Effect of satellite-emitted frequency sequences on Phakopsora pachyrhizi control, on soybean nutrition and yield

The obstacles in Phakopsora pachyrhizi management result especially from susceptible soybean genotypes and resistant fungal strains. The objective of the current study was to evaluate the applicability of the emission of extremely low and specific frequencies by Effatha technology in the soybean Asian rust control, nutrition, and its impact on yield. The in-vivo test followed the detached leaves method, with six treatments: frequencies 1 and 2 individually and in association; the conventional chemical treatment (fungicide azoxystrobin + benzovindiflupyr); and witnesses in presence and absence of the fungus. Frequency 1 relates to inhibition of the enzyme succinate dehydrogenase and 2 to ubiquinone oxidase. In the field, frequencies 1 and 2 associated (with the same fungicidal action of the in-vivo study); nutritional frequency; application of azoxystrobin + benzovindiflupyr + mancozeb, and control without application were evaluated. In vivo, the fungicide provided 85% control of the disease symptoms, against 65% of frequencies 1 and 2 in association, which showed a higher efficiency compared to the isolated frequencies. In the field, the rate of increase of symptoms were reduced by all treatments compared to the control. At the end of the soybean cycle, the conventional fungicide resulted in 33% severity against 56% of frequencies 1 and 2 associated, and 69.2% of the control. The emission of the frequency for increased nutrient efficiency stood out positively on yield in relation to all the other ones. The conventional application provided the highest weight of 1,000 grains, possibly a direct reflection of the better control of the disease.

Mapping of corn phenological stages using NDVI from OLI and MODIS sensors / Mapeamento dos estádios fenológicos da cultura do milho utilizando o NDVI derivado dos sensores OLI e MODIS

Crop phenology knowledge is relevant to a series of actions related to its management and can be accessed through vegetation indexes. Thus, this study aimed to evaluate the use of the Normalized Difference Vegetation Index (NDVI), from images of OLI and MODIS sensors, to obtain phenological information from corn crops. To this end, we evaluated two corn cropping areas, irrigated by a central pivot, and located western Bahia state, Brazil. These areas were managed with high technology and had no record of biotic and abiotic stresses. NDVI showed a well-defined temporal pattern throughout the corn cycle, with a rapid increase at the beginning, stabilization at intermediate stages, and decreases at the end of the cycle. Excellent fits for polynomial equations were obtained to estimate NDVI as a function of days after sowing (DAS), with R² values of 0.96 and 0.95 for images of OLI and MODIS sensors, respectively. This demonstrates that both sensors could characterize corn canopy changes over time. NDVI ranges were correlated with the main phenological stages (PE), using the direct relationship between both variables (NDVI and PE) with days after sowing (DAS). For the beginning and end of each phenological stage, NDVI ranges were validated through model identity testing. NDVI proved to be a suitable parameter to assess corn phenology accurately and remotely. Finally, NDVI was also an important tool for detecting biotic and abiotic stresses throughout the crop cycle, and hence for decision making based on corn phenology.(AU)

O conhecimento da fenologia das culturas é relevante para uma série de ações relacionadas ao seu manejo e pode ser acessada por meio de índices de vegetação. Portanto, objetivou-se com este trabalho avaliar o potencial do uso do Índice de Vegetação por Diferença Normalizada (NDVI), calculado a partir de imagens dos sensores OLI e MODIS para obter informações fenológicas da cultura do milho. Para tanto, foram utilizadas duas áreas de cultivo de milho irrigadas por sistema de pivô central na região oeste do estado da Bahia, Brasil. Estas áreas foram manejadas com alta tecnologia e sem registro de ocorrência de estresses bióticos e abióticos. O NDVI apresentou um padrão temporal bem definido ao longo do ciclo de desenvolvimento, com rápido incremento no início do desenvolvimento, estabilização nos estádios intermediários, e decréscimos na parte final do ciclo. Excelentes ajustes para as equações polinomiais foram obtidos para estimar o NDVI em função dos dias após a semeadura (DAS), com R² de 0,96 e 0,95 para as imagens do sensor OLI e MODIS, respectivamente, demonstrando que os sensores são capazes de caracterizar temporalmente as modificações do dossel da cultura do milho ao longo do ciclo. Intervalos de valores de NDVI foram correlacionados com os principais estádios fenológicos (EF) da cultura do milho, utilizando a relação direta de ambas as variáveis (NDVI e EF) com os dias após a semeadura (DAS). Os intervalos de valores de NDVI para o início e final de cada estádio fenológico foram validados através do teste de identidade do modelo, tornando o NDVI uma variável adequada para ser utilizada no acesso a fenologia do milho com precisão de maneira remota. Por fim, esses valores também são uma importante ferramenta para detecção de problemas bióticos e abióticos ao longo do ciclo de cultivo e para tomadas de decisão baseadas na fenologia da cultura.(AU)

Applying the NDVI from satellite images in delimiting management zones for annual crops

The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socio-economic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman's correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 r 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.

