Soybean crop area estimation by Modis/Evi data

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 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

Produtividade da cultura da soja no Rio Grande do Sul com dados EVI/MODIS

Nowadays, agricultural commodity market requires more efficient and better control of information on crop production. Due to characteristics of Brazil, in this market, the attainment of reliable and timely agricultural estimates is very important. The objective of this study was to evaluate the coupled models performance of crop area and crop productivity estimates, for an adequate temporal and spatial resolution. The productivity model proposed is complementarily added to the previously developed model of crop area estimate. The coupled model results were compared to Brazilian Institute of Geography and Statistics (IBGE) data, in a State and municipality level. Both, MODIS Crop Detection Model (MCDM) and MODIS Productivity Detection Model (MPDM) were developed at the National Institute for Space Research (INPE) and use analysis of the temporal-spectral profile of agricultural crop development, achieved with EVI/MODIS images. Results indicate that application of the coupled model is able to generate timely crop area and productivity estimates for soybean in the Rio Grande do Sul in a municipality level. In a State level, estimates of MPDM from 2001 to 2008, obtained R 2 =0.89, with a higher deviation for 2002. R 2 =0.96 were obtained by subtracting harvest of 2002 from the analysis. Kriging productivity maps, of MPDM and IBGE, were compared and shown a visual similarity in their spatial distribution and development trend.Pages: 55-6

Income Driven Patterns of the Urban Environment

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

Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja

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

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

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

Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier

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

Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja Integration of NOAA/AVHRR images: cooperation network towards national soybean crop monitoring

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.<br>An early assessment of national crop development conditions while the plants are still in the fields is highly needed to calculate correctly projections for decision-making and policies related to government planning and food security. The aim of this study was to evaluate the suitability of NOAA /AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) to detect changes in vegetation conditions, due to water stress during soybean crop, by means a combination of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). Both LST and NDVI data were combined and compared in a pixel basis over a soybean crop area in Rio Grande do Sul State. The predicted theoretical inverse relationship for the combination of LST and NDVI was detected. An average increase of LST was observed in a normal crop cycle ( from 301.02 K to 308.36 K) compared to a crop cycle under water stress condition. An average reduction in NDVI was observed for normal crop cycle development (from 0.65 to 0.53) compared to a crop cycle under drought-induced effects. It was observed a higher correlation of municipality yield with LST (R2=0.78) than NDVI (R2=0.59). Results obtained indicate that the aggregation of AVHRR images, from different institutions, provides the appropriate combination of spatial and temporal data LST and NDVI in order to detect the occurrence of drought stress, as well as its intensity, characterizing the conditions of the crop cycle development of soybean

Algorithm for Soybean Classification Using Medium Resolution Satellite Images

Abstract: An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R 2 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