Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy

Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy

Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Miss...

Full description

Saved in:
Bibliographic Details
Main Authors: Gabriele Delogu, Miriam Perretta, Eros Caputi, Alessio Patriarca, Cassandra Carroll Funsten, Fabio Recanatesi, Maria Nicolina Ripa, Lorenzo Boccia
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
PRISMA sensor
hyperspectral data
forest cover classification
forestry monitoring
tree stands
tree species
Online Access:https://www.mdpi.com/2072-4292/16/24/4788
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hyperspectral imagery and advanced classification techniques can significantly enhance remote sensing’s role in forest monitoring. Thanks to recent missions, such as the Italian Space Agency’s PRISMA (PRecursore IperSpettrale della Missione Applicativa—Hyperspectral PRecursor of the Application Mission), hyperspectral data in narrow bands spanning visible/near infrared to shortwave infrared are now available. In this study, hyperspectral data from PRISMA were used with the aim of testing the applicability of PRISMA with different band sizes to classify tree species in highly biodiverse forest environments. The Serre Regional Park in southern Italy was used as a case study. The classification focused on forest category classes based on the predominant tree species in sample plots. Ground truth data were collected using a global positioning system together with a smartphone application to test its contribution to facilitating field data collection. The final result, measured on a test dataset, showed an F1 greater than 0.75 for four classes: fir (0.81), pine (0.77), beech (0.90), and holm oak (0.82). Beech forests showed the highest accuracy (0.92), while chestnut forests (0.68) and a mixed class of hygrophilous species (0.69) showed lower accuracy. These results demonstrate the potential of hyperspectral spaceborne data for identifying trends in spectral signatures for forest tree classification.
ISSN:2072-4292

Similar Items