V. 81 N. 2s (2026): Atti della Conferenza Foreste vetuste e antichi alberi: un tesoro di natura, vita e cultura. Firenze 1 ottobre 2025 / Vallombrosa 2-3 ottobre 2025
Articoli Scientifici

Caratterizzazione dei gradienti di maturità e vetustà nelle foreste italiane: un approccio integrato tra telerilevamento e inventari forestali

PDF (English)
  • Costanza Borghi,
  • Francesco Solano,
  • Giovanni D'Amico,
  • Titomanlio Pepe,
  • Mattia Niccoli,
  • Elia Vangi,
  • Francesco Parisi,
  • Davide Travaglini,
  • Giuseppe Modica,
  • Giancarlo Papitto,
  • Gianluca Piovesan,
  • Gherardo Chirici
Costanza Borghi
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
Francesco Solano
Department of Agriculture and Forest Sciences, University of Tuscia, via San Camillo de Lellis s/n, 01100 Viterbo, Italy
Giovanni D'Amico
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
Titomanlio Pepe
Department of Ecological and Biological Sciences, University of Tuscia; largo dell’Università s/n, 01100 Viterbo, Italy
Mattia Niccoli
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
Elia Vangi
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
Francesco Parisi
Department of Biosciences and Territory, University of Molise; contrada Fonte Lappone - 86090 Pesche (IS), Italy. Institute of Technologies and Environmental Intelligence (CNR-ITIAm), National Research Council of Italy, strada provinciale 35d, n. 9 - 00010 Montelibretti (RM), Italy.
Davide Travaglini
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
Giuseppe Modica
Dipartimento di Scienze Veterinarie, Università degli Studi di Messina; viale G. Palatucci s/n - 98168 Messina, Italy.
Giancarlo Papitto
Arma dei Carabinieri, Comando Unità Forestali Ambientali Agroalimentari, Via. G. Carducci 5, Rome, 00187, Italy
Gianluca Piovesan
Department of Ecological and Biological Sciences, University of Tuscia, Largo dell’Università s/n, 01100 Viterbo, Italy
Gherardo Chirici
Department of Agriculture, Food, Environment and Forestry, University of Florence; via San Bonaventura 13 - 50145 Firenze, Italy.
DOI: https://doi.org/10.36253/ifm-1250

Pubblicato 2026-10-06

Parole chiave

  • forestry,
  • machine learning,
  • structural complexity,
  • explainable artificial intelligence,
  • Natural Restoration Law

Come citare

Borghi, C., Solano, F., D’Amico, G., Pepe, T., Niccoli, M., Vangi, E., Parisi, F., Travaglini, D., Modica, G., Papitto, G., Piovesan, G., & Chirici, G. (2026). Caratterizzazione dei gradienti di maturità e vetustà nelle foreste italiane: un approccio integrato tra telerilevamento e inventari forestali. L’Italia Forestale E Montana, 81(2s), 118–139. https://doi.org/10.36253/ifm-1250

Copyright (c) 2026 Costanza Borghi, Francesco Solano, Giovanni D'Amico, Titomanlio Pepe, Mattia Niccoli, Elia Vangi, Francesco Parisi, Davide Travaglini, Giuseppe Modica, Giancarlo Papitto, Gianluca Piovesan, Gherardo Chirici

Creative Commons License

Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale 4.0 Internazionale.

Abstract

Mature and Old-Growth-like Forests (MOGF) are critical for biodiversity conservation and climate regulation, yet they remain rare and fragmented across Europe. This study aims to ecologically validate a high-resolution (30 m) predictive map of MOGF probability across Italy by integrating it with independent ground-based data from the 2015 National Forest Inventory (INFC2015). Utilizing a multi-source approach, we analyzed 6993 inventory sampling plots across diverse forest types and environmental gradients. Statistical analysis confirmed that higher MOGF probabilities strongly correlate with indicators of advanced stand development, including older age classes (> 120 years), multilayered vertical structures, and high canopy cover (> 80%). MOGF probability is also significantly higher within strictly protected areas (e.g., National Park Zone A) and topographically remote sites characterized by high altitudes and steep slopes. Quantitative correlations also link MOGF probability to an increase in above-ground biomass and deadwood. While the study successfully validates the spatial patterns of forest maturity, limitations include the current lack of national-scale 3D LiDAR data for deadwood modeling and the lack of microhabitat indicators in the INFC2015. However, the anticipated inclusion of these metrics in future national frameworks will further refine the monitoring of these ecosystems.

