A spatially explicit dataset of upper canopy tree species composition of public forests of the Autonomous Province of Trento, Italy

This dataset provides spatially explicit information on the tree species composition of the upper canopy layer for forest management units located in public forest areas of the Autonomous Province of Trento in Italy. This area is characterized by a large variety of tree species with climatic regions ranging from sub-Mediterranean to Alpine. The data are distributed as a GeoPackage containing 121,177 polygon geometries representing forest management units derived from the provincial forest management system. For each unit, the dataset reports the total upper canopy cover and the relative species-specific canopy cover contributing to that layer. These values originate from visual estimations by forest practitioners during the development of forest management plans. Attributes include the survey year, unit area, total upper canopy cover percentage, and the relative species-specific cover for 45 individual tree species, alongside two aggregated classes for minor broadleaved and conifer species. The dataset provides a spatially consistent representation of forest composition across about 65 % of the provincial forest area. The dataset supports and has already been used for applications in forest management planning, forest ecology, and remote sensing products calibration and validation. Three specific applications where the dataset has already been implemented are: evaluating the impacts of extreme climatic events on different forest types, modelling species distribution under climate change scenarios, and benchmarking Geospatial Foundation Models.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2026-03-31
DOI
https://doi.org/10.1016/j.dib.2026.112736
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A spatially explicit dataset of upper canopy tree species composition of public forests of the Autonomous Province of Trento, Italy

Davide Andreatta, Michele Dalponte
Data in Brief
Species Distribution and Climate Change
article

A spatially explicit dataset of upper canopy tree species composition of public forests of the Autonomous Province of Trento, Italy

Davide Andreatta, Michele Dalponte
article en

Abstract

This dataset provides spatially explicit information on the tree species composition of the upper canopy layer for forest management units located in public forest areas of the Autonomous Province of Trento in Italy. This area is characterized by a large variety of tree species with climatic regions ranging from sub-Mediterranean to Alpine. The data are distributed as a GeoPackage containing 121,177 polygon geometries representing forest management units derived from the provincial forest management system. For each unit, the dataset reports the total upper canopy cover and the relative species-specific canopy cover contributing to that layer. These values originate from visual estimations by forest practitioners during the development of forest management plans. Attributes include the survey year, unit area, total upper canopy cover percentage, and the relative species-specific cover for 45 individual tree species, alongside two aggregated classes for minor broadleaved and conifer species. The dataset provides a spatially consistent representation of forest composition across about 65 % of the provincial forest area. The dataset supports and has already been used for applications in forest management planning, forest ecology, and remote sensing products calibration and validation. Three specific applications where the dataset has already been implemented are: evaluating the impacts of extreme climatic events on different forest types, modelling species distribution under climate change scenarios, and benchmarking Geospatial Foundation Models.

Data in BriefVol. 66
Fondazione Edmund Mach (IT)
European Commission, HORIZON EUROPE Framework Programme
Life in Land
Openalex Percentile: Top 20%
Species Distribution and Climate Change
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.