Research article

Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains

Mária Havašová , Tomáš Bucha, Ján Ferenčík, Rastislav Jakuš

Mária Havašová
Institute of Forest Ecology, Slovak Academy of Sciences, Ľudovíta Štúra 2, 960 53 Zvolen, Slovakia. Email:
Tomáš Bucha
National Forest Centre, T. G. Masaryka 22, 960 92 Zvolen, Slovakia
Ján Ferenčík
Research station Tatra national park, 059 60 Tatranská Lomnica, Slovakia
Rastislav Jakuš
Institute of Forest Ecology, Slovak Academy of Sciences, Ľudovíta Štúra 2, 960 53 Zvolen, Slovakia, Department of Forest Protection and Entomology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 1176, 165 21 Praha 6 - Suchdol, Czech Republic

Online First: May 18, 2015
Havašová, M., Bucha, T., Ferenčík, J., Jakuš, R. 2015. Applicability of a vegetation indices-based method to map bark beetle outbreaks in the High Tatra Mountains. Annals of Forest Research DOI:10.15287/afr.2015.388

Automatic identification of forest patches disturbed by the spruce bark beetle Ips typographus L. is crucial to reveal the rules of following bark beetle outbreaks on the landscape scale. Landsat imagery provides free resources to outline past and present gradations of bark beetle outbreaks (BBOs). The objective of this study is to identify the most sensitive vegetation index through different method of vegetation index differencing to identify past and actual bark beetle outbreaks. Six Landsat Thematic Mapper (TM) images, from 2005–2009 and 2011, were converted into selected vegetation indices (VIs) sensitive to conifer tree health in a Norway spruce–dominated forest in the High Tatra Mountains. The Vegetation Condition Index (VCI), Moisture Stress Index (MSI), Normalised Difference Moisture Index (NDMI), Normalised Difference Vegetation Index (NDVI),   Disturbance Index (DI) and Changed Disturbance Index (DI´) were calculated separately for every year, and the methodology of vegetation index differencing was applied to multiple two-year time periods (2005–2006, 2006–2007, 2007–2008, 2008–2009 and 2010–2011), thus producing the Changed Vegetation Index (ΔVI). A set of thresholds was established on ΔVI to classify disturbed and undisturbed forest due to BBOs; the sensitivity of different VIs to identify BBO was equally evaluated. The highest accuracies of classifications were reached in 2007 and 2011 (kappa index of agreement >70% and >40%, respectively), which were characterised by an epidemic phase of a BBO. All selected VIs were highly sensitive to BBOs, except for NDVI. The stable threshold value for change detection is not widely applicable to detect past forest disturbances caused by bark beetles, however. Finally, for further research of the epidemic phases of BBOs, we recommend the utilisation of the vegetation indices VCI, MSI and NDMI to detect BBOs because of their simplicity and easy interpretability.

Aho K.,2014.. asbio: A collection of statistical tools for biologists. R package version 1.0-5.

Ardö J., 1998. Remote Sensing of Forest Decline in theCzechRepublic. PhD thesis, Department of Physical Geography,LundUniversity,Lund,Sweden, 47 p.

Bucha T., 1999. Classification of tree species composition inSlovakiafrom satelite images as a part of monitoring of forest ecosystem biodiversity. Acta Instituti Forestalis 9: 65–84.

Bucha T., Stibig H.J., 2008. Analysis of MODIS imagery for detection of clear cuts in the boreal forest in north-west Russia. Remote Sensing of Environment: 112(5): 2416–2429. DOI: 10.1016/j.rse.2007.11.008

Cohen J., 1960. ACoefficient of Agreement for Nominal Scales. Educational and Psychological Measurement: 20(1): 37–46. DOI: 10.1177/001316446002000104

Collins J.B., Woodcock C.E.,1996. An Assessment of Several Linear Change Detection Techniques for MappingForestMortality Using Multitemporal Landsat TM Data. Remote Sensing of Environment: 77: 66–77. DOI: 10.1016/0034-4257(95)00233-2

Coops N.C., Waring R.H., Wulder M.A., White J.C., 2009.Prediction and assesment of bark beete-induced mortality of lodgepole pine using estimates of stand vigor derived from remotely sensed data. Remote Sensing of Environment: 113(5): 1058–1066Coppin P.R.,BauerM.E., 1994. Processing of Multitemporal Landsat TM Imagery to Change Features. IEEE Transactions on Geoscience and Remote Sensing: 32(4): 918–927

