Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery
- 346 Downloads
Biological infestations in forests, e.g. the insect outbreaks, have been shown as favoured by future climate change trends. In Europe, the European spruce bark beetle (Ips typographus L.) is one of the main agents causing substantial economic disturbances in forests. Therefore, studies on spatio-temporal characterization of the area affected by bark beetle are of major importance for rapid post-attack management. We aimed at spatially detecting damage classes by combining multidate remote sensing data and a non-parametric classification. As study site served a part of the Bavarian Forest National Park (Germany). For the analysis, we used 10 geometrically rectified scenes of Landsat and SPOT sensors in the period between 2001 and 2011. The main objective was to explore the potential of medium-resolution data for classifying the attacked areas. A further aim was to explore if the temporally adjacent infested areas are able to be separated. The random forest (RF) model was applied using the reference data drawn from high-resolution aerial imagery. The results indicate that the sufficiently large patches of visually identifiable damage classes can be accurately separated from non-attacked areas. In contrast to those, the other mortality classes (current year, current year 1 and current year 2 infested classes) were mostly classified with higher commission or omission errors as well as higher classification biases. The available medium-resolution satellite images, combined with properly acquired reference data, are concluded to be adequate tools to map area-based infestations at advanced stages. However, the quality of reference data, the size of infested patches and the spectral resolution of remotely sensed data are the decisive factors in case of smaller areas. Further attempts using auxiliary height information and spatially enhanced data may refine such an approach.
KeywordsBark beetle (Ips typographus L.) Central Europe Random Forest Medium-resolution data Aerial photography
This study was accomplished using the multidate SPOT data provided by the Planet Action initiative launched by Astrium GEO and SPOT Image. We appreciate the BFNP administration, in particular Dr. Jörg Müller and Dr. Marco Heurich, for providing permission to use the polygon-based infestation data for validation.
- Bangdiwala, K. (1987). Using SAS software graphical procedures for the observer agreement chart. In Proceedings of SAS user’s group international conference (Vol. 12, pp. 1083–1088).Google Scholar
- Bivand (2013). R-package maptools userguide. http://cran.r-project.org/web/packages/maptools/maptools.pdf. Accessed 28 Feb 2013.
- Breiman, L. (1984). Classification and regression trees. Wadsworth statistics/probability series. Belmont: Wadsworth International Group.Google Scholar
- Breiman, L., & Cutler, A. (2008). Random forests. Tech. rep. http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm. Accessed 15 Dec 2012.
- Coggins, S.B., Coops, N.C., Hilker, T., Wulder, M.A. (2013). Augmenting forest inventory attributes with geometric optical modelling in support of regional susceptibility assessments to bark beetle infestations. International Journal of Applied Earth Observation and Geoinformation, 21, 444–452.CrossRefGoogle Scholar
- Edburg, S., Hicke, J., Brooks, P., Pendall, E., Ewers, B., Norton, U., Gochis, D., Gutmann, E., Meddens, A. (2012). Cascading impacts of bark beetle-caused tree mortality on coupled biogeophysical and biogeochemical processes. Frontiers in Ecology and the Environment, 10(8), 416–424.CrossRefGoogle Scholar
- Gregoire, J.C., & Evans, H. (2004). Damage and control of bawbilt organisms an overview. In F. Lieutier, K.R. Day, A. Battisti, J.C. Gregoire, H.F. Evans (Eds.), Bark and wood boring insects in living trees in Europe, a synthesis (pp. 19–37). Springer Netherlands.Google Scholar
- Heurich, M., Fahse, L., Reinelt, A. (2001). Die buchdruckermassenvermehrung im nationalpark bayerischer wald. In Waldentwicklung im bergwald nach windwurf und borkenkäferbefall. Tech. rep., Wissenschaftliche Schriftenreihe der Nationalparkverwaltung Bayerischer Wald. Band 16.Google Scholar
- Hicke, J.A., Allen, C.D., Desai, A.R., Dietze, M.C., Hall, R.J., (Ted) Hogg, E.H., Kashian, D.M., Moore, D., Raffa, K.F., Sturrock, R.N., Vogelmann, J. (2012). Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Global Change Biology, 18(1), 7–34.CrossRefGoogle Scholar
- Hijmans, R., & Van Etten, J. (2012). R-package Raster userguide. R Development Core Team. http://cran.r-project.org/web/packages/raster/raster.pdf. Accessed 28 February 2013.
- Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., Falkowski, M.J. (2008). Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112(5), 2232–2245. Earth Observations for Terrestrial Biodiversity and Ecosystems Special Issue.CrossRefGoogle Scholar
- Jactel, H., & Vodde, F. (2011). Prevalence of biotic and abiotic hazards in European forests. Tech. Rep. 86, EFI Technical Report 66. European Forest Institute.Google Scholar
- Keitt, T., Rowlingson, B., Bivand, R. (2013). R package rgdal userguide. http://cran.r-project.org/web/packages/rgdal/rgdal.pdf. Accessed 28 Feb 2013.
- Klein, W. (1982). Estimating bark beetle-killed lodgepole pine with high altitude panoramic photography. Photogrammetric Engineering and Remote Sensing, 48, 733–737.Google Scholar
- Koski, V., Skroppa, T., Paule, L., Wolf, H., Turok, J. (1997). Technical guidelines for genetic conservation of Norway spruce (Picea abies (L.) Karst.). Tech. rep., European Forest Genetic Ressources Programme.Google Scholar
- Leica Geosystems (2002). Stereo analyst user’s guide. Leica Geosystems GIS & Mapping Division, Atlanta, GA, USA.Google Scholar
- Liaw, A., & Wiener, M. (2012). R-package random forest userguide. R Development Core Team. http://cran.r-roject.org/web/packages/randomForest/randomForest.pdf. Accessed 28 February 2013.
- Meyer, D., Zeileis, A., Hornik, K., Friendly, M. (2012). R-package vcd userguide. R Development Core Team. http://http://cran.r-project.org/web/packages/vcd/vcd.pdf. Accessed 28 February 2013.
- Moravec, I., Cudlin, P., Polak, T., Havlicek, F. (2002). Spruce bark beetle (Ips typographus L.) infestation and norway spruce status: is there a causal relationship? Wiss Mitt Bohmerwald, 8, 255–264.Google Scholar
- Pebesma et al. (2012). R-package sp userguide. R Development Core Team. http://cran.r-project.org/web/packages/sp/sp.pdf. Accessed 28 February 2013.
- R Development Core Team (2011). R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org. Accessed 25 Nov 2012.
- Rall, H., & Martin, K. (2002). Luftbildauswertung zur waldentwicklung im nationalpark bayerischer wald 2001. Tech. rep., Administration of NP Bavarian Forest (Ed.), Berichte aus dem Nationalpark, 1.Google Scholar
- Röder, J., Ortiz, S., Lyytikäinen-Saarenmaa, P., Holopainen, M., Hyypää, J., Karjalainen, M., Koch, B. (2009). EO application development data user element, due innovators ii—insect combat. Deliverable 2. project: Esrin/ao/1-5781/08/i-ec, Deliverable 2. Project: ESRIN/AO/1-5781/08/I-EC.Google Scholar
- Wallace, J., Li, M., Traylen, A. (2009). Forest vegetation monitoring and runoff in water supply catchments affected by drying climate. In Geoscience and remote sensing symposium, 2009 IEEE international, IGARSS 2009 (Vol. 3, pp. III–939–III–942).Google Scholar
- White, J., & Wulder, M. (2006). Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-M multispectral imagery. Mountain Pine Beetle Initiative Working Paper Series, Pacific Forestry Centre.Google Scholar
- Wulder, M.A., Dymond, C.C., White, J.C. (2005). Remote sensing in the survey of mountain pine beetle impacts: review and recommendations. Information report bc-x-401., Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre.Google Scholar
- Wulder, M.A., White, J.C., Carroll, A.L., Coops, N.C. (2009). Challenges for the operational detection of mountain pine beetle green attack with remote sensing. The Forestry Chronicle, 85(1), 32–38. http://pubs.cif-ifc.org/doi/pdf/10.5558/tfc85032-1.Google Scholar
- Xie, Y., Sha, Z., Yu, M. (2008). Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1(1), 9–23. http://jpe.oxfordjournals.org/content/1/1/9.full.pdf+html.CrossRefGoogle Scholar