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Mapping European Spruce Bark Beetle (IPS Typograhus, L.) : Infestation using Desing Image Spectroscopy and Sentinel-2 Satellite Data
Nagbija, Kwmame Raymond (2025) Mapping European Spruce Bark Beetle (IPS Typograhus, L.) : Infestation using Desing Image Spectroscopy and Sentinel-2 Satellite Data.
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| Abstract: | In recent years, forest disturbances worldwide have increased significantly due to factors such as insect outbreaks and wildfires, which are often intensified by prolonged droughts linked to climate change. In Central Europe, the European spruce bark beetle (Ips typographus L.) is the primary cause of damage, to temperate forest resulting in millions of tons of dead Norway spruce (Picea abies). Traditional detection methods, such as field surveys and aerial photography, are inadequate for large-scale monitoring of such damage and often fail to identify early infestations when intervention is most effective. Satellite remote sensing offers a promising approach for early and large-scale detection of vegetation stress. This study investigated the potential of DLR Earth Sensing Imaging Spectrometer (DESIS) and Sentinel-2 data for assessing bark beetle infestation in Spruce stands in Bavarian Forest National Park (BFNP), Germany. The spectral variability of spruce canopies infested by bark beetles during the green and red attack phases were assessed using Sentinel-2 and DESIS image spectroscopy data and the spectral bands sensitive to early-stage bark beetle infestation (green attack) (i.e., in June)- were identified. Next, the potential of these data for mapping the bark beetle infestation was examined using two Random Forest classification models. To analyze spectral reflectance variations across infestation phases, first the location of healthy and infested canopies was identified using field data and then the reflectance values of these canopies were extracted from Sentinel-2 and DESIS imagery and processed in MATLAB R2024b to generate spectral profiles. The red edge position (REP) was calculated for all infestation phases using the polynomial fitting method. A one-way ANOVA test was applied to all 230 DESIS and 10 Sentinel- 2 spectral bands to identify spectral regions/bands sensitive to green attack infestation. Subsequently, two Random Forest models were developed for discriminating against healthy and infested canopies (Model A, used significant bands and vegetation indices, and Model B, which used only the ANOVA-significant bands). Two infestation maps were generated using these models for DESIS and Sentinel-2 datasets. The results showed that canopies affected by green and red attack exhibited increased reflectance in the visible spectrum and decreased reflectance in the near-infrared region. The analysis demonstrated that REP shifted toward shorter wavelengths as the infestation progressed, indicating stress and chlorophyll degradation. The ANOVA test identified bands in red edge, and near infrared region as significant for detecting bark beetles green attack in both datasets. The Random Forest model results revealed that using reflectance data from significant bands and vegetation indices could allow an enhanced discrimination of healthy and infested canopies during green attack (733 pixels (47%) out of 1,568 pixels in DESIS data, and 1,476 pixels (41%) out of 3,572 pixels in Sentinel-2 data). The findings demonstrate the value of satellite hyperspectral remote sensing data for the early detection of bark beetle infestations, providing a cost-effective tool to enhance forest health monitoring and management during critical early infestation stages |
| Item Type: | Essay (Master) |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Programme: | Geoinformation Science and Earth Observation MSc (75014) |
| Link to this item: | https://purl.utwente.nl/essays/107254 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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