{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:31:02Z","timestamp":1775147462977,"version":"3.50.1"},"reference-count":44,"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\/"}],"funder":[{"DOI":"10.13039\/501100003500","name":"Universit\u00e0 degli Studi di Padova","doi-asserted-by":"publisher","award":["UNI-IMPRESA 2018"],"award-info":[{"award-number":["UNI-IMPRESA 2018"]}],"id":[{"id":"10.13039\/501100003500","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0\u2013100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation.<\/jats:p>","DOI":"10.3390\/rs13081541","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"1541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0948-1691","authenticated-orcid":false,"given":"Marco","family":"Piragnolo","sequence":"first","affiliation":[{"name":"Department of Land, Environment, Agriculture and Forestry, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"},{"name":"CIRGEO, Interdepartmental Research Center of Geomatics, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-6406","authenticated-orcid":false,"given":"Francesco","family":"Pirotti","sequence":"additional","affiliation":[{"name":"Department of Land, Environment, Agriculture and Forestry, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"},{"name":"CIRGEO, Interdepartmental Research Center of Geomatics, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"}]},{"given":"Carlo","family":"Zanrosso","sequence":"additional","affiliation":[{"name":"Department of Land, Environment, Agriculture and Forestry, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9515-7657","authenticated-orcid":false,"given":"Emanuele","family":"Lingua","sequence":"additional","affiliation":[{"name":"Department of Land, Environment, Agriculture and Forestry, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2089-3892","authenticated-orcid":false,"given":"Stefano","family":"Grigolato","sequence":"additional","affiliation":[{"name":"Department of Land, Environment, Agriculture and Forestry, University of Padua, Viale dell\u2019Universit\u00e0 16, 35020 Legnaro, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.1007\/s10980-020-01147-w","article-title":"The Influence of Land Abandonment on Forest Disturbance Regimes: A Global Review","volume":"35","author":"Mantero","year":"2020","journal-title":"Landsc. Ecol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4013","DOI":"10.1111\/gcb.15118","article-title":"Climate Change Causes Critical Transitions and Irreversible Alterations of Mountain Forests","volume":"26","author":"Albrich","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Diaz, H.F. (2003). Climatic Change in Mountain Regions: A Review of Possible Impacts. Climate Variability and Change in High Elevation Regions: Past, Present & Future. Advances in Global Change Research, Springer.","DOI":"10.1007\/978-94-015-1252-7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1046\/j.1365-2486.2003.00684.x","article-title":"Natural Disturbances in the European Forests in the 19th and 20th Centuries","volume":"9","author":"Schelhaas","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s11027-010-9243-0","article-title":"Assessing Risk and Adaptation Options to Fires and Windstorms in European Forestry","volume":"15","author":"Schelhaas","year":"2010","journal-title":"Mitig. Adapt. Strat. Glob. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11069-019-03642-z","article-title":"Satellite-Based Analysis of the Spatial Patterns of Fire- and Storm-Related Forest Disturbances in the Ural Region, Russia","volume":"97","author":"Shikhov","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46397","DOI":"10.1038\/srep46397","article-title":"Increasing Large Scale Windstorm Damage in Western, Central and Northern European forests, 1951\u20132010","volume":"7","author":"Gregow","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agrformet.2009.08.010","article-title":"Increasing Storm Damage to Forests in Switzerland from 1858 to 2007","volume":"150","author":"Usbeck","year":"2010","journal-title":"Agric. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.foreco.2013.06.017","article-title":"Impacts and Underlying Factors of Landscape-Scale, Historical Disturbance of Mountain Forest Identified Using Archival Documents","volume":"305","author":"Wild","year":"2013","journal-title":"Ecol. Manag."},{"key":"ref_10","unstructured":"Gardiner, B., Blennow, K., Carnus, J.-M., Fleischer, P., Ingemarson, F., Landmann, G., Lindner, M., Marzano, M., Nicoll, B., and Orazio, C. (2010). Destructive Storms in European Forests: Past and Forthcoming Impacts. Final Report to European Commission-DG Environment, European Forest Institute."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Biolchi, S., Denamiel, C., Devoto, S., Korbar, T., Macovaz, V., Scicchitano, G., Vilibi\u0107, I., and Furlani, S. (2019). Impact of the October 2018 Storm Vaia on Coastal Boulders in the Northern Adriatic Sea. Water (Switzerland), 11.","DOI":"10.3390\/w11112229"},{"key":"ref_12","first-page":"3","article-title":"Forest Damage Inventory After the \u201cVaia\u201d Storm in Italy","volume":"16","author":"Chirici","year":"2019","journal-title":"Riv. Selvic. Ed. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/essd-12-257-2020","article-title":"A Spatially Explicit Database of Wind Disturbances in European Forests over the Period 2000\u20132018","volume":"12","author":"Forzieri","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"023001","DOI":"10.1103\/PhysRevE.93.023001","article-title":"Critical Wind Speed at Which Trees Break","volume":"93","author":"Virot","year":"2016","journal-title":"Phys. Rev. E"},{"key":"ref_15","unstructured":"(2021, January 18). Copernicus Emergency Management Service Mapping. Available online: https:\/\/emergency.copernicus.eu\/mapping\/list-of-components\/EMSR334."