Vol. 77 No. 1 (2022)
Scientific Articles

Automatic mapping of Italian forest disturbances between 1985 and 2019 using Landsat imagery and Google Earth Engine

Saverio Francini
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura, 13 - 50145 Firenze, Italy.
Costanza Borghi
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura, 13 - 50145 Firenze, Italy.
Giovanni D'Amico
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura, 13 - 50145 Firenze, Italy.
Stefano Santi
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura, 13 - 50145 Firenze, Italy.
Davide Travaglini
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura, 13 - 50145 Firenze, Italy.

Published 2022-03-30


  • remote sensing,
  • algorithms,
  • forest disturbance

How to Cite

Francini, S. ., Borghi, C. ., D’Amico, G., Santi, S., & Travaglini, D. . (2022). Automatic mapping of Italian forest disturbances between 1985 and 2019 using Landsat imagery and Google Earth Engine. L’Italia Forestale E Montana, 77(1), 5-21. https://doi.org/10.36253/ifm-1616


Forests play a key role in the carbon cycle and the fight against climate change. Long-term monitoring of forest dynamics represents a key element for understanding forests transformations due to forest harvestings and disturbances including fires, wind storms, frost or drought events, and pathogen attacks. This work aims at mapping and evaluating the forest disturbances that have occurred in Italy since 1985, using Landsat satellite imagery and apposite algorithms. We predicted about 1.8 million forest disturbances occurring during the observation period. Disturbances ranged between 27.923 ha in 2014 and 261.733 ha in 1985. Most of the forest disturbances have been identified in Sicilia and Calabria. Commission errors fluctuated between 29% in 2012 and 65% in 2001 while omission errors were between 8% in 2014 and 88% in 2003. The results that we present in this work can increase our understanding of Italian forests, and serve as basis for future research, while the methodology we applied can support the production of official statistics on forest disturbances.