PDF (English)

Riferimenti bibliografici

  1. Adiningrat, D. P., Skidmore, A., Schlund, M., Wang, T., Abdullah, H., & Heurich, M. (2025). Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe. Remote Sensing, 17(2), 251. https://doi.org/10.3390/rs17020251
  2. Agnoletti, M., Piras, F., Venturi, M., & Santoro, A. (2022). Cultural values and forest dynamics: The Italian forests in the last 150 years. Forest Ecology and Management, 503, 119655. https://doi.org/10.1016/j.foreco.2021.119655
  3. Alberdi, I., Gschwantner, T., Bosela, M., Redmond, J., Riedel, T., Snorrason, A., Gasparini, P., Braendli, U.-B., Fridman, J., Tomter, S., Kulbokas, G., Lanz, A., & Vidal, C. (2016). Harmonisation of Data and Information on the Potential Supply of Wood Resources. In National Forest Inventories (pp. 55–79). Springer International Publishing. https://doi.org/10.1007/978-3-319-44015-6_3
  4. Amiri, N., Krzystek, P., Heurich, M., & Skidmore, A. (2019). Classification of tree species as well as standing dead trees using triplewavelength als in a temperate forest. Remote Sensing, 11(22), 2614. https://doi.org/10.3390/rs11222614
  5. Badalamenti, E., La Mela Veca, D. S., Costa, M., Giardina, G., La Mantia, T., Laschi, A., Maetzke, F. G., Petroncini, S., Sala, G., & Bueno, R. S. (2025). Old-growthness level assessed by structural heterogeneity indices in Mediterranean Quercus pubescens forests. Forest Ecosystems, 14, 100365. https://doi.org/10.1016/j.fecs.2025.100365
  6. Barbati, A., Salvati, R., Ferrari, B., Di Santo, D., Quatrini, A., Portoghesi, L., Travaglini, D., Iovino, F., & Nocentini, S. (2012). Assessing and promoting old-growthness of forest stands: Lessons from research in Italy. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology, 146(1), 167–174. https://doi.org/10.1080/11263504.2011.650730
  7. Barredo, J. I., Brailescu, C., Teller, A., Sabatini, F. M., Mauri, A., & Janouskova, K. (2021). Mapping and assessment of primary and old-growth forests in Europe (Number EUR 30661 EN). https://doi.org/10.2760/797591
  8. Blasi, C., Burrascano, S., Maturani, A., & Sabatini, F. M. (2010a). Old-growth forests in Italy. A thematic contribution to the National Biodiversity Strategy. 1–28. https://www.mite.gov.it/sites/default/files/archivio/biblioteca/protezione_natura/foreste_vetuste_eng.pdf
  9. Blasi, C., Marchetti, M., Chiavetta, U., Aleffi, M., Audisio, P., Azzella, M. M., Brunialti, G., Capotorti, G., Del Vico, E., Lattanzi, E., Persiani, A. M., Ravera, S., Tilia, A., & Burrascano, S. (2010b). Multi‐taxon and forest structure sampling for identification of indicators and monitoring of old‐growth forest. Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology, 144(1), 160–170. https://doi.org/10.1080/11263500903560538
  10. Borghi, C., Francini, S., D’Amico, G., Valbuena, R., & Chirici, G. (2025). Advancements in Forest Monitoring: Applications and Perspectives of Airborne Laser Scanning and Complementarity with Satellite Optical Data. Land, 14(3), 567. https://doi.org/10.3390/land14030567
  11. Borghi, C., Francini, S., McRoberts, R. E., Parisi, F., Lombardi, F., Nocentini, S., Maltoni, A., Travaglini, D., & Chirici, G. (2023). Country-wide assessment of biodiversity, naturalness, and old-growth status using National Forest Inventory data. European Journal of Forest Research. https://doi.org/10.1007/s10342-023-01620-6
  12. Borghi, C., Solano, F., D’Amico, G., Pepe, T., Niccoli, M., Vangi, E., Parisi, F., Travaglini, D., Modica, G., Papitto, G., Chiarucci, A., Piovesan, G., & Chirici, G. (2026). Explainable artificial intelligence for mapping mature and old-growth forests of Italy. Journal of Biogeography.
  13. Breidenbach, J., Waser, L. T., Debella-Gilo, M., Schumacher, J., Rahlf, J., Hauglin, M., Puliti, S., & Astrup, R. (2021). National mapping and estimation of forest area by dominant tree species using Sentinel-2 data. Canadian Journal of Forest Research, 51(3), 365–379. https://doi.org/10.1139/cjfr-2020-0170
