Browsing by Subject "kNN"

In Finland collation of data needed in forest planning is changing from traditional standwise field inventory to area-based airborne laser scanning and aerial photography inventorying. The aim of this study was to compare prediction accuracy of stand total volume and diameter distribution using following methods: MSN, PRM, ML, FMM and Weibull-distribution. Results were calculated separately for pine, spruce, birch and other tree species. In addition need of calculation time and storage space was established. Sampling plot and stand information used in this study were collected in the vicinity of Evo, Finland. Total of 249 sampling plots were measured. 12 clear-cutting areas measured by logging machine were used as reference data. In addition area-based laser scanning and aerial photographbased features were used in estimation of variables of interest. Results were calculated in all 12 stands and stands which area was over 0,5 hectares (8 pcs). Number of grid neighbours differed between 1 and 10. Depending on method and number of neighbours the relative RMSE and bias of stand total volume varied between 20,76 – 52,86 % and -12,04 – 46,54 % respectively in all stands and between 6,74 – 59,41 % and -8,04 – 49,59 % respectively in stands which area was over 0,5 hectares. Calculation time varied strongly depending on method and number of neighbours. With more developed programming and programs calculation times could decrease substantially. Storage space needed in saving information is not an issue in tested methods even in large-scale applications. According to diameter distribution PRM-method predicts narrow distribution if sampling plot consists of only few trees nearly same size. This affected results especially in PRM2.

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.