This work proposes a workflow methodology that acquires and processes Google Earth imagery to generate 3D Virtual Field Environments (VFE) using SfM algorithm processing software. These models can be visualized in MOS...
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This work proposes a workflow methodology that acquires and processes Google Earth imagery to generate 3D Virtual Field Environments (VFE) using SfM algorithm processing software. These models can be visualized in MOSIS V2 (Multi Outcrop Sharing and interpretation System - vizlab) software and can transport earth science academics to any high-resolution environment from Google Earth in immersive virtual reality (iVR) with a set of geological tools for interpretation and teaching.
This work evaluates robust statistical assessments for 3D models generated by the process of photogrammetry using images captured from Google Earth screenshots in a method named Printgrammetry. This method can make 3D...
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This work evaluates robust statistical assessments for 3D models generated by the process of photogrammetry using images captured from Google Earth screenshots in a method named Printgrammetry. This method can make 3D models more accessible to be explored in other ways, including virtual reality and use in other programs with incorporated tools that are not available in the Google Earth. To verify the quality of the Printgrammetry model we proposed the use of skewness-adjusted robust statistics, an approach that avoids the exclusion of relevant data. The results confirmed that the proposed outlier detection method is more realistic to the skewness of the data than the usual outlier detection methods. The robust statistical methods adequate to our data assessed the quality of the Printgrammetry model.
The virtual reality is becoming an important asset in the recent years bringing immersion to simple tasks as gaming, modeling, and learning. Following this trend, the MOSIS (Multi Outcrop Sharing and Interpretation Sy...
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The virtual reality is becoming an important asset in the recent years bringing immersion to simple tasks as gaming, modeling, and learning. Following this trend, the MOSIS (Multi Outcrop Sharing and Interpretation System) was created to help earth scientists and other users to visualize and study outcrops, important sources of information to the Oil & Gas industry. Basing on the user's feedback some updates were identified to improve the user experience. Thus, a new version of the software (MOSIS V2) was developed, with updates in both, interface and toolset. New possibilities and the model scalability were introduced in the toolset. MOSIS V2 was well graded in SUS (System Usability Scale) which has validated the changes that were made.
Concrete is the building material most widely used in the world due to its characteristics, which enable the constrution of large buildings, long span bridges or even special artworks. However, many of these structure...
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This paper presents a comparative study about 3D reconstruction based on active and passive sensors, mainly LiDAR - Terrestrial Laser Scanner (TLS) and raster images (photography), respectively. An accuracy analysis h...
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Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures durin...
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Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures during key development periods. A land surface temperature (LST) data series from MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua satellite was processed between 2003 and 2012 that covered the entire state of Rio Grande do Sul, in Brazil. Enhanced vegetation index (EVI) data from MODIS on the Terra satellite were used to monitor the LST during different phenological stages. Spatially interpolated maps of soybean yield distributions were generated using data obtained from Instituto Brasileiro de Geografia e Estatística (IBGE) at state and municipality levels. The results indicate that canopy-LST occurrence in mid-February, during the grain filling, is most correlated to yield reduction (R2 = 0.82 and RMSD = 14.4%). At the state level, the average yield is 2003 kg·ha-1 with a standard deviation of 308 kg·ha-1. The overall average of the canopy-LST is 305.0 K (31.8°C) with a standard deviation of 1.9 K. The slope of the downward linear relationship between canopy-LST and yield was -28.7%. These results indicate that monitoring heat wave events can provide important information for characterising agriculture vulnerability.
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 ...
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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.
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