{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T22:57:11Z","timestamp":1785279431919,"version":"3.55.0"},"reference-count":81,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Connecting European Facility Programme of the European Union","award":["INEA\/CEF\/ICT\/A2018\/1815462"],"award-info":[{"award-number":["INEA\/CEF\/ICT\/A2018\/1815462"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle\u2013infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetles.<\/jats:p>","DOI":"10.3390\/rs14133135","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T22:43:28Z","timestamp":1656542608000},"page":"3135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9850-8985","authenticated-orcid":false,"given":"Michele","family":"Dalponte","sequence":"first","affiliation":[{"name":"Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach, 38098 San Michele all\u2019Adige, TN, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4867-1837","authenticated-orcid":false,"given":"Yady Tatiana","family":"Solano-Correa","sequence":"additional","affiliation":[{"name":"Environmental Research Group, Biology Department, University of Cauca, Calle 5 #4-70, Popay\u00e1n 190003, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3150-8395","authenticated-orcid":false,"given":"Lorenzo","family":"Frizzera","sequence":"additional","affiliation":[{"name":"Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach, 38098 San Michele all\u2019Adige, TN, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7697-5793","authenticated-orcid":false,"given":"Damiano","family":"Gianelle","sequence":"additional","affiliation":[{"name":"Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach, 38098 San Michele all\u2019Adige, TN, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","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":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1038\/s41467-021-21399-7","article-title":"Emergent Vulnerability to Climate-Driven Disturbances in European Forests","volume":"12","author":"Forzieri","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Eagar, C., and Adams, M.B. (1992). The Dendroecology of Red Spruce Decline. Ecology and Decline of Red Spruce in the Eastern United States, Springer. Ecological Studies.","DOI":"10.1007\/978-1-4612-2906-3"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Eagar, C., and Adams, M.B. (1992). Ecology and Decline of Red Spruce in the Eastern United States, Springer. Ecological Studies.","DOI":"10.1007\/978-1-4612-2906-3"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"438","DOI":"10.2307\/2995893","article-title":"Decline of Red Spruce in the Adirondacks, New York","volume":"111","author":"Scott","year":"1984","journal-title":"Bull. Torrey Bot. Club"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.tree.2019.06.002","article-title":"Bark Beetle Population Dynamics in the Anthropocene: Challenges and Solutions","volume":"34","author":"Biedermann","year":"2019","journal-title":"Trends Ecol. Evol."},{"key":"ref_7","unstructured":"(1974). Southern Pine Beetle Suppression Strategy in Southeastern U.S.: Environmental Impact Statement, Northwestern University."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s13595-013-0265-0","article-title":"Drought-Induced Decline and Mortality of Silver Fir Differ among Three Sites in Southern France","volume":"71","author":"Cailleret","year":"2014","journal-title":"Ann. For. Sci."},{"key":"ref_9","unstructured":"Niemann, K.O., and Visintini, F. (2005). Assessment of Potential for Remote Sensing Detection of Bark Beetle-Infested Areas during Green Attack: A Literature Review."},{"key":"ref_10","unstructured":"Weng, Q. (2019). Remote Sensing for Sustainability, Taylor & Francis. [1st ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rse.2005.03.007","article-title":"Detection of Red Attack Stage Mountain Pine Beetle Infestation with High Spatial Resolution Satellite Imagery","volume":"96","author":"White","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.foreco.2005.09.021","article-title":"Surveying Mountain Pine Beetle Damage of Forests: A Review of Remote Sensing Opportunities","volume":"221","author":"Wulder","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/S0034-4257(03)00112-3","article-title":"Sensitivity of the Thematic Mapper Enhanced Wetness Difference Index to Detect Mountain Pine Beetle Red-Attack Damage","volume":"86","author":"Skakun","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"102335","article-title":"Early Detection of Bark Beetle Infestation in Norway Spruce Forests of Central Europe Using Sentinel-2","volume":"100","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s40725-021-00142-x","article-title":"Bark Beetle Outbreaks in Europe: State of Knowledge and Ways Forward for Management","volume":"7","author":"Krokene","year":"2021","journal-title":"Curr. For. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Solano-Correa, Y.T., Carcereri, D., Bovolo, F., and Bruzzone, L. (2019, January 9\u201312). Identification of Non-Photosynthetic Vegetation Areas in Sentinel-2 Satellite Image Time Series. Proceedings of the SPIE Remote Sensing: Image and Signal Processing for Remote Sensing XXV, Strasbourg, France.","DOI":"10.1117\/12.2533761"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Solano-Correa, Y.T., Bovolo, F., Bruzzone, L., and Fern\u00e1ndez-Prieto, D. (2018, January 22\u201327). Automatic Derivation of Cropland Phenological Parameters by Adaptive Non-Parametric Regression of Sentinel-2 NDVI Time Series. Proceedings of the IGARSS 2018, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519264"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.foreco.2013.07.043","article-title":"Forecasting Potential Bark Beetle Outbreaks Based on Spruce Forest Vitality Using Hyperspectral Remote-Sensing Techniques at Different Scales","volume":"308","author":"Lausch","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fernandez-Carrillo, A., Pato\u010dka, Z., Dobrovoln\u00fd, L., Franco-Nieto, A., and Revilla-Romero, B. (2020). Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data. Remote Sens., 12.","DOI":"10.3390\/rs12213634"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"283","DOI":"10.14358\/PERS.69.3.283","article-title":"Mountain Pine Beetle Red-Attack Forest Damage Classification Using Stratified Landsat TM Data in British Columbia, Canada","volume":"69","author":"Franklin","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","first-page":"295","article-title":"Applicability of a Vegetation Indices-Based Method to Map Bark Beetle Outbreaks in the High Tatra Mountains","volume":"58","author":"Bucha","year":"2015","journal-title":"Ann. For. Res."},{"key":"ref_22","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_23","unstructured":"Yang, S. (2019). Detecting Bark Beetle Damage with Sentinel-2 Multi-Temporal Data in Sweden. [Master\u2019s Thesis, Lund University]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119984","DOI":"10.1016\/j.foreco.2021.119984","article-title":"Comparison of Field Survey and Remote Sensing Techniques for Detection of Bark Beetle-Infested Trees","volume":"506","year":"2022","journal-title":"For. Ecol. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hellwig, F.M., Stelmaszczuk-G\u00f3rska, M.A., Dubois, C., Wolsza, M., Truckenbrodt, S.C., Sagichewski, H., Chmara, S., Bannehr, L., Lausch, A., and Schmullius, C. (2021). Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements. Remote Sens., 13.","DOI":"10.3390\/rs13224659"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1080\/01431160600944028","article-title":"Detecting Mountain Pine Beetle Red Attack Damage with EO-1 Hyperion Moisture Indices","volume":"28","author":"White","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","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_29","first-page":"101900","article-title":"Timing of Red-Edge and Shortwave Infrared Reflectance Critical for Early Stress Detection Induced by Bark Beetle (Ips typographus, L.) Attack","volume":"82","author":"Abdullah","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Abdullah, H., Darvishzadeh, R., Skidmore, A.K., and Heurich, M. (2019). Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation. Remote Sens., 11.","DOI":"10.3390\/rs11040398"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gomez, D.F., Ritger, H.M.W., Pearce, C., Eickwort, J., and Hulcr, J. (2020). Ability of Remote Sensing Systems to Detect Bark Beetle Spots in the Southeastern US. Forests, 11.","DOI":"10.3390\/f11111167"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1080\/01431160903186277","article-title":"Curve Fitting of Time-Series Landsat Imagery for Characterizing a Mountain Pine Beetle Infestation","volume":"31","author":"Goodwin","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112240","DOI":"10.1016\/j.rse.2020.112240","article-title":"Early Detection of Forest Stress from European Spruce Bark Beetle Attack, and a New Vegetation Index: Normalized Distance Red & SWIR (NDRS)","volume":"255","author":"Huo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5696","DOI":"10.3390\/rs6065696","article-title":"Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data","volume":"6","author":"Liang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.foreco.2014.02.037","article-title":"Spatial and Temporal Patterns of Landsat-Based Detection of Tree Mortality Caused by a Mountain Pine Beetle Outbreak in Colorado, USA","volume":"322","author":"Meddens","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2015.09.019","article-title":"Characterizing Spectral\u2013Temporal Patterns of Defoliator and Bark Beetle Disturbances Using Landsat Time Series","volume":"170","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112560","DOI":"10.1016\/j.rse.2021.112560","article-title":"Detecting Subtle Change from Dense Landsat Time Series: Case Studies of Mountain Pine Beetle and Spruce Beetle Disturbance","volume":"263","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_38","first-page":"102206","article-title":"Mapping Forest Windthrows Using High Spatial Resolution Multispectral Satellite Images","volume":"93","author":"Dalponte","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","unstructured":"Sboarina, C., and Cescatti, A. (2004). II Clima Del Trentino\u2013Distribuzione Spaziale Delle Principali Variabili Climatiche [The Climate of Trentino\u2013Spatial Distribution of the Principal Climatic Variables], Centro di Ecologia Alpina, Viote del Monte Bondone. Report 33."},{"key":"ref_40","unstructured":"(2022, June 14). Meteotrentino. Available online: http:\/\/storico.meteotrentino.it\/web.htm?ppbm=T0409&rs&1&df."},{"key":"ref_41","unstructured":"(2021, August 04). ESA, S.-2 Spatial\u2014Resolutions\u2014Sentinel-2 MSI\u2014User Guides\u2014Sentinel Online\u2014Sentinel Online. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-2-msi\/resolutions\/spatial."},{"key":"ref_42","unstructured":"(2021, March 15). Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_43","first-page":"1120612021","article-title":"LidR: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yu, X., Hyypp\u00e4, J., Litkey, P., Kaartinen, H., Vastaranta, M., and Holopainen, M. (2017). Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning. Remote Sens., 9.","DOI":"10.3390\/rs9020108"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.isprsjprs.2010.04.003","article-title":"Range and AGC Normalization in Airborne Discrete-Return LiDAR Intensity Data for Forest Canopies","volume":"65","author":"Korpela","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-Centric Mapping of Forest Carbon Density from Airborne Laser Scanning and Hyperspectral Data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5611","DOI":"10.1002\/ece3.4089","article-title":"How to Map Forest Structure from Aircraft, One Tree at a Time","volume":"8","author":"Dalponte","year":"2018","journal-title":"Ecol. Evol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ecolind.2017.10.066","article-title":"Predicting Stem Diameters and Aboveground Biomass of Individual Trees Using Remote Sensing Data","volume":"85","author":"Dalponte","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Nguyen, H.M., Demir, B., and Dalponte, M. (2019). A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11242948"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Versace, S., Gianelle, D., Frizzera, L., Tognetti, R., Garf\u00ec, V., and Dalponte, M. (2019). Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data. Remote Sens., 11.","DOI":"10.3390\/rs11232734"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.rse.2017.09.007","article-title":"Utility of Multitemporal Lidar for Forest and Carbon Monitoring: Tree Growth, Biomass Dynamics, and Carbon Flux","volume":"204","author":"Zhao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"Hijmans, R.J., Etten, J.V., Sumner, M., Cheng, J., Baston, D., Bevan, A., Bivand, R., Busetto, L., Canty, M., and Fasoli, B. (2022, June 22). Raster: Geographic Data Analysis and Modeling. Available online: https:\/\/cran.r-project.org\/web\/packages\/raster\/index.html."},{"key":"ref_53","unstructured":"Leutner, B., Horning, N., Schwalb-Willmann, J., and Hijmans, R.J. (2022, June 22). RStoolbox: Tools for Remote Sensing Data Analysis. Available online: https:\/\/cran.r-project.org\/web\/packages\/RStoolbox\/index.html."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/BF00031911","article-title":"GEMI: A Non-Linear Index to Monitor Global Vegetation from Satellites","volume":"101","author":"Pinty","year":"1992","journal-title":"Vegetatio"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS Terrestrial Chlorophyll Index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/10106049109354290","article-title":"Mapping Burns and Natural Reforestation Using Thematic Mapper Data","volume":"6","author":"Caselles","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_63","unstructured":"Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kortrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000). Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground-Based Multispectral Data, American Society of Agronomy."},{"key":"ref_64","unstructured":"Rouse, J., Haas, R.H., Schell, J.A., and Deering, D. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and Limits of Vegetation Indices for LAI and APAR Assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_67","first-page":"750","article-title":"High Spectral Resolution: Determination of Spectral Shifts between the Red and the near Infrared","volume":"11","author":"Guyot","year":"1988","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"530","DOI":"10.2111\/05-201R.1","article-title":"Remote Sensing for Grassland Management in the Arid Southwest","volume":"59","author":"Marsett","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_69","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_70","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":"Photogramm. Eng. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A Review of Vegetation Indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e6227","DOI":"10.7717\/peerj.6227","article-title":"Individual Tree Crown Delineation and Tree Species Classification with Hyperspectral and LiDAR Data","volume":"2019","author":"Dalponte","year":"2019","journal-title":"PeerJ"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1139\/er-2018-0034","article-title":"Application of Machine-Learning Methods in Forest Ecology: Recent Progress and Future Challenges","volume":"26","author":"Liu","year":"2018","journal-title":"Environ. Rev."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sothe, C., Dalponte, M., de Almeida, C.M., Schimalski, M.B., Lima, C.L., Liesenberg, V., Miyoshi, G.T., and Tommaselli, A.M.G. (2019). Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data. Remote Sens., 11.","DOI":"10.3390\/rs11111338"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating Search Methods in Feature Selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recognit. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Arag\u00f3n-Roy\u00f3n, F., Jim\u00e9nez-V\u00edlchez, A., Arauzo-Azofra, A., and Ben\u00edtez, J.M. (2020). FSinR: An Exhaustive Package for Feature Selection. arXiv.","DOI":"10.32614\/CRAN.package.FSinR"},{"key":"ref_77","unstructured":"(2021, August 04). CCI-LC, E. ESA CCI Land Cover Website. Available online: http:\/\/www.esa-landcover-cci.org\/."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.5194\/essd-12-1217-2020","article-title":"Annual Dynamics of Global Land Cover and Its Long-Term Changes from 1982 to 2015","volume":"12","author":"Liu","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Klou\u010dek, T., Kom\u00e1rek, J., Surov\u00fd, P., Hrach, K., Janata, P., and Va\u0161\u00ed\u010dek, B. (2019). The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens., 11.","DOI":"10.3390\/rs11131561"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1109\/TGRS.2018.2890404","article-title":"A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images","volume":"57","author":"Paris","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","unstructured":"(2021, December 09). PlanetScope Introducing Next-Generation PlanetScope Imagery. Available online: https:\/\/www.planet.com\/pulse\/introducing-next-generation-planetscope-monitoring\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:37Z","timestamp":1760139637000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":81,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133135"],"URL":"https:\/\/doi.org\/10.3390\/rs14133135","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}