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Bernardo Friedrich Theodor Rudorff
National Institute of Space Research, Brazil

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Journal article
Published: 01 December 2015 in Renewable and Sustainable Energy Reviews
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ACS Style

Ricardo De Oliveira Bordonal; Rattan Lal; Daniel Alves Aguiar; Eduardo Barretto de Figueiredo; Luciano Ito Perillo; Marcos Adami; Bernardo Friedrich Theodor Rudorff; Newton La Scala Jr.. Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane ( Saccharum officinarum ) plantation in south-central Brazil. Renewable and Sustainable Energy Reviews 2015, 52, 547 -556.

AMA Style

Ricardo De Oliveira Bordonal, Rattan Lal, Daniel Alves Aguiar, Eduardo Barretto de Figueiredo, Luciano Ito Perillo, Marcos Adami, Bernardo Friedrich Theodor Rudorff, Newton La Scala Jr.. Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane ( Saccharum officinarum ) plantation in south-central Brazil. Renewable and Sustainable Energy Reviews. 2015; 52 ():547-556.

Chicago/Turabian Style

Ricardo De Oliveira Bordonal; Rattan Lal; Daniel Alves Aguiar; Eduardo Barretto de Figueiredo; Luciano Ito Perillo; Marcos Adami; Bernardo Friedrich Theodor Rudorff; Newton La Scala Jr.. 2015. "Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane ( Saccharum officinarum ) plantation in south-central Brazil." Renewable and Sustainable Energy Reviews 52, no. : 547-556.

Journal article
Published: 01 June 2013 in Boletim de Ciências Geodésicas
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A avaliação das mudanças na paisagem é fundamental para a eficiência na gestão territorial. O objetivo deste trabalho é parametrizar e calibrar um modelo de mudança de cobertura e uso, além de validar as simulações associadas à expansão canavieira em Arealva-SP, no período compreendido entre 2005 e 2010. Os mapas inicial e final foram corregistrados e, após a rasterização, foi realizada uma tabulação cruzada, gerando-se um mapa de mudanças e a respectiva matriz de transição. O modelo adotado foi o Dinamica EGO, e seu desempenho foi avaliado por meio de um método baseado no conceito de incerteza de localização (fuzziness of location), no qual a representação de uma célula é influenciada por ela mesma, e, em menor magnitude, pela sua vizinhança. Há predominância de pastagens e baixo índice de área de vegetação nativa. As mudanças mais relevantes estão relacionadas à expansão canavieira e à retração de pastagens. O valor da similaridade fuzzy entre o mapa simulado e o mapa-referência, para a janela de tamanho 11x11 e função de decaimento constante, foi de 0.52. Foi possível aprimorar o conhecimento dos fatores direcionadores das mudanças de cobertura e uso, propiciando a revelação das forçantes dessas mudanças.

ACS Style

Rodrigo De Campos Macedo; Cláudia Almeida; João Roberto Dos Santos; Bernardo Friedrich Theodor Rudorff. Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira. Boletim de Ciências Geodésicas 2013, 19, 313 -337.

AMA Style

Rodrigo De Campos Macedo, Cláudia Almeida, João Roberto Dos Santos, Bernardo Friedrich Theodor Rudorff. Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira. Boletim de Ciências Geodésicas. 2013; 19 (2):313-337.

Chicago/Turabian Style

Rodrigo De Campos Macedo; Cláudia Almeida; João Roberto Dos Santos; Bernardo Friedrich Theodor Rudorff. 2013. "Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira." Boletim de Ciências Geodésicas 19, no. 2: 313-337.

Letter
Published: 19 October 2012 in Remote Sensing
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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.

ACS Style

Marcos Adami; Marcio Pupin Mello; Daniel Alves Aguiar; Bernardo Friedrich Theodor Rudorff; Arley Ferreira De Souza. A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil. Remote Sensing 2012, 4, 3201 -3214.

AMA Style

Marcos Adami, Marcio Pupin Mello, Daniel Alves Aguiar, Bernardo Friedrich Theodor Rudorff, Arley Ferreira De Souza. A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil. Remote Sensing. 2012; 4 (10):3201-3214.

Chicago/Turabian Style

Marcos Adami; Marcio Pupin Mello; Daniel Alves Aguiar; Bernardo Friedrich Theodor Rudorff; Arley Ferreira De Souza. 2012. "A Web Platform Development to Perform Thematic Accuracy Assessment of Sugarcane Mapping in South-Central Brazil." Remote Sensing 4, no. 10: 3201-3214.