  14. Bruening, J. M., Dubayah, R. O., Pederson, N., Poulter, B., & Calle, L. (2024). Definition criteria determine the success of old-growth mapping. Ecological Indicators, 159(January), 111709. https://doi.org/10.1016/j.ecolind.2024.111709
  15. Burrascano, S., Ripullone, F., Bernardo, L., Borghetti, M., Carli, E., Colangelo, M., Gangale, C., Gargano, D., Gentilesca, T., Luzzi, G., Passalacqua, N., Pelle, L., Rivelli, A. R., Sabatini, F. M., Schettino, A., Siclari, A., Uzunov, D., & Blasi, C. (2018). It’s a long way to the top: Plant species diversity in the transition from managed to old‐growth forests. Journal of Vegetation Science, 29(1), 98–109. https://doi.org/10.1111/jvs.12588
  16. Capotorti, G., Zavattero, L., Anzellotti, I., Burrascano, S., Frondoni, R., Marchetti, M., Marignani, M., Smiraglia, D., & Blasi, C. (2012). Do National Parks play an active role in conserving the natural capital of Italy? Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology, 146(2), 258–265. https://doi.org/10.1080/11263504.2012.695298
  17. Chirici, G., Giannetti, F., McRoberts, R. E., Travaglini, D., Pecchi, M., Maselli, F., Chiesi, M., & Corona, P. (2020). Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data. International Journal of Applied Earth Observation and Geoinformation, 84, 101959. https://doi.org/10.1016/j.jag.2019.101959
  18. Chiti, E., & Piovesan, G. (2025). Reclaiming strict protection via the European Green Deal Frontiers in Ecology and the Environment, 2–4. https://doi.org/10.1002/fee.70011
  19. Crampe, E. A., Segura, C., & Jones, J. A. (2021). Fifty years of runoff response to conversion of old‐growth forest to planted forest in the H. J. Andrews Forest, Oregon, USA. Hydrological Processes, 35(5). https://doi.org/10.1002/hyp.14168
  20. D’Amico, G., Chirici, G., Campetella, G., Canullo, R., Cervellini, M., Chelli, S., Di Biase, R. M., Fattorini, L., Floris, A., Franceschi, S., Francini, S., Giannetti, F., Marcelli, A., Marcheselli, M., Mattioli, W., Notarangelo, M., Papitto, G., Parisi, F., Pisani, C., … Corona, P. (2025). National Forest Inventory in Italy: new perspectives for forest monitoring. Annals of Forest Science, 82(1), 35. https://doi.org/10.1186/s13595-025-01303-9
  21. de Assis Barros, L., & Elkin, C. (2021). An index for tracking old-growth value in disturbance-prone forest landscapes. Ecological Indicators, 121, 107175. https://doi.org/10.1016/j.ecolind.2020.107175
  22. De Laurentis, D., Papitto, G., Gasparini, P., Di Cosmo, L., & Floris, A. (2021). Italian Forests - Selected Results of the Third National Forest Inventory INFC2015. Council for Agricultural Research and Economics, Research Centre for Forestry and Wood (CREA-FL).
  23. De Laurentis, D., & Piovesan, G. (2023). Foreste d’Italia. Inventario Forestale Nazionale.
  24. Dellasala, D. A., Kormos, C. F., Keith, H., Mackey, B., Young, V., Rogers, B., & Mittermeier, R. A. (2020). Primary Forests Are Undervalued in the Climate Emergency. BioScience, 70(6), 445–445. https://doi.org/10.1093/biosci/biaa030
  25. Di Paolo, S., Giuliarelli, D., Ferrari, B., Barbati, A., & Corona, P. (2010). Support of multispectral very high resolution remotely sensed imagery for old-growth beech forest detection. L’Italia Forestale e Montana, 65(5), 519–527. https://doi.org/10.4129/ifm.2010.5.04
  26. Dietenberger, S., Mueller, M. M., Stöcker, B., Dubois, C., Arlaud, H., Adam, M., Hese, S., Meyer, H., & Thiel, C. (2025). Accurate Mapping of Downed Deadwood in a Dense Deciduous Forest Using UAV-SfM Data and Deep Learning. Remote Sensing, 17(9), 1610. https://doi.org/10.3390/rs17091610
  27. Dufour-Pelletier, S., A. Tremblay, J., Hébert, C., Lachat, T., & Ibarzabal, J. (2020). Testing the Effect of Snag and Cavity Supply on Deadwood-Associated Species in a Managed Boreal Forest. Forests, 11(4), 424. https://doi.org/10.3390/f11040424
  28. Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56(293), 52–64.
  29. Eionet. (2021). Natura 2000 Barometer. https://www.eea.europa.eu/data-and-maps/dashboards/natura-2000-barometer
