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A. Gusso
Institute of Geosciences, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

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Articles
Published: 01 January 2019 in European Journal of Remote Sensing
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The objective of this study is to validate the feasibility of a reflectance-based model for soybean crop area classification in advance of the county scale statistics from the United States Department of Agriculture (USDA). This classification method is named Reflectance-based North American Model (RNAM). It operates through the analysis of the main physically driven characteristics of farm fields and their specific radiometric profile obtained from Operational Land Imager (OLI) onboard Landsat 8. The state area of Illinois/US was selected because it is the largest soybean producer and accounted for nearly 35 percent of the total soybeans production in US. Farm fields within a set of 32 counties were analyzed for six crop years between 2013 to 2018. Results obtained from RNAM were compared to official estimates of USDA at county level. Coefficients R2 ranged between 0.92 and 0.96, indicating good agreement of the estimates. Results from RNAM were also validated with the geospatial reference map Cropland Data Layer (CDL) of soybeans from USDA. The overall map accuracy found was 93.86% with Kappa Index of Agreement of 0.795. Thus, RNAM was considered able to provide timely thematic soybean maps, in late September, in advance of the county scale statistics from USDA.

ACS Style

Aníbal Gusso; Wenxuan Guo; Silvia Beatriz Alves Rolim. Reflectance-based Model for Soybean Mapping in United States at Common Land Unit Scale with Landsat 8. European Journal of Remote Sensing 2019, 52, 522 -531.

AMA Style

Aníbal Gusso, Wenxuan Guo, Silvia Beatriz Alves Rolim. Reflectance-based Model for Soybean Mapping in United States at Common Land Unit Scale with Landsat 8. European Journal of Remote Sensing. 2019; 52 (1):522-531.

Chicago/Turabian Style

Aníbal Gusso; Wenxuan Guo; Silvia Beatriz Alves Rolim. 2019. "Reflectance-based Model for Soybean Mapping in United States at Common Land Unit Scale with Landsat 8." European Journal of Remote Sensing 52, no. 1: 522-531.

Articles
Published: 01 January 2018 in European Journal of Remote Sensing
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During the studied time window, between 2003 and 2010, there was an important increase of land use conversion into new soybean areas (first-time-use) in Mato Grosso state (MT) in Brazil. Uncertainties of future scenario of Brazilian agriculture and increase in the frequency of extreme events, such as the occurrence of high temperatures, is highly likely to produce yield loss on summer crops. The MT is the largest producer of soybeans and accounted for 28.2% of the national production in 2013. The objective of this study was to investigated specific characterization of land surface temperature distribution over the soybean crop fields canopies (canopy-LST) due to massive land use conversion into new soybean areas and its impacts on yield. Satellite imagery data from Aqua and Terra/MODIS sensors (Moderate Resolution Imaging Spectroradiometer) were compared to official agricultural statistics covering eight densely cultivated regions in the studied period. Results show that within the period from flowering to grain filling canopy-LST exhibits a non-negligible relation to yield. It is expected an additional loss of 4.9% on soybean yield for each 1oC of canopy-LST above the obtained optimal level of canopy-LST with 28.4oC, associated to the higher yield averages. The difference between overall average of canopy-LST and air temperature was found 4.2 oC.

ACS Style

Anibal Gusso. Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier. European Journal of Remote Sensing 2018, 51, 901 -910.

AMA Style

Anibal Gusso. Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier. European Journal of Remote Sensing. 2018; 51 (1):901-910.

Chicago/Turabian Style

Anibal Gusso. 2018. "Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier." European Journal of Remote Sensing 51, no. 1: 901-910.

Agronomy
Published: 01 December 2017 in Acta Amazonica
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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.

ACS Style

Anibal Gusso; Jorge Ricardo Ducati; Virindiana Colet Bortolotto. Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers. Acta Amazonica 2017, 47, 281 -292.

AMA Style

Anibal Gusso, Jorge Ricardo Ducati, Virindiana Colet Bortolotto. Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers. Acta Amazonica. 2017; 47 (4):281-292.

Chicago/Turabian Style

Anibal Gusso; Jorge Ricardo Ducati; Virindiana Colet Bortolotto. 2017. "Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers." Acta Amazonica 47, no. 4: 281-292.

Journal article
Published: 15 February 2017 in Sustainability
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This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation–Impervious–Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1.263 million in 1991 to 1.409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.

ACS Style

Anibal Gusso; André Silva; John Boland; Leticia Lenz; Conrad Philipp. Income Driven Patterns of the Urban Environment. Sustainability 2017, 9, 275 .

