Please login first
Marcio Pupin Mello   Dr.  Graduate Student or Post Graduate 
Timeline See timeline
Marcio Pupin Mello published an article in July 2014.
Top co-authors See all
E. J. Pebesma

100 shared publications

Institute for Geoinformatics, University of Münster, 48149 Münster, Germany

Clement Atzberger

73 shared publications

Institute of Surveying, Remote Sensing & Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria

Marcos Adami

54 shared publications

INPE, National Institute For Space Research, Brazil

Bernardo Friedrich Theodor Rudorff

33 shared publications

Instituto Nacional de Pesquisas Espaciais, São José dos Campos, Brasil

Paul Aplin

29 shared publications

School of Geography, Edge Hill University, Ormskirk L39 4QP, UK

Publication Record
Distribution of Articles published per year 
(2010 - 2014)
Total number of journals
published in
Publications See all
PROCEEDINGS-ARTICLE 0 Reads 1 Citation Near real time yield estimation for sugarcane in Brazil combining remote sensing and official statistical data Marcio Pupin Mello, Clement Atzberger, Antonio Roberto Forma... Published: 01 July 2014
2014 IEEE Geoscience and Remote Sensing Symposium, doi: 10.1109/igarss.2014.6947635
DOI See at publisher website
Conference 0 Reads 0 Citations Assessment of suitable observation conditions for a monthly operational remote sensing based crop monitoring system Isaque Daniel Rocha Eberhardt, Marcio Pupin Mello, Rodrigo R... Published: 01 July 2014
2014 IEEE Geoscience and Remote Sensing Symposium, doi: 10.1109/igarss.2014.6946886
DOI See at publisher website
Article 0 Reads 2 Citations Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations Marcio Pupin Mello, Joel Risso, Clement Atzberger, Paul Apli... Published: 15 November 2013
Remote Sensing, doi: 10.3390/rs5115999
DOI See at publisher website ABS Show/hide abstract
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet.
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Spatial statistic to assess remote sensing acreage estimates: An analysis of sugarcane in São Paulo State, Brazil Marcio Pupin Mello, Daniel Alves Aguiar, Bernardo Friedrich ... Published: 01 July 2013
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, doi: 10.1109/igarss.2013.6723768
DOI See at publisher website
Article 0 Reads 5 Citations STARS: A New Method for Multitemporal Remote Sensing Marcio Pupin Mello, Carlos A. O. Vieira, Bernardo F. T. Rudo... Published: 01 April 2013
IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2012.2215332
DOI See at publisher website
Article 2 Reads 6 Citations A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil Marcos Adami, Marcio Pupin Mello, Daniel Alves Aguiar, Berna... Published: 19 October 2012
Remote Sensing, doi: 10.3390/rs4103201
DOI See at publisher website ABS Show/hide abstract
The ability to monitor sugarcane expansion in Brazil, the world’s largest producer and exporter of sugar and second largest producer of ethanol, is important due to its agricultural, economic, strategic and environmental relevance. With the advent of flex fuel cars in 2003 the sugarcane area almost doubled over the last decade in the South-Central region of Brazil. Using remote sensing images, the sugarcane cultivation area was annually monitored and mapped between 2003 and 2012, a period of major sugarcane expansion. The objective of this work was to assess the thematic mapping accuracy of sugarcane, in the crop year 2010/2011, with the novel approach of developing a web platform that integrates different spatial and temporal image resolutions to assist interpreters in classifying a large number of points selected by stratified random sampling. A field campaign confirmed the suitability of the web platform to generate the reference data set. An overall accuracy of 98% with an area estimation error of −0.5% was achieved for the sugarcane map of 2010/11. The accuracy assessment indicated that the map is of excellent quality, offering very accurate sugarcane area estimation for the purpose of agricultural statistics. Moreover, the web platform showed to be very effective in the construction of the reference dataset.