Mapping Canopy Chemical trait and Land Surface Temperature over Samaria National Park using Thermal Satellite and Unmanned Aerial Vehicle Data
The topic is also suitable for GEM students in track 3 – GEM for Ecosystems & Natural Resources.
Suggested electives: Quantitative Remote Sensing of Vegetation Parameters (Q1) | Thermal infrared remote sensing: from theory to applications (Q1)
Fieldwork will be conducted in Samaria National Park, Crete, Greece
Recently, mortality and reduction in forest stand densities for Brutia pine (Pinus brutia) have been found over a specific part of Samaria National Park, Greece, which can have economic and environmental impacts on this ecosystem. This issue might be attributed to several factors, such as insect infestation, increasing (and successive) dry years, or a combination of both. In this respect, understanding the reason behind this issue is an essential step for managing the National Park to minimise economic loss and detect insect infestation early. As is expected, insect infestation affects a tree’s physiological and chemical status by attacking the needles of the Pines and thus reducing the tree’s photosynthetic capacity or disrupting the transport of water in the tree, leading to tree mortality. Therefore, assessment and mapping of the canopy chemical trait (e.g., water, nitrogen, and chlorophyll) and land surface temperature using UAV as a rapid remote sensing approach for monitoring and estimating the vegetation parameters are imperative to the Samaria National Park authorities.
This research topic aims to understand the link between canopy chemical traits and land surface temperature by means of UAV data (visible and thermal infrared) as a rapid remote sensing approach over the insect-infested forest. In this respect, the student would carry out the field data collection. The candidate will work with multispectral drone imagery, as well as satellite imagery (e.g., Landsat-8 or Sentinel-2). The student is expected to collect in situmeasurements such as vegetation chemical properties, leaf area index, and other vegetation structural parameters if required. Further, the student will become familiar with the pre-processing for thermal infrared and visible-shortwave infrared remotely sensed data, as well as computing land surface temperature and reflectance obtained by UAV and Satellite imagery. According to the field measurements, statistical or physical approaches would be applied for analysis, and further, the results would be validated using in situ measurements.
- Avdan, U., & Jovanovska, G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of sensors, 2016.
- Wang, Z., Wang, T., Darvishzadeh, R., Skidmore, A. K., Jones, S., Suarez, L., ... & Hearne, J. (2016). Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote sensing, 8(6), 491.