Journal article
Published: 01 September 2012 in Pesquisa Agropecuária Brasileira
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O objetivo deste trabalho foi avaliar o desempenho do índice de vegetação realçado (EVI) e do índice de vegetação da diferença normalizada (NDVI) - ambos do sensor "moderate resolution imaging spectroradiometer" (Modis) -, para discriminar áreas de soja das áreas de cana‑de‑açúcar, pastagem, cerrado e floresta, no Estado do Mato Grosso. Foram utilizadas imagens adquiridas em dois períodos: durante a entressafra e por ocasião do pleno desenvolvimento da cultura da soja. Para cada classe analisada, foram selecionadas 31 amostras de mapas de referência e avaliadas as diferenças nos valores de cada índice de vegetação, para a classe soja, foram avaliadas frente às demais classes, por meio do teste de Tukey‑Kramer. Em seguida, foram avaliadas as diferenças entre os índices de vegetação, por meio do teste de Wilcoxon pareado. O NDVI apresentou melhor desempenho na discriminação das áreas de soja na entressafra, particularmente com uso das imagens do dia do ano (DA) 161 a 273, enquanto o EVI apresentou melhor desempenho no período de pleno desenvolvimento da cultura, especificamente com uso das imagens de DA 353 a 33. Portanto, o melhor resultado para classificação da soja, no Estado do Mato Grosso, via séries temporais do sensor Modis, pode ser obtida por meio do uso combinado do NDVI na entresssafra e do EVI no pleno desenvolvimento da soja.

ACS Style

Joel Risso; Yosio Edemir Shimabukuro; Rodrigo Rizzi; Bernardo Friedrich Theodor Rudorff; Marcos Adami; Antônio Roberto Formaggio; Rui Dalla Valle Epiphanio. Índices de vegetação Modis aplicados na discriminação de áreas de soja. Pesquisa Agropecuária Brasileira 2012, 47, 1317 -1326.

AMA Style

Joel Risso, Yosio Edemir Shimabukuro, Rodrigo Rizzi, Bernardo Friedrich Theodor Rudorff, Marcos Adami, Antônio Roberto Formaggio, Rui Dalla Valle Epiphanio. Índices de vegetação Modis aplicados na discriminação de áreas de soja. Pesquisa Agropecuária Brasileira. 2012; 47 (9):1317-1326.

Chicago/Turabian Style

Joel Risso; Yosio Edemir Shimabukuro; Rodrigo Rizzi; Bernardo Friedrich Theodor Rudorff; Marcos Adami; Antônio Roberto Formaggio; Rui Dalla Valle Epiphanio. 2012. "Índices de vegetação Modis aplicados na discriminação de áreas de soja." Pesquisa Agropecuária Brasileira 47, no. 9: 1317-1326.

Journal article
Published: 27 August 2012 in Remote Sensing
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Coffee is the second most valuable traded commodity worldwide. Brazil is the world’s largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.

ACS Style

Tiago Bernardes; Maurício Alves Moreira; Marcos Adami; Angélica Giarolla; Bernardo Friedrich Theodor Rudorff. Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery. Remote Sensing 2012, 4, 2492 -2509.

AMA Style

Tiago Bernardes, Maurício Alves Moreira, Marcos Adami, Angélica Giarolla, Bernardo Friedrich Theodor Rudorff. Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery. Remote Sensing. 2012; 4 (9):2492-2509.

Chicago/Turabian Style

Tiago Bernardes; Maurício Alves Moreira; Marcos Adami; Angélica Giarolla; Bernardo Friedrich Theodor Rudorff. 2012. "Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery." Remote Sensing 4, no. 9: 2492-2509.

Journal article
Published: 01 June 2012 in Scientia Agricola
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Many researchers have shown the potential of Synthetic Aperture Radar (SAR) images for agricultural applications, particularly for monitoring regions with limitations in terms of acquiring cloud free optical images. Recently, Brazil and Germany began a feasibility study on the construction of an orbital L-band SAR sensor referred to as MAPSAR (Multi-Application Purpose SAR). This sensor provides L-band images in three spatial resolutions and polarimetric, interferometric and stereoscopic capabilities. Thus, studies are needed to evaluate the potential of future MAPSAR images. The objective of this study was to evaluate multipolarized MAPSAR images simulated by the airborne SAR-R99B sensor to distinguish coffee, cotton and pasture fields in Brazil. Discrimination among crops was evaluated through graphical and cluster analysis of mean backscatter values, considering single, dual and triple polarizations. Planting row direction of coffee influenced the backscatter and was divided into two classes: parallel and perpendicular to the sensor look direction. Single polarizations had poor ability to discriminate the crops. The overall accuracies were less than 59 %, but the understanding of the microwave interaction with the crops could be explored. Combinations of two polarizations could differentiate various fields of crops, highlighting the combination VV-HV that reached 78 % overall accuracy. The use of three polarizations resulted in 85.4 % overall accuracy, indicating that the classes pasture and parallel coffee were fully discriminated from the other classes. These results confirmed the potential of multipolarized MAPSAR images to distinguish the studied crops and showed considerable improvement in the accuracy of the results when the number of polarizations was increased

ACS Style

Wagner Fernando Silva; Bernardo Friedrich Theodor Rudorff; Antonio Roberto Formaggio; Waldir Renato Paradella; José Claudio Mura. Simulated multipolarized MAPSAR images to distinguish agricultural crops. Scientia Agricola 2012, 69, 201 -209.