Applying the NDVI from satellite images in delimiting management zones for annual crops

The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socio-economic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman's correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 r 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.(AU)

Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil / Calendário agrícola de soja e milho baseado em imagens de satélite de moderada resolução para o sul do Brasil

Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied cropyears, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.

O conhecimento do calendário agrícola das culturas é essencial para melhor estimar e prever o cultivo de culturas em larga escala. O objetivo deste estudo foi estimar a data da semeadura (SD), a data de data de máximo desenvolvimento vegetativo (DMVD) e a data da colheita (HD) de soja e milho no estado do Paraná, Brasil. Datas de 120 fazendas e o Índice de Vegetação Aprimorado (EVI) do Espectrorradiômetro de Imagem de Resolução Moderada (MODIS) de 2011 a 2014 foram utilizados em uma análise de tendência sazonal para obter padrões sazonais de soja e milho. Os resultados indicam que a maioria da soja é semeada em outubro e a DMVD ocorre entre o segundo decêndio de dezembro e o primeiro decêndio de janeiro. Devido à variabilidade espacial do SD, à diferença nos ciclos de maturação das cultivares e à variação climática regional, a HD da soja variou bastante durante as safras estudadas, variando de meados de fevereiro a final de março. A SD do milho é anterior a da soja e ocorre principalmente no final de setembro a meados de outubro. O DMVD ocorre principalmente em dezembro e a HD está distribuída entre janeiro e março no Paraná. Ao comparar as datas estimadas comas datas observadas, o erro médio (ME) variou de 0,2 dias antes a 3,3 dias após a data observada para a soja com a raiz do erro quadrático médio (RMSE) de 1,93 a 14,73 dias. Para o milho, o ME variou de10,3 dias a 18,5 dias após a data observada, com RMSE de 18,02 a 27,82 dias.

Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil / Calendário agrícola de soja e milho baseado em imagens de satélite de moderada resolução para o sul do Brasil

Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied cropyears, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.(AU)

O conhecimento do calendário agrícola das culturas é essencial para melhor estimar e prever o cultivo de culturas em larga escala. O objetivo deste estudo foi estimar a data da semeadura (SD), a data de data de máximo desenvolvimento vegetativo (DMVD) e a data da colheita (HD) de soja e milho no estado do Paraná, Brasil. Datas de 120 fazendas e o Índice de Vegetação Aprimorado (EVI) do Espectrorradiômetro de Imagem de Resolução Moderada (MODIS) de 2011 a 2014 foram utilizados em uma análise de tendência sazonal para obter padrões sazonais de soja e milho. Os resultados indicam que a maioria da soja é semeada em outubro e a DMVD ocorre entre o segundo decêndio de dezembro e o primeiro decêndio de janeiro. Devido à variabilidade espacial do SD, à diferença nos ciclos de maturação das cultivares e à variação climática regional, a HD da soja variou bastante durante as safras estudadas, variando de meados de fevereiro a final de março. A SD do milho é anterior a da soja e ocorre principalmente no final de setembro a meados de outubro. O DMVD ocorre principalmente em dezembro e a HD está distribuída entre janeiro e março no Paraná. Ao comparar as datas estimadas comas datas observadas, o erro médio (ME) variou de 0,2 dias antes a 3,3 dias após a data observada para a soja com a raiz do erro quadrático médio (RMSE) de 1,93 a 14,73 dias. Para o milho, o ME variou de10,3 dias a 18,5 dias após a data observada, com RMSE de 18,02 a 27,82 dias.(AU)

Seasonal dynamics of vegetation indices as a criterion for grouping grassland typologies

