PRISMA hyperspectral satellite images for mapping Specific Leaf Area (SLA) in a boreal forest
Quantitative remote sensing of vegetation parameters (Q5)
- A suitable candidate would be interested in remote sensing and quantification of biophysical variables
- Fieldwork is not necessary; however, a possible field visit can be discussed with the supervisors if the student is interested.
The goal of this study, as part of the larger BIOSPACE project, is to map and model Specific Leaf Area (SLA) and its spatial variation in a boreal forest in Finland (warm summer humid continental climate) using PRISMA hyperspectral satellite data. The student will benefit from the existing satellite images and field samples collected during the earlier campaigns in Finland's EVO forest national park during summer 2020-2021. He/she will become familiar with (i) calculation of SLA using collected leaf samples and (ii) pre-processing of the hyperspectral satellite data. The study will then evaluate whether SLA in forest canopy can be estimated using statistical models and hyperspectral satellite data. Once the model is established, the estimated SLA will be validated using collected field data, and the SLA map of the study area will be generated accordingly.
Specific Leaf Area (SLA) is one of the essential leaf functional traits for assessing functional diversity. It plays a vital role in ecosystem modelling, linking plant carbon and water cycles and is defined as the leaf area per unit of dry leaf mass (m2/kg). SLA is an indicator for plant physiological processes such as growth rate and light capture hence, providing information on the spatial variation of photosynthetic capacity and leaf nitrogen content. The measurement of SLA is mainly limited to destructive sampling, which is time-consuming and technically challenging. Despite being time-consuming and labour intensive, it does not permit the measurement of changes over time or space. Previous studies that used hyperspectral remote sensing data for accurate estimation of SLA utilised lab-based and airborne hyperspectral measurements and have been proven accurate and successful. The new generation of hyperspectral satellites (e.g., PRISMA and DESIS) has opened a new ear for monitoring leaf functional traits in vegetation canopies at a low cost and with high accuracy. However, the retrieval of these types of traits such as SLA from hyperspectral satellite data has not yet been investigated. This study is the first attempt on using hyperspectral satellite images for mapping SLA of the forest canopies.
No need for adaptation. The MSc topic addresses environmental degradation and climate change issues.