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Anibal Gusso   Dr.  University Educator/Researcher 
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Anibal Gusso published an article in October 2012.
Top co-authors See all
John Boland

85 shared publications

Centre for Industrial and Applied Mathematics, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, SA 5095, Australia

Denise C. Fontana

38 shared publications

Universidade Federal do Rio Grande do Sul, Brazil

Jorge Ricardo Ducati

27 shared publications

Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Damien Arvor

9 shared publications

French National Center for Scientific Research (CNRS), LETG, Université Rennes, UMR 6554 F-35000, France

Diego A. G Lopez

2 shared publications

Universidad Nacional de Rosario, Rosario, Argentina

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Distribution of Articles published per year 
(2012)
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Publications
Article 7 Reads 4 Citations Algorithm for Soybean Classification Using Medium Resolution Satellite Images Anibal Gusso, Jorge Ricardo Ducati Published: 18 October 2012
Remote Sensing, doi: 10.3390/rs4103127
DOI See at publisher website ABS Show/hide abstract
An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales.
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