  30. Franklin, J. F., & Van Pelt, R., (2004). Spatial Aspects of Structural Complexity in Old-Growth Forests. Journal of Forestry, 102(May), 22–28.
  31. Fraser, J. S., Pile Knapp, L. S., Graham, B., Jenkins, M. A., Kabrick, J., Saunders, M., Spetich, M., & Shifley, S. (2023). Carbon dynamics in old-growth forests of the Central Hardwoods Region, USA. Forest Ecology and Management, 537(March), 120958. https://doi.org/10.1016/j.foreco.2023.120958
  32. Gasparini, P., Di Cosmo, L., Floris, A., & De Laurentis, D. (2022). Italian national forest inventory - methods and results of the third survey: Inventario nazionale delle foreste e dei serbatoi forestali di carbonio - metodi e risultati della terza indagine. Springer.
  33. Giannetti, F., Chirici, G., Vangi, E., Corona, P., Maselli, F., Chiesi, M., D’Amico, G., & Puletti, N. (2022). Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy. Forests, 13(12), 1989. https://doi.org/10.3390/f13121989
  34. Gilhen-Baker, M., Roviello, V., Beresford-Kroeger, D., & Roviello, G. N. (2022). Old growth forests and large old trees as critical organisms connecting ecosystems and human health. A review. Environmental Chemistry Letters, 20(2), 1529–1538. https://doi.org/10.1007/s10311-021-01372-y
  35. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  36. Gramm, J., Guo, J., Hüffner, F., Niedermeier, R., Piepho, H. P., & Schmid, R. (2007). Algorithms for compact letter displays: Comparison and evaluation. Computational Statistics and Data Analysis, 52(2), 725–736. https://doi.org/10.1016/j.csda.2006.09.035
  37. Gschwantner, T., Alberdi, I., Balázs, A., Bauwens, S., Bender, S., Borota, D., Bosela, M., Bouriaud, O., Cañellas, I., Donis, J., Freudenschuß, A., Hervé, J.-C., Hladnik, D., Jansons, J., Kolozs, L., Korhonen, K. T., Kucera, M., Kulbokas, G., Kuliešis, A., … Zell, J. (2019). Harmonisation of stem volume estimates in European National Forest Inventories. Annals of Forest Science, 76(1), 24. https://doi.org/10.1007/s13595-019-0800-8
  38. Hagge, J., Müller, J., Bässler, C., Brandl, R., Schuldt, A., Thorn, S., & Seibold, S. (2024). Change in saproxylic beetle, fungi and bacteria assemblages along horizontal and vertical gradients of sun-exposure in forest. Biological Conservation, 291, 110493. https://doi.org/10.1016/j.biocon.2024.110493
  39. Hahn, K., & Christensen, M. (2005). Dead wood in European forest reserves–a reference for forest management. Monitoring and Indicators of Forest Biodiversity in Europe - From Ideas to Operationality, 181.
  40. Haibo He, & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
  41. Hirschmugl, M., Sobe, C., Di Filippo, A., Berger, V., Kirchmeir, H., & Vandekerkhove, K. (2023). Review on the Possibilities of Mapping Old-Growth Temperate Forests by Remote Sensing in Europe. Environmental Modeling & Assessment. https://doi.org/10.1007/s10666-023-09897-y
  42. Keith, H., Kun, Z., Hugh, S., Svoboda, M., Mikolá, M., Adam, D., Filippo, A. Di, Dutc, I., Garbarino, M., Horváth, F., & Ivkovich, V. (2024). Carbon carrying capacity in primary forests shows potential for mitigation achieving the European Green Deal 2030 target. https://doi.org/10.1038/s43247-024-01416-5
  43. Kozák, D., Mikoláš, M., Svitok, M., Bače, R., Paillet, Y., Larrieu, L., Nagel, T. A., Begovič, K., Čada, V., Diku, A., Frankovič, M., Janda, P., Kameniar, O., Keren, S., Kjučukov, P., Lábusová, J., Langbehn, T., Málek, J., Mikac, S., … Svoboda, M. (2018). Profile of tree-related microhabitats in European primary beech-dominated forests. Forest Ecology and Management, 429, 363–374. https://doi.org/10.1016/j.foreco.2018.07.021
  44. Lamonaca, A., Corona, P., & Barbati, A. (2008). Exploring forest structural complexity by multi-scale segmentation of VHR imagery. Remote Sensing of Environment, 112(6), 2839–2849. https://doi.org/10.1016/j.rse.2008.01.017
  45. Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 7(11), 1778–1789. https://doi.org/10.1038/s41559-023-02206-6