AMA Style

Wagner Fernando Silva, Bernardo Friedrich Theodor Rudorff, Antonio Roberto Formaggio, Waldir Renato Paradella, José Claudio Mura. Simulated multipolarized MAPSAR images to distinguish agricultural crops. Scientia Agricola. 2012; 69 (3):201-209.

Chicago/Turabian Style

Wagner Fernando Silva; Bernardo Friedrich Theodor Rudorff; Antonio Roberto Formaggio; Waldir Renato Paradella; José Claudio Mura. 2012. "Simulated multipolarized MAPSAR images to distinguish agricultural crops." Scientia Agricola 69, no. 3: 201-209.

Journal article
Published: 23 May 2012 in Sustainability
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The Soy Moratorium is an initiative to reduce deforestation rates in the Amazon biome based on the hypothesis that soy is a deforestation driver. Soy planted in opened areas after July 24th, 2006 cannot be commercialized by the associated companies to the Brazilian Association of Vegetable Oil Industries (ABIOVE) and the National Association of Cereal Exporters (ANEC), which represent about 90% of the Brazilian soy market. The objective of this work is to present the evaluation of the fourth year of monitoring new soy plantations within the Soy Moratorium context. With the use of satellite images from the MODIS sensor, together with aerial survey, it was possible to identify 147 polygons with new soy plantations on 11,698 ha. This soy area represents 0.39% of the of the total deforested area during the moratorium, in the three soy producing states of the Amazon biome, and 0.6% of the cultivated soy area in the Amazon biome, indicating that soy is currently a minor deforestation driver. The quantitative geospatial information provided by an effective monitoring approach is paramount to the implementation of a governance process required to establish an equitable balance between environmental protection and agricultural production.

ACS Style

Bernardo F. T. Rudorff; Marcos Adami; Joel Risso; Daniel Alves De Aguiar; Bernardo Pires; Daniel Amaral; Leandro Fabiani; Izabel Cecarelli. Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative. Sustainability 2012, 4, 1074 -1088.

AMA Style

Bernardo F. T. Rudorff, Marcos Adami, Joel Risso, Daniel Alves De Aguiar, Bernardo Pires, Daniel Amaral, Leandro Fabiani, Izabel Cecarelli. Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative. Sustainability. 2012; 4 (5):1074-1088.

Chicago/Turabian Style

Bernardo F. T. Rudorff; Marcos Adami; Joel Risso; Daniel Alves De Aguiar; Bernardo Pires; Daniel Amaral; Leandro Fabiani; Izabel Cecarelli. 2012. "Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative." Sustainability 4, no. 5: 1074-1088.

Journal article
Published: 02 April 2012 in Sustainability
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The use of biofuels to mitigate global carbon emissions is highly dependent on direct and indirect land use changes (LUC). The direct LUC (dLUC) can be accurately evaluated using remote sensing images. In this work we evaluated the dLUC of about 4 million hectares of sugarcane expanded from 2005 to 2010 in the South-central region of Brazil. This region has a favorable climate for rain-fed sugarcane, a great potential for agriculture expansion without deforestation, and is currently responsible for almost 90% of Brazilian’s sugarcane production. An available thematic map of sugarcane along with MODIS and Landast images, acquired from 2000 to 2009, were used to evaluate the land use prior to the conversion to sugarcane. A systematic sampling procedure was adopted and the land use identification prior to sugarcane, for each sample, was performed using a web tool developed to visualize both the MODIS time series and the multitemporal Landsat images. Considering 2000 as reference year, it was observed that sugarcane expanded: 69.7% on pasture land; 25.0% on annual crops; 0.6% on forest; while 3.4% was sugarcane land under crop rotation. The results clearly show that the dLUC of recent sugarcane expansion has occurred on more than 99% of either pasture or agriculture land.

ACS Style

Marcos Adami; Bernardo Friedrich Theodor Rudorff; Ramon Morais Freitas; Daniel Alves Aguiar; Luciana Miura Sugawara; Marcio Pupin Mello. Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil. Sustainability 2012, 4, 574 -585.

AMA Style

Marcos Adami, Bernardo Friedrich Theodor Rudorff, Ramon Morais Freitas, Daniel Alves Aguiar, Luciana Miura Sugawara, Marcio Pupin Mello. Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil. Sustainability. 2012; 4 (4):574-585.

