Browsing by Subject "lentolaserkeilaus"

Sort by: Order: Results:

Now showing items 1-4 of 4
  • Nisula, Kalle (Helsingin yliopisto, 2019)
    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.
  • Rintarunsala, Juhani (Helsingin yliopisto, 2018)
    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.
  • Nummela, Henna (Helsingin yliopisto, 2018)
    Tarkka tieto puutavaralajeista on olennainen puukaupassa, sillä noin 75 % metsänomistajien kantorahatuloista kertyy tukista. Puunostajille on puolestaan tärkeää, että korjattu puutavara vastaa määrältään ja laadultaan toimitustavoitteita, jotta asiakkaalle voidaan toimittaa lopputuotteet ajallaan. Tarpeeksi luotettavaa ja tarkkaa tietoa puutavaralajeista ei kuitenkaan saada nykymuotoisten inventointien tiedoista, joten tarkempien tietojen saamiseksi täytyy nykyään tehdä erillinen leimikon suunnittelu maastossa. Monilähteinen puutason inventointi on leimikon ennakkomittaukseen tarkoitettu menetelmä, jossa yhdistetään lentolaserkeilausaineistoa ja puukartta. Puukartta olisi tarkoitus tuottaa edellisen metsänkäsittelytoimenpiteen yhteydessä esimerkiksi maastolaserkeilauksella. Tämän Pro gradu -työn tarkoituksena oli testata monilähteistä puutason inventoinnilla kolmella päätehakkuukypsällä leimikolla. Jokaiselle leimikolle ennustettiin monilähteisellä puutason inventoinnilla ja yksinpuintulkinnalla koko puuston tukki- ja kuitupuukertymä, keskiläpimitta sekä runkolukusarja. Lisäksi ennustettiin jokaisen leimikon pääpuulajin eli männyn (Pinus sylvestris L.) tukki- ja kuitupuukertymä, keskiläpimitta sekä runkolukusarja monilähteisellä puutason inventoinnilla ja yksinpuintulkinnalla. Runkolukusarjoille laskettiin virheindeksit. Monilähteisen puutason inventoinnin ja yksinpuintulkinnan tuloksia vertailtiin hakkuukoneaineistoon. Lisäksi monilähteisen puutason inventoinnin ja yksinpuintulkinnan tuloksia vertailtiin toisiinsa. Monilähteinen puutason inventointi yliarvioi koko puuston keskiläpimitan keskimäärin 0,5 cm:llä. Mäntyjen keskiläpimitan ennustuksessa ei ollut eroa verrattuna hakkuukoneaineistoon. Kuitupuukertymän ennustaminen monilähteisellä puutason inventoinnilla oli epätarkinta: keskimäärin 29,9 % aliarvio koko puustolla ja 19,5 % yliarvio männyillä. Tukkikertymän ennustaminen oli tarkkaa, koko puustolla monilähteinen puutason inventointi antoi keskimäärin 6,8 % yliarvion ja männyillä vain 2,8 % aliarvion. Runkolukusarjojen virheindeksi oli koko puustolla välillä 0,26 – 0,38 ja männyillä välillä 0,17 – 0,25 monilähteisellä puutason inventoinnilla. Yksinpuintulkinnalla puolestaan koko puuston keskiläpimitta oli 0,2 cm aliarvio ja männyillä vain 0,1 cm aliarvio. Kuitupuukertymä oli yksinpuintulkinnalla 1,3 % aliarvio ja männyillä 10,1 % yliarvio. Tukkikertymä oli 26,4 % yliarvio yksinpuintulkinnalla ja mäntyjen tukkikertymäkin 16,2 % yliarvio. Virheindeksi runkolukusarjoille yksinpuintulkinnalla vaihteli välillä 0,26 – 0,42 ja männyillä välillä 0,14 – 0,28. Monilähteinen puutason inventointi operatiivisessa käytössä vaatisi lisätutkimuksia mm. automaattisesta puiden ja puulajin tunnistamisesta maastolaserkeilausaineistosta sekä automaattisesta lentolaserkeilaus- ja maastolaserkeilausaineiston yhdistämisestä.
  • Mäkinen, Antti (Helsingin yliopisto, 2020)
    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.