  46. Larrieu, L., Paillet, Y., Winter, S., Bütler, R., Kraus, D., Krumm, F., Lachat, T., Michel, A. K., Regnery, B., & Vandekerkhove, K. (2018). Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization. Ecological Indicators, 84, 194–207. https://doi.org/10.1016/j.ecolind.2017.08.051
  47. Lindenmayer, D. B., & Laurance, W. F. (2017). The ecology, distribution, conservation and management of large old trees. Biological Reviews, 92(3), 1434–1458. https://doi.org/10.1111/brv.12290
  48. Lombardi, F., Lasserre, B., Chirici, G., Tognetti, R., & Marchetti, M. (2012). Deadwood occurrence and forest structure as indicators of old-growth forest conditions in Mediterranean mountainous ecosystems. Ecoscience, 19(4), 344-355. https://doi.org/10.2980/19-4-3506
  49. Lundberg, S. M., & Lee, S.-I. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Decem(Section 2), 4766–4775.
  50. Lutz, J. A., Furniss, T. J., Johnson, D. J., Davies, S. J., Allen, D., Alonso, A., Anderson‐Teixeira, K. J., Andrade, A., Baltzer, J., Becker, K. M. L., Blomdahl, E. M., Bourg, N. A., Bunyavejchewin, S., Burslem, D. F. R. P., Cansler, C. A., Cao, K., Cao, M., Cárdenas, D., Chang, L., … Zimmerman, J. K. (2018). Global importance of large‐diameter trees. Global Ecology and Biogeography, 27(7), 849–864. https://doi.org/10.1111/geb.12747
  51. Marchetti, M., & Blasi, C. (2010). Old-growth forests in Italy: towards a first network. L’Italia Forestale e Montana, 65(6), 679–698. https://doi.org/10.4129/ifm.2010.6.02
  52. Marchi, N., Pirotti, F., & Lingua, E. (2018). Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives. Remote Sensing, 10(9), 1356. https://doi.org/10.3390/rs10091356
  53. Markham, K., Frazier, A. E., Singh, K. K., & Madden, M. (2023). A review of methods for scaling remotely sensed data for spatial pattern analysis. Landscape Ecology, 38(3), 619–635. https://doi.org/10.1007/s10980-022-01449-1
  54. Martin-Benito, D., Pederson, N., Férriz, M., & Gea-Izquierdo, G. (2021). Old forests and old carbon: A case study on the stand dynamics and longevity of aboveground carbon. Science of The Total Environment, 765, 142737. https://doi.org/10.1016/j.scitotenv.2020.142737
  55. Martin, M., Cerrejón, C., & Valeria, O. (2021a). Complementary airborne LiDAR and satellite indices are reliable predictors of disturbance-induced structural diversity in mixed old-growth forest landscapes. Remote Sensing of Environment, 267, 112746. https://doi.org/10.1016/j.rse.2021.112746
  56. Martin, M., Fenton, N. J., & Morin, H. (2020). Boreal old-growth forest structural diversity challenges aerial photographic survey accuracy. Canadian Journal of Forest Research, 50(2), 155–169. https://doi.org/10.1139/cjfr-2019-0177
  57. Martin, M., Fenton, N. J., & Morin, H. (2021b). Tree-related microhabitats and deadwood dynamics form a diverse and constantly changing mosaic of habitats in boreal old-growth forests. Ecological Indicators, 128, 107813. https://doi.org/10.1016/j.ecolind.2021.107813
  58. Mattioli, W., Romano, R., Botticelli, D., Chirici, G., D’Amico, G., Giuliarelli, D., Pecchi, M., & Corona, P. (2025). The Forest Map of Italy (CFI2020): an updated portrait of Italian forests. Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, 22(3), 39–44. https://doi.org/10.3832/efor4836-022
  59. Mayer, M., Prescott, C. E., Abaker, W. E. A., Augusto, L., Cécillon, L., Ferreira, G. W. D., James, J., Jandl, R., Katzensteiner, K., Laclau, J.-P., Laganière, J., Nouvellon, Y., Paré, D., Stanturf, J. A., Vanguelova, E. I., & Vesterdal, L. (2020). Tamm Review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. Forest Ecology and Management, 466, 118127. https://doi.org/10.1016/j.foreco.2020.118127
  60. McGee, G. G. (2018). Biological diversity in eastern old growth. In Ecology and Recovery of Eastern Old-Growth Forests (pp. 197–216). Springer.
  61. McRoberts, R. E., Stehman, S. V., Liknes, G. C., Næsset, E., Sannier, C., & Walters, B. F. (2018). The effects of imperfect reference data on remote sensing-assisted estimators of land cover class proportions. ISPRS Journal of Photogrammetry and Remote Sensing, 142, 292–300. https://doi.org/10.1016/j.isprsjprs.2018.06.002
