Monitoring multiple parameters in complex water scenarios using a low-cost open-source data acquisition platform.

Water monitoring faces challenges that are driven by the infrastructure, protection, financial resources, science and innovation policies, among others. A modular, low-cost, fully open-source and small-sized Unmanned Surface Vessel (USV) called EMAC-USV (EMAC: Estación de Monitoreo Ambiental Costero), is proposed for monitoring bathymetry and water quality parameters (i.e. temperature, suspended solids concentration and hydrocarbon concentration) in complex water scenarios. A detailed description of each part of the platform as well as all electronic connections and functioning is presented.The field works were carried out in two small waste stabilization ponds and in a portion of the main tidal channel of the Bahía Blanca port. The EMAC-USV is the result of a cautious design, regarding the balancing performance, communications, payload capacity, among others.

Structure-from-motion approach for characterization of bioerosion patterns using UAV imagery.

The aim of this work is to evaluate the applicability of the 3D model obtained through Structure-from-Motion (SFM) from unmanned aerial vehicle (UAV) imagery, in order to characterize bioerosion patterns (i.e., cavities for roosting and nesting) caused by burrowing parrots on a cliff in Bahía Blanca, Argentina. The combined use of SFM-UAV technology was successfully applied for the 3D point cloud model reconstruction. The local point density, obtained by means of a sphere of radius equal to 0.5 m, reached a mean value of 9749, allowing to build a high-resolution model (0.013 m) for resolving fine spatial details in topography. To test the model, we compared it with another point cloud dataset which was created using a low cost do-it-yourself terrestrial laser scanner; the results showed that our georeferenced model had a good accuracy. In addition, an innovative method for the detection of the bioerosion features was implemented, through the processing of data provided by SFM like color and spatial coordinates (particularly the y coordinate). From the 3D model, we also derived topographic calculations such as slope angle and surface roughness, to get associations between the surface topography and bioerosion features.