Annika Kangas, Mari Myllymäki, Lauri Mehtätalo. (2023). Understanding uncertainty in forest resources maps. Silva Fennica vol. 57 no. 2 article id 22026. https://doi.org/10.14214/sf.22026

Keywords: autocorrelation; ensemble modelling; kriging; quantile; random forest; sequential Gaussian simulation

Highlights: Forest resources maps without uncertainty assessment may lead to false impression of precision; Suitable tools for visualization of map products are lacking; Kriging method provided accurate uncertainty assessment for pixel-level predictions; Quantile random forest algorithm slightly underestimated the pixel-level uncertainties; With simulation it is possible to assess the uncertainty also for landscape-level characteristics.

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

Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Myllymäki, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-2713-7088 E-mail: mari.myllymaki@luke.fi
Mehtätalo, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8128-0598 E-mail: lauri.mehtatalo@luke.fi

article id 1218, category Research article

Mikko Niemi, Mikko Vastaranta, Jussi Peuhkurinen, Markus Holopainen. (2015). Forest inventory attribute prediction using airborne laser scanning in low-productive forestry-drained boreal peatlands. Silva Fennica vol. 49 no. 2 article id 1218. https://doi.org/10.14214/sf.1218

Keywords: remote sensing; forest technology; forest management planning; mapping; k-NN estimation; random forests

Highlights: Following current forest inventory practises, stem volume was predicted in low-productive drained peatlands (LPDPs) with a root mean square error (RMSE) of 13.7 m³ ha^–1; When 30 reference plots measured from LPDPs were added to the prediction, RMSE was decreased to 10.0 m³ ha^–1; Additional reference plots from LPDPs did not affect the forest inventory attribute predictions in productive forests.

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Nearly 30% of Finland’s land area is covered by peatlands. In Northern parts of the country there is a significant amount of low-productive drained peatlands (LPDPs) where the average annual stem volume growth is less than 1 m³ ha^–1. The re-use of LPDPs has been considered thoroughly since Finnish forest legislation was updated and the forest regeneration prerequisite was removed from LPDPs in January 2014. Currently, forestry is one of the re-use alternatives, thus detailed forest resource information is required for allocating activities. However, current forest inventory practices have not been evaluated for sparse growing stocks (e.g., LPDPs). The purpose of our study was to evaluate the suitability of airborne laser scanning (ALS) for mapping forest inventory attributes in LPDPs. We used ALS data with a density of 0.8 pulses per m², 558 field-measured reference plots (500 from productive forests and 58 from LPDPs) and k nearest neighbour (k-NN) estimation. Our main aim was to study the sensitivity of predictions to the number of LPDP reference plots used in the k-NN estimation. When the reference data consisted of 500 plots from productive forest stands, the root mean square errors (RMSEs) for the prediction accuracy of Lorey’s height, basal area and stem volume were 1.4 m, 2.7 m² ha^–1 and 13.7 m³ ha^–1 in LPDPs, respectively. When 30 additional reference plots were allocated to LPDPs, the respective RMSEs were 1.1 m, 1.7 m² ha^–1 and 10.0 m³ ha^–1. Additional reference plot allocation did not affect the predictions in productive forest stands.

Niemi, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: mikko.t.niemi@helsinki.fi
Vastaranta, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: mikko.vastaranta@helsinki.fi
Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland E-mail: jussi.peuhkurinen@arbonaut.com
Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland E-mail: markus.holopainen@helsinki.fi

Category : Complex remote sensing-assisted forest surveys – Discussion article

article id 24063, category Complex remote sensing-assisted forest surveys – Discussion article

Sara Franceschi, Caterina Pisani, Lorenzo Fattorini, Piermaria Corona. (2025). Statistical considerations for enhanced forest resource mapping. Silva Fennica vol. 59 no. 2 article id 24063. https://doi.org/10.14214/sf.24063

Keywords: design-based inference; consistency; kNN mapping; pseudo-population bootstrap; Random Forest mapping

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

This paper examines forest resource mapping from a statistical perspective, highlighting the opportunity to use a design-based approach to ensure inferential congruency with the estimation of averages and totals of forest attributes. Traditionally, in forest surveys estimates of averages and totals are obtained using design-unbiased estimators, with known variance expressions that can be easily estimated using standard sampling methodologies. The paper emphasizes the prominent role of kNN and Random Forest techniques in forest mapping while addressing the methodological limitations identified over more than thirty years of forest literature in efforts to estimate map precision. The critical importance of design-based map consistency, often overlooked in forest literature, is discussed and clarified, demonstrating that it allows for the development of design-based estimators of map precision through bootstrap resampling from the estimated maps.

Franceschi, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy https://orcid.org/0000-0001-6675-4540 E-mail: franceschi2@unisi.it
Pisani, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy E-mail: caterin.pisani@unisi.it
Fattorini, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy E-mail: lorenzo.fattorini@unisi.it
Corona, CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, 52100 Arezzo, Italy E-mail: piermaria.corona@crea.gov.it

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