This study aimed to characterize the average seasonal pattern of the vegetation in southern grassland in Brazil, and the variability found in the time series of vegetation indices. It also sought to identify similarities in the seasonal pattern of different grassland typologies. Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Feb to Dec, 2000 to 2014 were analyzed for ten regions. The grassland typologies studied showed EVI and NDVI profiles consistent with the seasonal dynamics of grassland vegetation under the influence of a subtropical climate, with highest values in the indices during the warm seasons of the year (spring and summer) and lowest in the colder seasons (autumn and winter). Considering the values of EVI and NDVI, grassland typologies were allocated to four groups with similar temporal profiles. Among the groups formed from the EVI index it is possible to identify differences between grassland typologies during the autumn and winter, while the NDVI showed differences only in winter as compared to the other seasons.

Seasonal dynamics of vegetation indices as a criterion for grouping grassland typologies

This study aimed to characterize the average seasonal pattern of the vegetation in southern grassland in Brazil, and the variability found in the time series of vegetation indices. It also sought to identify similarities in the seasonal pattern of different grassland typologies. Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Feb to Dec, 2000 to 2014 were analyzed for ten regions. The grassland typologies studied showed EVI and NDVI profiles consistent with the seasonal dynamics of grassland vegetation under the influence of a subtropical climate, with highest values in the indices during the warm seasons of the year (spring and summer) and lowest in the colder seasons (autumn and winter). Considering the values of EVI and NDVI, grassland typologies were allocated to four groups with similar temporal profiles. Among the groups formed from the EVI index it is possible to identify differences between grassland typologies during the autumn and winter, while the NDVI showed differences only in winter as compared to the other seasons.(AU)

Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm / Produtividade e demanda hídrica do milho estimado pelo algoritmo de Priestley-Taylor modificado para satélite

The water demand of crops, as well as the relation of this variable to productivity and other important factors related to the sustainable management of agriculture, makes it relevant to estimate parameters that help in the most assertive and efficient decision-making in the agricultural environment. In this context, the work aims to estimate the actual evapotranspiration (ETa), biomass (Bio), water productivity (WP) and crop productivity (P), using the Landsat-8 satellite, through the Modified Satellite Priestley-Taylor Algorithm (MS-PT). For this, ETa was estimated for maize culture irrigated by central pivots, using the MS-PT with six images of Landsat-8, which were free of clouds. The ETa estimate was accurate in the first 60 days after emergence (DAE) of the crop. Subsequently, the variables Bio, P, and WP were estimated using the ETa and the assumptions of the Monteith (1972) model. Therefore, we sequentially calculated the dry biomass, crop productivity and water productivity. ETa presented a high correlation with Bio from the second image (06/10/2015), due to the canopy closure of the crop and, consequently, the predominance of transpiration in the evapotranspiration phenomenon. The water productivity was constant throughout the maximum vegetative stage until the reproductive phase R4 of the crop, verifying in this interval the best efficiency in the conversion of water in biomass. From the obtained results, it is verified that the set of algorithms used in the estimation of the parameters demonstrated the potential to increase the capacity to handle agriculture in a more efficient, assertive and sustainable way.

A demanda hídrica das culturas, assim como a relação dessa com variáveis de produtividade e outros importantes fatores relacionados ao manejo da agricultura sustentável, faz com seja relevante a estimação de parâmetros que auxiliam de modo assertivo e eficiente a tomada de decisão no ambiente agrícola. Nesse contexto, o objetivo desse trabalho foi estimar a evapotranspiração real (ETa), biomassa (Bio), produtividade da água (WP) e a produtividade da cultura (P), utilizando imagens do satélite Landsat-8, por meio do algoritmo Priestley-Taylor modificado para satélite (MS-PT). Para isso, estimou-se a ETa para a cultura do milho irrigado por pivôs central, utilizando o MS-PT com seis imagens do Landsat-8, as quais encontravam-se livre de nuvens. A estimativa da ETa foi acurada nos primeiros 60 dias após a emergência (DAE) da cultura. Posteriormente, as variáveis Bio, P, e WP foram estimadas utilizando a ETa e os pressupostos do modelo de Monteith (1972). A ETa apresentou alta correlação com a Bio a partir da segunda imagem (10/06/2015), em função do fechamento dossel da cultura e consequentemente a predominância da transpiração no fenômeno de evapotranspiração. A WP foi constante durante o máximo crescimento vegetativo até a fase reprodutiva da cultura denominada R4, sendo verificado nessa amplitude de tempo a melhor eficiência da conversão de água em biomassa. A partir dos resultados obtidos, verifica-se que esse conjunto de algoritmos utilizados para estimativa dos parâmetros relacionados a produtividade do milho mostraram o potencial de crescimento que se tem para melhorar a capacidade de como lidar com uma agricultura mais eficiente, assertiva e sustentável.

Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm / Produtividade e demanda hídrica do milho estimado pelo algoritmo de Priestley-Taylor modificado para satélite

The water demand of crops, as well as the relation of this variable to productivity and other important factors related to the sustainable management of agriculture, makes it relevant to estimate parameters that help in the most assertive and efficient decision-making in the agricultural environment. In this context, the work aims to estimate the actual evapotranspiration (ETa), biomass (Bio), water productivity (WP) and crop productivity (P), using the Landsat-8 satellite, through the Modified Satellite Priestley-Taylor Algorithm (MS-PT). For this, ETa was estimated for maize culture irrigated by central pivots, using the MS-PT with six images of Landsat-8, which were free of clouds. The ETa estimate was accurate in the first 60 days after emergence (DAE) of the crop. Subsequently, the variables Bio, P, and WP were estimated using the ETa and the assumptions of the Monteith (1972) model. Therefore, we sequentially calculated the dry biomass, crop productivity and water productivity. ETa presented a high correlation with Bio from the second image (06/10/2015), due to the canopy closure of the crop and, consequently, the predominance of transpiration in the evapotranspiration phenomenon. The water productivity was constant throughout the maximum vegetative stage until the reproductive phase R4 of the crop, verifying in this interval the best efficiency in the conversion of water in biomass. From the obtained results, it is verified that the set of algorithms used in the estimation of the parameters demonstrated the potential to increase the capacity to handle agriculture in a more efficient, assertive and sustainable way.(AU)

A demanda hídrica das culturas, assim como a relação dessa com variáveis de produtividade e outros importantes fatores relacionados ao manejo da agricultura sustentável, faz com seja relevante a estimação de parâmetros que auxiliam de modo assertivo e eficiente a tomada de decisão no ambiente agrícola. Nesse contexto, o objetivo desse trabalho foi estimar a evapotranspiração real (ETa), biomassa (Bio), produtividade da água (WP) e a produtividade da cultura (P), utilizando imagens do satélite Landsat-8, por meio do algoritmo Priestley-Taylor modificado para satélite (MS-PT). Para isso, estimou-se a ETa para a cultura do milho irrigado por pivôs central, utilizando o MS-PT com seis imagens do Landsat-8, as quais encontravam-se livre de nuvens. A estimativa da ETa foi acurada nos primeiros 60 dias após a emergência (DAE) da cultura. Posteriormente, as variáveis Bio, P, e WP foram estimadas utilizando a ETa e os pressupostos do modelo de Monteith (1972). A ETa apresentou alta correlação com a Bio a partir da segunda imagem (10/06/2015), em função do fechamento dossel da cultura e consequentemente a predominância da transpiração no fenômeno de evapotranspiração. A WP foi constante durante o máximo crescimento vegetativo até a fase reprodutiva da cultura denominada R4, sendo verificado nessa amplitude de tempo a melhor eficiência da conversão de água em biomassa. A partir dos resultados obtidos, verifica-se que esse conjunto de algoritmos utilizados para estimativa dos parâmetros relacionados a produtividade do milho mostraram o potencial de crescimento que se tem para melhorar a capacidade de como lidar com uma agricultura mais eficiente, assertiva e sustentável.(AU)

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.

Two approaches to calculate TVDI in humid subtropical climate of southern Brazil

Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.(AU)

Analysis of the landscape complexity and heterogeneity of the Pantanal wetland / Análise da complexidade e heterogeneidade da paisagem do Pantanal

This is the first report on analysis of habitat complexity and heterogeneity of the Pantanal wetland. The Pantanal encompasses a peculiar mosaic of environments, being important to evaluate and monitor this area concerning conservation of biodiversity. Our objective was to indirectly measure the habitat complexity and heterogeneity of the mosaic forming the sub-regions of the Pantanal, by means of remote sensing. We obtained free images of Normalized Difference Vegetation Index (NDVI) from the sensor MODIS and calculated the mean value (complexity) and standard deviation (heterogeneity) for each sub-region in the years 2000, 2008 and 2015. The sub-regions of Poconé, Canoeira, Paraguai and Aquidauana presented the highest values of complexity (mean NDVI), between 0.69 and 0.64 in the evaluated years. The highest horizontal heterogeneity (NDVI standard deviation) was observed in the sub-region of Tuiuiú, with values of 0.19 in the years 2000 and 2015, and 0.21 in the year 2008. We concluded that the use of NDVI to estimate landscape parameters is an efficient tool for assessment and monitoring of the complexity and heterogeneity of the Pantanal habitats, applicable in other regions.(AU)

