You are viewing Version 2, the most recent version of this dataset.
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Date of publication: August 1, 2023

Version 2

Date of publication: August 1, 2023

Type of change: Metadata

Description: changed: title, entity-value, moved some of the summary into the comment

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Canopy Chlorophyll Content for the Bavarian Forest National Park

by Elnaz Neinavaz

The canopy chlorophyll content for the Bavarian Forest National Park (BFNP) in Germany at 10 meters resolution on monthly basis for the year 2020. Dataset generated under Showcase. 4, pilot 4.3 myVARIABLE of the e-shape project. Canopy Chlorophyll Content (CCC) is the total amount of chlorophyll a and b pigments in a contiguous group of plants per unit ground area often expressed in mg/m2 (Gitelson et al., 2005). It is a product of leaf chlorophy ...(continue reading)

Data: netCDF (137.96MB)
Metadata: ACDD (JSON) | EML (XML)

Canopy Chlorophyll Contente-shapeRemote Sensing-EBVsBavarian Forest National Park

The title of the dataset.
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The canopy chlorophyll content for the Bavarian Forest National Park (BFNP) in Germany at 10 meters resolution on monthly basis for the year 2020. Dataset generated under Showcase. 4, pilot 4.3 myVARIABLE of the e-shape project. Canopy Chlorophyll Content (CCC) is the total amount of chlorophyll a and b pigments in a contiguous group of plants per unit ground area often expressed in mg/m2 (Gitelson et al., 2005). It is a product of leaf chlorophyll content (i.e., the chlorophyll content of a fresh green leaf divided by its one-side area (µmg/cm2)) and the leaf area index. CCC is a terrestrial ecosystem function EBV that describes the distribution of chlorophyll pigments within the 3D canopy surface. This defines the total photosynthetically active radiation absorbed by the canopy (Gitelson et al., 2003; Ustin et al., 2009). The chlorophyll content is one of a constellation of co-evolved traits that vary together across species in relation to contrasting environmental conditions (Reich et al., 2003). Monitoring the dynamics of the CCC helps to understand the adaptation of forest, crop, and other plant canopies to such factors (Feret et al. 2017). As a result, the amount and spatial distribution of chlorophyll content is vital for measuring and understanding plant growth, ecosystem primary productivity and ecosystem dynamics. References can be found in the comment field.
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The method of production of the original data.
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Sentinel-2A images with 10-meter resolution were acquired in 2018 (from April to October), 2019 (from March to October) and 2020 (from March to September) on a monthly basis, on a relatively cloud-free date(s) (< 10% cloud cover), and were used to predict the Canopy Chlorophyll Content (CCC) for the Bavarian Forest National Park. For retrieval of the CCC, two simple ratio vegetation indices optimized for forests and non-forest vegetation were applied (Ali et al., 2020a; Ali et al., 2020b). Reference: Ali, A. M., Darvishzadeh, R., Skidmore, A., Heurich, M., Paganini, M., Heiden, U., & M\u00fccher, S. (2020a). Evaluating prediction models for mapping canopy chlorophyll content across biomes. Remote Sensing, 12(11), 1788. A. M., Darvishzadeh, R., Skidmore, A., Gara, T. W., O\u2019Connor, B., Roeoesli, C., ... & Paganini, M. (2020b). Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation, 87, 102037.
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://e-shape.eu/
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. e.neinavaz@utwente.nl
e.neinavaz@utwente.nl
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Essential Biodiversity Variables

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Genetic composition
Intraspecific genetic diversity
Genetic differentiation
Effective population size
Inbreeding
Other
Species populations
Species distributions
Species abundances
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Species traits
Morphology
Physiology
Phenology
Movement
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Community composition
Community abundance
Taxonomic and phylogenetic diversity
Trait diversity
Interaction diversity
Other
Ecosystem functioning
Primary productivity
Ecosystem phenology
Ecosystem disturbances
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Ecosystem structure
Live cover fraction
Ecosystem distribution
Ecosystem Vertical Profile
Other
Ecosystem services
Pollination
Other
Cross-cutting

Biological entity

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Species
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Ecosystems
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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. http://rsebv.itc.utwente.nl/

Metric

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Scenario

Spatial domain

Global
Continental/Regional
National
Sub-national/Local
Name of the continent/region/country/area, separated by comma
southWest lat: 5413740, lon: 366150
northEast lat: 5442900, lon: 398040

Temporal domain

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decadal
annually
monthly
weekly
daily
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Environmental domain *

Terrestrial
Marine
Freshwater
Miscellaneous information about the data, not captured elsewhere. E-shape is a unique initiative that combines decades of public investment in Earth Observation and cloud capabilities into services for the decision-makers, the citizens, the industry and the researchers. It allows Europe to position itself as a global force in Earth observation by leveraging Copernicus, using existing European capacities and improving user uptake of the data from GEO assets. In the e-shape initiative, 27 cloud-based pilot applications under the seven thematic areas address societal challenges, foster entrepreneurship and support sustainable development in alignment with the three main GEO priorities (e.g., SDGs, Paris Agreement and Sendaï Framework). References: Féret, J, B., A.A. Gitelson, S.D. Noble, S. Jacquemoud PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle Remote Sens. Environ., 193 (2017), pp. 204-215 Gitelson, A.A, Andrés Viña Verónica Ciganda Donald C. Rundquist Timothy J. Arkebauer (2005), Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32(8). https://doi.org/10.1029/2005GL022688 Gitelson, A. A., Vina, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5), 52–54. Retrieved from http://www.sciencedirect.com/science/article/B6WPY-49H70PP-497/2/4cd9159e3f39a07a15c0f430abd8bbd4 Reich, P. B., Buschena, C., Tjoelker, M. G., Wrage, K., Knops, J., Tilman, D. & Machado, J. L. 2003. Variation in growth rate and ecophysiology among 34 grassland and savanna species under contrasting N supply: a test of functional group differences. New Phytologist, 157, 617-631. Ustin, S. L., Gitelson, A. A., Jacquemoud, S., Schaepman, M., Asner, G. P., Gamon, J. A., & Zarco-Tejada, P. (2009). Retrieval of foliar information about plant pigment systems from high-resolution spectroscopy. Remote Sensing of Environment, 113(Supplement 1), S67–S77. Retrieved from http://www.sciencedirect.com/science/article/B6V6V-4W7RJYR-1/2/58a4cb652674c7aea4caed7df189aacc. Gitelson, A.A, Andrés Viña Verónica Ciganda Donald C. Rundquist Timothy J. Arkebauer (2005), Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32(8). https://doi.org/10.1029/2005GL022688 Links to the data: https://dikke.itc.utwente.nl:5001/sharing/1hPVX83c8 https://dikke.itc.utwente.nl:5001/sharing/np2KkXNg6 https://dikke.itc.utwente.nl:5001/sharing/67YGOTlvc