Chicago/Turabian Style

Marcos Adami; Bernardo Friedrich Theodor Rudorff; Ramon Morais Freitas; Daniel Alves Aguiar; Luciana Miura Sugawara; Marcio Pupin Mello. 2012. "Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil." Sustainability 4, no. 4: 574-585.

Journal article
Published: 01 March 2012 in Pesquisa Agropecuária Brasileira
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ACS Style

Anibal Gusso; Antônio Roberto Formaggio; Rodrigo Rizzi; Marcos Adami; Bernardo Friedrich Theodor Rudorff. Soybean crop area estimation by Modis/Evi data. Pesquisa Agropecuária Brasileira 2012, 47, 425 -435.

AMA Style

Anibal Gusso, Antônio Roberto Formaggio, Rodrigo Rizzi, Marcos Adami, Bernardo Friedrich Theodor Rudorff. Soybean crop area estimation by Modis/Evi data. Pesquisa Agropecuária Brasileira. 2012; 47 (3):425-435.

Chicago/Turabian Style

Anibal Gusso; Antônio Roberto Formaggio; Rodrigo Rizzi; Marcos Adami; Bernardo Friedrich Theodor Rudorff. 2012. "Soybean crop area estimation by Modis/Evi data." Pesquisa Agropecuária Brasileira 47, no. 3: 425-435.

Journal article
Published: 15 February 2012 in Atmosphere
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Sugarcane is an important crop for the Brazilian economy and roughly 50% of its production is used to produce ethanol. However, the common practice of pre-harvest burning of sugarcane straw emits particulate material, greenhouse gases, and tropospheric ozone precursors to the atmosphere. Even with policies to eliminate the practice of pre-harvest sugarcane burning in the near future, there is still significant environmental damage. Thus, the generation of reliable inventories of emissions due to this activity is crucial in order to assess their environmental impact. Nevertheless, the official Brazilian emissions inventory does not presently include the contribution from pre-harvest sugarcane burning. In this context, this work aims to determine sugarcane straw burning emission factors for some trace gases and particulate material smaller than 2.5 μm in the laboratory. Excess mixing ratios for CO2, CO, NOX, UHC (unburned hydrocarbons), and PM2.5 were measured, allowing the estimation of their respective emission factors. Average estimated values for emission factors (g kg−1 of burned dry biomass) were 1,303 ± 218 for CO2, 65 ± 14 for CO, 1.5 ± 0.4 for NOX, 16 ± 6 for UHC, and 2.6 ± 1.6 for PM2.5. These emission factors can be used to generate more realistic emission inventories and therefore improve the results of air quality models.

ACS Style

Daniela De Azeredo França; Karla Maria Longo; Turibio Gomes Soares Neto; José Carlos Santos; Saulo R. Freitas; Bernardo F. T. Rudorff; Ely Vieira Cortez; Edson Anselmo; Jr. João Andrade Carvalho. Pre-Harvest Sugarcane Burning: Determination of Emission Factors through Laboratory Measurements. Atmosphere 2012, 3, 164 -180.

AMA Style

Daniela De Azeredo França, Karla Maria Longo, Turibio Gomes Soares Neto, José Carlos Santos, Saulo R. Freitas, Bernardo F. T. Rudorff, Ely Vieira Cortez, Edson Anselmo, Jr. João Andrade Carvalho. Pre-Harvest Sugarcane Burning: Determination of Emission Factors through Laboratory Measurements. Atmosphere. 2012; 3 (1):164-180.

Chicago/Turabian Style

Daniela De Azeredo França; Karla Maria Longo; Turibio Gomes Soares Neto; José Carlos Santos; Saulo R. Freitas; Bernardo F. T. Rudorff; Ely Vieira Cortez; Edson Anselmo; Jr. João Andrade Carvalho. 2012. "Pre-Harvest Sugarcane Burning: Determination of Emission Factors through Laboratory Measurements." Atmosphere 3, no. 1: 164-180.

Journal article
Published: 13 December 2011 in Remote Sensing
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Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of São Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice to 2014, a “Green Ethanol” Protocol was established in 2007. The present work aims at analyzing five years of continuous sugarcane harvest monitoring, based on remote sensing images, to evaluate the effectiveness of the Protocol, thus helping decision makers to establish public policies to meet the Protocol’s expected goals. During the last five crop years, sugarcane acreage expanded by 1.5 million ha, which was compensated by a correspondent increase in the green harvested land. However, no significant reduction was observed in the amount of pre-harvest burned land over the same period. Based on the current trend, this goal is likely to be achieved one or two years later (2015–2016), which will be five or six years ahead of 2021 as the goal in the State Law number 11241 states. We thus conclude that the“Green Ethanol” Protocol has been effective with a positive impact on the increase of GH, especially on recently expanded sugarcane fields.

