%0 Articles %T Developing laser scanning applications for mapping and monitoring single tree characteristics for the needs of urban forestry %A Tanhuanpää, Topi %D 2016 %J Dissertationes Forestales %V 2016 %N 230 %R doi:10.14214/df.230 %U http://dissertationesforestales.fi/article/2013 %X

Urban forests provide various ecosystem services. However, they also require fairly intensive management, which can be supported with up-to-date tree-level data. Until recently, the data have been collected using traditional field measurements. Laser scanning (LS) techniques provide efficient means for acquiring detailed three-dimensional (3D) data from the vegetation. The objective of this dissertation was to develop methods for mapping and monitoring urban forests at tree level.

In substudy I, a method (MS-STI) utilizing multiple data sources was developed for extracting tree-level attributes. The method combined airborne laser scanning (ALS), field measurements, and tree locations. The field sample was generalized using the non-parametric nearest neighbor (NN) approach. The relative root mean square error (RMSE) of diameter at breast height (DBH) varied between 18.8–33.8%.

The performance of MS-STI was assessed in substudy II by applying it to an existing tree register. 88.8% of the trees were successfully detected, and the relative RMSE of DBH for the most common diameter classes varied between 21.7–24.3%.

In substudy III, downed trees were mapped from a recreational forest area by detecting changes in the canopy. 97.7% of the downed trees were detected and the commission error was 10%. Species group, DBH, and volume were estimated for all downed trees using ALS metrics and existing allometric models. For the DBH, the relative RMSE was 20.8% and 34.1% for conifers and deciduous trees respectively.

Finally, in substudy IV, a method utilizing terrestrial laser scanning (TLS) and tree basic density was developed for estimating tree-level stem biomass for urban trees. The relative RMSE of the stem biomass estimates varied between 8.4–10.5%.

The dissertation demonstrates the applicability of LS data in assessing tree-level attributes for urban forests. The methods developed show potential in providing the planning and management of urban forests with cost-efficient and up-to-date tree-level data.