  62. Meyer, P., Aljes, M., Culmsee, H., Feldmann, E., Glatthorn, J., Leuschner, C., & Schneider, H. (2021). Quantifying old-growthness of lowland European beech forests by a multivariate indicator for forest structure. Ecological Indicators, 125, 107575. https://doi.org/10.1016/j.ecolind.2021.107575
  63. Mikolāš, M., Piovesan, G., Ahlström, A., Donato, D. C., Gloor, R., Hofmeister, J., Keeton, W. S., Muys, B., Sabatini, F. M., Svoboda, M., & Kuemmerle, T. (2023). Protect old-growth forests in Europe now. Science, 380(6644), 466–466. https://doi.org/10.1126/science.adh2303
  64. Mikoláš, M., Ujházy, K., Jasík, M., Wiezik, M., Gallay, I., Polák, P., Vysoký, J., Čiliak, M., Meigs, G. W., Svoboda, M., Trotsiuk, V., & Keeton, W. S. (2019). Primary forest distribution and representation in a Central European landscape: Results of a large-scale field-based census. Forest Ecology and Management, 449, 117466. https://doi.org/10.1016/j.foreco.2019.117466
  65. Mikusiński, G., Bubnicki, J. W., Churski, M., Czeszczewik, D., Walankiewicz, W., & Kuijper, D. P. J. (2018). Is the impact of loggings in the last primeval lowland forest in Europe underestimated? The conservation issues of Białowieża Forest. Biological Conservation, 227, 266–274. https://doi.org/10.1016/j.biocon.2018.09.001
  66. Motta, R. (2002). Old-growth forests and silviculture in the Italian Alps: the case-study of the strict reserve of Paneveggio (TN). Plant Biosystems - An International Journal Dealing with All Aspects of Plant Biology, 136(2), 223–231.
  67. Motta, R., Garbarino, M., Lingua, E., Lombardi, F., & Tognetti, R. (2022). The current Italian research and its role in the establishment of an Italian network of “old-growth forests.” Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, 19(2), 36–48. https://doi.org/10.3832/efor4110-019
  68. Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., Börger, L., Bennett, D. J., Choimes, A., Collen, B., Day, J., De Palma, A., Díaz, S., Echeverria-Londoño, S., Edgar, M. J., Feldman, A., Garon, M., Harrison, M. L. K., Alhusseini, T., … Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), 45–50. https://doi.org/10.1038/nature14324
  69. O’Brien, L., Schuck, A., Fraccaroli, C., Pötzelsberger, E., Winkel, G., & Lindner, M., (2021). Protecting old-growth forests in Europe A review of scientific evidence to inform policy implementation. 104. https://doi.org/10.36333/rs1
  70. Oliver, C. D., & Larson, B. C., (1996). Forest stand dynamics (Vol. 520). Wiley New York.
  71. Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., Nabuurs, G.-J., Shvidenko, A., Hashimoto, S., Lerink, B., Schepaschenko, D., Castanho, A., & Murdiyarso, D. (2024). The enduring world forest carbon sink. Nature, 631(8021), 563–569. https://doi.org/10.1038/s41586-024-07602-x
  72. Parisi, F., Francini, S., Borghi, C., & Chirici, G. (2022). An open and georeferenced dataset of forest structural attributes and microhabitats in central and southern Apennines (Italy). Data in Brief, 43, 108445. https://doi.org/10.1016/j.dib.2022.108445
  73. Parisi, F., Lombardi, F., Sciarretta, A., Tognetti, R., Campanaro, A., Marchetti, M., & Trematerra, P. (2016). Spatial patterns of saproxylic beetles in a relic silver fir forest (Central Italy), relationships with forest structure and biodiversity indicators. Forest Ecology and Management, 381, 217–234. https://doi.org/10.1016/j.foreco.2016.09.041
  74. Parisi, F., Mazziotta, A., Vangi, E., Tognetti, R., Travaglini, D., Marchetti, M., D’Amico, G., Francini, S., Borghi, C., & Chirici, G. (2023). Exposure elevation and forest structure predict the abundance of saproxylic beetles’ communities in mountain managed beech forests. IForest - Biogeosciences and Forestry, 16(3), 155–164. https://doi.org/10.3832/ifor4264-016
  75. Pesklevits, A., Duinker, P. N., & Bush, P. G. (2011). Old-growth forests: Anatomy of a wicked problem. Forests, 2(1), 343–356. https://doi.org/10.3390/f2010343
