Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.
To the best of our knowledge, none of the deployment efforts reviewed here address the same scenario as ours: having (multi-year) long term deployments, based on cooperative efforts of several (heterogeneous) teams, using commercially available equipment from multiple manufacturers, with an integrated effort of data retrieval, quality control and data availability through an easy to use Web-based platform. We believe this is a more realistic scenario for ground-based environmental monitoring efforts. The current efforts within the Life Under Your Feet project ( http: lifeunderyourfeet.org ) @cite_23 are the closest to our own, also having long term, spatially distributed deployments with a Web-based data visualization interface integrated with geolocation information. However, they do not seem to deal with heterogeneous equipment and data formats, nor offer filtering aggregation options, derived datasets or quality information in their data management solution.
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A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sam- pling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of au- tonomous vehicles carrying environmental sensors to efficiently sample the ocean. We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Six of these gliders were coordinated autonomously for 24 days straight using feed- back laws that scale with the number of vehicles. These feedback laws were systematically computed using recently developed methodology to produce desired collective motion patterns, tuned to the spatial and tem- poral scales in the sampled fields for the purpose of reducing statistical uncertainty in field estimates. The implementation was designed to allow for adaptation of coordinated sampling patterns using human-in-the- loop decision making, guided by optimization and prediction tools. The results demonstrate an innovative tool for ocean sampling and provide a proof of concept for an important field robotics endeavor that integrates coordinated motion control with adaptive sampling. C