An effective online platform for crowd classification of coastal wetland loss. Issue 1 (7th December 2022)

• Record Type: Journal Article
• Authors: Spatharioti, Sofia Eleni; Boetsch, Eliza; Eustis, Scott; Gandhi, Kutub; Rota, Matt; Apte, Archana; Cooper, Seth; Wylie, Sara
• Is Part Of: Conservation science and practice. Volume 5:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-07
• Subjects: citizen science -- climate change -- crowdsourcing -- infrared aerial imagery -- Louisiana -- oil and gas; Biodiversity conservation -- Periodicals; Biodiversity conservation; Periodicals; 333.951605
• DOI: 10.1111/csp2.12844
• Languages: English
• ISSNs: 2578-4854

Abstract

Abstract: Wetland loss is increasing rapidly, and there are gaps in public awareness of the problem. By crowdsourcing image analysis of wetland morphology, academic and government studies could be supplemented and accelerated while engaging and educating the public. The Land Loss Lookout (LLL) project crowdsourced mapping of wetland morphology associated with wetland loss and restoration. We demonstrate that volunteers can be trained relatively easily online to identify characteristic wetland morphologies, or patterns present on the landscape that suggest a specific geomorphological process. Results from a case study in coastal Louisiana revealed strong agreement among nonexpert and expert assessments who agreed on classifications at least 83% and at most 94% of the time. Participants self‐reported increased knowledge of wetland loss after participating in the project. Crowd‐identified morphologies are consistent with expectations, although more work is needed to directly compare LLL results with previous studies. This work provides a foundation for using crowd‐based wetland loss analysis to increase public awareness of the issue, and to contribute to land surveys or train machine learning algorithms.