Johanna Jääskeläinen, Lauri Korhonen, Mikko Kukkonen, Petteri Packalen, Matti Maltamo. (2024). Individual tree inventory based on uncrewed aerial vehicle data: how to utilise stand-wise field measurements of diameter for calibration? Silva Fennica vol. 58 no. 3 article id 23042. https://doi.org/10.14214/sf.23042

Keywords: laser scanning; calibration; mixed-effects model; single-tree detection

Highlights: A practical scheme to improve the accuracy of predicted tree and stand attributes in an uncrewed aerial vehicle based individual tree inventory; Accuracy was considerably improved with data from 2–4 sample trees from the target stand; Calibrated existing models and the construction of local models performed equally well; The laborious task of constructing a local model can be avoided by using a calibrated transferred model.

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

Uncrewed aerial vehicles (UAV) have great potential for use in forest inventories, but in practice they can be expensive for relatively small inventory areas as a large number of field measurements are needed for model construction. One proposed solution is to transfer previously constructed models to a new inventory area and to calibrate these with a small number of local field measurements. Our objective was to compare calibration of general models and the construction of new models to determine the best approach for UAV-based forest inventories. Our material included field measurements and UAV-based laser scanning data, from which individual trees were automatically identified. A general mixed-effects model for diameter at breast height (DBH) had been formulated earlier based on data from a geographically wider area. It was calibrated to the study area with field measurements from 2–10 randomly selected calibration trees. The calibrated diameters were used to calculate the diameter of a basal area median tree (DGM), tree volumes, and the volume of all trees at plot-level. Next, new DBH-models were formulated based on the 2–10 randomly selected trees and calibrated with plot-level random effects estimated during model construction. Finally, plot-specific height-diameter regression models were formulated by randomly selecting 10 trees from each plot. Calibration reduced the prediction errors of all variables. An increase in the number of calibration trees decreased error rates by 1–6% depending on the variable. Calibrated predictions from the general mixed-effects model were similar to the separately formulated mixed-effects models and plot-specific regression models.

Jääskeläinen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0009-0004-4127-7863 E-mail: johanna.jaaskelainen@uef.fi
Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi
Kukkonen, Natural Resources Institute Finland, Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-4206-1680 E-mail: mikko.kukkonen@luke.fi
Packalen, Natural Resources Institute Finland, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-1804-0011 E-mail: petteri.packalen@luke.fi
Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9904-3371 E-mail: matti.maltamo@uef.fi

Category : Research article

article id 10360, category Research article

Mikko Kukkonen, Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen. (2021). Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements. Silva Fennica vol. 55 no. 1 article id 10360. https://doi.org/10.14214/sf.10360

Keywords: forest inventory; satellite image; open data; drone; stereo matching; unmanned aircraft system

Highlights: A UAS-based species-specific forest inventory approach that avoids new field measurements is presented; Models were constructed using previously measured training plots and remotely sensed data; Bi-seasonal Sentinel-2 data were beneficial in the prediction of species-specific volumes; RMSE values associated with the prediction of volumes by tree species and total volume at the validation plot level were 33.4–62.6% and 9.0%, respectively.

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

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.

Kukkonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kukkonen@uef.fi
Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi

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