{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:01:12Z","timestamp":1775624472168,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>On the 29th of October 2018, a storm named \u201cVaia\u201d hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters\u2019 fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60\u20130.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way.<\/jats:p>","DOI":"10.3390\/rs13081530","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"1530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of \u201cVaia\u201d Storm in Friuli Venezia Giulia Region (North-Eastern Italy)"],"prefix":"10.3390","volume":"13","author":[{"given":"Valentina","family":"Olmo","sequence":"first","affiliation":[{"name":"Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy"},{"name":"Department of Life Science, University of Trieste, Via L. Giorgieri 10, 34127 Trieste, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9722-6692","authenticated-orcid":false,"given":"Enrico","family":"Tordoni","sequence":"additional","affiliation":[{"name":"Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, 51005 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3635-8501","authenticated-orcid":false,"given":"Francesco","family":"Petruzzellis","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Via delle Scienze 206, 33100 Udine, Italy"},{"name":"Department of Life Science, University of Trieste, Via L. Giorgieri 10, 34127 Trieste, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0946-4496","authenticated-orcid":false,"given":"Giovanni","family":"Bacaro","sequence":"additional","affiliation":[{"name":"Department of Life Science, University of Trieste, Via L. Giorgieri 10, 34127 Trieste, Italy"}]},{"given":"Alfredo","family":"Altobelli","sequence":"additional","affiliation":[{"name":"Department of Life Science, University of Trieste, Via L. Giorgieri 10, 34127 Trieste, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1093\/forestry\/cpv048","article-title":"Characterising Forest Structure Using Combinations of Airborne Laser Scanning Data, RapidEye Satellite Imagery and Environmental Variables","volume":"89","author":"Dash","year":"2016","journal-title":"Forestry"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Van Westen, C.J. (2013). Remote Sensing and GIS for Natural Hazards Assessment and Disaster Risk Management. Treatise on Geomorphology, Elsevier.","DOI":"10.1016\/B978-0-12-374739-6.00051-8"},{"key":"ref_3","unstructured":"I.P.C.C (2019). Summary for Policymakers. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, IPCC\/IGES."},{"key":"ref_4","unstructured":"Gardiner, B., Schuck, A.R.T., Schelhaas, M.J., Orazio, C., Blennow, K., and Nicoll, B. (2013). Living with Storm Damage to Forests: What Science Can Tell Us, European Forest Institute."},{"key":"ref_5","first-page":"15","article-title":"Storm damage in Europe\u2013An overview","volume":"Volume 3","author":"Gardiner","year":"2013","journal-title":"Living with Storm Damage to Forests: What Science Can Tell Us"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/nclimate1635","article-title":"Consequences of Widespread Tree Mortality Triggered by Drought and Temperature Stress","volume":"3","author":"Anderegg","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nowacki, G.J. (1998). The Effects of Wind Disturbance on Temperate Rain Forest Structure and Dynamics of Southeast Alaska.","DOI":"10.2737\/PNW-GTR-421"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1890\/0012-9658(2000)081[2539:WDFCAS]2.0.CO;2","article-title":"Windthrow Disturbance, Forest Composition, and Structure in the Bull Run Basin, Oregon","volume":"81","author":"Sinton","year":"2000","journal-title":"Ecology"},{"key":"ref_9","first-page":"31","article-title":"Mechanics of wind damage","volume":"Volume 3","author":"Gardiner","year":"2013","journal-title":"Living with Storm Damage to Forests: What Science Can Tell Us"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Perera, A.H., Sturtevant, B.R., and Buse, L.J. (2015). Modeling Windthrow at Stand and Landscape Scales. Simulation Modeling of Forest Landscape Disturbances, Springer International Publishing.","DOI":"10.1007\/978-3-319-19809-5"},{"key":"ref_11","unstructured":"Carraro, V. (2019). La Furia del Vento che ha Danneggiato le Foreste dell\u2019arco Alpino, Centro Studi per L\u2019ambiente Alpino L."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3832\/efor3070-016","article-title":"Forest damage inventory after the \u201cVaia\u201d storm in Italy","volume":"16","author":"Chirici","year":"2019","journal-title":"Forest"},{"key":"ref_13","unstructured":"(2019, October 17). Directorate Space, Security and Migration, European Commission Joint Research Centre (EC JRC). Copernicus Emergency Management Service. Available online: https:\/\/emergency.copernicus.eu\/."