Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests
Biodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Forests and Global Change |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ffgc.2023.1020477/full |
| _version_ | 1828010967027417088 |
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| author | Francesco Parisi Elia Vangi Elia Vangi Elia Vangi Saverio Francini Saverio Francini Giovanni D’Amico Giovanni D’Amico Gherardo Chirici Gherardo Chirici Marco Marchetti Fabio Lombardi Davide Travaglini Sonia Ravera Elena De Santis Roberto Tognetti |
| author_facet | Francesco Parisi Elia Vangi Elia Vangi Elia Vangi Saverio Francini Saverio Francini Giovanni D’Amico Giovanni D’Amico Gherardo Chirici Gherardo Chirici Marco Marchetti Fabio Lombardi Davide Travaglini Sonia Ravera Elena De Santis Roberto Tognetti |
| author_sort | Francesco Parisi |
| collection | DOAJ |
| description | Biodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required for performing field surveys. In this context, remote sensing is a powerful tool, continuously providing consistent and open access data at a different range of spatial and temporal scales. In particular, the Sentinel-2 (S2) mission has great potential to produce reliable proxies for biological diversity. In beech forests of two Italian National Parks, we sampled the beetle fauna, breeding birds, and epiphytic lichens. First, we calculated Shannon’s entropy and Simpson’s diversity. Then, to produce variables for biodiversity assessment, we exploited S2 data acquired in the 4 years 2017–2021. S2 images were used to construct spectral bands and photosynthetic indices time series, from which 91 harmonic metrics were derived. For each taxon and multi-taxon community, we assessed the correlation with S2 harmonic metrics, biodiversity indices, and forest structural variables. Then, to assess the potential of the harmonic metrics in predicting species diversity in terms of Shannon’s and Simpson’s biodiversity indices, we also fit a random forests model between each diversity index and the best 10 harmonic metrics (in terms of absolute correlation, that is, the magnitude of the correlation) for each taxon. The models’ performance was evaluated via the relative root mean squared error (RMSE%). Overall, 241 beetle, 27 bird, and 59 lichen species were recorded. The diversity indices were higher for the multi-taxon community than for the single taxa. They were generally higher in the CVDA site than in GSML, except for the bird community. The highest correlation values between S2 data and biodiversity indices were recorded in CVDA for multi-taxon and beetle communities (| r| = 0.52 and 0.38, respectively), and in GSML for lichen and beetle communities (| r| = 0.34 and 0.26, respectively). RMSE% ranged between 2.53 and 9.99, and between 8.1 and 16.8 for the Simpson and Shannon index, respectively. The most important variables are phase and RMSE of red-Edge bands for bird and lichen communities, while RMSE and time of tassel cap and from EVI indices for beetles and multi-taxon diversity. Our results demonstrate that S2 data can be used for identifying potential biodiversity hotspots, showing that the herein presented harmonic metrics are informative for several taxa inhabiting wood, giving concrete support to cost-effective biodiversity monitoring and nature-based forest management in complex mountain systems. |
| first_indexed | 2024-04-10T09:08:02Z |
| format | Article |
| id | doaj.art-bff933ba4ccf4b2e8b926f47cf34bf9d |
| institution | Directory Open Access Journal |
| issn | 2624-893X |
| language | English |
| last_indexed | 2024-04-10T09:08:02Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Forests and Global Change |
| spelling | doaj.art-bff933ba4ccf4b2e8b926f47cf34bf9d2023-02-21T05:44:55ZengFrontiers Media S.A.Frontiers in Forests and Global Change2624-893X2023-02-01610.3389/ffgc.2023.10204771020477Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forestsFrancesco Parisi0Elia Vangi1Elia Vangi2Elia Vangi3Saverio Francini4Saverio Francini5Giovanni D’Amico6Giovanni D’Amico7Gherardo Chirici8Gherardo Chirici9Marco Marchetti10Fabio Lombardi11Davide Travaglini12Sonia Ravera13Elena De Santis14Roberto Tognetti15geoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyDipartimento Bioscienze e Territorio, Università degli Studi del Molise, Contrada Fonte Lappone, Pesche, ItalyForest Modelling Laboratory, Institute for Agriculture and Forestry Systems in Mediterranean, National Research Council of Italy (CNR-ISAFOM), Perugia, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyFondazione