PRISMA hyperspectral satellite data for mapping boreal forest nitrogen content
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 leaf and canopy nitrogen content and their spatial variations 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) measurements of leaf nitrogen content using collected leaf samples and the latest technology for lab chemistry and (ii) pre-processing of the hyperspectral satellite data. The study will then evaluate whether the nitrogen content in forest leaves and canopies can be estimated using narrow-band vegetation indices, statistical models and hyperspectral satellite data. Once the model is established, the estimated nitrogen at leaf and canopy levels can be validated using collected field data, and the nitrogen maps of the study area will then be generated.
Nitrogen plays a critical role in the terrestrial ecosystem and climate systems. Although nitrogen is only a relatively small constituent (0.2-6.4%) in leaves, the retrieval of nitrogen using remote sensing data has been investigated in many studies. Traditionally, lab-based spectrophotometric approaches have been used to determine leaf nitrogen content. These techniques are time-consuming and labour intensive and do not permit the measurement of changes over time or space. Previous studies that used hyperspectral remote sensing data for retrieving nitrogen content in forest canopies benefited from lab 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 biochemical properties such as nitrogen content in vegetation canopies at a low cost and with high accuracy. However, the retrieval of essential vegetation biochemical properties such as leaf nitrogen from hyperspectral satellite data has not yet been investigated. This study is the first attempt on using hyperspectral satellite images for mapping forest nitrogen content at leaf and canopy levels.
No need for adaptation. The MSc topic addresses environmental degradation and forest nutrient cycle issues.