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 (R 2 ) 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.

This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.

Sanitary landfill remains the most common methodology for final treatment and disposal of municipal solid waste worldwide, the cost per tonne depends on its scale. The bigger the landfill, the cheaper the cost of treatment, so the consortium of municipalities is the solution to achieve an economic scale. However, the growth of waste production introduces pressure for adequate solutions and therefore has been increasing sanitary landfill site selection studies. This study proposes a methodology for siting sanitary landfills and optimising the transport of municipal solid waste for a locality in the state of São Paulo, Brazil. Environmental, social, and economic criteria were established. Their correlated attributes were categorised into suitability levels and weighted according to multiple decision analysis. The data were organised and mapped within a geographic information system. Considering sites where landfills are prohibited, two scenarios were generated. The Mixed-Integer Quadratic Programming mathematical model is used to minimise the costs of transporting municipal solid waste and operating sanitary landfills. In Scenario 1, the results indicated that 64% of the area was suitable as a potential sanitary landfill site, 9% of the area exhibited medium suitability, and 27% of the area was classified as restricted. In Scenario 2, the results indicated that 25% of the area was suitable as a potential sanitary landfill site, 4% of the area had medium suitability, and 71% of the area was classified as restricted. The optimal solutions for Scenario 1 and Scenario 2 enabled sites to be determined for five landfills and four landfills, respectively.