|
|
Improved quantification of forest health using multi scale remote sensing observations
![[ForestRS]](ForestRS.png "Forest") Forests experience periods of decline in response to a variety of causes, but there is a growing awareness that atmospheric pollution, climate change, pests and pathogens may pose a serious threat to forest ecosystems (UNECE, 2004). The assessment and monitoring of forest condition over time is of the highest importance for developing optimal forest management practices. Forest decline is the result of complex physiological processes that change tree biochemical and structural variables, such as the concentration, composition and efficiency in light harvesting of foliar pigments, and leaf area index (LAI). These variables directly influence vegetation optical properties particularly in the visible and infrared wavelengths, as has been confirmed with optical indices acquired at narrow spectral resolution (in the order of 5–10 nm) and known as hyperspectral data. The central aim my PhD project is to utilise passive optical remote sensing to improve the identification of early onset forest decline such that spatial patterns can be elucidated and management can be improved.
|
![[University Logo]](/icons/unilogo_25476c.gif "Link to The University of Edinburgh Home Page"){kind=icon}