared information is essential to spectral discrimination.Conclusion: The use of chemicals, typical to conventional viticulture, has an impact on leaf composition and cell structure, being an important factor to imprint a characteristic reflectance pattern to these vineyards; the contribution to the integrated reflectance from inter-row vegetation is probably also a differentiating factor. Both causes act synergistically to build a significant spectral difference between conventional and organic vineyards.Significance and impact of the study: Remote Sensing techniques can be used as a first approach to vineyard monitoring, producing relevant information on viticultural methods, which can be used as early indicators of the need for field inspection or conventional laboratory analysis.
PROCEEDINGS-ARTICLE 5 Reads 0 Citations Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, Brazil Anibal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez... Published: 01 July 2014
IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium, doi: 10.1109/igarss.2014.6946560
DOI See at publisher website
Article 6 Reads 2 Citations Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazi... Anibal Gusso, Damien Arvor, Jorge Ricardo Ducati, Mauricio R... Published: 10 April 2014
The Scientific World Journal, doi: 10.1155/2014/863141
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R 2 = 0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state.