per il Futuro delle Città, Firenze, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyCREA-Research Centre for Forestry and Wood, Arezzo, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyFondazione per il Futuro delle Città, Firenze, ItalyDipartimento Bioscienze e Territorio, Università degli Studi del Molise, Contrada Fonte Lappone, Pesche, ItalyDipartimento di Agraria, Università Mediterranea di Reggio Calabria, Reggio Calabria, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyDipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Università degli Studi di Palermo, Palermo, ItalygeoLAB–Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Firenze, ItalyDipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Campobasso, ItalyBiodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required for performing field surveys. In this context, remote sensing is a powerful tool, continuously providing consistent and open access data at a different range of spatial and temporal scales. In particular, the Sentinel-2 (S2) mission has great potential to produce reliable proxies for biological diversity. In beech forests of two Italian National Parks, we sampled the beetle fauna, breeding birds, and epiphytic lichens. First, we calculated Shannon’s entropy and Simpson’s diversity. Then, to produce variables for biodiversity assessment, we exploited S2 data acquired in the 4 years 2017–2021. S2 images were used to construct spectral bands and photosynthetic indices time series, from which 91 harmonic metrics were derived. For each taxon and multi-taxon community, we assessed the correlation with S2 harmonic metrics, biodiversity indices, and forest structural variables. Then, to assess the potential of the harmonic metrics in predicting species diversity in terms of Shannon’s and Simpson’s biodiversity indices, we also fit a random forests model between each diversity index and the best 10 harmonic metrics (in terms of absolute correlation, that is, the magnitude of the correlation) for each taxon. The models’ performance was evaluated via the relative root mean squared error (RMSE%). Overall, 241 beetle, 27 bird, and 59 lichen species were recorded. The diversity indices were higher for the multi-taxon community than for the single taxa. They were generally higher in the CVDA site than in GSML, except for the bird community. The highest correlation values between S2 data and biodiversity indices were recorded in CVDA for multi-taxon and beetle communities (| r| = 0.52 and 0.38, respectively), and in GSML for lichen and beetle communities (| r| = 0.34 and 0.26, respectively). RMSE% ranged between 2.53 and 9.99, and between 8.1 and 16.8 for the Simpson and Shannon index, respectively. The most important variables are phase and RMSE of red-Edge bands for bird and lichen communities, while RMSE and time of tassel cap and from EVI indices for beetles and multi-taxon diversity. Our results demonstrate that S2 data can be used for identifying potential biodiversity hotspots, showing that the herein presented harmonic metrics are informative for several taxa inhabiting wood, giving concrete support to cost-effective biodiversity monitoring and nature-based forest management in complex mountain systems.https://www.frontiersin.org/articles/10.3389/ffgc.2023.1020477/fullecological indicatorsforest structuremountain forestsspecies diversityremote sensing |
| spellingShingle | Francesco Parisi Elia Vangi Elia Vangi Elia Vangi Saverio Francini Saverio Francini Giovanni D’Amico Giovanni D’Amico Gherardo Chirici Gherardo Chirici Marco Marchetti Fabio Lombardi Davide Travaglini Sonia Ravera Elena De Santis Roberto Tognetti Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests Frontiers in Forests and Global Change ecological indicators forest structure mountain forests species diversity remote sensing |
| title | Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests |
| title_full | Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests |
| title_fullStr | Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests |
| title_full_unstemmed | Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests |
| title_short | Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests |
| title_sort | sentinel 2 time series analysis for monitoring multi taxon biodiversity in mountain beech forests |
| topic | ecological indicators forest structure mountain forests species diversity remote sensing |
| url | https://www.frontiersin.org/articles/10.3389/ffgc.2023.1020477/full |
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