  1. Abram N.J., McGregor H.V., Tierney J.E., Evans M.N., McKay N.P., Kaufman, D.S., 2016 - Early
  2. onset of industrial-era warming across the oceans and continents. Nature, 536: 411-418. https://doi.org/10.1038/nature19082
  3. Ascoli D., Chirici G., Francini S., Marchetti M., Motta R., Vacchiano G., 2021 - Forest harvesting in
  4. Europe: a healthy scientific debate. Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, 18: 35-37. https://doi.org/10.3832/efor3892-018
  5. Canadell J.G., Raupach M.R., 2008 - Managing Forests for Climate Change Mitigation. Science,
  6. (5882): 1456-1457. https://doi.org/10.1126/science.1155458
  7. Cannell M.G.R., 2003 - Carbon sequestration and biomass energy offset: theoretical, potential and achievable capacities globally, in Europe and the UK. Biomass and Bioenergy, 24: 97-116. https://doi.org/10.1016/S0961-9534(02)00103-4
  8. Chirici G., Giannetti F., McRoberts R.E., Travaglini D., Pecchi M., Maselli F., Chiesi M., Corona
  9. P., 2020 - Wall-to-wall spatial prediction of growing stock volume based on Italian National
  10. 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
  11. Chirici G., Giannetti F., Travaglini D., Nocentini S., Francini S., D’Amico G., Calvo E. et al., 2019 -
  12. Forest damage inventory after the “Vaia” storm in Italy. Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, 16: 3-9. https://doi.org/10.3832/efor3070-016
  13. Ciancio O., Nocentini S., 2011 - Biodiversity conservation and systemic silviculture: Concepts and applications. Plant Biosystems - An International Journal Dealing with all Aspect Plant Biology, 145: 411-418. https://doi.org/10.1080/11263504.2011.558705
  14. Cohen W.B., Yang Z., Healey S.P., Kennedy R.E., Gorelick N., 2018 - A LandTrendr multispectral
  15. ensemble for forest disturbance detection. Remote Sensing of Environment, 205: 131-140. https://doi.org/10.1016/j.rse.2017.11.015
  16. Corona P., Marchetti M., 2007 - Outlining multi-purpose forest inventories to assess the ecosystem approach in forestry. Plant Biosystems - An International Journal Dealing with all Aspect Plant Biology, 141: 243-251. https://doi.org/10.1080/11263500701401836
  17. D’Amico G., Vangi E., Francini S., Giannetti F., Nicolaci A., Travaglini D., Massai L. et al., 2021
  18. - Are we ready for a National Forest Information System? State of the art of forest maps and airborne laser scanning data availability in Italy. iForest - Biogeosciences and Forestry, 14: 144-154. https://doi.org/10.3832/ifor3648-014
  19. Drever C.R., Peterson G., Messier C., Bergeron Y., Flannigan M., 2006 - Can forest management based on natural disturbances maintain ecological resilience? Canadian Journal of Forest Research, 36: 2285-2299. https://doi.org/10.1139/x06-132
  20. Dynesius M., Hylander K., 2007 - Resilience of bryophyte communities to clear-cutting of boreal stream-side forests. Biological Conservation, 135: 423-434. https://doi.org/10.1016/j.biocon.2006.10.010.
  21. FAO ,2020 - Global Forest Resources Assessment 2020.
  22. Foga S., Scaramuzza P.L., Guo S., Zhu Z., Dilley R.D., Beckmann T., Schmidt G.L. et al., 2017 - Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194: 379-390. https://doi.org/10.1016/j.rse.2017.03.026
  23. Forzieri G., Girardello M., Ceccherini G., Spinoni J., Feyen L., Hartmann H., Beck P.S.A. et al., 2021 - Emergent vulnerability to climate-driven disturbances in European forests. Nature Communications, 12: 1081. https://doi.org/10.1038/s41467-021-21399-7
  24. Francini S., D’Amico G., Mencucci M., Seri G., Gravano, E., Chirici G., 2021a - Remote sensing and automatic procedures: useful tools to monitor forest harvesting. Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, 18: 27-34. https://doi.org/10.3832/efor3835-018
  25. Francini S., McRoberts R.E., D’Amico G., Coops N.C., Hermosilla T., White J.C., Wulder M.A. et
  26. al., 2022 - An open science and open data approach for the statistically robust estimation of forest disturbance areas. International Journal of Applied Earth Observation and Geoinformation, 106: 102663.https://doi.org/10.1016/j.jag.2021.102663
  27. Francini S., McRoberts R.E., Giannetti F., Marchetti M., Scarascia Mugnozza G., Chirici G., 2021b - The Three Indices Three Dimensions (3I3D) algorithm: a new method for forest disturbance mapping and area estimation based on optical remotely sensed imagery. International
  28. Journal of Remote Sensing, 42: 4697-4715. https://doi.org/10.1080/01431161.2021.1899334
  29. Francini S., McRoberts R.E., Giannetti F., Mencucci M., Marchetti M., Scarascia Mugnozza G., Chirici G., 2020 - Near-real time forest change detection using PlanetScope imagery. European Journal of Remote Sensing, 53: 233-244. https://doi.org/10.1080/22797254.2020.1806734
  30. Giannetti F., Pegna R., Francini S., McRoberts R.E., Travaglini D., Marchetti M., Scarascia Mugnozza G., Chirici G., 2020 - A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series. Remote Sensing, 12, 3720.
  31. https://doi.org/10.3390/rs12223720
  32. Gomes V., Queiroz G., Ferreira K., 2020 - An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sensing, 12: 1253. https://doi.org/10.3390/rs12081253
  33. 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
  34. Griffiths P., van der Linden S., Kuemmerle T., Hostert P., 2013 - A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6: 2088-2101.
  35. https://doi.org/10.1109/JSTARS.2012.2228167
  36. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A., Thau D. et al., 2013 - High-Resolution Global Maps of 21st Century Forest Cover Change. Science, 342 (6160): 850-853. https://doi.org/10.1126/science.1244693
