You are viewing the Initial Version, the most recent version of this dataset.
1 version(s) available
Date of publication: February 25, 2022

Version 1

Date of publication: February 25, 2022

Type of change:

Description:

preview image

Vegetation Phenology in Finland

by Kristin Böttcher

Datasets present the yearly maps of the start of vegetation active period (VAP) in coniferous forests and deciduous vegetation during 2001-2019 in Finland. The start of the vegetation active period is defined as the day when coniferous trees start to photosynthesize and for deciduous vegetation as the day when trees unfold new leaves in spring. The datasets were derived from satellite observations of the Moderate Resolution Imaging Spectroradiome ...(continue reading)

Data: netCDF (310.71kB)
Metadata: ACDD (JSON) | EML (XML)

BorealGreen-upPhenologyVegetationStart of season

The title of the dataset.
The date on which this version of the data was created in YYYY-MM-DD format.
A paragraph describing the dataset.
Hover to see a suggestion for a good description. Allowed:
Datasets present the yearly maps of the start of vegetation active period (VAP) in coniferous forests and deciduous vegetation during 2001-2019 in Finland. The start of the vegetation active period is defined as the day when coniferous trees start to photosynthesize and for deciduous vegetation as the day when trees unfold new leaves in spring. The datasets were derived from satellite observations of the Moderate Resolution Imaging Spectroradiometer (MODIS).
Provide the DOI number of associated publications. Click Plus to add DOIs.
https://doi.org/
10.1016/j.rse.2013.09.022
https://doi.org/
10.3390/rs8070580
The method of production of the original data.
Hover to see a suggestion for a minimum description. Moderate Resolution Imaging Spectrometer (MODIS) Terra Level 1B (1 km, 500 m) were manually selected from the Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC). From 2009 onwards data were obtained from the satellite receiving station of the Finnish Meteorological Institute (FMI) in Sodankylä, Finland and gap-filled with data from LAADS DAAC. MODIS Level 1B data were calibrated to top-of-atmosphere reflectances and projected to a geographic latitude/longitude grid (datum WGS-84) using the software envimon by Technical Research Centre of Finland (VTT). Fractional Snow Cover (FSC) and the Normalized Difference Water Index (NDWI) were calculated from MODIS top-of-atmosphere reflectances. Cloud covered observations were removed using an automatic cloud masking algorithm by the Finnish Environment Institute. For the extraction of the start of the VAP in coniferous forest, FSC was averaged at a spatial resolution of 0.05 x 0.05 degrees for the MODIS pixels with dominant coverage of coniferous forest. A sigmoid function was fitted to the averaged FSC-time series and the start of the VAP was determined based on a threshold value. For the extraction of the VAP in deciduous vegetation, daily NDWI time series were averaged for MODIS pixels with vegetation cover into the same spatial grid (0.05 x 0.05 degrees). The day of the VAP was determined from NDWI time series based on a threshold value. The yearly maps of the VAP were smoothed with a median filter to remove spurious outliers and fill spatial gaps. Open water areas were masked.
The coverage content type describes the general content type of the resource (multiple selection possible).
The name of the Project.
The URL from the project website. https://monimet.fmi.fi/index.php?style=warm
The name of the person or other creator type principally responsible for creating this data.
The email of the person or other creator type principally responsible for creating this data. Kristin.Bottcher@ymparisto.fi
Kristin.Bottcher@ymparisto.fi
The names of the co-creators responsible for creating this data. Click Plus to add co-creators.
Select between Creative Commons (CC) or Non-CC license.
Please select the CC license from the list. We recommend the use of CC BY 4.0

Essential Biodiversity Variables

Select the EBV class and the EBV name for the dataset. For cross-cutting use the comment at the bottom of the page for further information.
Genetic composition
Intraspecific genetic diversity
Genetic differentiation
Effective population size
Inbreeding
Other
Species populations
Species distributions
Species abundances
Other
Species traits
Morphology
Physiology
Phenology
Movement
Other
Community composition
Community abundance
Taxonomic and phylogenetic diversity
Trait diversity
Interaction diversity
Other
Ecosystem functioning
Primary productivity
Ecosystem phenology
Ecosystem disturbances
Other
Ecosystem structure
Live cover fraction
Ecosystem distribution
Ecosystem Vertical Profile
Other
Ecosystem services
Pollination
Other
Cross-cutting

Biological entity

Select the entity type of the dataset.
Species
Communities
Ecosystems
Other
None
A description of the range of taxa or ecosystem types addressed in the dataset. E.g. "300 species of mammals”, “Forests”, etc.
The reference as a URL. N/A

Metric

Provide the name, description, units of minimum 1 metric. Click Plus to add metrics.

Scenario

Spatial domain

Global
Continental/Regional
National
Sub-national/Local
Name of the continent/region/country/area, separated by comma
southWest lat: 57.8, lon: 14.1
northEast lat: 71.2, lon: 35.2

Temporal domain

The targeted time period between each value in the dataset.
decadal
annually
monthly
weekly
daily
Other
Irregular
Single time
Select the temporal extent of the dataset.
When the dataset represents a Single time, then use the same start and end date.
-

Environmental domain *

Terrestrial
Marine
Freshwater
Miscellaneous information about the data, not captured elsewhere. The products were compared with ground observations. The start of the VAP in coniferous forest was well correlated with the day when the Gross Primary Production (GPP) exceeded 15% of its summer maximum at 3 eddy covariance measurement sites in Finland (R2=0.7). The accuracy was 9 days for the period 2001-2016. The satellite product was in average 3 days late compared to the ground observations. The accuracy was higher (6 days, R2=0.84) and no bias was observed in pine forest compared to spruce forest that showed larger deviations to ground observations. The start of the VAP in deciduous vegetation corresponded well with visual observations of the bud break of birch from the phenological network of the Natural Resource Institute of Finland (Luke). The accuracy was 7 days for the period 2001-2015 based on 84 site-years. The bias was negligible (0.4 days).