},{"key":"ref_14","first-page":"3757","article-title":"Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management","volume":"3757","author":"Chang","year":"2016","journal-title":"Biogeosci. Eur. Geosci. Union"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_16","unstructured":"(2019, November 11). Copernicus Emergency Management Service EMSR334: Wind Storm in North-East of Italy. Available online: https:\/\/emergency.copernicus.eu\/mapping\/list-of-components\/EMSR334."},{"key":"ref_17","unstructured":"Osmer, O. (2014). Metereologico regionale del FVG. Il Clima del Friuli-Venezia Giulia, ARPA FVG\u2013OSMER."},{"key":"ref_18","unstructured":"(2019, August 11). ARPAOSMER Climate Data. Available online: https:\/\/www.meteo.fvg.it\/clima\/clima_fvg."},{"key":"ref_19","unstructured":"GRASS Development Team (2019, November 01). Geographic Resources Analysis Support System (GRASS) Software; Open Source Geospatial Foundation. Available online: http:\/\/grass.osgeo.org."},{"key":"ref_20","first-page":"48","article-title":"La nuova Carta degli habitat del Friuli Venezia Giulia, base per la valutazione ecologica del territorio","volume":"16","author":"Giorgi","year":"2017","journal-title":"Reticula"},{"key":"ref_21","unstructured":"Copernicus Open Access Hub (2019, September 30). Sentinel-2 Data. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Stych, P., Lastovicka, J., Hladky, R., and Paluba, D. (2019). Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8020071"},{"key":"ref_23","first-page":"2355","article-title":"Novel Algorithms for Remote Sensing of Chlorophyll Content in Higher Plant Leaves","volume":"Volume 4","author":"Gitelson","year":"1996","journal-title":"Proceedings of the IGARSS \u201996. 1996 International Geoscience and Remote Sensing Symposium, Lincoln, NE, USA, 27\u201331 May 1996"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolind.2013.01.041","article-title":"NDVI Saturation Adjustment: A New Approach for Improving Cropland Performance Estimates in the Greater Platte River Basin, USA","volume":"30","author":"Gu","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Einzmann, K., Immitzer, M., B\u00f6ck, S., Bauer, O., Schmitt, A., and Atzberger, C. (2017). Windthrow Detection in European Forests with Very High-Resolution Optical Data. Forests, 8.","DOI":"10.3390\/f8010021"},{"key":"ref_27","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Elleithy, K., Sobh, T., Iskander, M., Kapila, V., Karim, M.A., and Mahmood, A. (2010). Stereo Spectral Imaging System for Plant Health Characterization. Technological Developments in Networking, Education and Automation, Springer.","DOI":"10.1007\/978-90-481-9151-2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-Wave Infrared Bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jag.2016.06.020","article-title":"Performance of Vegetation Indices from Landsat Time Series in Deforestation Monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S0034-4257(01)00191-2","article-title":"Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain","volume":"77","author":"Ceccato","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-Destructive Optical Detection of Pigment Changes during Leaf Senescence and Fruit Ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_34","first-page":"183","article-title":"Estimation of Canopy-Average Surface-Specific Leaf Area Using Landsat TM Data","volume":"66","author":"Lymburner","year":"2000","journal-title":"Photogram. Eng. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yu, Y., Saatchi, S., Heath, L.S., LaPoint, E., Myneni, R., and Knyazikhin, Y. (2010). Regional Distribution of Forest Height and Biomass from Multisensor Data Fusion. J. Geophys. Res. Biogeosci., 115.","DOI":"10.1029\/2009JG000995"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis, Springer.","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"ref_38","unstructured":"Pinheiro, J., Bates, D., DebRoy, S., and Sakar, D. (2021, February 15). nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1-152. Available online: https:\/\/CRAN.R-project.org\/package=nlme."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1111\/j.2041-210x.2012.00261.x","article-title":"A General and Simple Method for Obtaining R2 from Generalized Linear Mixed Effects Models","volume":"4","author":"Nakagawa","year":"2013","journal-title":"Methods Ecol. Evol."},{"key":"ref_40","unstructured":"Jaeger, B. (2021, February 15). r2glmm: Computes R Squared for Mixed (Multilevel) Models. R Package Version 0.1.2. Available online: https:\/\/CRAN.R-project.org\/package=r2glmm."},{"key":"ref_41","unstructured":"Lenth, R. (2021, February 15). emmeans: Estimated Marginal Means, Aka Least-Squares Means. R Package Version 1.5.4. Available online: https:\/\/CRAN.R-project.org\/package=emmeans."