Este é o primeiro trabalho sobre análise da complexidade e heterogeneidade de habitats do Pantanal. O Pantanal é constituído por um mosaico de ambientes com características peculiares, sendo importante a avaliação e o monitoramento dessa área voltado para a conservação da biodiversidade. O objetivo do estudo é mensurar de forma indireta a complexidade e a heterogeneidade do mosaico de habitats os quais formam as sub-regiões do Pantanal, por meio do sensoriamento remoto. Foram obtidas, gratuitamente, imagens de índice de vegetação por diferença normalizada (NDVI) do sensor MODIS e calculado o valor de média (complexidade) e desvio padrão (heterogeneidade) para cada sub-regiões do Pantanal, para os anos de 2000, 2008 e 2015. Os pantanais de Poconé, Canoeira, Paraguai e Aquidauana são as regiões que apresentaram os maiores valores de complexidade (NDVI médio), variando entre 0.69 a 0.64 para os anos avaliados. Maior heterogeneidade (NDVI desvio padrão) foi observada na sub-região pantaneira do Tuiuiú, sendo o valor para os anos de 2000 e 2015 igual a 0.19 e para o ano de 2008 o valor de 0.21, o que implica que a região tem a maior heterogeneidade horizontal quando comparada com as demais sub-regiões. Constata-se que o uso de NDVI na estimativa de parâmetros da paisagem é uma ferramenta eficiente para o reconhecimento e monitoramento da complexidade e heterogeneidade de habitats do Pantanal, replicável em outras regiões.(AU)

Analysis of the landscape complexity and heterogeneity of the Pantanal wetland

Abstract This is the first report on analysis of habitat complexity and heterogeneity of the Pantanal wetland. The Pantanal encompasses a peculiar mosaic of environments, being important to evaluate and monitor this area concerning conservation of biodiversity. Our objective was to indirectly measure the habitat complexity and heterogeneity of the mosaic forming the sub-regions of the Pantanal, by means of remote sensing. We obtained free images of Normalized Difference Vegetation Index (NDVI) from the sensor MODIS and calculated the mean value (complexity) and standard deviation (heterogeneity) for each sub-region in the years 2000, 2008 and 2015. The sub-regions of Poconé, Canoeira, Paraguai and Aquidauana presented the highest values of complexity (mean NDVI), between 0.69 and 0.64 in the evaluated years. The highest horizontal heterogeneity (NDVI standard deviation) was observed in the sub-region of Tuiuiú, with values of 0.19 in the years 2000 and 2015, and 0.21 in the year 2008. We concluded that the use of NDVI to estimate landscape parameters is an efficient tool for assessment and monitoring of the complexity and heterogeneity of the Pantanal habitats, applicable in other regions.

Resumo Este é o primeiro trabalho sobre análise da complexidade e heterogeneidade de habitats do Pantanal. O Pantanal é constituído por um mosaico de ambientes com características peculiares, sendo importante a avaliação e o monitoramento dessa área voltado para a conservação da biodiversidade. O objetivo do estudo é mensurar de forma indireta a complexidade e a heterogeneidade do mosaico de habitats os quais formam as sub-regiões do Pantanal, por meio do sensoriamento remoto. Foram obtidas, gratuitamente, imagens de índice de vegetação por diferença normalizada (NDVI) do sensor MODIS e calculado o valor de média (complexidade) e desvio padrão (heterogeneidade) para cada sub-região do Pantanal, para os anos de 2000, 2008 e 2015. Os pantanais de Poconé, Canoeira, Paraguai e Aquidauana são as regiões que apresentaram os maiores valores de complexidade (NDVI médio), variando entre 0.69 a 0.64 para os anos avaliados. Maior heterogeneidade (NDVI desvio padrão) foi observada na sub-região pantaneira do Tuiuiú, sendo o valor para os anos de 2000 e 2015 igual a 0.19 e para o ano de 2008 o valor de 0.21, o que implica que a região tem a maior heterogeneidade horizontal quando comparada com as demais sub-regiões. Constata-se que o uso de NDVI na estimativa de parâmetros da paisagem é uma ferramenta eficiente para o reconhecimento e monitoramento da complexidade e heterogeneidade de habitats do Pantanal, replicável em outras regiões.

