Abstract
The monitoring of water ecosystems requires consistent and accurate sensor measurements, usually provided from traditional in-situ environmental monitoring systems. Such infrastructure, however, is expensive, hard to maintain and available only in limited areas that had been affected by extreme phenomena and require continuous monitoring. Due to climate change, the monitoring of larger areas and extended water ecosystems is imperative, raising the question of whether this monitoring can be disengaged from the in-situ monitoring systems. Due to climate change and extreme weather phenomena, more citizens are affected by environmental issues and become aware of the need to contribute to their monitoring. As a result, they are willing to offer their time to support the collection of scientific data. Collecting such data from volunteers, with no technical knowledge and while using low-cost equipment such as smart phones and portable sensors, raises the question of data quality and consistency. We present here a novel integrated toolbox that can support the organization of crowd-sourcing activities, ensure the engagement of the participants, the data collection in a consistent way, enforce extensive data quality controls and provide to local authorities and scientists access to the collected information in a uniform way, through widely accepted standards.
Highlights
Environmental monitoring is based on time-series of data collected over long periods of time from expensive and hard to maintain in-situ sensors available only in specific areas
The information, in the context of monitoring water ecosystems that has been defined as very important is the monitoring of land cover and land use (LC/LU) changes, the water level and the water velocity as well as the recording of environmental parameters such as air temperature and soil moisture
In case that two points of interest (PoIs), as defined in the Campaign Manager, have a significant distance or the user is delayed in any other way, e.g., people in the team taking longer to complete the PoI resulting in the user being inactive for more than three minutes, the application creates a intermediate PoI requesting LC/LU information
Summary
Environmental monitoring is based on time-series of data collected over long periods of time from expensive and hard to maintain in-situ sensors available only in specific areas. Data collected in the context of citizen science projects are very specific, measuring data in customized ways that facilitate the collection from people without scientific knowledge and are rarely mapped to generic formats or provided using widely accepted standards. The answer to this problem, comes through the utilization of the appropriate standards [18], that allow the uniform provision of data between different citizen science projects [19,20]. The filtering functionalities of the SensorThings API allow the spatiotemporal discovery of information among the collected measurements and encourage their re-usability
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