AMA Style

Anibal Gusso, André Silva, John Boland, Leticia Lenz, Conrad Philipp. Income Driven Patterns of the Urban Environment. Sustainability. 2017; 9 (2):275.

Chicago/Turabian Style

Anibal Gusso; André Silva; John Boland; Leticia Lenz; Conrad Philipp. 2017. "Income Driven Patterns of the Urban Environment." Sustainability 9, no. 2: 275.

Sensoriamento remoto
Published: 01 February 2017 in Pesquisa Agropecuária Brasileira
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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

ACS Style

Anibal Gusso; Damien Arvor; Jorge Ricardo Ducati. Model for soybean production forecast based on prevailing physical conditions. Pesquisa Agropecuária Brasileira 2017, 52, 95 -103.

AMA Style

Anibal Gusso, Damien Arvor, Jorge Ricardo Ducati. Model for soybean production forecast based on prevailing physical conditions. Pesquisa Agropecuária Brasileira. 2017; 52 (2):95-103.

Chicago/Turabian Style

Anibal Gusso; Damien Arvor; Jorge Ricardo Ducati. 2017. "Model for soybean production forecast based on prevailing physical conditions." Pesquisa Agropecuária Brasileira 52, no. 2: 95-103.

Preprint
Published: 16 December 2016
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This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1,263 million in 1991 to 1,409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.

ACS Style

Anibal Gusso; Conrad Philipp; André Silva; John Boland; Leticia Lenz. Economic Driven Patterns of the Urban Environment. 2016, 1 .

AMA Style

Anibal Gusso, Conrad Philipp, André Silva, John Boland, Leticia Lenz. Economic Driven Patterns of the Urban Environment. . 2016; ():1.

Chicago/Turabian Style

Anibal Gusso; Conrad Philipp; André Silva; John Boland; Leticia Lenz. 2016. "Economic Driven Patterns of the Urban Environment." , no. : 1.

Journal article
Published: 16 March 2015 in Sustainability
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For a reliable assessment of sustainability in big cities, it is imperative to evaluate urban ecosystem conditions and the environment of the cities undergoing economic growth. Urban green spaces are valuable sources of evapotranspiration, which is generated by trees and vegetation; these spaces mitigate urban heat islands in cities. Land surface temperature (LST) is closely related to the distribution of land-use and land-cover characteristics and can be used as an indicator of urban environment conditions and development. This study evaluates the patterns of LST distribution through time by employing the thermal spatial distribution signature procedure using thermal infrared data obtained from Landsat-5 Thematic Mapper. A set of 18 images, between 1985 and 2010, was used to study the urban environment during summer in 47 neighborhoods of Porto Alegre, Brazil. On a neighborhood scale, results show a non-linear inverse correlation (R² = 0.55) between vegetation index and LST. The overall average of the LST is 300.23 K (27.8 °C) with a standard deviation of 1.25 K and the maximum average difference of 2.83 K between neighborhoods. Results show that the Thermal Spatial Distribution Signature (TSDS) analysis can help multi-temporal studies for the evaluation of UHI through time.

ACS Style

Anibal Gusso; Cristina Cafruni; Fabiane Bordin; Mauricio Roberto Veronez; Leticia Lenz; Sabrina Crija. Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management. Sustainability 2015, 7, 3129 -3145.

AMA Style

Anibal Gusso, Cristina Cafruni, Fabiane Bordin, Mauricio Roberto Veronez, Leticia Lenz, Sabrina Crija. Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management. Sustainability. 2015; 7 (3):3129-3145.

Chicago/Turabian Style

Anibal Gusso; Cristina Cafruni; Fabiane Bordin; Mauricio Roberto Veronez; Leticia Lenz; Sabrina Crija. 2015. "Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management." Sustainability 7, no. 3: 3129-3145.

Conference paper
Published: 05 November 2014 in Proceedings of The 4th World Sustainability Forum
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The evaluation of the urban ecosystem conditions and environment while cities that are still growing economically, are highly necessary for reliable assessment of sustainability in big cities. The urban green spaces are valuable sources of evapotranspiration process generated by trees and vegetation which mitigates urban heat islands (UHI) in the cities. The Land Surface Temperature (LST) is closely related to the distribution of Land Use and Land Cover (LULC) characteristics and can be used as an indicator of the urban environment conditions and development. This research evaluates the patterns of LST distribution by means the Thermal Spatial Distribution Signature (TSDS) procedure using Thermal Infrared (TIR) data obtained from Landsat-5 Thematic Mapper (TM). A set of eighteen images, between 1985 and 2009, were used to study the urban environment during the summer season, in 47 neighborhoods in the city of Porto Alegre, Brazil. At neighborhood scale, results show a non-linear inverse correlation (R2=0.55) between vegetation index and LST. The overall average of the LST is 300.23 K (27.8˚C) with a standard deviation of 1.25 K. The max difference found between neighborhoods was 2.83 K.