Analysis of the landscape complexity and heterogeneity of the Pantanal wetland

Abstract This is the first report on analysis of habitat complexity and heterogeneity of the Pantanal wetland. The Pantanal encompasses a peculiar mosaic of environments, being important to evaluate and monitor this area concerning conservation of biodiversity. Our objective was to indirectly measure the habitat complexity and heterogeneity of the mosaic forming the sub-regions of the Pantanal, by means of remote sensing. We obtained free images of Normalized Difference Vegetation Index (NDVI) from the sensor MODIS and calculated the mean value (complexity) and standard deviation (heterogeneity) for each sub-region in the years 2000, 2008 and 2015. The sub-regions of Poconé, Canoeira, Paraguai and Aquidauana presented the highest values of complexity (mean NDVI), between 0.69 and 0.64 in the evaluated years. The highest horizontal heterogeneity (NDVI standard deviation) was observed in the sub-region of Tuiuiú, with values of 0.19 in the years 2000 and 2015, and 0.21 in the year 2008. We concluded that the use of NDVI to estimate landscape parameters is an efficient tool for assessment and monitoring of the complexity and heterogeneity of the Pantanal habitats, applicable in other regions.

Resumo Este é o primeiro trabalho sobre análise da complexidade e heterogeneidade de habitats do Pantanal. O Pantanal é constituído por um mosaico de ambientes com características peculiares, sendo importante a avaliação e o monitoramento dessa área voltado para a conservação da biodiversidade. O objetivo do estudo é mensurar de forma indireta a complexidade e a heterogeneidade do mosaico de habitats os quais formam as sub-regiões do Pantanal, por meio do sensoriamento remoto. Foram obtidas, gratuitamente, imagens de índice de vegetação por diferença normalizada (NDVI) do sensor MODIS e calculado o valor de média (complexidade) e desvio padrão (heterogeneidade) para cada sub-região do Pantanal, para os anos de 2000, 2008 e 2015. Os pantanais de Poconé, Canoeira, Paraguai e Aquidauana são as regiões que apresentaram os maiores valores de complexidade (NDVI médio), variando entre 0.69 a 0.64 para os anos avaliados. Maior heterogeneidade (NDVI desvio padrão) foi observada na sub-região pantaneira do Tuiuiú, sendo o valor para os anos de 2000 e 2015 igual a 0.19 e para o ano de 2008 o valor de 0.21, o que implica que a região tem a maior heterogeneidade horizontal quando comparada com as demais sub-regiões. Constata-se que o uso de NDVI na estimativa de parâmetros da paisagem é uma ferramenta eficiente para o reconhecimento e monitoramento da complexidade e heterogeneidade de habitats do Pantanal, replicável em outras regiões.

Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers / Análise da expansão do cultivo da soja no sul da Amazônia brasileira utilizando imagens de satélite e sua relação com forçantes econômicas

ABSTRACT 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.(AU)

RESUMO A dinâmica agrícola relacionada à expansão da soja tem sido reconhecida como um dos principais fatores da excessiva conversão da cobertura do solo no Estado de Mato Grosso, no sudoeste da Amazônia brasileira. A hipótese de que o mercado de soja exerce influência no uso do solo, foi investigada pela associação de indicadores econômicos com a dinâmica da cultura de soja no Mato Grosso. Integramos séries históricas de dados de satélite para expansão da área de cultivo de soja e de suas duas principais variáveis econômicas associadas (preço de venda e custo de produção). Enfocamos a relação entre lucro (a diferença entre as médias do preço da soja e do custo de produção) e a conversão do uso do solo para soja de 2001 a 2013. A análise espacial explícita revelou que a precisão global na comparação entre o mapa resultante de first-time-use de cultivo de soja em 2013 foi de 96,75%, com índice Kappa de 0,63. Entretanto, a divergência obtida entre os indicadores de erro por comissão e omissão, sugerem que a maior parte da expansão da soja ocorrida antes de 2013 (5,57 milhões de ha) esteve sob influência de uma intensa dinâmica de uso do solo. Embora seja claro não haver relação direta entre a taxa de desmatamento ou tendências de expansão (taxa de uso pela primeira vez) e o lucro, os resultados sugerem fortemente (R2=0,81) que o lucro exerce influência direta e não-negligenciável na evolução do uso do solo consolidado com soja no estado de Mato Grosso.(AU)

