This work intends to lay the foundations for a theoretical framework of citizen science combining social and organizational implications with the support of information technologies. The proposed theoretical framework moves towards a shared and common research process between experts and citizens to deal with environmental and social challenges. The role and capacity of online communities is explored and their engagement capacity by means of web-based digital platforms supporting crowdsourcing activities. In this contribution, authors highlight the most common practices, methods and issues of citizen science approaches adopted from multidisciplinary application fields to obtain insights for designing a new participative approach for organizational studies. To reach this goal, authors illustrate the results of a systematic meta-review analysis, consisting of an accurate selection and revision of journal review articles in order to highlight concepts, methods, research design approaches and tools adopted in citizen science approaches.
IntroductionMonitoring water levels of ephemeral streams is a difficult yet important task in hydrology, especially when studying minor river flows in remote areas. The installation of flow gauging stations on upstream tributaries is impacted by the lack of economic resources, by accessibility problems and unstable morphological conditions of riverbeds avoiding the implementation of distributed observation networks at large scales. This major challenge in hydrology may be addressed by eventually adopting image-analysis approaches that constitute an effective parsimonious river flow monitoring method, but the demonstration of such techniques is still an open research topic.MethodologyThis study focuses on the testing of a novel technique that employs a white pole “staff gauge” to be photographed using a phototrap (i.e., named stage-cam which is a high-speed camera trigger system). This technology shows to be particularly efficient for observing flood events that represent the most difficult scenario for streamflow monitoring. Furthermore, the testing of this innovative hydrological data-gathering method is performed by adopting citizen science and participatory image analysis to assess the value and effectiveness of non-expert volunteers to operationalize this novel method. Citizen engagement may be essential for supporting distributed flow monitoring supporting large scale image analysis algorithm calibration associated to a continuous series of phototrap images. The Montecalvello watershed, located near Rome, is selected for this pilot case study.ResultsResults of the conducted tests, involving the University of Tuscia student community, are presented toward the demonstration of the effectiveness of citizen science to collect valid quantitative hydrological observations, which may correlate consistently with expert estimates. To better interpret results, the authors consider mean absolute error (MAE) and mean absolute relative error (MARE) as synthetic indices to determine the uncertainties associated to voluntary observations. Low margins of error return positive feedback on the adopted methodology.DiscussionThis research promotes the use of participatory approaches for addressing an actual hydrological monitoring challenge. In addition, it fosters increased citizen knowledge and awareness of the importance and value of hydrological monitoring of small ungauged river basins.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.