ACS Style

Anibal Gusso; Fabiane Bordin; Mauricio Veronez; Cristina Cafruni; Leticia Lenz; Sabrina Crija. Multitemporal Analysis of Thermal Distribution Characteristics for Urban Heat Islands Management. Proceedings of The 4th World Sustainability Forum 2014, 1 .

AMA Style

Anibal Gusso, Fabiane Bordin, Mauricio Veronez, Cristina Cafruni, Leticia Lenz, Sabrina Crija. Multitemporal Analysis of Thermal Distribution Characteristics for Urban Heat Islands Management. Proceedings of The 4th World Sustainability Forum. 2014; ():1.

Chicago/Turabian Style

Anibal Gusso; Fabiane Bordin; Mauricio Veronez; Cristina Cafruni; Leticia Lenz; Sabrina Crija. 2014. "Multitemporal Analysis of Thermal Distribution Characteristics for Urban Heat Islands Management." Proceedings of The 4th World Sustainability Forum , no. : 1.

Conference paper
Published: 01 July 2014 in 2014 IEEE Geoscience and Remote Sensing Symposium
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Increases in the frequency of extreme events, such as the occurrence of high temperatures, are prone to produce severe effects on summer crop yields especially soybeans and maize. Under a climate change scenario, the physical parameters of the Earth's surface, such as temperature, water availability and evapotranspiration, are expected to change over the next decades. We investigated the variability of soybean yields associated with crop canopy temperatures during key development that are sensitive to the occurrence of high temperatures in Mato Grosso State, Brazil. In the present paper, we propose that the temperature fluctuations around the optimum level in the crop canopy can cause favorable effects on soybean yields in MT State/Brazil. In order to evaluate the above mentioned hypothesis, we investigated the effects of canopy temperature on soybean yield during flowering to the grain filling periods using Aqua and Terra/MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, between 2003 and 2010. Comparison of spatially interpolated maps show that yield variations are positively related to canopy-LST during of flowering period, with R 2 =0.60 and RMSD=6.2%. Overall results show that increases in canopy-LST temperature in Mato Grosso State, during flowering/grain filling periods, are related to higher soybean yield averages.

ACS Style

Anibal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Damien Arvor; Luiz Gonzaga Da Silveira. Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, Brazil. 2014 IEEE Geoscience and Remote Sensing Symposium 2014, 859 -862.

AMA Style

Anibal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Damien Arvor, Luiz Gonzaga Da Silveira. Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, Brazil. 2014 IEEE Geoscience and Remote Sensing Symposium. 2014; ():859-862.

Chicago/Turabian Style

Anibal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Damien Arvor; Luiz Gonzaga Da Silveira. 2014. "Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, Brazil." 2014 IEEE Geoscience and Remote Sensing Symposium , no. : 859-862.

Research article
Published: 10 April 2014 in The Scientific World Journal
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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.

ACS Style

Anibal Gusso; Damien Arvor; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira. Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil. The Scientific World Journal 2014, 2014, 1 -9.

AMA Style

Anibal Gusso, Damien Arvor, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Luiz Gonzaga Da Silveira. Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil. The Scientific World Journal. 2014; 2014 (1):1-9.

Chicago/Turabian Style

Anibal Gusso; Damien Arvor; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Luiz Gonzaga Da Silveira. 2014. "Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil." The Scientific World Journal 2014, no. 1: 1-9.

Journal article
Published: 01 January 2014 in Revista Brasileira de Geografia Física
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ACS Style

Aníbal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Leonardo Campos Inocêncio. Integrating Aqua and Terra/MODIS satellite Data for the Evaluation of Heat Stress Impacts on Summer Crops. Revista Brasileira de Geografia Física 2014, 7, 1 .

AMA Style

Aníbal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Leonardo Campos Inocêncio. Integrating Aqua and Terra/MODIS satellite Data for the Evaluation of Heat Stress Impacts on Summer Crops. Revista Brasileira de Geografia Física. 2014; 7 (1):1.

