Browsing by Subject "urban tree"

In order to achieve carbon neutrality and slow down climate change, it is necessary not only to curb greenhouse gas emissions but also to remove carbon from the atmosphere. In agricultural sector, adding biochars to soils has proven to be one of the most effective methods to sequester carbon. Using biochars in urban planting soils could be simple and effective way to create carbon sinks also in urban environment. Wood based biochars are already available in the market and their viability as soil amendment is supported by an extensive body of research. Despite large evidence from agricultural and greenhouse sectors, research focused on biochars in planting soils for urban trees is scarce. To assess and demonstrate viability of biochars as component of urban planting soils, an experiment was established in 2019-2020 in Hyväntoivonpuisto-park in Helsinki. The experiment consists of four tree species and nine different planting soils, seven of which include biochars. Three of the planting soils were structural soils installed below a sealed surface. Tree growth in planting soils was followed and biomass accumulation was estimated with allometric equations. Planting soil nutrient composition was analysed at the time of soil application and nine months later after first growing season. Pyrogenic carbon fractions were analysed by BPCA analysis. Planting soil physical and hydrological properties were analysed by water retention curves with pF range of 0 – 4.2. After two growing seasons, biochars had increased tree growth in two planting soils compared to the control while in other planting soils with biochar, the growth was similar to control. In structural soils biochars had improved growth, but there were no statistically significant pairwise differences between treatments. Biochars increased the macroporosity of planting soils, indicating they could improve aeration and water conductivity in planting soils. Effects to total porosity and water retention capacity were mixed, but highest total porosity and water retention capacity was observed in planting soil with the highest amount of biochar. Planting soil nutrient composition varied a lot due to different raw materials, limiting the possibilities of making mechanistic analysis of effects of biochars. These results indicate that biochars are viable and safe constituent for planting soils which may increase tree growth by improving soil physical properties and improve carbon sinks in urban infrastructure.

Urban trees and forests are important for human well-being and the diversity of urban nature. Urban forests maintain biodiversity, improve air quality and offer aesthetic and recreational value. The urban trees have also some negative effects. Trees in bad condition can cause harm or danger to humans property. Dense and shady urban forests may cause feelings of insecurity and tree pollen can cause health problems. The urban trees require intensive management and their condition must be constantly monitored. Maximizing the benefits of urban trees and minimizing disadvantages requires detailed data on urban trees. For this reason, many municipalities and cities maintain a tree register with accurate information on individual city trees. Traditionally, data on urban trees have been collected and updated by field surveys, which is laborious and expensive. New laser scanning methods that produce accurate three-dimensional information offer the opportunity to automatically update the tree register. Interest in utilizing them in urban tree mapping and monitoring has been growing rapidly in recent years. This thesis studied ALS-based individual tree detection methods in urban tree mapping. The aim of this study was to determine whether the accuracy of the automatically generated canopy map from ALS-data could be improved by a semi-automatic method. Initially, a detailed canopy map of trees was produced by automated method. Tree candidates were deliniated from the surface model by utilizing watershed segmentation. The canopy segmentation produced by the automated method was visually modified and incorrectly delimited canopy segments were corrected. This resulted in a semi-automatically produced canopy map. The results of the automatic and semi-automatic canopy segmentation method were compared by determining the detection accuracy of the trees and the modeling accuracy of the tree diameter. The results were compared with the number and the diameter of trees measured in the field. Non-parametric random forest method and the nearest neighbor (kNN) method were used in the diameter modeling process. The study area consisted of nine Helsinki hospital areas with a total area of 47,2 ha. There were 4365 trees and 37 different tree species measured in the field. The automatic method produced 6860 trees and the semi-automatic method produced 3500 trees. Thus, the automatic method produced an overestimation of 57.2% and the semi-automatic method produced an underestimation of 19.5 % compared to the reference trees. The largest overestimation by the automatic method was in the Koskela study area (221.6 %) and the smallest underestimation was produced by the semi-automatic method in the Suursuo study area (75.5 %). 63 % of the canopy segments produced by the automatic method were commission errors and 33% of the canopy segments produced by semi-automatic method were commission errors. With the automatic method, the absolute RMSE of the diameter prediction was 12,84 cm and 10,99 cm with semi-automatic method. The diameter predictions of the whole data were 6 % more accurate with the semi-automatic method. The results of the study showed that the accuracy of the automatically generated canopy map from the laser scanning data can be improved by the semi-automatic method. Tree mapping accuracy improved in terms of both tree detection accuracy and diameter modeling accuracy. Based on the results of the study, it can be stated that the semi-automatic method is useful especially in parkland areas, but in densely wooded forest areas there is still issues to solve make this method practical. The benefits of a semi-automated method should be assessed by comparing the workload with the results. Based on this study, the semi-automatic individual tree detection method used in this work could be useful in the operational mapping and monitoring of urban trees.