Canadian Forest Service Publications
Forest stand age classification using time series of photogrammetrically derived digital surface models. Vastaranta, M., Niemi, M., Wulder, M.A., White, J.C., Nurminen, K., Litkey, P., Honkavaara, E., Holopainen, M., & Hyyppä, J. 2015. Scandinavian Journal of Forest Research
Available from: Pacific Forestry Centre
Catalog ID: 36124
CFS Availability: PDF (download)
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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 (DSMs) that were normalized to canopy height models (CHMs) using an airborne laser scanning (ALS) 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 intra-stand 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 data bases.
Plain Language Summary
Study area: Finland International research team, led by Centre of Excellence in Laser Scanning Research, University of Helsinki. Digital photogrammetry is advertised as an alternate to lidar for vertical structure. Testing of the new and alternate technology under range of conditions required. Focus of this study is on stand age using historic photography, similar to those available in photo archives in Canada. Age characterization is possible for a 68-year period. Accuracy of age estimation is about 90%, with limitations related to scale and quality of older photos, as well as the time gaps between photos.
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