Predicting tree species based on the geometry and intensity of aerial laser scanning point cloud of treetops

Kranjec, Nina (author), Triglav Čekada, Mihaela (author), Kobal, Milan (author)

Based on the laser point clouds of 240 individual trees that were also identified in the field, we developed decision trees to distinguish deciduous and coniferous trees and individual tree species: Picea abies, Larix decidua, Pinus sylvestris, Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior. The volume of the upper part of the tree crown (height of 3 m) and the average intensity of the laser reflections were used as explanatory variables. There were four aerial laser datasets: May 2012, September 2012, March 2013 and July 2015. We found that the combination of the volume and the average intensity of the first three laser datasets was the most reliable for predicting the selected tree species (60% model performance). A slightly poorer model performance was obtained if only the average intensity of the first three datasets was used (54% model performance). The worst model performance was given by the intensities (31 % model performance) or the volumes (21 % model performance) of dataset 4, which represents the national laser scanning of Slovenia (LSS). The best performing was the deciduous and coniferous separation, which achieved 75% and 95% success based on the test data (combination of volume and average intensity of the first three laser datasets). Using only the LSS intensities, deciduous and coniferous trees could be separated with 81% success.

• Language: English
• Keywords: lidar, intensity, the geometry of tree, tree species, machine learning, Lithuania, machine learning
• Work type: Scientific work
• Typology: 1.01 - Original Scientific Article
• Organization: FGG - Faculty of Civil and Geodetic Engineering
• Publication status: Published
• Publication version: Version of Record
• Article acceptance date: 20.04.2021
• Publication date: 27.05.2021
• Year: 2021
• Number of pages: Str. 234-259
• Numbering: Letn. 65, št. 2
• PID: 20.500.12556/RUL-132733
• UDC: 528.715:633/635.055
• ISSN on article: 0351-0271
• DOI: 10.15292/geodetski-vestnik.2021.02.234-259
• COBISS.SI-ID: 69702403
• Publication date in RUL: 02.11.2021

Record is a part of a journal

• Title: Geodetski vestnik. glasilo Zveze geodetov Slovenije
• Shortened title: Geod. vestn.
• Publisher: Zveza geodetov Slovenije
• ISSN: 0351-0271
• COBISS.SI-ID: 5091842

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 27.05.2021

Secondary language

• Language: Slovenian
• Title: Napovedovanje drevesnih vrst iz geometrije in intenzitete oblaka aerolaserskih točk vrhov drevesnih krošenj

Abstract

Na osnovi laserskih oblakov točk 240 posameznih dreves, ki smo jih identificirali tudi na terenu, smo razvili odločitvena drevesa za ločevanje listavcev in iglavcev ter posameznih drevesnih vrst (rdeči bor, navadna bukev, gorski javor, veliki jesen, evropski macesen, navadna smreka). Kot pojasnjevalne spremenljivke smo uporabili volumen zgornjega dela drevesne krošnje (višine 3 m) in povprečno intenziteto laserskih odbojev. Uporabili smo štiri nize aerolaserskih podatkov: iz maja 2012, septembra 2012, marca 2013 in julija 2015. Ugotovili smo, da najzanesljivejše rezultate za napovedovanje izbranih drevesnih vrst daje kombinacija volumna in povprečne intenzitete prvih treh laserskih nizov (uspešnost modela 60 %). Nekoliko nižjo uspešnost modela dobimo, če uporabimo samo povprečno intenziteto prvih treh nizov (54 %). Najslabšo uspešnost modela daje intenziteta niza 4, ki predstavlja lasersko skeniranje Slovenije (LSS ) (31 %) oziroma volumen (21 %). Uspešnejše je razločevanje listavcev in iglavcev, ki na testnih podatkih dosega uspešnost 75 % oziroma 95 % (kombinacija volumna in povprečne intenzitete združenih prvih treh laserskih nizov). Če uporabimo samo intenzitete LSS, listavce in iglavce lahko ločimo z uspešnostjo 81 %.

• Keywords: lidar, intenziteta, geometrija drevesa, drevesne vrste, strojno učenje, odločitveno drevo

Projects

• Funder: Other - Other funder or multiple funders
• Name: Čezmejni projekt Slovenija - Avstrija 2011-2014 - Naravne nesreče brez meja
• Acronym: NH-WF