Chicago/Turabian Style

Aníbal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Leonardo Campos Inocêncio. 2014. "Integrating Aqua and Terra/MODIS satellite Data for the Evaluation of Heat Stress Impacts on Summer Crops." Revista Brasileira de Geografia Física 7, no. 1: 1.

Journal article
Published: 01 January 2014 in Agricultural Sciences
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Periods in the soybean summer cycle that are sensitiveto the occurrence of high temperatures were studied. An analysis was performedon the variability of soybean yields associated with crop canopy temperaturesduring key development periods. A land surface temperature (LST) data seriesfrom MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aquasatellite was processed between 2003 and 2012 that covered the entire state ofRio Grande do Sul, in Brazil. Enhancedvegetation index (EVI) data from MODIS on the Terra satellite were used tomonitor the LST during different phenological stages. Spatially interpolatedmaps of soybean yield distributions were generated using data obtained fromInstituto Brasileiro de Geografia eEstatística (IBGE) at state and municipality levels. The results indicate thatcanopy-LST occurrence in mid-February, during the grain filling, is mostcorrelated to yield reduction (R2 = 0.82 and RMSD = 14.4%). At the state level, the average yield is 2003 kg·ha-1 with a standarddeviation of 308 kg·ha-1. The overall average of the canopy-LST is305.0 K (31.8°C) with a standarddeviation of 1.9 K. The slope of the downward linear relationship betweencanopy-LST and yield was -28.7%. These results indicate that monitoring heat wave events can provide important information for characterising agriculture vulnerability.

ACS Style

Anibal Gusso; Jorge Ducati; Mauricio Roberto Veronez; Victor Sommer; Luiz Gonzaga Da Silveira Junior. Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil. Agricultural Sciences 2014, 05, 353 -364.

AMA Style

Anibal Gusso, Jorge Ducati, Mauricio Roberto Veronez, Victor Sommer, Luiz Gonzaga Da Silveira Junior. Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil. Agricultural Sciences. 2014; 05 (04):353-364.

Chicago/Turabian Style

Anibal Gusso; Jorge Ducati; Mauricio Roberto Veronez; Victor Sommer; Luiz Gonzaga Da Silveira Junior. 2014. "Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil." Agricultural Sciences 05, no. 04: 353-364.

Journal article
Published: 01 April 2013 in Revista Ceres
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Uma avaliação inicial das condições do desenvolvimento da safra nacional, enquanto as plantas ainda estão nos campos, é altamente necessária para o cálculo correto das projeções na tomada de decisão e políticas relacionadas com o planejamento governamental e segurança alimentar. O objetivo deste trabalho foi avaliar a adequação dos dados NOAA/AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) em detectar mudanças nas condições da vegetação, devidas à ocorrência de estresse hídrico, na soja, por meio de uma combinação do índice NDVI (Normalized Difference Vegetation Index) e da LST (Land Surface Temperature). Os dados LST e NDVI foram combinados e comparados pixel a pixel, sobre uma área de cultivo de soja, no Rio Grande do Sul. A relação teórica inversa prevista na combinação de LST e NDVI foi detectada. Foi observado que ocorre um aumento médio na LST em uma safra de ciclo normal (de 301,02 K para 308,36 K), quando comparada a uma safra sob condição de estresse hídrico, no desenvolvimento da cultura. Uma redução média do NDVI foi observada no ciclo normal (de 0,65 para 0,53), comparada com uma safra sob efeitos ocasionados pela estiagem no desenvolvimento da cultura. Foi observado maior correlação da produtividade municipal com LST (R2=0,78) do que com o NDVI (R2 = 0,59). Os resultados obtidos indicam que a integração de imagens do sensor AVHRR, proveniente de diferentes instituições, proporciona a adequada combinação espacial e temporal dos dados LST e NDVI, a fim de detectar a ocorrência de estresse hídrico, bem como sua intensidade, caracterizando as condições do ciclo de desenvolvimento da soja.

ACS Style

Anibal Gusso. Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja. Revista Ceres 2013, 60, 194 -204.

AMA Style

Anibal Gusso. Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja. Revista Ceres. 2013; 60 (2):194-204.

Chicago/Turabian Style

Anibal Gusso. 2013. "Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja." Revista Ceres 60, no. 2: 194-204.

