Improving LIDAR-based tree species mapping in Central European mixed forests using multitemporal digital aerial colour-infrared photographs
Abstract: Digital colour-infrared (CIR) aerial photographs, which have been collected routinely in many parts of the world, are an invaluable data source for the monitoring and assessment of forest resources. Yet, the potential of these data for automated individual tree species mapping remains largely unexplored. One way to maximize the usefulness of digital CIR aerial photographs for individual tree species mapping is to integrate them with modern and complementary remote sensing technologies such as the light detection and ranging (LiDAR) system and 3D segmentation algorithms. In this study, we examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping for a temperate mixed forest in eastern Germany. Our results showed that the texture features captured by multi-temporal digital CIR orthophotos under different view-illumination conditions were species-specific. As a consequence, combining these texture features with LiDAR metrics significantly improved tree species mapping accuracy (overall accuracy: 77.4%, kappa: 0.68) compared to using LiDAR data alone (overall accuracy: 69.3%, kappa: 0.58). Among various texture features, the average gray level in the near-infrared band was found to contribute most to the classification. Our results suggest that the synergic use of multi-temporal digital aerial photographs and airborne LiDAR data has the potential to accurately classify individual tree species in Central European mixed forests
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
International journal of applied earth observation and geoinformation. - 84 (2020) , 101970, ISSN: 0303-2434
- Ereignis
-
Veröffentlichung
- (wo)
-
Freiburg
- (wer)
-
Universität
- (wann)
-
2021
- Urheber
- DOI
-
10.1016/j.jag.2019.101970
- URN
-
urn:nbn:de:bsz:25-freidok-1936586
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:52 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Shi, Yifang
- Wang, Tiejun
- Skidmore, Andrew K.
- Heurich, Marco
- Universität
Entstanden
- 2021
Ähnliche Objekte (12)
Waldentwicklung im Bergwald nach Windwurf und Borkenkäferbefall
Evaluierung und Entwicklung von Methoden zur automatisierten Erfassung von Waldstrukturen aus Daten flugzeuggetragener Fernerkundungssensoren
Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
LiDAR reveals a preference for intermediate visibility by a forest‐dwelling ungulate species
Classification of tree species as well as standing dead trees using triple wavelength lidar in a temperate forest
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Identifying standing dead trees in forest areas based on 3D single tree detection from full waveform LiDAR data
Der Luchs : die Rückkehr der Pinselohren ; Nationalpark Bayerischer Wald
Wolf, Luchs und Bär in der Kulturlandschaft : Konflikte, Chancen, Lösungen im Umgang mit großen Beutegreifern
Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects
Waldentwicklung im Bergwald nach Windwurf und Borkenkäferbefall
Evaluierung und Entwicklung von Methoden zur automatisierten Erfassung von Waldstrukturen aus Daten flugzeuggetragener Fernerkundungssensoren
Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
LiDAR reveals a preference for intermediate visibility by a forest‐dwelling ungulate species
Classification of tree species as well as standing dead trees using triple wavelength lidar in a temperate forest
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Identifying standing dead trees in forest areas based on 3D single tree detection from full waveform LiDAR data
Der Luchs : die Rückkehr der Pinselohren ; Nationalpark Bayerischer Wald
Wolf, Luchs und Bär in der Kulturlandschaft : Konflikte, Chancen, Lösungen im Umgang mit großen Beutegreifern
Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects
Waldentwicklung im Bergwald nach Windwurf und Borkenkäferbefall
Evaluierung und Entwicklung von Methoden zur automatisierten Erfassung von Waldstrukturen aus Daten flugzeuggetragener Fernerkundungssensoren
Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
LiDAR reveals a preference for intermediate visibility by a forest‐dwelling ungulate species
Classification of tree species as well as standing dead trees using triple wavelength lidar in a temperate forest
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat
Identifying standing dead trees in forest areas based on 3D single tree detection from full waveform LiDAR data
Der Luchs : die Rückkehr der Pinselohren ; Nationalpark Bayerischer Wald
Wolf, Luchs und Bär in der Kulturlandschaft : Konflikte, Chancen, Lösungen im Umgang mit großen Beutegreifern