  37. Hermosilla T., Wulder M.A., White J.C., Coops N.C., Hobart G.W., 2015 - Regional detection,
  38. characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived timeseries metrics. Remote Sensing of Environment, 170: 121-132. https://doi.org/10.1016/j.rse.2015.09.004
  39. Jin S., Sader S.A., 2005 - MODIS time-series imagery for forest disturbance detection and quantification of patch size effects. Remote Sensing of Environvironment, 99: 462-470. https://doi.org/10.1016/j.rse.2005.09.017
  40. Keith H., Mackey B., Berry S., Lindenmayer D., Gibbons P., 2009 - Estimating carbon carrying
  41. capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error. Global Change Biology, 16 (11): 2971-2989.https://doi.org/10.1111/j.1365-2486.2009.02146.x
  42. Kennedy R.E., Yang Z., Cohen W.B., 2010 - Detecting trends in forest disturbance and recovery
  43. using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment, 114: 2897-2910. https://doi.org/10.1016/j.rse.2010.07.008
  44. Kubat M., Holte R.C., Matwin S., 1998 - Machine learning for the detection of oil spills in satellite radarimages. Machine Learning, 30: 195-215. https://doi.org/10.1023/a:1007452223027
  45. Marcelli A., Mattioli W., Puletti N., Chianucci F., Gianelle D., Grotti M., Chirici G. et al., 2020 -
  46. Large-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information. Silva Fennica, 54. https://doi.org/10.14214/sf.10247
  47. Millar C.I., Stephenson, N.L., 2015 - Temperate forest health in an era of emerging megadisturbance. Science, 349 (6250): 823-826. https://doi.org/10.1126/science.aaa9933
  48. Moriondo M., Good P., Durao R., Bindi M., Giannakopoulos C., Corte-Real J., 2006 - Potential
  49. impact of climate change on fire risk in the Mediterranean area. Climate Research, 31: 85-95.
  50. https://doi.org/10.3354/cr031085
  51. Nabuurs G.-J., 1996 - Significance of wood products in forest sector carbon balances (MJ Apps & DT Price, A c. di). Forest eco. Springer, Berlin.
  52. Nocentini S., 2015 - Managing forests as complex adaptive systems: an issue of theory and method. In: Atti del Secondo Congresso Internazionale di Selvicoltura = Proceedings of the Second International Congress of Silviculture. Accademia Italiana di Scienze
  53. Forestali, p. 913-918.
  54. Nocentini S., Buttoud G., Ciancio O., Corona, P., 2017 - Managing forests in a changing world: the need for a systemic approach. A review. Forest Systems, 26, eR01. https://doi.org/10.5424/fs/2017261-09443
  55. Olofsson P., Foody G.M., Herold M., Stehman S.V., Woodcock C.E., Wulder M.A., 2014 - Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148: 42-57. https://doi.org/10.1016/j.rse.2014.02.015
  56. Parisi F., Frate L., Lombardi F., Tognetti R., Campanaro A., Biscaccianti A.B., Marchetti M., 2020 - Diversity patterns of Coleoptera and saproxylic communities in unmanaged forests of Mediterranean mountains. Ecological Indicators, 110, 105873. https://doi.org/10.1016/j.ecolind.2019.105873.
  57. Patto J.V., Rosa R., 2022 - Adapting to frequent fires: Optimal forest management revisited. Journal of Environmental Economics and Management revisited, 111, 102570. https://doi.org/10.1016/j.jeem.2021.102570
  58. Riccioli F., Fratini R., Marone E., Fagarazzi C., Calderisi M., Brunialti G., 2020 - Indicators of
  59. sustainable forest management to evaluate the socioeconomic functions of coppice in Tuscany, Italy. Socioeconomic Planning Sciences, 70, 100732. https://doi.org/10.1016/j.seps.2019.100732
  60. Seidl R., Schelhaas M.-J., Lexer M.J., 2011 - Unraveling the drivers of intensifying forest disturbance regimes in Europe. Global Change Biology, 17. https://doi.org/10.1111/j.1365-2486.2011.02452.x
  61. Senf C., Buras A., Zang C.S., Rammig A., Seidl R., 2020 - Excess forest mortality is consistently linked to drought across Europe. Nature Communications, 11, 6200. https://doi.org/10.1038/s41467-020-19924-1
  62. Senf C., Seidl, R., 2020 - Mapping the forest disturbance regimes of Europe. Nature Sustainability, 4: 63-70.https://doi.org/10.1038/s41893-020-00609-y
  63. Senf C., Seidl R., 2021 - Storm and fire disturbances in Europe: Distribution and trends. Global Change Biology, 27: 3605-3619. https://doi.org/10.1111/gcb.15679
  64. Stephens S.L., Burrows N., Buyantuyev A., Gray R.W., Keane R.E., Kubian R., Liu S. et al.,
  65. - Temperate and boreal forest mega-fires: characteristics and challenges. Frontiers in Ecology and the Environment, 12: 115-122. https://doi.org/10.1890/120332
  66. Tognetti R., Smith M., Panzacchi P., 2022 - Climate-Smart Forestry in Mountain Regions. Springer International Publishing, Cham.
  67. White J.C., Wulder M.A., Hobart G.W., Luthe J.E., Hermosilla T., Griffiths P., Coops N.C. et al., 2014 - Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science. Canadian Journal of Remote Sensing, 40: 192-212. https://doi.org/10.1080/07038992.2014.945827
  68. Woodcock C.E., Allen R., Anderson M., Belward A., Bindschadler R., Cohen W., Gao F. et al.,
  69. - Free Access to Landsat Imagery. Science, 320 (5879): 1011-1011. https://doi.org/10.1126/science.320.5879.1011a
  70. Zhu Z., Wang S., Woodcock C.E., 2015 - Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2
  71. images. Remote Sensing of Environment, 159: 269-277. https://doi.org/10.1016/j.rse.2014.12.014
  72. Zhu Z., Woodcock C.E., 2012 - Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83-94. https://doi.org/10.1016/j.rse.2011.10.028