Journal article
Published: 01 January 2013 in International Journal of Geosciences
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Attaining reliable and timely agricultural estimates is very important everywhere, and in Brazil, due to its characteristics, this is especially true. In this study, estimations of crop production were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) images. The objective was to evaluate the coupled model (CM) performance of crop area and crop yield estimates based solely on MODIS/EVI as input data in Rio Grande do Sul State, which is characterized by high variability in seasonal soybean yields, due to different crop development conditions. The resulting production estimates from CM were compared to official agricultural statistics of Brazilian Institute of Geography and Statistics (IBGE) and the National Company of Food Supply (CONAB) at different levels from 2000/2001 to 2010/2011 crop years. Results obtained with CM indicate that its application is able to generate timely production estimates for soybean both at municipality and local levels. Validation estimates with CM at State level obtained R2 = 0.95. Combining all cropping years at municipality level, estimates were highly correlated to official statistics from IBGE, with R2 = 0.91 and RMSD = 10,840 tons. Spatially interpolated comparisons of yield maps obtained from the CM estimates and IBGE data also showed visual similarity in their spatial distribution. Local level comparisons were performed and presented R2 = 0.95. Implications of this work point out that time-series analysis of production estimates are able to provide anticipated spatial information prior to the soybean harvest.

ACS Style

Anibal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Damien Arvor; Silveira Jr. Luiz Gonzaga da. Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data. International Journal of Geosciences 2013, 04, 1233 -1241.

AMA Style

Anibal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Damien Arvor, Silveira Jr. Luiz Gonzaga da. Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data. International Journal of Geosciences. 2013; 04 (09):1233-1241.

Chicago/Turabian Style

Anibal Gusso; Jorge Ricardo Ducati; Mauricio Roberto Veronez; Damien Arvor; Silveira Jr. Luiz Gonzaga da. 2013. "Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data." International Journal of Geosciences 04, no. 09: 1233-1241.

Journal article
Published: 18 October 2012 in Remote Sensing
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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.

ACS Style

Anibal Gusso; Jorge Ricardo Ducati. Algorithm for Soybean Classification Using Medium Resolution Satellite Images. Remote Sensing 2012, 4, 3127 -3142.

AMA Style

Anibal Gusso, Jorge Ricardo Ducati. Algorithm for Soybean Classification Using Medium Resolution Satellite Images. Remote Sensing. 2012; 4 (10):3127-3142.

Chicago/Turabian Style

Anibal Gusso; Jorge Ricardo Ducati. 2012. "Algorithm for Soybean Classification Using Medium Resolution Satellite Images." Remote Sensing 4, no. 10: 3127-3142.

Journal article
Published: 17 July 2012 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Evaluation of the MODIS Crop Detection Algorithm (MCDA) procedure for estimating historical planted soybean crop areas was done on fields in Mato Grosso State, Brazil. MCDA is based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite data of the MODIS (Moderate Resolution Imaging Spectroradiometer) imager, and was previously developed for soybean area estimation in Rio Grande do Sul State, Brazil. According to the MCDA approach, in Mato Grosso soybean area estimates can be provided in December (1st forecast), using images from the sowing period, and in February (2nd forecast), using images from sowing and maximum crop development period. The results obtained by the MCDA were compared with Brazilian Institute of Geography and Statistics (IBGE) official estimates of soybean area at municipal level. Coefficients of determination were between 0.93 and 0.98, indicating a good agreement, and also the suitability of MCDA to estimations performed in Mato Grosso State. On average, the MCDA results explained 96% of the variation of the data estimated by the IBGE. In this way, MCDA calibration was able to provide annual thematic soybean maps, forecasting the planted area in the State, with results which are comparable to the official agricultural statistics.

ACS Style

A. Gusso; J. R. Ducati. SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2012, I-7, 215 -219.

AMA Style

A. Gusso, J. R. Ducati. SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2012; I-7 ():215-219.

Chicago/Turabian Style

A. Gusso; J. R. Ducati. 2012. "SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-7, no. : 215-219.

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.

Conference paper
Published: 01 July 2010 in 2010 IEEE International Geoscience and Remote Sensing Symposium
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Bayesian Network (BN) techniques can be used to represent the causal relationships among random variables on probabilistic models. Only few studies have applied these techniques to remote sensing and other spatial data integrated in geographic information systems. The objective of the present work was to map soybean plantation using minimum of EVI (M), range of EVI (R) and terrain slope (L) as input variables in the BN. Soybean plantations were evaluated in the state of Rio Grande do Sul, Brazil during the 2000/01 crop year. The probability function was discretized with five different numbers of intervals. Results were improved with the increase of the number of intervals. Best soybean mapping result presented sensitivity, specificity and overall accuracy indices equal to 77.62, 77.56 and 77.58%, respectively, indicating that the method is promising and has potential to be improved with the use of additional input variables.

