Shaohui Zhang, Lauri Korhonen, Timo Nummenmaa, Simone Bianchi, Matti Maltamo. (2024). How to implement the data collection of leaf area index by means of citizen science and forest gamification? Silva Fennica vol. 58 no. 5 article id 24044. https://doi.org/10.14214/sf.24044

Keywords: forest canopy; crowdsourcing; hinge angle; plant area index; smartphones

Highlights: Citizen science and gamification are proposed for collecting in situ forest leaf area index data; LAI can be estimated by taking smartphone images of forest canopies at 57° zenith angle; Twenty smartphone images per plot are enough to obtain accurate LAI measurements; Additional images may be required in forests with dense or uneven canopy structure.

Abstract | Full text in HTML | Full text in PDF | Author Info

Leaf area index (LAI) is a critical parameter that influences many biophysical processes within forest ecosystems. Collecting in situ LAI measurements by forest canopy hemispherical photography is however costly and laborious. As a result, there is a lack of LAI data for calibration of forest ecosystem models. Citizen science has previously been tested as a solution to obtain LAI measurements from large areas, but simply asking citizen scientists to collect forest canopy images does not stimulate enough interest. As a response, this study investigates how gamified citizen science projects could be implemented with a less laborious data collection scheme. Citizen scientists usually have only mobile phones available for LAI image collection instead of cameras suitable for taking hemispherical canopy images. Our simulation results suggest that twenty directional canopy images per plot can provide LAI estimates that have an accuracy comparable to conventional hemispherical photography with twelve images per plot. To achieve this result, the mobile phone images must be taken at the 57° hinge angle, with four images taken at 90° azimuth intervals at five spread-out locations. However, more images may be needed in forests with large LAI or uneven canopy structure to avoid large errors. Based on these findings, we propose a gamified solution that could guide citizen scientists to collect canopy images according to the proposed scheme.

Zhang, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80101 Joensuu, Finland https://orcid.org/0000-0001-7876-9635 E-mail: shaohui.zhang@uef.fi
Korhonen, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi
Nummenmaa, Tampere University, Kalevantie 4, FI-33100 Tampere, Finland https://orcid.org/0000-0002-9896-0338 E-mail: timo.nummenmaa@tuni.fi
Bianchi, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-9544-7400 E-mail: simone.bianchi@luke.fi
Maltamo, School of Forest Sciences, University of Eastern Finland, Yliopistokatu 7, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9904-3371 E-mail: matti.maltamo@uef.fi

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