Title (eng)
Green chromatic coordinates (GCC) dataset of Phenocam from 2021
Phenology of the reed belt of Lake Neusiedl
Analyst
Pamela Alessandra Baur
Description (eng)
Datasets corresponding to the publication: Buchsteiner, C.; Baur, P.A.; Glatzel, S. (2023): Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning. Remote Sensing, 15, 3961. doi.org/10.3390/rs15163961 Temporal dataset of the Phenocam attached to the Eddy Covariance Tower, facing northwest and located in the eastern reed belt of Lake Neusiedl (near Ilmitz). The Phenocam has been studying the phenological changes of the reed plant (Phragmites australis) in 2021 and is equipped with a normal digital camera that captures RGB images every hour during the day. The Phenocam dataset has a daily time stamp and contains processed and filtered Green Chromatic Coordinates (GCC) values. Format of Phenocam datset: CSV Temporal interval: daily Temporal coverage: 2021 Coordinates of Phenocam (Eddy Covariance Tower): 47.769150°N, 16.758482°E
Keywords (eng)
ReedPhragmites australisLake NeusiedlIllmitzgreen chromatic coordinatesRGBvegetation indexPhenocamphenology
Subject (eng)
ÖFOS 2012 -- 105405 -- Geoecology
Subject (eng)
ÖFOS 2012 -- 106030 -- Plant ecology
Subject (eng)
ÖFOS 2012 -- 106026 -- Ecosystem research
Type (eng)
dataset
Language
[eng]
Persistent identifier
https://phaidra.univie.ac.at/o:1676006
DOI
10.25365/phaidra.397
Version type (eng)
author's original
Date created
2023
Temporal coverage
{ "@language": "eng", "@value": "2021" }
Depicted / Represented place
Reed Belt of Lake Neusiedl Research Site
[Reed Belt of Lake Neusiedl Research Site--Illmitz--Neusiedl am See--Burgenland--Austria]
Association (eng)
Department of Geography and Regional Research
Access rights (eng)
open access
License
CC BY-SA 4.0 International
Bibliography
Cites (eng)
Buchsteiner, C.; Baur, P.A. (2023): Spatial and intra-annual datasets on reed ecosystem dynamics at Lake Neusiedl of 2021 using RGB drone imagery, Phenocam and Deep Learning. V1. PHAIDRA Repositorium (University of Vienna).https://doi.org/10.25365/phaidra.397