Understanding the impact of a land surface temperature change as a proxy of drought on net primary productivity in the European mixed forest

M-GEO
M-SE
FORAGES
M-SE Core knowledge areas
Spatial Information Science (SIS)
Spatial Planning for Governance (SPG)
Technical Engineering (TE)
Additional Remarks

The topic is open for also suitable for GEM students in track 1 – GEM for Urban-Rural Interactions.

The topic is open for also suitable for GEM students in track 3 – GEM for Ecosystems & Natural Resources.

Suggested electives: Thermal infrared remote sensing: from theory to applications (Q5), Advanced Image Analysis (Q5) 

A suitable candidate should be interested in thermal infrared remote sensing and quantification of the ECVs (Essential climate variables) and EBVs (Essential Biodiversity variables)   

Topic description

Droughts are exacerbated by climate change, which makes them more frequent, prolonged, and catastrophic. This weather-related hazard is intensifying and profoundly influencing climate, forestry, and food security, amongst other things. During past years, many monthly temperature records in Europe have been broken, sometimes consecutively. The European Drought Observatory recently reported that since the beginning of 2022, Europe has been experiencing a severe to exceptional drought, and forecasts for the upcoming months indicate continued drier-than-average conditions. Water scarcity and heat stress result in lower productivity in forest ecosystems, spreading diseases and pests and damaging urban infrastructure. The relationship between temperature and NPP has been investigated before. However, LST is a crucial environmental component that can be viewed as a proxy for drought events in determining the energy flux between the atmosphere and the Earth's surface. In essence, determining imbalances in LST patterns provides insight into the impact of drought on natural ecosystems, particularly in forest ecosystems. Therefore, this topic aims to investigate the LST change variation in the mixed forest ecosystem over the course of the past decade at the European scale and understand its link with NPP for the mixed forest ecosystems.

Topic objectives and methodology

This study intends to investigate the link between land surface temperature (LST) and net primary productivity (NPP) over the past 8 years using Sentinel-3/ MODIS 8 satellite imagery. The satellite data will be obtained from the Copernicus data hub and/or Landsat collection. The student will generate and utilize available land cover maps to extract the European mixed forest. There are diverse methods to calculate LST using remote sensing data; the most common are single-channel and split-window algorithms. For the computation of NPP, LPJ-Guess as a process-based dynamic vegetation-terrestrial ecosystem model will be applied. Various methods, such as multivariate models and machine learning approaches, will be investigated to better understand the relationship between LST and NPP.

References for further reading
  • Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of sensors, 2016. 
  • Witmer, F. D. (2015). Remote sensing of violent conflict: eyes from above. International Journal of Remote Sensing, 36(9), 2326-2352. 
  • Smith, B. (2001). LPJ-GUESS-an ecosystem modelling framework. Department of Physical Geography and Ecosystems Analysis, INES, Sölvegatan12, 22362.