Forest stand age classification using time series of photogrammetrically derived digital surface models

ABSTRACT

In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256 m²) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.

Keywords:

• time series
• image matching
• forestry
• photogrammetry
• lidar
• forest management

Acknowledgements

The authors are grateful to the National Land Survey for scanning the images for this investigation and for the open topographic data sets. The authors also acknowledge the Finnish Defence Intelligence Agency for the film materials and Tornator Oyj for the stand register data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research carried out in this study was financially supported by the Academy of Finland (Project No. 273806 and Centre of Excellence in Laser Scanning Research (CoE-LaSR)) and the Finnish Ministry of Agriculture and Forestry (DNro. 350/311/2012). Elements of this research were also supported by the Canadian Wood Fibre Centre (CWFC) of the Canadian Forest Service, Natural Resources Canada.