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.
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.
Monitoring the vulnerability of soybean to heat waves and their impacts in Mato Grosso state, BrazilPublished: 01 July 2014 by Institute of Electrical and Electronics Engineers (IEEE) in 2014 IEEE Geoscience and Remote Sensing Symposium
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 R2=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.
Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazi...Published: 01 January 2014 by Hindawi Limited in The Scientific World Journal
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 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.
Integrating Aqua and Terra/MODIS satellite Data for the Evaluation of Heat Stress Impacts on Summer CropsPublished: 01 January 2014 by Instituto Geológico in Revista Brasileira de Geografia Física