Open Georeferenced Field Data on Forest Types and Species for Biodiversity Assessment and Remote Sensing Applications

Patrizia Gasparini, Lucio Di Cosmo, Antonio Floris, Federica Murgia and Maria Rizzo ()
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Patrizia Gasparini: Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Foreste e Legno, 38123 Trento, Italy
Lucio Di Cosmo: Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Foreste e Legno, 38123 Trento, Italy
Antonio Floris: Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Foreste e Legno, 38123 Trento, Italy
Federica Murgia: Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Foreste e Legno, 38123 Trento, Italy
Maria Rizzo: Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Foreste e Legno, 38123 Trento, Italy

Data, 2025, vol. 10, issue 3, 1-9

Abstract: Forest ecosystems are important for biodiversity conservation, climate regulation and climate change mitigation, soil and water protection, and the recreation and provision of raw materials. This paper presents a dataset on forest type and tree species composition for 934 georeferenced plots located in Italy. The forest type is classified in the field consistently with the Italian National Forest Inventory (NFI) based on the dominant tree species or species group. Tree species composition is provided by the percent crown cover of the main five species in the plot. Additional data on conifer and broadleaves pure/mixed condition, total tree and shrub cover, forest structure, sylvicultural system, development stage, and local land position are provided. The surveyed plots are distributed in the central–eastern Alps, in the central Apennines, and in the southern Apennines; they represent a wide range of species composition, ecological conditions, and silvicultural practices. Data were collected as part of a project aimed at developing a classification algorithm based on hyperspectral data. The dataset was made publicly available as it refers to forest types and species widespread in many countries of Central and Southern Europe and is potentially useful to other researchers for the study of forest biodiversity or for remote sensing applications.

Keywords: forest classification; forest inventory; forest monitoring; mobile GIS; hyperspectral; multispectral; ground truth; PRISMA satellite mission; field surveys (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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