Analysis of hyperspectral bands for the health diagnosis of tree species

Forests play an important role in providing timber, soil and water conservation, and control of global climate and environmental change through carbon sequestration. Thus, the health status of tree species is worthy of major concern. Every year various tree species especially rare tree species disappear from natural disasters or plant disease and insect pests. However, many trees can be saved if effective measures are taken through early diagnostics of health. Due to the potential of hyperspectral data in detecting the changes of tree leaves in colors, moisture, and materials due to natural disasters or plant disease and insects, hyperspectral data can be used to monitor the health status of trees and achieve the health diagnosis of trees and forests. In this study, a hyperspectral radiometer with spectrum range of 350 nm to 2500 nm from ASD company was used to observe high-density hyperspectral changes of tree leaves due to defoliation for the tree species of metasequoia glyptostroboides and ginkgo biloba, with time period of September 2013 to December 2013. First order differential and its logarithmic transformations were employed for processing the hyspectral data. The results showed that the peak location of visible light reflectance for the tree species was highly related to their health status. The peaks of spectral reflectance appeared at the locations of 550 nm and 650 nm identified the healthy and dead leaves, respectively. In addition, the peaks appeared at yellow band implied the sub-healthy of trees. This finding has critical significance for early health diagnosis of tree species.