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Browsing by Subject "yksinpuintulkinta"

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  • Monilähteinen puutason inventointi leimikon ennakkomittauksessa 
    Nummela, Henna (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.
  • Puistopuiden kartoitustarkkuuden parantaminen puoliautomaattisesti tuotetulla latvuskartalla 
    Mäkinen, Antti (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.
  • Puistopuiden kartoitustarkkuuden parantaminen puoliautomaattisesti tuotetulla latvuskartalla 
    Mäkinen, Antti (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.

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