Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis

摘要

Quantification of vegetation properties plays an important role in the assessment of ecosystem functions with leaf dry mater content (LDMC) and specific leaf area (SLA) being two key functional traits. For the first time, these two leaf traits have been estimated from the airborne images (HySpex) using the INFORM radiative transfer model and Continuous Wavelet Analysis (CWA). Ground truth data, were collected for 33 sample plots during a field campaign in July 2013 in the Bavarian Forest National Park, Germany, concurrent with the hyperspectral overflight. The INFORM model was used to simulate the canopy reflectance of the test site and the simulated spectra were transformed to wavelet features by applying CWA. Next, the top 1% strongly correlated wavelet features with the LDMC and SLA were used to develop predictive (regression) models. The two leaf traits were then retrieved using the CWA transformed HySpex imagery and the predictive models. The results were validated using R-2 and the RMSE of the estimated and measured variables.