ACS Style

Daniel Alves Aguiar; Bernardo Friedrich Theodor Rudorff; Wagner Fernando Silva; Marcos Adami; Marcio Pupin Mello. Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil. Remote Sensing 2011, 3, 2682 -2703.

AMA Style

Daniel Alves Aguiar, Bernardo Friedrich Theodor Rudorff, Wagner Fernando Silva, Marcos Adami, Marcio Pupin Mello. Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil. Remote Sensing. 2011; 3 (12):2682-2703.

Chicago/Turabian Style

Daniel Alves Aguiar; Bernardo Friedrich Theodor Rudorff; Wagner Fernando Silva; Marcos Adami; Marcio Pupin Mello. 2011. "Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil." Remote Sensing 3, no. 12: 2682-2703.

Conference paper
Published: 02 November 2011 in Proceedings of The 1st World Sustainability Forum
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The Soy Moratorium is an initiative to reduce deforestation rates in the Amazon biome based on the hypothesis that soybean is a deforestation driver. Farmers that planted soybean in that biome in opened areas after its declaration, July 24th, 2006, would not have their production commercialized nor supported with any financial aid through purchases or crop financing by the associated companies to the Brazilian Association of Vegetable Oil Industries (ABIOVE) and the National Association of Cereal Exporters (ANEC). ABIOVE and ANEC represent about 90% of the Brazilian soybean market. Brazil has a long term project to monitor the deforested areas in the Brazilian Amazon Biome using remote sensing images. Every year a map with new deforested polygons is available on the Internet (www.prodes.inpe.br). Therefore, it is possible to monitor the deforested polygons after the Moratorium date in order to identify annual crops in these polygons using remote sensing images. The crop detection method based on satellite images facilitate and reduce costs of the monitoring procedure to select possible soybean fields. The MODIS satellite images are not able to classify soybean crop at early growth stages with high accuracy, however, they play an important role in the pre-selection of these possible soybean fields. Therefore, crop detection method also uses Landsat like images, aerial survey and, field work. In the last crop, 3,571 deforested polygons with more than 25 ha and deforested after the Moratorium declaration were identified nearby the soybean producing region in the Amazon Biome. Using satellite imagery analysis procedure, 293 of these deforested polygons were selected, indicating to have annual crops. Soybean was detected in 147 of 293 polygons, covering an area of 11,698 ha. In 2011, the soybean was cultivated only in 0.39% of the recently deforested areas in Amazon Biome during the Moratorium period. In terms of the total soybean area cultivated in Brazil and in the Amazon Biome, 11,698 ha represents 0.05% and 0.60%, respectively. It seems that the Soy Moratorium is having an inhibitory effect on recent deforestation in the Amazon Biome, but the soy crop certainly has not been a major driver of deforestation during the last four years as indicated by the numbers. The quantitative geospatial information provided by an effective monitoring approach is paramount to the implementation of a governance process required to establish an equitable balance between environmental protection and agricultural production.

ACS Style

Bernardo Rudorff; Marcos Adami; Joel Risso; Daniel Aguiar; Bernardo Pires; Daniel Amaral; Leandro Fabiani; Izabel Cecarelli. Remote Sensing Images to Detect Soy Plantations in the Amazon Biome – the Soy Moratorium Initiative. Proceedings of The 1st World Sustainability Forum 2011, 1 .

AMA Style

Bernardo Rudorff, Marcos Adami, Joel Risso, Daniel Aguiar, Bernardo Pires, Daniel Amaral, Leandro Fabiani, Izabel Cecarelli. Remote Sensing Images to Detect Soy Plantations in the Amazon Biome – the Soy Moratorium Initiative. Proceedings of The 1st World Sustainability Forum. 2011; ():1.

Chicago/Turabian Style

Bernardo Rudorff; Marcos Adami; Joel Risso; Daniel Aguiar; Bernardo Pires; Daniel Amaral; Leandro Fabiani; Izabel Cecarelli. 2011. "Remote Sensing Images to Detect Soy Plantations in the Amazon Biome – the Soy Moratorium Initiative." Proceedings of The 1st World Sustainability Forum , no. : 1.