  76. Pinna, M. (1978). L’atmosfera e il clima (Vol. 5). Utet.
  77. Piovesan, G., Baliva, M., Biondi, F., Palli, J., Calcagnile, L., Chiarucci, A., Manicone, R., Quarta, G., Quilghini, G., Siclari, A., & Cannon, C. H. (2026).Ancient oaks reveal rewilding of Mediterranean forests after the Black Death. Proceedings of the National Academy of Sciences, 123(24), e2529341123. https://doi.org/10.1073/pnas.2529341123
  78. Piovesan, G., & Biondi, F. (2021). On tree longevity. New Phytologist, 231(4), 1318–1337. https://doi.org/10.1111/nph.17148
  79. Pretzsch, H., Hilmers, T., Biber, P., Avdagić, A., Binder, F., Bončina, A., Bosela, M., Dobor, L., Forrester, D. I., & Lévesque, M. (2020). Evidence of elevation-specific growth changes of spruce, fir, and beech in European mixed mountain forests during the last three centuries. Canadian Journal of Forest Research, 50(7), 689–703.
  80. Puettmann, K., Messier, C., & Bauhus, J., (2009). Silviculture for old-growth attributes. Forest Ecology and Management, 258(4), 525–537. https://doi.org/10.1016/j.foreco.2009.01.053
  81. R Core Team. (2026). The R Project for Statistical Computing. https://www.r-project.org/
  82. Renftle, M., Trittenbach, H., Poznic, M., & Heil, R. (2024). What do algorithms explain? The issue of the goals and capabilities of Explainable Artificial Intelligence (XAI). Humanities and Social Sciences Communications, 11(1), 760. https://doi.org/10.1057/s41599-024-03277-x
  83. Sabatini, F. M., Bluhm, H., Kun, Z., Aksenov, D., Atauri, J. A., Buchwald, E., Burrascano, S., Cateau, E., Diku, A., Duarte, I. M., Fernández López, Á. B., Garbarino, M., Grigoriadis, N., Horváth, F., Keren, S., Kitenberga, M., Kiš, A., Kraut, A., Ibisch, P. L., … Kuemmerle, T. (2021). European primary forest database v2.0. Scientific Data, 8(1), 220. https://doi.org/10.1038/s41597-021-00988-7
  84. Sabatini, F. M., Burrascano, S., Keeton, W. S., Levers, C., Lindner, M., Pötzschner, F., Verkerk, P. J., Bauhus, J., Buchwald, E., Chaskovsky, O., Debaive, N., Horváth, F., Garbarino, M., Grigoriadis, N., Lombardi, F., Marques Duarte, I., Meyer, P., Midteng, R., Mikac, S., … Kuemmerle, T. (2018). Where are Europe’s last primary forests? Diversity and Distributions, 24(10), 1426–1439. https://doi.org/10.1111/ddi.12778
  85. Savoie, J.-M., Brin, A., Cateau, E., Dejean, S., Hannoire, C., Larrieu, L., Valladares, L., Blanc, F., Brustel, H., & Corriol, G. (2016). Inventaire et évaluation des vieilles forêts des Pyrénées de Midi-Pyrénées. 5. Rencontres Naturalistes de Midi-Pyrénées, 88–91.
  86. Schulze, K., Malek, Ž., & Verburg, P. H. (2019). Towards better mapping of forest management patterns: A global allocation approach. Forest Ecology and Management, 432, 776–785. https://doi.org/10.1016/j.foreco.2018.10.001
  87. Seibold, S., Brandl, R., Buse, J., Hothorn, T., Schmidl, J., Thorn, S., & Müller, J. (2015). Association of extinction risk of saproxylic beetles with ecological degradation of forests in Europe. Conservation Biology, 29(2), 382–390. https://doi.org/10.1111/cobi.12427
  88. Šēnhofa, S., Jaunslaviete, I., Šņepsts, G., Jansons, J., Liepa, L., & Jansons, Ā. (2020). Deadwood Characteristics in Mature and Old-Growth Birch Stands and their Implications for Carbon Storage. Forests, 11(5), 536. https://doi.org/10.3390/f11050536
  89. Seymour, R. S., White, A. S., & DeMaynadier, P. G. (2002). Natural disturbance regimes in northeastern North America - evaluating silvicultural systems using natural scales and frequencies. Forest Ecology and Management, 155(1–3), 357–367. https://doi.org/10.1016/S0378-1127(01)00572-2
  90. Shapiro, S. S., Wilk, M. B., & Chen, H. J. (1968). A comparative study of various tests for normality. Journal of the American Statistical Association, 63(324), 1343–1372.
  91. Solano, F., Mansi, C., Baliva, M., Canestrelli, D., Chiarucci, A., Manicone, R., Maugeri, M., Modica, G., Palli, J., Quilghini, G., & Piovesan, G. (2025). Mediterranean strictly protected forests are cooler. Agricultural and Forest Meteorology, 375(September), 110858. https://doi.org/10.1016/j.agrformet.2025.110858
