Thesis subject
MSc thesis topic: Early Detection of Bark Beetle Infestation Using Sentinel-2, Thermal Sensors, and Biophysical Models
Bark beetles threaten European forests, especially under drought and extreme weather. Early detection is key but challenging with traditional methods. This study integrates Sentinel-2, ECOSTRESS, Landsat thermal, and biophysical models to detect stress before visible symptoms. We assess chlorophyll, water content, LAI, and thermal anomalies in Picea abies forests (2016–2022) using radiative transfer models (PROSAIL) and machine learning.
Background
Traditional monitoring detects infestations late. Sentinel-2 (optical) and thermal sensors (ECOSTRESS, Landsat, Sentinel-3 or MODIS TIR time series) can reveal early physiological stress. RTMs improve plant trait retrieval for proactive forest management. This study analyzes a 2016–2022 time series of Picea abies forests in Northern France to track infestation progression.
Relevance to research/projects at GRS or other groups
This research is a collaboration between Carlos Camino (GRS, Wageningen University & Research), Jean-Baptiste Féret (INRAE, France), and Kenji Ose at Joint Research Centre (JRC, European Commission). It aligns with efforts in remote sensing for forest health monitoring and early detection of bark beetle infestations.
Objectives and Research questions
This study develops an early detection approach for bark beetle infestations by integrating Sentinel-2, thermal satellite time series (e.g., ECOSTRESS, Landsat thermal, MODIS TIR and Sentinel-3), and RTMs (PROSAIL). By analyzing a 2016–2022 time series, we identify key spectral and thermal stress indicators to improve plant trait retrieval and support proactive forest management.
- Which thermal and SWIR bands are most effective for early bark beetle detection?
- How does combining thermal data and SWIR bands improve detection accuracy?
- How can RTMs and thermal sensors enhance plant trait retrieval under stress?
- What advantages does a multi-sensor approach (thermal + SWIR) offer over traditional methods?
Requirements
- Required: Geoscripting, Remote Sensing, Advance Earth Observation
- Optional: Spatial Modelling and Statistics, Deep Learning
Literature and information
- Anderson, M. C., Yang, Y., Xue, J., Knipper, K. R., Yang, Y., Gao, F., Hain, C. R., Kustas, W. P., Cawse-Nicholson, K., Hulley, G., Fisher, J. B., Alfieri, J. G., Meyers, T. P., Prueger, J., Baldocchi, D. D., & Rey-Sanchez, C. (2021). Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sensing of Environment, 252.
- Abdullah, H., Skidmore, A. K., Darvishzadeh, R., & Heurich, M. (2019a). Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat-8. Remote Sensing in Ecology and Conservation, 5(1), 87–106.
- Abdullah, H., Skidmore, A. K., Darvishzadeh, R., & Heurich, M. (2019b). Timing of red-edge and shortwave infrared reflectance critical for early stress detection induced by bark beetle (Ips typographus, L.) attack. International Journal of Applied Earth Observation and Geoinformation, 82(June), 101900.
- Verrelst, J., Malenovský, Z., Van der Tol, C., Camps-Valls, G., Gastellu-Etchegorry, J. P., Lewis, P., North, P., & Moreno, J. (2019). Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics, 40(3), 589–629.
Expected reading list before starting the thesis research
- Bárta, V., Lukeš, P., & Homolová, L. (2021). Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2. International Journal of Applied Earth Observation and Geoinformation, 100.
- Abdullah, H., Darvishzadeh, R., Skidmore, A. K., & Heurich, M. (2019). Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation. Remote Sensing, 11(4).
Theme(s): Modelling & visualisation; Integrated Land Monitoring