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Research Article | Open Access

The effects of data aggregation on long-term projections of forest stands development

Kobra Maleki( ), Rasmus Astrup, Nicolas Cattaneo, Wilson Lara Henao, Clara Antón-Fernández
Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, Ås, NO-1431, Norway
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Abstract

Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 ​m ​× ​16 ​m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 ​m3·ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.

Keywords

Scots pineNorway spruceDominant speciesGrowth and yield modelsBroadleavesForest resource mapStand variability

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Forest Ecosystems
Volume 11 Issue 3,
June 2024
Article number: 100199
DOI: 10.1016/j.fecs.2024.100199
Cite this article:
Maleki K, Astrup R, Cattaneo N, et al. The effects of data aggregation on long-term projections of forest stands development. Forest Ecosystems, 2024, 11(3): 100199. https://doi.org/10.1016/j.fecs.2024.100199

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Received: 29 January 2024
Revised: 19 April 2024
Accepted: 24 April 2024
Published: 29 April 2024
© 2024 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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