Topi Tanhuanpää

Assessing above-ground biomass of open-grown urban trees: A comparison between existing models and a volume-based approach

Abstract Assessment of the amount of carbon sequestered and the value of ecosystem services provided by urban trees requires reliable data. Predicting the proportions and allometric relationships of individual urban trees with models developed for trees in rural forests may result in significant errors in biomass calculations. To better understand the differences in biomass accumulation and allocation between urban and rural trees, two existing biomass models for silver birch (Betula pendula Roth) were tested for their performance in assessing the above-ground biomass (AGB) of 12 urban trees. In addition, the performance of a volume-based method utilizing accurate terrestrial laser scanning (TLS) data and stem density was evaluated in assessing urban tree AGB. Both tested models underestimated the total AGB of single trees, which was mainly due to a substantial underestimation of branch biomass. The volume-based method produced the most accurate estimates of stem biomass. The results suggest that biomass models originally based on sample trees from rural forests should not be used for urban, open-grown trees, and that volume-based methods utilizing TLS data are a promising alternative for non-destructive assessment of urban tree AGB.

Allocating tree crown pruning with ALS-data - A case study in the city of Helsinki

Trees are an essential part of urban environments. In urban areas trees are located for example among buildings, on roadsides and in parks. When growing outside of their typical natural forest habitat, the trees need to be actively managed to ensure their well-being and the safety of the citizens. One example of urban tree management is the need to make sure that the street areas are sufficiently lit. It means that trees growing very close to street-lamps need to be pruned occasionally. In this case study the need of pruning the urban trees in the city of Helsinki was studied by means of airborne laser-scanning (ALS) and GIS-analysis. An ALS-based tree register containing the main attributes and the spatial location of the trees was compared in the analysis to a lamppost register. The lamppost register included the types and the xy-locations of the lampposts. The aim of the analysis was to map the trees and more specifically tree crowns that had an effect on the lampposts and thus needed to be pruned. First, tree crowns were delineated from the ALS-data. A GIS-analysis was used to map the trees that had a crown segment influencing to a lamppost and then the tree was classified to be pruned. The classification was then compared to a field data collected from the same areas to validate the results. Based on the results, it seems it is possible to use ALS-data and GIS in planning of the tree management in urban areas.

Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping

During the past decade, airborne laser scanning (ALS) has established its status as the state-of-the-art method for detailed forest mapping and monitoring. Current operational forest inventory widely utilizes ALS-based methods. Recent advances in sensor technology and image processing have enabled the extraction of dense point clouds from digital stereo imagery (DSI). Compared with ALS data, the DSI-based data are cheap and the point cloud densities can easily reach that of ALS. In terms of point density, even the high-altitude DSI-based point clouds can be sufficient for detecting individual tree crowns. However, there are significant differences in the characteristics of ALS and DSI point clouds that likely affect the accuracy of tree detection. In this study, the performance of high-altitude DSI point clouds was compared with low-density ALS in detecting individual trees. The trees were extracted from DSI- and ALS-based canopy height models (CHM) using watershed segmentation. The use of both smoothed and unsmoothed CHMs was tested. The results show that, even though the spatial resolution of the DSI-based CHM was better, in terms of detecting the trees and the accuracy of height estimates, the low-density ALS performed better. However, utilizing DSI with shorter ground sample distance (GSD) and more suitable image matching algorithms would likely enhance the accuracy of DSI-based approach.

Deriving canopy metrics of urban trees from airborne laser scanning data

This study describes an automatic method for assessing various crown metrics for urban trees. We used high resolution (>20 points/m2) airborne laser scanning (ALS) data to derive four key characteristics for roadside trees at individual tree level. The tree level ALS point clouds were filtered with alpha shapes to exclude non-tree objects and measurements were taken directly from the filtered point clouds. The root mean square error (RMSE) of crown length and width, crown base height, and crown volume were 1.04 m, 0.68 m, 0.57 m, and 74.65 m3 respectively. The introduced method may be utilized in urban biomass estimations as well as monitoring the state and wellbeing of individual urban trees.