Detecting individual dead trees using airborne laser scanning

Author:

Heinaro, Einari [claim]

Description:

The global crises – climate change and biodiversity loss – have created a need for precise and wide-scale information of forests. Airborne laser scanning (ALS) provides a means for collecting such information, as it enables mapping large areas efficiently with a resolution sufficient for object-level information extraction. Deadwood is an important component of the forest environment, as it stores carbon and provides a habitat for a wide variety of species. Mapping deadwood provides information about the valuable areas regarding biodiversity, which can be used in, e.g., conservation and restoration planning. The aim of this thesis was to develop automated methodology for detecting individual fallen and standing dead trees from ALS data. Studies I and II presented a line detection based method for detecting fallen trees and evaluated its performance on a moderate-density ALS dataset (point density approx. 15 points/m2) and a high point density unmanned aerial vehicle borne laser scanning (ULS) dataset (point density approx. 285 points/m2). In addition, the studies inspected the dataset, methodology, and forest structure related factors affecting the performance of the method. The studies found that the length and diameter of fallen trees significantly impact their detection probability, and that the majority of large fallen trees can be identified from ALS data automatically. Furthermore, study I found that the amount and type of undergrowth and ground vegetation, as well as the size of surrounding living trees determine how accurately fallen trees can be mapped from ALS data. Moreover, study II found that increasing the point density of the laser scanning dataset does not automatically improve the performance of fallen tree detection, unless the methodology is adjusted to consider the increase in noise and detail in the point cloud. Study III inspected the feasibility of high-density discrete return ULS data for mapping individual standing dead trees. The individual tree detection method developed in the study ...

Publisher:

Helsingin yliopisto ; Helsingfors universitet ; University of Helsinki

Contributors:

University of Helsinki, Faculty of Agriculture and Forestry ; Doctoral Programme in Sustainable Use of Renewable Natural Resources ; Helsingin yliopisto, maatalous-metsätieteellinen tiedekunta ; Uusiutuvien luonnonvarojen kestävän käytön tohtoriohjelma ; Helsingfors universitet, agrikultur-forstvetenskapliga fakulteten ; Doktorandprogrammet i hållbart utnyttjande av förnybara naturresurser ; Gobakken, Terje ; Tanhuanpää, Topi

Year of Publication:

2023-10-18T09:49:49Z

Document Type:

Doctoral dissertation (article-based) ; Artikkeliväitöskirja ; Artikelavhandling ; Text ; 4112 Metsätiede ; 4112 Skogsvetenskap ; 4112 Forestry ; doctoralThesis ; [Doctoral and postdoctoral thesis]

Language:

eng

Subjects:

forest sciences

Rights:

Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. ; This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. ; Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. ; CC BY-NC-ND 4.0

Terms of Re-use:

CC-BY-NC-ND

Relations:

URN:ISBN:978-951-651-777-6 ; Helsingin yliopisto, 2023, Dissertationes Forestales. 2323-9220 ; Dissertationes Forestales ; URN:ISSN:1795-7389 ; https://doi.org/10.14214/df.343 ; http://hdl.handle.net/10138/566272

URL:

http://hdl.handle.net/10138/566272

Content Provider:

HELDA – Helsingin yliopiston avoin julkaisuarkisto
HELDA – University of Helsinki Open Repository Flag of Finland