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

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Authors: Michele Dalponte, Davide Andreatta
Cited by: 1
References: 2

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.

Fields of Study & Subjects

Species Distribution and Climate Change Remote Sensing and LiDAR Applications Remote Sensing in Agriculture

Publication details

Journal / source
Data in Brief
Publisher
Elsevier BV
Volume
66
Pages
112736–112736
Publication date
31 March 2026
Language
EN