Applying a logistic-Gaussian complex signal model to restore surface hyperspectral reflectance of an old-growth tree species in cool temperate forest

This paper applied a Logistic-Gaussian complex signal model (LGM) to restore spectroradiometer-obtained canopy SWIR reflectance signals of red cypress, an old-growth conifer species. This species distributes within a temperate forest in Taiwan over a wide range of altitude from 1000 to 2800 m. A large amount of reflectance data of trees in a variety of light environments was used for this study. Although the reflectance curve of tree crowns is decreased as the directly incident light is blocked by shadow of uphill tree crowns, the particular pattern of tree reflectance curve in such a noisy region remained similar. Results showed that the pronounced noise in the region from 1350 to 1410 nm can be removed using the LGM complex signal model. In other words, the reflectance signals of trees in such spectral areas can be restored successfully. Briefly, the research revealed that the model shows acceptable ability to fix the noise problem in the water-sorption spectral wavelengths under a variety of incident radiance. This is valuable for the use of remotely sensed data for continuous monitoring of water stress in forest canopy.