title: Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery.
abstract: 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.
authors.affiliation: Department of Remote Sensing in cooperation with German Aerospace Center (DLR), University of Würzburg, Oswald-Külpe-Weg 86, 97074, Würzburg, Germany, hooman.latifi@uni-wuerzburg.de.
authors.forename: Hooman
authors.initials: H
authors.lastname: Latifi
authors.forename: Bastian
authors.initials: B
authors.lastname: Schumann
authors.forename: Markus
authors.initials: M
authors.lastname: Kautz
authors.forename: Stefan
authors.initials: S
authors.lastname: Dech
mesh_terms: D000818:Animals; D057231:Climate Change; D001517:Coleoptera / Q000502:physiology*; D004784:Environmental Monitoring; D005858:Germany; D028222:Picea / Q000469:parasitology*; D010935:Plant Diseases / Q000469:parasitology*; D063809:Satellite Imagery; D018531:Spacecraft
references.citation: J Chem Inf Comput Sci. 2003 Nov-Dec;43(6):1947-58
references.pmid: 14632445
references.citation: Nature. 2008 Apr 24;452(7190):987-90
references.pmid: 18432244