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Urban Trees - Detection, Delineation, Quantification, and Characterisation based on VHR Remote Sensing

Leichtle, Tobias und Zehner, Markus und Kühnl, Marlene und Martin, Klaus und Taubenböck, Hannes (2021) Urban Trees - Detection, Delineation, Quantification, and Characterisation based on VHR Remote Sensing. In: REAL CORP 2021, Seiten 1029-1039. REAL CORP 2021, 2021-09-07 - 2021-09-10, Wien, Österreich. ISBN 978-3-9504945-0-1. ISSN 2521-8050.

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Offizielle URL: https://archive.corp.at/cdrom2021/papers2021/CORP2021_143.pdf

Kurzfassung

Trees play a vital role in the urban ecosystem, providing benefits for society, ecology and economy. In particular in urban areas, trees mitigate the urban heat island effect, filter air pollution, regulate microclimate and hydrology, bond carbon dioxide, and provide spaces for recreation and leisure, among others. Despite these diverse positive effects, detailed information on the number, location, dimensions and other characteristics of urban trees remains scarce. For this reason, most cities in Germany currently aim to establish a tree information system for efficient and targeted management of their tree inventory. However, traditional terrestrial surveying is time-consuming and costly and therefore only suitable to a limited extent. In addition, the municipal tree cadastre usually only includes urban trees on public propertyand thus does not cover the complete stock. Against this background, remote sensing acquisitions with very-high spatial resolution (VHR) of less than one meter offer promising capabilities for area-wide detection, delineation, and characterization of urban trees. In this study, we use VHR aerial imagery as well as a derived canopy height model (CHM) for detection and delineation of urban trees. Different methods for individual tree detection using local maximum (LM) filtering andLaplacian of Gaussian (LoG) blob detectionare compared and evaluated. For tree crown delineation, marker-controlled watershed segmentation (MCWS), clustering using Voronoi tessellation, and region growing are implemented as segmentationtechniques. The detection of individual trees and delieation of tree crowns are validated against about1,000 reference trees from visual interpretation via stereophotogrammetry.In addition, we relate our results to street tree location data of Munich, which was derived from mobile terrestrial laser scanning (TLS).The characterization of urban trees is realized based on the 3-dimensional shape of individual tree segments as well as auxillary data sets of land use and building density. According to our analyses, there are 1.54 million trees in Munich.Compared to available reference trees, tree detection was evaluated with highest values of F-score, precision, and recall of 0.95, 0.99, and 0.94,respectively. Results of tree crown segmentation revealed an overall accuracy of 88.1 % compared to crowns of reference trees. Based on auxillary land use information, urban trees were categorized into street trees, (public) park trees, as well as trees in (private) residential gardens.In Munich, 9.1 % are characterized as street trees, 38.4 % are allocated in residential gardens and 33.1 % stand in public parks. The remaining 19.4 % oftree segments were found onother land use such as agricultural areas, parking lots, or along railroad tracks. According to these categories, the height and crown area of urban trees are analyzed and related to the distance to the city center. In a more general manner, this analysis was performed in relation to the building density in Munich. As expected, relatively few trees were found close to the city center and generally on areas with high building density. However, these areas are particularly associated with the greatest challenges in the context of sustainable and climate change-adapted urban development.In this study, we demonstrate that information derived from remote sensing contributes new spatial and quantitative knowledge on urban trees, providing the basis for sustainable management and informed decision-making in cities.

elib-URL des Eintrags:https://elib.dlr.de/143807/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Urban Trees - Detection, Delineation, Quantification, and Characterisation based on VHR Remote Sensing
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Leichtle, Tobiastobias.leichtle (at) dlr.dehttps://orcid.org/0000-0002-0852-4437NICHT SPEZIFIZIERT
Zehner, MarkusMarkus.Zehner (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kühnl, MarleneMarlene.Kuehnl (at) slu-web.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Martin, Klausklaus.martin (at) slu-web.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Taubenböck, HannesHannes.Taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126NICHT SPEZIFIZIERT
Datum:September 2021
Erschienen in:REAL CORP 2021
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Seitenbereich:Seiten 1029-1039
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Schrenk, ManfredNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Popovich, Vasily V.NICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zeile, PeterNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Elisei, PietroNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Beyer, ClemensNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Ryser, JudithNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Stöglehner, GernotNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Name der Reihe:Proceedings of the REAL CORP 2021
ISSN:2521-8050
ISBN:978-3-9504945-0-1
Status:veröffentlicht
Stichwörter:characterization, very-high spatial resolution (VHR), urban trees, remote sensing, urban ecosystem
Veranstaltungstitel:REAL CORP 2021
Veranstaltungsort:Wien, Österreich
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:7 September 2021
Veranstaltungsende:10 September 2021
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Leichtle, Tobias
Hinterlegt am:21 Sep 2021 13:06
Letzte Änderung:24 Apr 2024 20:43

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