  92. Solano, F., Praticò, S., Piovesan, G., Chiarucci, A., Argentieri, A., & Modica, G. (2021). Characterizing historical transformation trajectories of the forest landscape in Rome’s metropolitan area (Italy) for effective planning of sustainability goals. Land Degradation & Development, 32(16), 4708–4726. https://doi.org/10.1002/ldr.4072
  93. Spracklen, B. D., & Spracklen, D. V. (2019). Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians. Forests, 10(2), 127. https://doi.org/10.3390/f10020127
  94. Svoboda, M., Janda, P., Bače, R., Fraver, S., Nagel, T. A., Rejzek, J., Mikoláš, M., Douda, J., Boublík, K., Šamonil, P., Čada, V., Trotsiuk, V., Teodosiu, M., Bouriaud, O., Biriş, A. I., Sýkora, O., Uzel, P., Zelenka, J., Sedlák, V., & Lehejček, J. (2014). Landscape-level variability in historical disturbance in primary Picea abies mountain forests of the Eastern Carpathians, Romania. Journal of Vegetation Science, 25(2), 386–401. https://doi.org/10.1111/jvs.12109
  95. Tíscar, P. A., & Lucas-Borja, M. E. (2016). Structure of old-growth and managed stands and growth of old trees in a Mediterranean Pinus nigra forest in southern Spain. Forestry, 89(2), 201–207.
  96. Tolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., Moutakanni, T., Bojanowski, P., Johns, T., White, B., Tiecke, T., & Couprie, C. (2024). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sensing of Environment, 300, 113888. https://doi.org/10.1016/j.rse.2023.113888
  97. Tomppo, E., Gschwantner, T., Lawrence, M., & McRoberts, R. E. (2010). National forest inventories: Pathways for common reporting. In E. Tomppo, T. Gschwantner, M. Lawrence, & R. E. McRoberts (Eds.), National Forest Inventories: Pathways for Common Reporting. Springer Netherlands. https://doi.org/10.1007/978-90-481-3233-1
  98. Vandekerkhove, K., Meyer, P., Kirchmeir, H., Piovesan, G., Hirschmugl, M., Larrieu, L., Kozàk, D., Mikolas, M., Nagel, T., Schmitt, C., & Blumröder, J. (2022). Old-growth criteria and indicators for beech forests (Fageta). (March), 1–51.
  99. Vangi, E., D’Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., McRoberts, R. E., & Chirici, G. (2021). The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume. Remote Sensing, 13(5), 1038. https://doi.org/10.3390/rs13051038
  100. Vargha, A., & Delaney, H. D. (1998). The Kruskal-Wallis test and stochastic homogeneity. Journal of Educational and behavioral Statistics, 23(2), 170-192. https://doi.org/10.3102/10769986023002170
  101. Veen, P., Fanta, J., Raev, I., Biriş, I.-A., de Smidt, J., & Maes, B. (2010). Virgin forests in Romania and Bulgaria: results of two national inventory projects and their implications for protection. Biodiversity and Conservation, 19, 1805–1819.
  102. Wadoux, A. M. J.-C., Heuvelink, G. B. M., de Bruin, S., & Brus, D. J. (2021). Spatial cross-validation is not the right way to evaluate map accuracy. Ecological Modelling, 457, 109692. https://doi.org/10.1016/j.ecolmodel.2021.109692
  103. Wirth, C., Gleixner, G., & Heimann, M. (2009a). Old-growth forests: function, fate and value–an overview. Old-Growth Forests, 3–10.
  104. Wirth, C., Messier, C., Bergeron, Y., Frank, D., & Fankhänel, A. (2009b). Old-Growth Forest Definitions: a Pragmatic View (pp. 11–33). https://doi.org/10.1007/978-3-540-92706-8_2
  105. Wood, S. N. (2017). Generalized Additive Models. Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279
  106. Wulder, M. A., White, J. C., Loveland, T. R., Woodcock, C. E., Belward, A. S., Cohen, W. B., Fosnight, E. A., Shaw, J., Masek, J. G., & Roy, D. P. (2016). The global Landsat archive: Status, consolidation, and direction. Remote Sensing of Environment, 185, 271–283. https://doi.org/10.1016/j.rse.2015.11.032
  107. Żmihorski, M., Chylarecki, P., Orczewska, A., & Wesołowski, T. (2018). Białowieża Forest: A new threat. Science, 361(6399), 238–238. https://doi.org/10.1126/science.aau2708

Puoi leggere altri articoli dello stesso autore/i

<< < 1 2 3 4 5 6 > >>