},{"key":"ref_42","unstructured":"Rcore Team R (2021). A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change Detection Techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.agrformet.2009.09.009","article-title":"Post-Hurricane Forest Damage Assessment Using Satellite Remote Sensing","volume":"150","author":"Wang","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"565","DOI":"10.30638\/eemj.2014.060","article-title":"Detection of Environmental Changes Due to Windthrows Using Landsat 7 ETM+ Satellite Images","volume":"13","author":"Vorovencii","year":"2014","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_46","unstructured":"Yektay, Z. (2019). Sentinel-2 Images for Detection of Wind Damage in Forestry. [Master Thesis, Aalto University]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1146\/annurev.fluid.40.111406.102135","article-title":"Effects of Wind on Plants","volume":"40","year":"2008","journal-title":"Annu. Rev. Fluid Mech."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.02.002","article-title":"Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Data","volume":"90","author":"Maki","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1111\/nph.15644","article-title":"Greater Focus on Water Pools May Improve Our Ability to Understand and Anticipate Drought-induced Mortality in Plants","volume":"223","author":"Anderegg","year":"2019","journal-title":"New Phytol."},{"key":"ref_50","first-page":"745409","article-title":"Remote Sensing of Canopy Water Content: Scaling from Leaf Data to MODIS","volume":"Volume 7454","author":"Gao","year":"2009","journal-title":"Remote Sensing and Modeling of Ecosystems for Sustainability VI, Proceeding of the SPIE Optical Engineering + Application, San Diego, CA, USA, 20 August 2009"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1016\/j.rse.2007.11.014","article-title":"Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery","volume":"112","author":"Yilmaz","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Marusig, D., Petruzzellis, F., Tomasella, M., Napolitano, R., Altobelli, A., and Nardini, A. (2020). Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline. Forests, 11.","DOI":"10.3390\/f11010077"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3390\/jimaging1010085","article-title":"Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy","volume":"1","author":"Grybas","year":"2015","journal-title":"J. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lambers, H., Chapin, F.S., and Pons, T.L. (2008). Plant Physiological Ecology, Springer.","DOI":"10.1007\/978-0-387-78341-3"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.3389\/fpls.2019.01749","article-title":"Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm","volume":"10","author":"Anderegg","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00484-016-1236-6","article-title":"Assessing Plant Senescence Reflectance Index-Retrieved Vegetation Phenology and Its Spatiotemporal Response to Climate Change in the Inner Mongolian Grassland","volume":"61","author":"Ren","year":"2017","journal-title":"Int. J. Biometeorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1751-1097.1999.tb01944.x","article-title":"Environmental Significance of Anthocyanins in Plant Stress Responses","volume":"70","year":"1999","journal-title":"Photochem. Photobiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/nature02403","article-title":"The Worldwide Leaf Economics Spectrum","volume":"428","author":"Wright","year":"2004","journal-title":"Nature"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00442-012-2279-y","article-title":"Responses of Leaf Structure and Photosynthetic Properties to Intra-Canopy Light Gradients: A Common Garden Test with Four Broadleaf Deciduous Angiosperm and Seven Evergreen Conifer Tree Species","volume":"170","author":"Wyka","year":"2012","journal-title":"Oecologia"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(70)80021-9","article-title":"Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation","volume":"1","author":"Knipling","year":"1970","journal-title":"Remote Sens. Environ."},{"key":"ref_61","first-page":"183","article-title":"Leaf Reflectance of Near-Infrared","volume":"40","author":"Gausman","year":"1974","journal-title":"Photogram. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.3732\/ajb.92.9.1425","article-title":"Leaf Architecture and Direction of Incident Light Influence Mesophyll Fluorescence Profiles","volume":"92","author":"Johnson","year":"2005","journal-title":"Am. J. Bot."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of Variability in Canopy Reflectance and the Convergent Properties of Plants: Tansley Review","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.foreco.2004.07.018","article-title":"Ecology and Management of the Spruce Bark Beetle Ips Typographus\u2014A Review of Recent Research","volume":"202","author":"Wermelinger","year":"2004","journal-title":"Forest Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:25Z","timestamp":1760161705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":64,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081530"],"URL":"https:\/\/doi.org\/10.3390\/rs13081530","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]}}}