The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

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.

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, withR2=0.97and 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.

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.

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.

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.