Conference paper
Published: 02 November 2011 in Proceedings of The 1st World Sustainability Forum
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Mitigation of global carbon emissions to prevent global warming potential using biofuels is highly dependent on direct and indirect land use change (LUC). There are still several uncertainties about how to assess the indirect LUC impacts of biofuels. However, direct LUC (dLUC) can be evaluated using remote sensing (RS). The present work has the aim to quantify the dLUC occurred during the recent sugarcane expansion for ethanol and sugar production concentrated in the South-Central region of Brazil. This region has a favorable climate for sugarcane production and also a great potential for agriculture expansion. Yearly monitoring from 2005 to 2010 using Landsat type imagery has shown that the sugarcane crop expanded during this period over 3.5 Mha in the South-Central region. To evaluate the dLUC in response to the expanded sugarcane area we used RS time series from the MODIS sensor transformed to the two-band enhanced vegetation index (EVI2), acquired from 2000 to 2009. The original sugarcane map was re-sampled to a pixel size of 250 x 250 m to be compatible with spatial resolution of the MODIS images. One percent of these pixels were systematically sampled covering 1035 pixels. Each of these pixels were carefully analyzed using a special developed web tool to visualize the entire MODIS time series overlaid with several Landsat-5 TM images acquired at key periods in order to correctly identify the land use/land cover prior to the sugarcane crop. Considering 2000 as reference year for the dLUC evaluation it was observed that: 69.8% of the sugarcane expanded on pasture land; 26.2% expanded on annual crops; 0.6% expanded on native vegetation; and 3.4 % was not sugarcane expansion but sugarcane renovation using crop rotation. It was interesting to notice that 35% of the pasture land in 2000 converted to sugarcane was first converted to annual crops. This practice is commonly adopted for one to two years on degraded pasture to improve the physical soil characteristics before introducing the sugarcane crop. It was also observed that the 0.6 % of native vegetation changed to sugarcane was previously converted to either annual crop (33%) or pasture land (67%). Although the analysis needs to be further refined the results clearly show that the dLUC observed during the recent sugarcane expansion for ethanol and sugar production in the South-Central region of Brazil is mainly occurring on pasture and agricultural land.

ACS Style

Marcos Adami; Bernardo Rudorff; Ramon Freitas; Daniel Aguiar; Luciana Sugawara; Marcio Mello. Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil. Proceedings of The 1st World Sustainability Forum 2011, 1 .

AMA Style

Marcos Adami, Bernardo Rudorff, Ramon Freitas, Daniel Aguiar, Luciana Sugawara, Marcio Mello. Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil. Proceedings of The 1st World Sustainability Forum. 2011; ():1.

Chicago/Turabian Style

Marcos Adami; Bernardo Rudorff; Ramon Freitas; Daniel Aguiar; Luciana Sugawara; Marcio Mello. 2011. "Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil." Proceedings of The 1st World Sustainability Forum , no. : 1.

Journal article
Published: 18 January 2011 in Remote Sensing
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The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome.

ACS Style

Bernardo Friedrich Theodor Rudorff; Marcos Adami; Daniel Alves Aguiar; Maurício Alves Moreira; Marcio Pupin Mello; Leandro Fabiani; Daniel Furlan Amaral; Bernardo Machado Pires. The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images. Remote Sensing 2011, 3, 185 -202.

AMA Style

Bernardo Friedrich Theodor Rudorff, Marcos Adami, Daniel Alves Aguiar, Maurício Alves Moreira, Marcio Pupin Mello, Leandro Fabiani, Daniel Furlan Amaral, Bernardo Machado Pires. The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images. Remote Sensing. 2011; 3 (1):185-202.

Chicago/Turabian Style

Bernardo Friedrich Theodor Rudorff; Marcos Adami; Daniel Alves Aguiar; Maurício Alves Moreira; Marcio Pupin Mello; Leandro Fabiani; Daniel Furlan Amaral; Bernardo Machado Pires. 2011. "The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images." Remote Sensing 3, no. 1: 185-202.

Journal article
Published: 01 June 2010 in Pesquisa Agropecuária Brasileira
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O objetivo deste trabalho foi avaliar o desempenho de um modelo probabilístico de amostragem estratificada por pontos, e definir um tamanho de amostra adequado para estimar a área cultivada com soja no Rio Grande do Sul. A área foi estratificada de acordo com a percentagem de soja cultivada em cada município do estado: menor que 20, de 20 a 40 e maior que 40%. Foram avaliadas estimativas obtidas por meio de seis tamanhos de amostras, resultantes da combinação de três níveis de significância (10, 5 e 1%) e dois valores de erro amostral (5 e 2,5%). Para cada tamanho de amostra, foram realizados 400 sorteios aleatórios. As estimativas foram avaliadas com base na área de soja obtida de um mapa temático de referência proveniente de uma cuidadosa classificação automática e visual de imagens multitemporais dos satélites TM/Landsat-5 e ETM+/Landsat-7 disponível para a safra 2000/2001. A área de soja no Rio Grande do Sul pode ser estimada por meio de um modelo de amostragem probabilística estratificada por pontos, sendo que a melhor estimativa é obtida para o maior tamanho amostral (1.990 pontos), com diferença de apenas -0,14% em relação à estimativa do mapa de referência e um coeficiente de variação de 6,98%.

