Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data
Remote sensing is viewed as a cost-effective alternative to intensive field surveys in assessing site factors that affect growth of Eucalyptus grandis over broad areas. The objective of this study was to assess the utility of hyperspectral remote sensing to discriminate between site qualities in E. grandis plantation in KwaZulu-Natal, South Africa. The relationships between physiology-based hyperspectral indicators and site quality, as defined by total available water (TAW), were assessed for E. grandis plantations through one-way analysis of variance (ANOVA). Canopy reflectance spectra for 68 trees (25 good, 25 medium and 18 poor sites) were collected on clear-sky days using an Analytical Spectral Device (ASD) spectroradiometer (350–2500 nm) from a raised platform. Foliar macronutrient concentrations for N, P, K, S, Ca, Mg and Na and their corresponding spectral features were also evaluated. The spectral signals for leaf water – normalized difference water index (NDWI), water band index (WBI) and moisture stress index (MSI) – exhibited significant differences (p , 0.05) between sites. The magnitudes of these indices showed distinct gradients from the poor to the good sites. Similar results were observed for chlorophyll indices. These results show that differences in site quality based on TAW could be detected via imaging spectroscopy of canopy water or chlorophyll content. Among themacronutrients, only K and Ca exhibited significant differences between sites. However, a Tukey post-hoc test showed differences between the good and medium or medium and poor sites, a trend not consistent with the TAW gradient. The study also revealed the capability of continuum-removed spectral features to provide information on the physiological state of vegetation. The normalized band depth index (NBDI), derived from continuum-removed spectra in the region of the red-edge, showed the highest potential to differentiate between sites in this study. The study thus demonstrated the capability of hyperspectral remote sensing of vegetation canopies in identifying the site factors that affect growth of E. grandis in KwaZulu Natal, South Africa.