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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data

Authors: Miettinen, Jukka; Adame, Patricia; Adolt, Radim; Alberdi, Iciar; Antropov, Oleg; Arnarsson, Ólafur; Astrup, Rasmus; +34 Authors

High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data

Abstract

[Methods] For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent. A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % the South-Eastern area. The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.

We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m2 for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.

European Commission: PathFinder – Towards an Integrated Consistent European LULUCF Monitoring and Policy Pathway Assessment Framework.

El dataset se puede consultar y descargar en el siguiente enlace https://doi.org/10.5281/zenodo.13143235

Peer reviewed

Keywords

Forestry, Remote sensing, Forest monitoring, Forest inventory

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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