Crist E.P., Cicone R.C.,1984. APhysically-Based Transformation of Thematic Mapper Data-The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing: GE-221(3): 256–263Fawcett T., 2006. An introduction to ROC analysis. Pattern Recognition Letters: 27(8): 861–874

Drusch M., Del Bello U., Carlier S., Colin O., Fernandez V., Gascon F., Hoersch B., Isola C. Laberinti P., Martimort P., Meygret A., Spoto F., Sy O., Marchese F., Bargellini P., 2012. Sentinel-2: ESA's Optical High-Resolution Missionfor GMES Operational Services. Remote Sensing of Environment: 120: 25–36. DOI: 10.1016/j.rse.2011.11.026

Goodwin N.R., Coops N.C., Wulder M.A., Gillanders S., Schroeder T.A., Nelson T., 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment: 112(9): 3680–3689. DOI: 10.1016/j.rse.2008.05.005

Griffiths P., Kuemmerle T., Baumann M., Radeloff V. C., Abrudan I. V., Lieskovsky J., Munteanu C., Ostapowicz K., Hostert, P. (2014). Forestdisturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment: 151, 72–88. DOI: 10.1016/j.rse. 2013.04.022

Hais M., Jonášová M., Langhammer J., Kučera T., 2009. Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data. Remote Sensing of Environment: 113(4): 835–845. DOI: 10.1016/j.rse.2008.12.012

Healey S.P., Cohen W.B., Zhigiang Y., Krankina O.N., 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment: 97(3): 301–310. DOI: 10.1016/j.rse.2005.05.009

Healey S.P., Zhigiang Y., Cohen W.B., Pierce D.J., 2006. Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sensing of Environment: 101(1): 115–126. DOI: 10.1016/j.rse.2005.12.006

Hijmans R.J., 2014. raster: raster: Geographic data analysis and modeling. R package version 2.2-31.

Huang C., Song K., Kim S., Townshend J.R.G., DavisP., Masek J.G., Goward S.N., 2008. Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment: 112(3): 970–985. DOI: 10.1016/j.rse.2007.07.023

JakubauskasM.E., Price K.P., 1997. Empirical Relationships between Structural and Spectral Factors of Yellowstone Lodgepole Pine Forests. Photogrammetric Engineering & Remote Sensing: 63(1): 1375–1381.

Jakuš R., Grodzki W., Ježík M., Jachym M., 2003. Definition of Spatial Patterns of Bark Beetle Ips typographus (L.) Outbreak Spreading in Tatra Mountains (Central Europe), Using GIS. In McManus M.L., Liebhold A.M. (ed.), Ecology, Survey and Management of Forest Insect,), 1-5 September 2002,)Kraków,Poland.USDAForestNortheastern Research Station,Delaware, pp. 25–32.

Jensen J.R., 1986. Introductory digital image processing. A remote sensing perspective.Prentice-Hall,New Jersey,USA. 379 p.

Jin S., Sader S.A., 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment: 94(3): 364–372. DOI: 10.1016/j.rse.2004.10.012

Kennedy R.E., Yang Z., Cohen W.B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment: 114(12): 2897–2910. DOI: 10.1016/j.rse. 2010.07.008

Knorn J., Kuemmerle T., Radeloff V. C., Szabo A., Mindrescu M., Keeton W. S., Abrudan I., Griffiths P., Gancz V., Hostert P., 2012. Forest restitution and protected area effectiveness in post-socialist Romania. Biological Conservation: 146(1): 204–212. DOI: 10.1016/j.biocon. 2011.12.020

Lapin M., Faško P., Melo M., Šťastný P., Tomlain J., 2002. Climatic regions. In Hrnčiarová T. (ed.), Landscape Atlas of theSlovakRepublic. Ministry of Environment of theSlovakRepublic,Banská Bystrica,Slovakia, 94p.

Latifi H., Schumann B., Kautz M., Dech S., 2013. Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery. Environmental monitoring and assessment: 186(1): 441–56. DOI: 10.1007/s10661-013-3389-7

Lefsky M.A., Cohen W.B., 2003. Selection of remotely sensed data. In Wulder M.A., Franklin S.E. (ed.), Remote Sensing of ForestEnvironments: Concepts and Case Studies. Kluwer Academic Publishers, Boston, USA, pp. 13–47. DOI: 10.1007/978-1-4615-0306-4_2

Lu D., Mausel P., Brondízio E., Moran E., 2004. Change detection techniques. International Journal of Remote Sensing: 25(12): pp. 2365–2401. DOI: 10.1080/0143116031000139863

Mancino, G, Nole A, Ripullone F,FerraraA (2013). Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion inBasilicata, southernItaly. iForest - Biogeosciences and Forestry 7(2): 76–85.

