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Research article - Peer-reviewed, 2021

Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data

Lindgren, Nils; Wastlund, Andre; Bohlin, Inka; Nystrom, Kenneth; Nilsson, Mats; Olsson, Hakan;

Abstract

Accurate and up-to-date data about growing stock volume are essential for forest management planning. Airborne Laser Scanning (ALS) is known for producing accurate wall-to-wall predictions but the data are at present collected at long time intervals. Digital Photogrammetry (DP) is cheaper and often more frequently available but known to be less accurate. This study investigates the potential of using contemporary DP data together with older ALS data and compares this with the case when only old ALS data are trained with recent field data. Combining ALS data from 2010 to 2011 with DP data from 2015, both trained with National Forest Inventory (NFI) field plot data from 2015, improved predictions of growing stock volume. Validation using data from 100 stands inventoried in 2015 gave an RMSE of 24.3% utilizing both old ALS data and recent DP data, 26.0% for old ALS only and 24.9% for recent DP only. If information about management actions were assumed available, combining old ALS and recent DP gave RMSE of 23.0%, only ALS 23.3% and only DP 23.8%.

Keywords

Forest inventory; forest growing stock volume; airborne laser scanning; digital photogrammetry; thinning; updating forest data

Published in

Scandinavian Journal of Forest Research

2021, volume: 36, number: 5, pages: 401-407
Publisher: TAYLOR AND FRANCIS AS

Authors' information

Lindgren, Nils
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Forest Science

Publication Identifiers

DOI: https://doi.org/10.1080/02827581.2021.1936153

URI (permanent link to this page)

https://res.slu.se/id/publ/112693