Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data

Remote Sens. 2013, 5(4), 1912-1931; doi:10.3390/rs5041912

Author

Sonia M. Ortiz ^1,* , Johannes Breidenbach ²

 and Gerald Kändler ¹

¹

Department of Biometrics, Forest Research Institute of Baden-Württemberg, Wonnhaldestr. 4, D-79100 Freiburg, Germany

²

Norwegian Forest and Landscape Institute, Postboks 115, N-1431 Ås, Norway

^*

Author to whom correspondence should be addressed. 

Received: 18 February 2013 / Revised: 4 April 2013 / Accepted: 4 April 2013 / Published: 16 April 2013

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Abstract 

Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m². TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen’s Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models. View Full-Text

Keywords: forest health;  satellite images;  supervised classification;  maximum entropy;  random forest;  generalized linear models

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