View in EDS

Can forest management inventory support national forest inventory to improve the municipal-level estimation of timber volume?

Saved in:
Bibliographic Details
Title: Can forest management inventory support national forest inventory to improve the municipal-level estimation of timber volume?
Authors: Räty, Janne1 (AUTHOR) janne.raty@luke.fi, Kukkonen, Mikko2 (AUTHOR), Kangas, Annika1 (AUTHOR), Strunk, Jacob3 (AUTHOR), Mäkipää, Raisa4 (AUTHOR), Packalen, Petteri5 (AUTHOR)
Source: Canadian Journal of Forest Research. 4/27/2026, Vol. 56, p1-14. 14p.
Subject Terms: *Forest management, *Forest surveys, Forest measurement, Linear statistical models, Remote sensing, Boosting algorithms
Geographic Terms: Finland
Abstract: National forest inventories (NFIs) provide unbiased statistics for forest resource monitoring. Since NFIs primarily target large areas, they are less effective for small areas, such as municipalities, due to low sampling intensity and precision. In Finland, forest management inventories (FMIs) provide stand-level information but are biased for domain-level estimations. We evaluated a model-assisted estimator that used NFI plots integrated with FMI plots for municipal-level estimations. We used linear and tree-boosting models in 619 synthetic municipalities. The assisting models had a fixed set of nine predictor variables extracted from aerial image-based point clouds and Sentinel-2 images. Our findings indicated that the assisting models that used FMI field plots did not show improved efficiency over models fitted with the NFI plots in the 20 km buffer zone that surrounded the municipality of interest. The model-assisted estimators explained 66%–69% of the variation of the NFI field data-based mean estimates. A modest improvement was feasible by fitting a model that used both NFI and FMI plots, although the marginal precision improvements achieved and the additional effort required to harmonize the FMI plots with the NFI plots was not justified. [ABSTRACT FROM AUTHOR]
Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
Description
Abstract:National forest inventories (NFIs) provide unbiased statistics for forest resource monitoring. Since NFIs primarily target large areas, they are less effective for small areas, such as municipalities, due to low sampling intensity and precision. In Finland, forest management inventories (FMIs) provide stand-level information but are biased for domain-level estimations. We evaluated a model-assisted estimator that used NFI plots integrated with FMI plots for municipal-level estimations. We used linear and tree-boosting models in 619 synthetic municipalities. The assisting models had a fixed set of nine predictor variables extracted from aerial image-based point clouds and Sentinel-2 images. Our findings indicated that the assisting models that used FMI field plots did not show improved efficiency over models fitted with the NFI plots in the 20 km buffer zone that surrounded the municipality of interest. The model-assisted estimators explained 66%–69% of the variation of the NFI field data-based mean estimates. A modest improvement was feasible by fitting a model that used both NFI and FMI plots, although the marginal precision improvements achieved and the additional effort required to harmonize the FMI plots with the NFI plots was not justified. [ABSTRACT FROM AUTHOR]
ISSN:00455067
DOI:10.1139/cjfr-2025-0319