ACS Style

Marcio Pupin Mello; Bernardo F. T. Rudorff; Marcos Adami; Rodrigo Rizzi; Daniel A. Aguiar; Anibal Gusso; Leila M. G. Fonseca. A simplified Bayesian Network to map soybean plantations. 2010 IEEE International Geoscience and Remote Sensing Symposium 2010, 351 -354.

AMA Style

Marcio Pupin Mello, Bernardo F. T. Rudorff, Marcos Adami, Rodrigo Rizzi, Daniel A. Aguiar, Anibal Gusso, Leila M. G. Fonseca. A simplified Bayesian Network to map soybean plantations. 2010 IEEE International Geoscience and Remote Sensing Symposium. 2010; ():351-354.

Chicago/Turabian Style

Marcio Pupin Mello; Bernardo F. T. Rudorff; Marcos Adami; Rodrigo Rizzi; Daniel A. Aguiar; Anibal Gusso; Leila M. G. Fonseca. 2010. "A simplified Bayesian Network to map soybean plantations." 2010 IEEE International Geoscience and Remote Sensing Symposium , no. : 351-354.

Journal article
Published: 01 February 2007 in Pesquisa Agropecuária Brasileira
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O objetivo deste trabalho foi avaliar a adequação do uso do sensor AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) para mapeamento da temperatura da superfície terrestre (TST) no Estado do Rio Grande do Sul, por meio da comparação entre três algoritmos clássicos. Foram comparados os métodos de Becker & Li, Sobrino et al. e Kerr et al. para estimativa das TST mínimas, utilizando imagens noturnas e logo após o amanhecer. Os dados de emissividade e TST foram obtidos por meio de combinações matemáticas da radiação detectada nas faixas do visível, infravermelho próximo e termal do sensor AVHRR/NOAA. O sensor AVHRR é adequado para o mapeamento de TST para as condições do tipo de cobertura do solo que predominam no Rio Grande do Sul, visto que a TST estimada pelos três métodos testados foi próxima à temperatura do ar medida nos locais selecionados. O método de Sobrino et al. foi o mais adequado.

ACS Style

Aníbal Gusso; Denise Cybis Fontana; Glauber Acunha Gonçalves. Mapeamento da temperatura da superfície terrestre com uso do sensor AVHRR/NOAA. Pesquisa Agropecuária Brasileira 2007, 42, 231 -237.

AMA Style

Aníbal Gusso, Denise Cybis Fontana, Glauber Acunha Gonçalves. Mapeamento da temperatura da superfície terrestre com uso do sensor AVHRR/NOAA. Pesquisa Agropecuária Brasileira. 2007; 42 (2):231-237.

Chicago/Turabian Style

Aníbal Gusso; Denise Cybis Fontana; Glauber Acunha Gonçalves. 2007. "Mapeamento da temperatura da superfície terrestre com uso do sensor AVHRR/NOAA." Pesquisa Agropecuária Brasileira 42, no. 2: 231-237.

Journal article
Published: 01 January 2004 in Gayana (Concepción)
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Aníbal Gusso; Jorge Ricardo Ducati; Carlos G Cotlier; Diego A. G Lopez. A SEARCH FOR CORRELATIONS BETWEEN SEA SURFACE TEMPERATURES AND OPTICAL REFLECTANCES IN CHILE AND BRAZIL DERIVED FROM AVHRR/NOAA IMAGES. Gayana (Concepción) 2004, 68, 259 -265.

AMA Style

Aníbal Gusso, Jorge Ricardo Ducati, Carlos G Cotlier, Diego A. G Lopez. A SEARCH FOR CORRELATIONS BETWEEN SEA SURFACE TEMPERATURES AND OPTICAL REFLECTANCES IN CHILE AND BRAZIL DERIVED FROM AVHRR/NOAA IMAGES. Gayana (Concepción). 2004; 68 (2):259-265.

Chicago/Turabian Style

Aníbal Gusso; Jorge Ricardo Ducati; Carlos G Cotlier; Diego A. G Lopez. 2004. "A SEARCH FOR CORRELATIONS BETWEEN SEA SURFACE TEMPERATURES AND OPTICAL REFLECTANCES IN CHILE AND BRAZIL DERIVED FROM AVHRR/NOAA IMAGES." Gayana (Concepción) 68, no. 2: 259-265.