Masek J.G., 2005. LEDAPS Disturbance Products : User ' s Guide and Algorithm Description (v . 2 - August 2007). NASA,GreenbeltMD,USA, 7 p.

Meddens A.J.H., Hicke J.A., Vierling L.A., Hudak A.T., 2013. Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sensing of Environment 132: 49–58. DOI: 10.1016/j.rse.2013.01.002

Meigs G.W., Kennedy R.E., Cohen W.B., 2011. ALandsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sensing of Environment 115(12): 3707–3718. DOI: 10.1016/j.rse.2011.09.009

Myneni R.B., Hall F.G., Selers P.J., Marshak A.L., 1995. The Interpretation of Spectral Vegetation Indexes. IEEE Transactions on Geoscience and Remote Sensing: 33(2): 481–486. DOI: 10.1109/36.377948

Nikolov C, Konôpka B, Kajba M, Kunca A, Janský L.,2014. Post disaster forest management and bark beetle outbreak in the Tatra National Park, Slovakia. Mountain Research and Development: 34(4): 326-335. DOI: 10.1659/MRD-JOURNAL-D-13-00017.1

Olsson H., 1993. Regression functions for multitemporal relative calibration of thematic mapper data over Boreal forest. Remote Sensing of Environment: 46: 89–102. DOI: 10.1016/0034-4257(93)90034-U

R Core Team2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna,Austria. URL

Rock B. N., Vogelmann J. E., Williams D. L., Vogelmann A. F., Hoshizaki T., 1986. Remote detection of forest damage. BioScience: 36(7): 439–445. DOI: 10.2307/1310339

Rouse J.W., Haas R.H., Deering D.W., Schell J.A., 1973. Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation.GoddarSpaceFlighCenter,Greenbelt,Maryland,USA. 87 p.

Schelhaas M.J., Nabuurs G.J., Schuck A., 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology: 9: 1620–1633. DOI: 10.1046/j.1365-2486.2003.00684.x

Singh A., 1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing:10(6): 989–1003. DOI: 10.1080/ 01431168908903939

Svoboda M., Fraver S., Janda P., Bače R., Zenáhlíková J., 2010. Natural development and regeneration of a Central European montane spruce forest. ForestEcology and Management: 260(5): 707–714. DOI: 10.1016/j.foreco.2010.05.027

Svoboda M., Janda P., Nagel T.A., Fraver S., Rejzek J., Bače J., 2012. Disturbance history of an old-growth sub-alpine Picea abies stand in the Bohemian Forest, Czech Republic. Journal of Vegetation Science: 23(1): 86–97. DOI: 10.1111/j.1654-1103.2011.01329.x

Vogelmann J.E., 1990. Comparison between two vegetation indices for measuring different types of forest damage in the north-eastern United States. International Journal of Remote Sensing: 11(12): 2281–2297. DOI: 10.1080/01431169008955175

Vogelmann J.E., Tolk B., Zhu Z., 2009. Monitoring forest changes in the southwestern United Statesusing multitemporal Landsat data. Remote Sensing of Environment: 113(8): 1739–1748. DOI: 10.1016/j.rse.2009.04.014

Vogelmann J.E., Xian G., Homer C., Tolk B., 2012. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment: 122: 92–105. DOI: 10.1016/j.rse.2011.06.027

Wulder M.A., Dymond C.C., White J.C., 2005. Remote sensing in the survey of mountain pine beetle impacts : Review and recommendations.CanadianForestService,Victoria,Canada.

67 p.

Wulder M.A., Dynomd C.C., White J.C., Leckie D.G., Caroll A.L., 2006. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. ForestEcology and Management: 221(1-3): 27–41. DOI: 10.1016/j.foreco.2005.09.021

Wulder M.A., Coops N.C., 2013. Make Earth observations open access.Nature: 513: 30–31. DOI: 10.1038/513030a

Xiaojun Y., Lo C.P., 2000. Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images. Photogrammetric Engineering & Remote Sensing: 10(6): 989–1003.

Zhu Z., Woodcock C.E., Olofsson P., 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment: 122: 75–91. DOI: 10.1016/j. rse.2011.10.030

No Supplimentary Material available for this article.
No metrics available for this article.

Related Articles

Related Authors


In Google Scholar

In Annals of Forest Research

In Google Scholar

  • Mária Havašová
  • Tomáš Bucha
  • Ján Ferenčík
  • Rastislav Jakuš
  • Mária Havašová
  • Tomáš Bucha
  • Ján Ferenčík
  • Rastislav Jakuš