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

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 m 2 .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. OPEN ACCESSRemote Sens. 2013, 5 1913

Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

“…The classification accuracies are comparable to the previous studies in identifying different types of infestations with various sensors [17,24,25,27], but due to the small sample size, it is difficult to make further conclusions about the performance. The conclusion whether this accuracy is sufficient or not, is dependent on the application.…”

“…The crown color transforms due to larval feeding in xylem and phloem tissues, prohibiting water flux from roots to the crown. The initial attack is not visible to the human eye (green attack) [17]. With thousands of attacking beetles per one Norway spruce tree, needles first turn yellow (yellow attack), then to reddish brown (red attack), and finally grey (tree mortality) [18].…”

“…Remote sensing, such as aerial images, multi-temporal satellite images and synthetic aperture radar (SAR) datasets, Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), TerraSAR-X and light detection and ranging (LiDAR), have been applied in forest health surveys and detection of disturbances by pest insects [17,[20][21][22][23][24]. Mid-and low-resolution remote sensing data have proven to be practical for insect outbreak surveys over wide landscapes.…”

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

“…Their results suggest these indices were able to differentiate between affected and unaffected areas in the green attack stage although the lack of detailed ground survey data limited the ability to conduct an accuracy assessment. A more recent study using RapidEye and TerraSAR-X data (independently and simultaneously) to detect green attack with a minimum mapping unit of 78.5 m 2 obtained accuracies for green-attack detection ranged from 13% for TerraSAR-X only to 67% for RapidEye only (Oritz et al 2013). When these data sources were combined, accuracy for greenattack detection was 73%.…”

Can I Trust My One-Class Classification?

“…In contrast to the aforementioned approaches, Maxent has been used more frequently for one-class land cover classification in applied studies [19,[47][48][49][50].…”