New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-0956
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone Lidar ; day:4 ; month:5 ; year:2019 ; pages:1-19
Surveys in geophysics ; (4.5.2019), 1-19
- Klassifikation
-
Geowissenschaften
- Urheber
-
Kellner, James R.
Armston, John
Birrer, Markus
Cushman, K. C.
Duncanson, Laura
Eck, Christoph
Falleger, Christoph
Imbach, Benedikt
Král, Kamil
Krůček, Martin
Trochta, Jan
Vrška, Tomáš
Zgraggen, Carlo
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s10712-019-09529-9
- URN
-
urn:nbn:de:101:1-2019062023104076247779
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:54 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Kellner, James R.
- Armston, John
- Birrer, Markus
- Cushman, K. C.
- Duncanson, Laura
- Eck, Christoph
- Falleger, Christoph
- Imbach, Benedikt
- Král, Kamil
- Krůček, Martin
- Trochta, Jan
- Vrška, Tomáš
- Zgraggen, Carlo
- SpringerLink (Online service)
Ähnliche Objekte (12)
Drone remote sensing for forest health monitoring
THE MEASUREMENT ACCURACY AND MEASUREMENT CHARACTERISTICS OF GREEN LiDAR DRONE
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
DroneSilient (drone + resilient): an anti-drone system
Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar
Review of airborne oceanic lidar remote sensing
REAL-TIME AND POST-PROCESSED GEOREFERENCING FOR HYPERPSPECTRAL DRONE REMOTE SENSING
Drone Decarbonisation
An ultra lightweight neural network for automatic modulation classification in drone communications
Use of LiDAR drone data for O-mapping and change detection in mountainous areas
Dynamic cloud service placement for live video streaming with a remote-controlled drone
IMPROVEMENT ON TIMING ACCURACY OF LIDAR FOR REMOTE SENSING
Drone remote sensing for forest health monitoring
THE MEASUREMENT ACCURACY AND MEASUREMENT CHARACTERISTICS OF GREEN LiDAR DRONE
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
DroneSilient (drone + resilient): an anti-drone system
Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar
Review of airborne oceanic lidar remote sensing
REAL-TIME AND POST-PROCESSED GEOREFERENCING FOR HYPERPSPECTRAL DRONE REMOTE SENSING
Drone Decarbonisation
An ultra lightweight neural network for automatic modulation classification in drone communications
Use of LiDAR drone data for O-mapping and change detection in mountainous areas
Dynamic cloud service placement for live video streaming with a remote-controlled drone
IMPROVEMENT ON TIMING ACCURACY OF LIDAR FOR REMOTE SENSING
Drone remote sensing for forest health monitoring
THE MEASUREMENT ACCURACY AND MEASUREMENT CHARACTERISTICS OF GREEN LiDAR DRONE
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
DroneSilient (drone + resilient): an anti-drone system
Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar
Review of airborne oceanic lidar remote sensing
REAL-TIME AND POST-PROCESSED GEOREFERENCING FOR HYPERPSPECTRAL DRONE REMOTE SENSING
Drone Decarbonisation
An ultra lightweight neural network for automatic modulation classification in drone communications
Use of LiDAR drone data for O-mapping and change detection in mountainous areas
Dynamic cloud service placement for live video streaming with a remote-controlled drone