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...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4788 |
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| author | Gabriele Delogu Miriam Perretta Eros Caputi Alessio Patriarca Cassandra Carroll Funsten Fabio Recanatesi Maria Nicolina Ripa Lorenzo Boccia |
| author_facet | Gabriele Delogu Miriam Perretta Eros Caputi Alessio Patriarca Cassandra Carroll Funsten Fabio Recanatesi Maria Nicolina Ripa Lorenzo Boccia |
| author_sort | Gabriele Delogu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ceaa5a5c780f470288f1bedddfd3ea29 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ceaa5a5c780f470288f1bedddfd3ea292025-08-20T02:43:21ZengMDPI AGRemote Sensing2072-42922024-12-011624478810.3390/rs16244788Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern ItalyGabriele Delogu0Miriam Perretta1Eros Caputi2Alessio Patriarca3Cassandra Carroll Funsten4Fabio Recanatesi5Maria Nicolina Ripa6Lorenzo Boccia7Department of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, Via del Paradiso, 47, 01100 Viterbo, ItalyDepartment of Architecture, University of Naples Federico II, Via Forno Vecchio, 36, 80134 Naples, ItalyDepartment of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, Via del Paradiso, 47, 01100 Viterbo, ItalyDepartment of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via S. Camillo de Lellis, 01100 Viterbo, ItalyDepartment of Architecture, University of Naples Federico II, Via Forno Vecchio, 36, 80134 Naples, ItalyDepartment of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via S. Camillo de Lellis, 01100 Viterbo, ItalyDepartment of Agricultural and Forestry Sciences (DAFNE), Tuscia University, Via S. Camillo de Lellis, 01100 Viterbo, ItalyDepartment of Architecture, University of Naples Federico II, Via Forno Vecchio, 36, 80134 Naples, ItalyHyperspectral 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.https://www.mdpi.com/2072-4292/16/24/4788PRISMA sensorhyperspectral dataforest cover classificationforestry monitoringtree standstree species |
| spellingShingle | Gabriele Delogu Miriam Perretta Eros Caputi Alessio Patriarca Cassandra Carroll Funsten Fabio Recanatesi Maria Nicolina Ripa Lorenzo Boccia Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy Remote Sensing PRISMA sensor hyperspectral data forest cover classification forestry monitoring tree stands tree species |
| title | Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy |
| title_full | Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy |
| title_fullStr | Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy |
| title_full_unstemmed | Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy |
| title_short | Leveraging the Potential of PRISMA Hyperspectral Data for Forest Tree Species Classification: A Case Study in Southern Italy |
| title_sort | leveraging the potential of prisma hyperspectral data for forest tree species classification a case study in southern italy |
| topic | PRISMA sensor hyperspectral data forest cover classification forestry monitoring tree stands tree species |
| url | https://www.mdpi.com/2072-4292/16/24/4788 |
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