You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery
Contributor:
Latifi, Hooman;
Fassnacht, Fabian E.;
Schumann, Bastian;
Dech, Stefan
imprint:
SAGE Publications, 2014
Published in:Progress in Physical Geography: Earth and Environment
Language:
English
DOI:
10.1177/0309133314550670
ISSN:
1477-0296;
0309-1333
Origination:
Footnote:
Description:
<jats:p> As major agents of biological disturbances, bark beetle infestations have been reported to account for a large portion of damage that occur in European forest stands. As a result, accurate spatiotemporal characterization of the vulnerable areas is crucial for subsequent post-infestation management. Remote sensing-assisted mapping of bark beetle-induced forest mortality has been an important research focus during the last decade. Due to the occurrence of mostly small- to medium-scale infestation patches in European stands, high-resolution optical data is commonly applied for mapping mortality. Despite this, we hypothesize the widely available satellite products to be potentially advantageous due to their multitemporal availability and reasonable costs. Here, we combined multi-date LANDSAT and SPOT scenes across an 11-year time span in which various epidemic and non-epidemic infestations occurred within the Bavarian Forest National Park in Germany. The aim was to map temporally adjacent mortality classes. The spectral, geometric and textural metrics extracted from the segmented imagery were applied to perform a full object-based classification, for which a digital terrain model was additionally employed. A number of potentially influential factors were also explored, including the spatial aggregation of image segments and the spatial enhancement of the multispectral imagery. The analysis resulted in a nearly perfect separation of non-infested and dead trees, while different levels of confusion were observed when classifying the transitional mortality classes. While the pan-sharpening of selected image scenes contributed to the stability of mapping results for non-infested and dead trees, no explicit trend was observed when aggregating small image segments prior to classification. Furthermore, combining the metrics from image objects and the digital terrain model suggested an obviously improved classification compared to the previously achieved pixel-based results across the same study site. In this paper, we thoroughly discuss the practical aspects of applying object-based image processing for monitoring bark beetle-induced forest mortality. </jats:p>