Additional measures of in situ water quality monitoring in natural environments can be obtained through remote sensing because certain elements in water modify its spectral behavior. One of the indicators of water quality is the presence of algae, and the aim of this study was to propose an alternative method for the quantification of chlorophyll in water by correlating spectral data, infrared images, and limnology data. The object of study was an artificial lake located at Unisinos University, São Leopoldo/RS, Brazil. The area has been mapped with a modified NGB (near infrared (N), green (G) and blue (B)) camera coupled to an unmanned aerial vehicle (UAV). From the orthorectified and georeferenced images, a modified normalized difference vegetation index (NDVImod) image has been generated. Additionally, 20 sampling points have been established on the lake. At these points, in situ spectral analysis with a spectroradiometer has been performed, and water samples have been collected for laboratory determination of chlorophyll concentrations. The correlation resulted in two models. The first model, based on the multivariate analysis of spectral data, and the second model, based on polynomial equations from NDVI, had coefficients of determination (R2) of 0.86 and 0.51, respectively. This study confirmed the applicability of remote sensing for water resource management using UAVs, which can be characterized as a quick and easy methodology.

Over the last two decades, rapid technological evolution has brought several advanced solutions for applications in various knowledge areas. Geotechnologies have allowed geoscientists to acquire vast amounts of spatialized digital data, producing equally large data sets. Light detection and ranging (lidar) technology, especially terrestrial laser scanners (TLSs), and the more recent reconstruction techniques that use multiple digital images have enabled geoscientists to improve the quality of their analyses and interpretations through digital outcrop models (DOMs) [1], [18]-[20]. The DOMs, also called virtual outcrops (VOs) [2], are a digital representation of data collected from surfaces that can be inspected, handled, and interpreted. From a geological point of view, outcrops provide an intermediary approach between the kilometric and millimetric work scales, and field data acquisition is often underexplored. Therefore, DOMs have become an intriguing way of integrating surface and subsurface data. This intermediary work scale allows for the acquisition of considerably more data using remote sensors, such as laser scanners, and photogrammetry algorithms, which produce a quality that is similar if not superior to standard field equipment. The high accuracy of the DOMs facilitates the recognition and interpretation of structures and features within a realistic three-dimensional (3-D) scenario [1], [3], [4].

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.

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.

Laser scanning and photogrammetry techniques have been broadly adopted by Oil&Gas industry for modeling petroleum reservoir analogues. Beyond the benefits of digital data itself, computer systems employed by geoscientists for interpretation and modeling tasks provide high quality rendering, point clouds surface meshes and photo-realistic textured models. But these systems, commonly, have used 2-D display, the 3-D models and information are projected on the screen, providing a limited visualization and restrictive toolset for interpretation. This work proposes to break this paradigm by developing a fully immersive system capable to virtually teleport the geoscientists to the fieldwork and provide a complete toolset for the outcrop's interpretation. Besides, the system has been evaluated and validated by geologists with different skills and it has emerged as an useful and attractive toolset for Oil&Gas industry.