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Abstract

Precision forestry seeks to optimize forest management by using site-specific information at fine spatial scales, often supported by remote sensing. Implementing this framework requires detailed ground-truth data of forest attributes and conditions, but the collection of such information across large areas is limited by cost and scalability. Complementary data sources can help address this demand by expanding the availability of tree-level information. This thesis explores how different data sources, namely close-range laser scanning, airborne laser scanning, harvester production reports, and synthetic point clouds, can expand data availability for precision forestry. Paper I develops and evaluates a method for deriving stem attributes such as diameter, taper, and volume from car-mounted mobile laser scanning acquired along forest roads, demonstrating its potential as an efficient source of tree-level reference data. Paper II assesses the suitability of mobile laser scanning as an alternative to conventional field plots for training airborne laser scanning-based models, showing that both sources can support tree-level modelling of diameter and volume. Paper III presents a pipeline for automatic tree species annotation by linking airborne laser scanning data with harvester production reports, demonstrating that data derived from forest operations can reduce the need for field surveys or manual labelling in species classification tasks. Finally, Paper IV introduces a semi-empirical simulation framework for generating synthetic stem defects in terrestrial laser scanning point clouds, using them to train a convolutional neural network for crook detection and discussing the implications of simulation. Taken together, the four studies show that complementary data sources have the potential to serve as ground-truth information in forest assessments, providing data of sufficient quality and quantity to support remote sensing-based forest inventories at tree-level. This thesis underlines both opportunities and limitations of these approaches, highlighting their relevance for integration into inventory frameworks.

Keywords

Precision forestry; remote sensing; forest inventory; laser scanning; mobile laser scanning; terrestrial laser scanning; harvester production reports; synthetic point clouds

Published in

Acta Universitatis Agriculturae Sueciae
2025, number: 2025:77
Publisher: Swedish University of Agricultural Sciences

SLU Authors

UKÄ Subject classification

Forest Science
Earth Observation

Publication identifier

  • DOI: https://doi.org/10.54612/a.7hln0kr0ta
  • ISBN: 978-91-8124-061-0
  • eISBN: 978-91-8124-107-5

Permanent link to this page (URI)

https://res.slu.se/id/publ/142478