{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T12:43:05Z","timestamp":1775047385240,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T00:00:00Z","timestamp":1562025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006206","name":"\u010cesk\u00e1 Zem\u011bd\u011blsk\u00e1 Univerzita v Praze","doi-asserted-by":"publisher","award":["GIGA 20184206"],"award-info":[{"award-number":["GIGA 20184206"]}],"id":[{"id":"10.13039\/501100006206","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002969","name":"Technologick\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["TJ01000428"],"award-info":[{"award-number":["TJ01000428"]}],"id":[{"id":"10.13039\/501100002969","id-type":"DOI","asserted-by":"publisher"}]},{"name":"OP RDE","award":["CZ.02.1.01\/0.0\/0.0\/15_003\/0000433"],"award-info":[{"award-number":["CZ.02.1.01\/0.0\/0.0\/15_003\/0000433"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkono\u0161e Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%\u201396% across the time periods. The performance of the indices based on near-infrared band was lower.<\/jats:p>","DOI":"10.3390\/rs11131561","type":"journal-article","created":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T02:15:04Z","timestamp":1562033704000},"page":"1561","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5469-6769","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Klou\u010dek","sequence":"first","affiliation":[{"name":"Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, Praha - Suchdol, 165 00 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3505-6755","authenticated-orcid":false,"given":"Jan","family":"Kom\u00e1rek","sequence":"additional","affiliation":[{"name":"Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, Praha - Suchdol, 165 00 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6637-8661","authenticated-orcid":false,"given":"Peter","family":"Surov\u00fd","sequence":"additional","affiliation":[{"name":"Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, Praha - Suchdol, 165 00 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7012-053X","authenticated-orcid":false,"given":"Karel","family":"Hrach","sequence":"additional","affiliation":[{"name":"Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, Praha - Suchdol, 165 00 Prague, Czech Republic"}]},{"given":"P\u0159emysl","family":"Janata","sequence":"additional","affiliation":[{"name":"The Krkonose Mountains National Park Administration, Dobrovsk\u00e9ho 3, 543 01 Vrchlab\u00ed, Czech Republic"}]},{"given":"Bed\u0159ich","family":"Va\u0161\u00ed\u010dek","sequence":"additional","affiliation":[{"name":"The Krkonose Mountains National Park Administration, Dobrovsk\u00e9ho 3, 543 01 Vrchlab\u00ed, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest disturbances under climate change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1016\/j.rse.2011.09.009","article-title":"A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests","volume":"115","author":"Meigs","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1641\/B580607","article-title":"Cross-scale Drivers of Natural Disturbances Prone to Anthropogenic Amplification: The Dynamics of Bark Beetle Eruptions","volume":"58","author":"Raffa","year":"2008","journal-title":"Bioscience"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.rse.2013.09.014","article-title":"Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality","volume":"140","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10661-013-3389-7","article-title":"Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery","volume":"186","author":"Latifi","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/JSTARS.2014.2346955","article-title":"Landsat time series and lidar as predictors of live and dead basal area across five bark beetle-affected forests","volume":"7","author":"Bright","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1890\/110173","article-title":"Cascading impacts of bark beetle-caused tree mortality on coupled biogeophysical and biogeochemical processes","volume":"10","author":"Edburg","year":"2012","journal-title":"Front. Ecol. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1007\/s10531-008-9409-1","article-title":"The European spruce bark beetle Ips typographus in a national park: From pest to keystone species","volume":"17","author":"Rettelbach","year":"2008","journal-title":"Biodivers. Conserv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aukema, J.E., Leung, B., Kovacs, K., Chivers, C., Britton, K.O., Englin, J., Frankel, S.J., Haight, R.G., Holmes, T.P., and Liebhold, A.M. (2011). Economic impacts of Non-Native forest insects in the continental United States. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0024587"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2015.09.019","article-title":"Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series","volume":"170","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hais, M., Wild, J., Berec, L., Br\u016fna, J., Kennedy, R., Braaten, J., and Bro\u017e, Z. (2016). Landsat imagery spectral trajectories-important variables for spatially predicting the risks of bark beetle disturbance. Remote Sens., 8.","DOI":"10.3390\/rs8080687"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1177\/0309133314550670","article-title":"Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery","volume":"38","author":"Latifi","year":"2014","journal-title":"Prog. Phys. Geogr."