Browsing by Subject "lentolaserkeilaus"

In Finland, forest road network has played a significant role in the society throughout history by serving landowners, stakeholders of timber trade, forest management operators, agricultural- and other entrepreneurs. Different forest recreational users such as berry pickers, mushroom pickers and hunters benefit also from good quality forest roads. Wide forest road network help also in preventing forest fires, building fires and it provides help for human and animal rescue missions. In Finland, large number of private forest roads have reached end of their working life and require therefore wide renovations in near future so that the high quality can be maintained. The large-scale determination of forest roads quality is vital so that situation of lower level road network can be followed, and decisions can be maid whether forest roads can be utilized in timber harvesting operations for example. The growing trend in size and weight of timber transport vehicles will cause more careful route planning to the harvest site when forest roads are in bad shape. Good quality forest roads will reduce fuel consumption in timber transport, vehicle damages and road damages. The main objective in this study was to determine the potential of open access geographic information data and especially open access low-density airborne laser scanning data to evaluate the quality of forest roads. Area-based laser scanning inventory method was used with reference data from field plots. Field data was collected from area of research in November 2018 and it consisted from predefined sample plots that were evaluated with the means of traditional forest road quality factors. The aim was to find these quality factors from ALS data and from other open access data and predict forest road quality class using non-parametric k-nearest neighbor method. The results show that metrics calculated from ALS data were quite important in evaluating forest road quality classes. Metrics that illustrate point height distribution, height averages and metrics extracted from digital elevation model which illustrate slope were the most significant in this study. The results show also that the correlation of individual metrics and forest road quality class from reference data was not very high. However, the quality class of forest roads could be predicted correctly at least 69,8 % accuracy when k-nearest neighbor method was used, and all metrics were used. The method used in this study can be utilized to predict forest road quality class relatively accurately, but the accuracy could still be improved. One way to improve this method would be to use high density ALS data and more accurate reference data. It could also be interesting to use this method in another area of research and inspect how the results would differ from this study.

As an internationally important topic for forestry, climate change has long been a topic of concern, as well as the ability of the forests to accumulate carbon. In addition, in Finland, these values have essentially been associated with the economic, cultural and social value of forests. In view of these values, it is important to be able to maintain forest resources at a sustainable level for all the different sectors. As far as sustainability is concerned, knowing the current state of forests is significant. This information is collected through the inventory of forests, and today it is mainly based on different remote sensing methods. In order to support reliable decisionmaking, forest information needs to be up-to-date and accurate. The aim of the thesis was to examine the accuracy of different tree attribute estimates and compare them between themselves and to investigate the accuracy of growth models in producing the estimates. In addition, the aim was to evaluate the effects of the accuracy of the remote sensing estimates on the determination of the timing harvests. The research area was located in boreal coniferous forest zone in Southern Finland, Evo (61.19˚N, 25.11˚E). The area comprised a 5 km x 5 km area, comprising about 2000 hectares of forest treated in different ways. Field measurements, aerial imagery, and airborne laser scanning data were generated using estimates for forest inventory attributes based on three different statistical features derived from the remote sensing data in the preparation of estimates. The forest inventory attributes were volume V, basal area-weighted mean diameter Dg, basal area-weighted mean height, number of the stems per hectare, and basal area G. In the prediction of the forest inventory attributes a non-parametric k-NN method was used, and random forest -algorithm was used in the selection of the nearest neighbors. Growth modeling was carried out using SIMO software. It can be seen from the results that, as a rule, more accurate results are obtained by producing airborne lasers canning estimates than by aerial imagery estimates. In addition, prediction precisions were better in coniferous trees than in deciduous trees. In forest inventory attribute estimates, especially the basal area G and volume V are generally underestimated, which is likely to delay the scheduled timing of harvests. Updating remote sensing estimates with growth models would appear to yield more biased estimates compared to the new remote sensing inventory.

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.

Horizontal visibility determines how far a person can see without any obstacles blocking their line of sight in a horizontal direction. Visibility is in great importance in numerous fields of study and lines of work, such as urban planning, wildlife surveillance and conservation and in military. Manmade obstacles, such as buildings, bridges or other built structures, and environmental elements, like topographical variations or vegetation, can cause noticeable obstruction of visibility in varying magnitude. Airborne laser scanning (ALS) based on light detection and ranging (LiDAR) technology has been used successfully to model built structures and environmental elements, such as forest inventory attributes. This study aims to find out how well horizontal visibility in Finnish boreal forests can be modelled and predicted using a predictive model based on 5 points/m2 ALS point cloud dataset provided by National Land Survey of Finland. The research was done in two separate locations, Karilasanden and Sipoonkorpi National Park, located in Uusimaa region in southern Finland. The ALS point clouds were scanned in summertime 2019 and 2020 for the two research areas. In addition, a total of 60 field measurements of horizontal visibility were collected in summertime 2023. Modelling of horizontal visibility was done with an area-based-approach on the ALS point cloud. The most suitable forest structure variables were selected using a linear regression model based on the measured data at the research areas. The variables were used to predict horizontal visibility with a wall-to-wall method for the whole study sites. Finally, map visualizations were created by generalizing vector polygons of horizontal visibility predictions, and by combining viewshed and horizontal visibility prediction results. The results indicate that number of forest structure metrics based on ALS data can be used for horizontal visibility modelling, including canopy density, median and lower quantile percentages of height distributions and relative density of points from 0.5 to 2.2-meter height. In addition, the 5 points/m2 ALS dataset from National Land Survey of Finland is sufficient dataset for estimation of horizontal visibility in Finnish boreal forests when the accuracy of the output is around tens of meters. Understandable and fit for use map visualization can be produced from the prediction results in various formats. Modelling of Finnish forest structure and using structure metrics as variables for modelling horizontal visibility can be useful for example in city planning for planning the structure of urban greenery or in planning phases of military operations. The available dataset is sufficient for rougher scale modelling, but to achieve finer scale modelling results, increasing the density of the point cloud could be tried or the ALS dataset could be accompanied with other datasets. Also, the availability of the data is somewhat limited since the data is not open. The combining of ALS datasets with TLS datasets for higher point cloud density, usage of TLS dataset for verification purposes of this research's methods or for gathering additional field measurements rose as possible future research topics, as well as the possibility of modifying the point cloud dataset in order to examine the most important forest structure variables more accurately.