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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Maps of forest tree species and pixel-level uncertainty derived from satellite observations and Swedish National Forest Inventory data

Authors: Abdi, Abdulhakim M.; Wang, Fan;

Maps of forest tree species and pixel-level uncertainty derived from satellite observations and Swedish National Forest Inventory data

Abstract

Description This repository contains the data, scripts, and documentation supporting the study “Mapping forest tree species and uncertainty using satellite observations and National Forest Inventory data: towards operational monitoring in Sweden” by Abdulhakim Abdi and Fan Wang. The materials include tree species classification raster, a pixel-level entropy raster representing classification uncertainty, and spatially continuous, entropy-weighted tree species fractions, as well as the R implementation for deriving these fractions. Together, these resources enable large-area analyses of forest composition, uncertainty propagation, and the spatial characterization of forest stands across southern Sweden (Skåne, Blekinge, Halland, Kronoberg, Jönköping, and Kalmar counties). Contents TreeSpecies_Classification_XGB.tif — Discrete raster (8-bit unsigned integer) of dominant tree species predicted by an XGBoost model trained on Sentinel-1/2 and topographic data. TreeSpecies_Entropy_XGB.tif — Continuous raster (16-bit unsigned integer) containing per-pixel Shannon entropy values (0–Hmax), quantifying classification uncertainty. Weighted_Fraction_XXXX.tif (XXXX = Tree species/class) — Continuous rasters (Float32, 0–100%) representing local, entropy-weighted fractional cover of each tree species computed within a moving Gaussian window. Each file corresponds to one of the eight dominant species (Norway spruce, Scots pine, Birch, Beech, Oak, Alder, Aspen, and Other species). An additional “Weighted_Fraction_Unknown.tif” layer represents the proportion of unclassified or masked pixels within the window. Convert_classification_to_entropy-weighted_tree_species_fractions.R — R script that computes local (moving-window) species fractions with optional entropy weighting, producing 0–100% fractional cover maps for each species and an additional “Unknown” fraction layer. Derivation of entropy-weighted tree species fractions.docx — Document detailing the steps taken to derive the entropy-weighted tree species fractions. Spatial characteristics Property Specification Geographic coverage Southern Sweden (Skåne, Blekinge, Halland, Kronoberg, Jönköping, and Kalmar counties) Extent 307020, 6132480 : 609300, 6450120 (EPSG:3006 – SWEREF99 TM) Projection Projected (UTM), units in meters Spatial resolution 10 × 10 m Raster dimensions 30,228 × 31,764 pixels Origin 307020, 6,450,120

Related Organizations
Keywords

Remote Sensing, Coniferous forest, Earth observation, Deciduous forest, Forest management, Forest Mapping, Forest production, Machine Learning/classification, Forest, Forest ecology

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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
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Average