ACS Style

Marcos Adami; Rodrigo Rizzi; Maurício Alves Moreira; Bernardo Friedrich Theodor Rudorff; Camila Cossetin Ferreira. Amostragem probabilística estratificada por pontos para estimar a área cultivada com soja. Pesquisa Agropecuária Brasileira 2010, 45, 585 -592.

AMA Style

Marcos Adami, Rodrigo Rizzi, Maurício Alves Moreira, Bernardo Friedrich Theodor Rudorff, Camila Cossetin Ferreira. Amostragem probabilística estratificada por pontos para estimar a área cultivada com soja. Pesquisa Agropecuária Brasileira. 2010; 45 (6):585-592.

Chicago/Turabian Style

Marcos Adami; Rodrigo Rizzi; Maurício Alves Moreira; Bernardo Friedrich Theodor Rudorff; Camila Cossetin Ferreira. 2010. "Amostragem probabilística estratificada por pontos para estimar a área cultivada com soja." Pesquisa Agropecuária Brasileira 45, no. 6: 585-592.

Journal article
Published: 09 April 2010 in Remote Sensing
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This study’s overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed data. In 2003 flex fuel automobiles started to enter the Brazilian consumer market causing a dramatic expansion of sugarcane areas from 2.57 million ha in 2003 to 4.45 million ha in 2008. Almost all the land use change, for the sugarcane expansion of crop year 2008/09, occurred on pasture and annual crop land, being equally distributed on each. It was also observed that during the 2008 harvest season, the burned sugarcane area was reduced to 50% of the total harvested area in response to a protocol that aims to cease sugarcane straw burning practice by 2014 for mechanized areas. This study indicates that remote sensing images have efficiently evaluated important characteristics of the sugarcane cultivation dynamic providing quantitative results that are relevant to the debate of sustainable ethanol production from sugarcane in Brazil.

ACS Style

Bernardo Friedrich Theodor Rudorff; Daniel Alves Aguiar; Wagner Fernando Silva; Luciana Miura Sugawara; Marcos Adami; Mauricio Alves Moreira. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sensing 2010, 2, 1057 -1076.

AMA Style

Bernardo Friedrich Theodor Rudorff, Daniel Alves Aguiar, Wagner Fernando Silva, Luciana Miura Sugawara, Marcos Adami, Mauricio Alves Moreira. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sensing. 2010; 2 (4):1057-1076.

Chicago/Turabian Style

Bernardo Friedrich Theodor Rudorff; Daniel Alves Aguiar; Wagner Fernando Silva; Luciana Miura Sugawara; Marcos Adami; Mauricio Alves Moreira. 2010. "Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data." Remote Sensing 2, no. 4: 1057-1076.

Journal article
Published: 01 January 2010 in Pesquisa Agropecuária Brasileira
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The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

ACS Style

Rui Dalla Valle Epiphanio; Antônio Roberto Formaggio; Bernardo Friedrich Theodor Rudorff; Eduardo Eiji Maeda; Alfredo José Barreto Luiz. Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil. Pesquisa Agropecuária Brasileira 2010, 45, 72 -80.

AMA Style

Rui Dalla Valle Epiphanio, Antônio Roberto Formaggio, Bernardo Friedrich Theodor Rudorff, Eduardo Eiji Maeda, Alfredo José Barreto Luiz. Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil. Pesquisa Agropecuária Brasileira. 2010; 45 (1):72-80.

Chicago/Turabian Style

Rui Dalla Valle Epiphanio; Antônio Roberto Formaggio; Bernardo Friedrich Theodor Rudorff; Eduardo Eiji Maeda; Alfredo José Barreto Luiz. 2010. "Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil." Pesquisa Agropecuária Brasileira 45, no. 1: 72-80.

Journal article
Published: 07 January 2009 in Sensors
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This paper describes the methodology applied to generate simulated multipolarized L-band SAR images of the MAPSAR (Multi-Application Purpose SAR) satellite from the airborne SAR R99B sensor (SIVAM System). MAPSAR is a feasibility study conducted by INPE (National Institute for Space Research) and DLR (German Aerospace Center) targeting a satellite L-band SAR innovative mission for assessment, management and monitoring of natural resources. Examples of simulated products and their applications are briefly discussed.

ACS Style

José Claudio Mura; Waldir Renato Paradella; Luciano Vieira Dutra; João Roberto Dos Santos; Bernardo Friedrich Theodor Rudorff; Fernando Pellon De Miranda; Mario Marcos Quintino Da Silva; Wagner Fernando Da Silva. MAPSAR Image Simulation Based on L-band Polarimetric Data from the SAR-R99B Airborne Sensor (SIVAM System). Sensors 2009, 9, 102 -117.

