Predicting <I>Sphaeropsis sapinea</I> Damage in <I>Pinus radiata</I> Canopies Using Spectral Indices and Spectral Mixture Analysis

Abstract

Maintaining the health and condition of the forest plantation estate is critical to ensuring there are no adverse losses in productivity. Within Australian Pinus radiata plantations a diverse range of damaging agents are present. One significant agent is a fungal pathogen Sphaeropsis sapinea. In this research, we detail the development of relationships between a range of individual crown health attributes representing symptoms of Sphaeropsis sapinea infection and high spatial and spectral resolution remotely sensed imagery characteristics. To do this, two methods were used; the first utilized vegetation spectral indices including simple and normalized difference ratios, and the second, linear spectral mixture analysis. Results indicate that spectral indices that utilize either chlorophyll absorption wavelengths at 680 nm with a non-chlorophyll region of the spectrum (such as 710 or 750 nm) or the slope of the upper red-edge between 710 and 740 nm were most significantly related to individual crown damage attributes. Linear unmixing analysis consistently extracted four fraction endmember images (sunlit canopy, soil, shadow, and non-photosynthetic vegetation (NPV)) from the 12 channel imagery. Multiple linear stepwise regression models developed using mixed fractional abundances provided similar results to those derived using spectral indices. The NPV and shadow endmembers, in order, were consistently identified as the most significant in these developed models.