},{"key":"ref_14","first-page":"199","article-title":"European spruce bark beetle (Ips typographus L.) green attack affects foliar reflectance and biochemical properties","volume":"64","author":"Abdullah","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lausch, A., Erasmi, S., King, D.J., Magdon, P., and Heurich, M. (2016). Understanding forest health with remote sensing-Part I-A review of spectral traits, processes and remote-sensing characteristics. Remote Sens., 8.","DOI":"10.3390\/rs8121029"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lausch, A., Erasmi, S., King, D.J., Magdon, P., and Heurich, M. (2017). Understanding forest health with Remote sensing-Part II-A review of approaches and data models. Remote Sens., 9.","DOI":"10.3390\/rs9020129"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2013.01.002","article-title":"Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery","volume":"132","author":"Meddens","year":"2013","journal-title":"Remote Sens. Environ."},{"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","first-page":"72","DOI":"10.1016\/j.ufug.2018.01.010","article-title":"Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft","volume":"30","author":"Honkavaara","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5589\/m13-027","article-title":"Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar","volume":"39","author":"Bright","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"01005","DOI":"10.1051\/matecconf\/201814501005","article-title":"Potential of multispectral imaging technology for assessment coniferous forests bitten by a bark beetle in Central Bulgaria","volume":"145","author":"Stoyanova","year":"2018","journal-title":"MATEC Web Conf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/10095020.2017.1416994","article-title":"Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands","volume":"21","author":"Brovkina","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_23","first-page":"711","article-title":"Use of a multispectral UAV photogrammetry for detection and tracking of forest disturbance dynamics","volume":"41","author":"Langhammer","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"9","article-title":"The potential of Unmanned Aerial Systems: A tool towards precision classification of hard-to-distinguish vegetation types?","volume":"71","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","first-page":"1","article-title":"Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring","volume":"8","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.apgeog.2019.02.002","article-title":"Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success","volume":"104","author":"Fogl","year":"2019","journal-title":"Appl. Geogr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2016.1264028","article-title":"Determining tree height and crown diameter from high-resolution UAV Determining tree height and crown diameter from high-resolution UAV imagery","volume":"38","author":"Panagiotidis","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4786","DOI":"10.1080\/01431161.2018.1434329","article-title":"Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems","volume":"39","author":"Panagiotidis","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"49","article-title":"Remote sensing of forest insect disturbances: Current state and future directions","volume":"60","author":"Senf","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., and Herrera, F. (2019). Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote Sens., 11.","DOI":"10.3390\/rs11060643"},{"key":"ref_33","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_34","unstructured":"(2018, June 20). Exelis Visual Information Solutions ENVI Help 2019. Available online: http:\/\/www.harrisgeospatial.com\/docs\/AtmosphericCo."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"968","DOI":"10.2134\/agronj2005.0200","article-title":"Aerial color infrared photography for determining early in-season nitrogen requirements in corn","volume":"98","author":"Sripada","year":"2006","journal-title":"Agron. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third ERTS Symposium, Washington DC, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote estimation of chlorophyll content in higher plant leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Noguchi, K., Gel, Y.R., Brunner, E., and Frank, K. (2012). Nparld: An R software package for the nonparametric analysis of longitudinal data in factorial experiments. J. Stat. Softw., 50.","DOI":"10.18637\/jss.v050.i12"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e5487","DOI":"10.7717\/peerj.5487","article-title":"Selecting appropriate variables for detecting grassland to cropland changes using high resolution satellite data","volume":"6","author":"Moravec","year":"2018","journal-title":"PeerJ"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.foreco.2016.11.004","article-title":"Spectral evidence of early-stage spruce beetle infestation in Engelmann spruce","volume":"384","author":"Foster","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.28","article-title":"Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery","volume":"5","author":"Richardson","year":"2018","journal-title":"Sci. Data"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1561\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:38Z","timestamp":1760187698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,2]]},"references-count":46,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131561"],"URL":"https:\/\/doi.org\/10.3390\/rs11131561","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,2]]}}}