AMA Style

José Claudio Mura, Waldir Renato Paradella, Luciano Vieira Dutra, João Roberto Dos Santos, Bernardo Friedrich Theodor Rudorff, Fernando Pellon De Miranda, Mario Marcos Quintino Da Silva, Wagner Fernando Da Silva. MAPSAR Image Simulation Based on L-band Polarimetric Data from the SAR-R99B Airborne Sensor (SIVAM System). Sensors. 2009; 9 (1):102-117.

Chicago/Turabian Style

José Claudio Mura; Waldir Renato Paradella; Luciano Vieira Dutra; João Roberto Dos Santos; Bernardo Friedrich Theodor Rudorff; Fernando Pellon De Miranda; Mario Marcos Quintino Da Silva; Wagner Fernando Da Silva. 2009. "MAPSAR Image Simulation Based on L-band Polarimetric Data from the SAR-R99B Airborne Sensor (SIVAM System)." Sensors 9, no. 1: 102-117.

Journal article
Published: 01 December 2008 in Pesquisa Agropecuária Brasileira
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O objetivo deste trabalho foi avaliar a viabilidade do uso de imagens do Landsat, para o mapeamento da área cultivada com soja, nas safras de 2000/2001 a 2006/2007, no Estado do Paraná. A análise dos "quick looks" das imagens dos sensores TM e ETM+ foi feita para selecionar as imagens úteis para o mapeamento da cultura da soja. Os "quick looks" foram classificados de acordo com a presença ou a ausência de nuvens e de problemas técnicos. Conforme os resultados, em nenhum dos sete anos teria sido possível mapear a área cultivada com soja, em todo o Estado, mesmo nos três anos-safra em que os satélites Landsat 5 e 7 operaram em conjunto. A presença de nuvens, detectada pelos sensores ópticos, deve ser levada em conta no mapeamento sistemático da área cultivada com culturas de verão, no Brasil.

ACS Style

Luciana Miura Sugawara; Bernardo Friedrich Theodor Rudorff; Marcos Adami. Viabilidade de uso de imagens do Landsat em mapeamento de área cultivada com soja no Estado do Paraná. Pesquisa Agropecuária Brasileira 2008, 43, 1777 -1783.

AMA Style

Luciana Miura Sugawara, Bernardo Friedrich Theodor Rudorff, Marcos Adami. Viabilidade de uso de imagens do Landsat em mapeamento de área cultivada com soja no Estado do Paraná. Pesquisa Agropecuária Brasileira. 2008; 43 (12):1777-1783.

Chicago/Turabian Style

Luciana Miura Sugawara; Bernardo Friedrich Theodor Rudorff; Marcos Adami. 2008. "Viabilidade de uso de imagens do Landsat em mapeamento de área cultivada com soja no Estado do Paraná." Pesquisa Agropecuária Brasileira 43, no. 12: 1777-1783.

Journal article
Published: 01 June 2008 in Sociedade & Natureza
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A cafeicultura, atividade de grande importância econômica e social para o país, é um segmento do agronegócio que não dispõe de informações atualizadas sobre o seu agroecossistema e perfil produtivo. O presente trabalho vem corroborar no sentido de desenvolver e/ou adequar metodologias fundamentadas em geotecnologias que possam contribuir significativamente para a delimitação e caracterização da cafeicultura em Minas Gerais. Como área de estudo piloto foram selecionados os municípios de Aguanil, Boa Esperança, Campo Belo e Cristais. No desenvolvimento do trabalho foram utilizadas imagens do sensor CCD a bordo do satélite CBERS, imagens do sensor TM a bordo do LANDSAT-5. As imagens foram classificadas de forma supervisionada com o classificador Maxver e posteriormente realizada a interpretação visual para a correção dos erros de omissão e inclusão. Os resultados mostraram que foi possível identificar e mapear as áreas cultivadas com café por meio de imagens de satélites de sensoriamento remoto.

ACS Style

Mauricio Alves Moreira; Marco Aurelio Barros; Bernardo Friedrich Theodor Rudorff. Geotecnologias no mapeamento da cultura do café em escala municipal. Sociedade & Natureza 2008, 20, 101 -110.

AMA Style

Mauricio Alves Moreira, Marco Aurelio Barros, Bernardo Friedrich Theodor Rudorff. Geotecnologias no mapeamento da cultura do café em escala municipal. Sociedade & Natureza. 2008; 20 (1):101-110.

Chicago/Turabian Style

Mauricio Alves Moreira; Marco Aurelio Barros; Bernardo Friedrich Theodor Rudorff. 2008. "Geotecnologias no mapeamento da cultura do café em escala municipal." Sociedade & Natureza 20, no. 1: 101-110.