Temporal profiles of vegetation indices for characterizing grazing intensity on natural grasslands in Pampa biome

The Pampa biome is an important ecosystem in Brazil that is highly relevant to livestock production. The objective of this study was to analyze the potential use of vegetation indices to discriminate grazing intensities on natural grasslands in the Pampa biome. Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Jan to Dec, 2000 to 2013 series, were analyzed for natural grassland experimental units managed under high (forage allowance of 5 ± 2 % live weight LW), moderate (13 ± 5 % LW) and low grazing intensity (19 ± 7 % LW). Regardless of intensity, the temporal profiles showed lower NDVI and EVI during winter, increased values in spring because of summer species regrowth, slightly decreased values in summer, especially in years when there is a water deficit, and increased values in the fall associated with the beginning of winter forage development. The average temporal profiles of moderate grazing intensity exhibited greater vegetation index values compared with low and high grazing intensities. The temporal profiles of less vegetation index were associated with lower green biomass accumulation caused by the negative impact of stocking rates on the leaf area index under high grazing intensity and a floristic composition with a predominance of tussocks under low grazing intensity. Vegetation indices can be used for distinguishing moderate grazing intensity from low and high intensities. The average EVI values can discriminate moderate grazing intensity during any season, and the NDVI values can discriminate moderate grazing intensity during spring and winter.

Temporal profiles of vegetation indices for characterizing grazing intensity on natural grasslands in Pampa biome

The Pampa biome is an important ecosystem in Brazil that is highly relevant to livestock production. The objective of this study was to analyze the potential use of vegetation indices to discriminate grazing intensities on natural grasslands in the Pampa biome. Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Jan to Dec, 2000 to 2013 series, were analyzed for natural grassland experimental units managed under high (forage allowance of 5 ± 2 % live weight LW), moderate (13 ± 5 % LW) and low grazing intensity (19 ± 7 % LW). Regardless of intensity, the temporal profiles showed lower NDVI and EVI during winter, increased values in spring because of summer species regrowth, slightly decreased values in summer, especially in years when there is a water deficit, and increased values in the fall associated with the beginning of winter forage development. The average temporal profiles of moderate grazing intensity exhibited greater vegetation index values compared with low and high grazing intensities. The temporal profiles of less vegetation index were associated with lower green biomass accumulation caused by the negative impact of stocking rates on the leaf area index under high grazing intensity and a floristic composition with a predominance of tussocks under low grazing intensity. Vegetation indices can be used for distinguishing moderate grazing intensity from low and high intensities. The average EVI values can discriminate moderate grazing intensity during any season, and the NDVI values can discriminate moderate grazing intensity during spring and winter.(AU)

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha1 to 0.19 t ha1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha1 to 0.35 t ha1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.

Correlation maps to assess soybean yield from EVI data in Paraná State, Brazil

Vegetation indices are widely used to monitor crop development and generally used as input data in models to forecast yield. The first step of this study consisted of using monthly Maximum Value Composites to create correlation maps using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor mounted on Terra satellite and historical yield during the soybean crop cycle in Paraná State, Brazil, from 2000/2001 to 2010/2011. We compared the ability of forecasting crop yield based on correlation maps and crop specific masks. We ran a preliminary regression model to test its ability on yield estimation for four municipalities during the soybean growing season. A regression model was developed for both methodologies to forecast soybean crop yield using leave-one-out cross validation. The Root Mean Squared Error (RMSE) values in the implementation of the model ranged from 0.037 t ha1 to 0.19 t ha1 using correlation maps, while for crop specific masks, it varied from 0.21 t ha1 to 0.35 t ha1. The model was able to explain 96 % to 98 % of the variance in estimated yield from correlation maps, while it was able to explain only 2 % to 67 % for crop specific mask approach. The results showed that the correlation maps could be used to predict crop yield more effectively than crop specific masks. In addition, this method can provide an indication of soybean yield prior to harvesting.(AU)