},{"key":"ref_16","first-page":"16","article-title":"Forest Mapping and Species Composition Using Supervised per Pixel Classification of Sentinel-2 Imagery","volume":"22","author":"Bolyn","year":"2018","journal-title":"Biotechnol. Agron. Soc. Environ."},{"key":"ref_17","first-page":"25","article-title":"Remote Sensing, Natural Hazards and the Contribution of ESA Sentinels Missions","volume":"6","author":"Poursanidis","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vaglio Laurin, G., Francini, S., Luti, T., Chirici, G., Pirotti, F., and Papale, D. (2020). Satellite Open Data to Monitor Forest Damage Caused by Extreme Climate-Induced Events: A Case Study of the Vaia Storm in Northern Italy. Int. J. Res.","DOI":"10.1093\/forestry\/cpaa043"},{"key":"ref_20","unstructured":"Huete, A., Justice, C., and Van Leeuwen, W. (1999). MODIS Vegetation Index (MOD13), The University of Arizona."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting Trend and Seasonal Changes In Satellite Image Time Series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0034-4257(93)90096-G","article-title":"Soil Background Effects on Reflectance-Based Crop Coefficients for Corn","volume":"46","author":"Bausch","year":"1993","journal-title":"Remote Sens. Envrion."},{"key":"ref_23","first-page":"1966","article-title":"Use of Vegetation Indices in Forested Regions: Issues of Linearity and Saturation","volume":"4","author":"Huete","year":"1997","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"S296","DOI":"10.4039\/tce.2016.11","article-title":"Remote Sensing of Forest Pest Damage: A Review and Lessons Learned from a Canadian Perspective","volume":"148","author":"Hall","year":"2016","journal-title":"Can. Entomol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"377","DOI":"10.5424\/fs\/2013223-04417","article-title":"Remote Monitoring of Forest Insect Defoliation. A review","volume":"22","author":"Olthoff","year":"2013","journal-title":"For. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1002\/rse2.93","article-title":"Sentinel-2 Accurately Maps Green-Attack Stage of European Spruce Bark Beetle (Ips typographus, L.) Compared with Landsat-8","volume":"5","author":"Abdullah","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.06.015","article-title":"Detection of Spruce Beetle-Induced Tree Mortality using High- and Medium-Resolution Remotely Sensed Imagery","volume":"168","author":"Hart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2006.03.012","article-title":"Assessment of Quickbird High Spatial Resolution Imagery to Detect Red Attack Damage Due to Mountain Pine Beetle Infestation","volume":"103","author":"Coops","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2005.12.010","article-title":"Estimating the Probability of Mountain Pine Beetle Red-Attack Damage","volume":"101","author":"Wulder","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.1016\/j.rse.2008.01.010","article-title":"Multi-Temporal Analysis of High Spatial Resolution Imagery for Disturbance Monitoring","volume":"112","author":"Wulder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing leaf pigment content and activity with a reflectometer","volume":"143","author":"Gamon","year":"1999","journal-title":"New Phytol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1016\/j.rse.2009.01.013","article-title":"Prediction and Assessment of Bark Beetle-Induced Mortality of Lodgepole Pine Using Estimates Of Stand Vigor Derived From Remotely Sensed Data","volume":"113","author":"Coops","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/0034-4257(84)90018-X","article-title":"Comparisons of the Dimensionality and Features of Simulated Landsat-4 MSS and TM data","volume":"14","author":"Crist","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3680","DOI":"10.1016\/j.rse.2008.05.005","article-title":"Estimation of Insect Infestation Dynamics Using a Temporal Sequence of Landsat Data","volume":"112","author":"Goodwin","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of Forest Harvest Type using Multiple Dates of Landsat TM Imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2018.01.017","article-title":"Understanding the Temporal Dimension of the Red-Edge Spectral Region for Forest Decline Detection using High-Resolution Hyperspectral and Sentinel-2a Imagery","volume":"137","author":"Hornero","year":"2018","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_38","unstructured":"Bruzzone, L., Bovolo, F., and Benediktsson, J.A. (2017). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, SPIE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1198\/106186006X94072","article-title":"Unsupervised Learning With Random Forest Predictors","volume":"15","author":"Shi","year":"2006","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_41","first-page":"572","article-title":"Data Mining with Neural Networks and Support Vector Machines Using the R\/rminer Tool","volume":"Volume 6171","author":"Cortez","year":"2010","journal-title":"Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2012.10.039","article-title":"Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models","volume":"225","author":"Cortez","year":"2013","journal-title":"Inf. Sci. (N. Y.)"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs4040810","article-title":"Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data","volume":"4","author":"Eckert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sanchez, A.H., Picoli, M.C.A., Camara, G., Andrade, P.R., Chaves, M.E.D., Lechler, S., Soares, A.R., Marujo, R.F.B., Sim\u00f5es, R.E.O., and Ferreira, K.R. (2020). Comparison of Cloud Cover Detection Algorithms on Sentinel-2 Images of the Amazon Tropical Forest. Remote Sens., 12.","DOI":"10.3390\/rs12081284"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:36Z","timestamp":1760161716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,15]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081541"],"URL":"https:\/\/doi.org\/10.3390\/rs13081541","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,15]]}}}