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Articles containing the keyword 'forest canopy'

Category : Research article

article id 24044, category Research article

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.

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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

article id 275, category Research article

Lauri Korhonen, Kari T. Korhonen, Pauline Stenberg, Matti Maltamo, Miina Rautiainen. (2007). Local models for forest canopy cover with beta regression. Silva Fennica vol. 41 no. 4 article id 275. https://doi.org/10.14214/sf.275

Keywords: beta regression; canopy cover; forest canopy

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Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.

Korhonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi
Korhonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ktk@nn.fi
Stenberg, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: ps@nn.fi
Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
Rautiainen, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: mr@nn.fi

article id 315, category Research article

Lauri Korhonen, Kari T. Korhonen, Miina Rautiainen, Pauline Stenberg. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica vol. 40 no. 4 article id 315. https://doi.org/10.14214/sf.315

Keywords: canopy cover; forest canopy; canopy closure; Cajanus tube; line intersect sampling; spherical densiometer; digital photographs

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Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.

Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi
Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ktk@nn.fi
Rautiainen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: mr@nn.fi
Stenberg, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ps@nn.fi

Category : Research note

article id 1518, category Research note

Francesco Chianucci. (2015). A note on estimating canopy cover from digital cover and hemispherical photography. Silva Fennica vol. 50 no. 1 article id 1518. https://doi.org/10.14214/sf.1518

Keywords: forest canopy; foliage cover; crown cover; fisheye photography; cover photography

Highlights: Comparison of fisheye (DHP) and cover (DCP) photography for estimating canopy cover; Digital photographic estimates validated against artificial images with known cover; Accuracy of cover estimates from DHP is influenced by mean gap size and actual cover; Accuracy of cover estimates from DCP is not influenced by mean gap size and actual cover.

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

Fast and accurate estimates of canopy cover are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used in forestry to estimate canopy cover. In this paper the accuracy of canopy cover estimates from two widely used canopy photographic methods, hemispherical photography (DHP) and cover photography (DCP) was evaluated. Canopy cover was approximated in DHP as the complement of gap fraction data at narrow viewing zenith angle range (0°–15°), which was comparable with that of DCP. The methodology was tested using artificial images with known canopy cover; this allowed exploring the influence of actual canopy cover and mean gap size on canopy cover estimation from photography. DCP provided robust estimates of canopy cover, whose accuracy was not influenced by variation in actual canopy cover and mean gap size, based on comparison with artificial images; by contrast, the accuracy of cover estimates from DHP was influenced by both actual canopy cover and mean gap size, because of the lower ability of DHP to detect small gaps within crown. The results were replicated in both DHP and DCP images collected in real forest canopies. Finally, the influence of canopy cover on foliage clumping index and leaf area index was evaluated using a theoretical gap fraction model. The main findings indicate that DCP can overcome the limits of indirect techniques for obtaining unbiased and precise estimates of canopy cover, which are comparable to those obtainable from direct, more labour-intensive techniques, being therefore highly suitable for routine monitoring and inventory purposes.

Chianucci, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria – Forestry Research Centre, viale Santa Margherita 80, 52100 Arezzo, Italy http://orcid.org/0000-0002-5688-2060 E-mail: fchianucci@gmail.com

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