2011 Volume 67 Issue 2 Pages 65-74
Numerous studies have used the satellite-derived Normalized Difference Vegetation Index (NDVI) to estimate the phenology of vegetation cover. However, little is known about the effect of species difference on the susceptibility of NDVI-based estimation approaches, such as the threshold approach and the abrupt variation approach, for estimating the phenology of forest trees. In this study, to clarify the utility of NDVI in cool temperate deciduous forests, which consist of many tree species, we investigated the effect of the species difference on the estimation accuracy of two traditional approaches at the scale of the individual tree. We observed a canopy NDVI of 6 tree species by using a high resolution spectral camera, and compared the NDVI-based estimate of the phenological stages (green-up, green peak, senescence and leaf fall) and the ground truth data on the basis of foliar chlorophyll content.
In the threshold approach, the optimal threshold value of NDVI was higher in the autumn leaf fall than the spring green-up. Species difference did not strongly affect the threshold of the green-up, but the threshold of the leaf fall was higher in tree species which flower in summer. The mean estimation error of the leafy period was +1.3 days in this approach when the simple threshold value was used for all species. In the abrupt variance approach the estimation error was larger and the leafy period was over estimated (mean: +26.1 days). The degree of overestimation in the leaf fall tended to be larger in species that flower and have a late abscission.
These results suggest that the threshold approach is a better method than the abrupt variation approach if the optimal threshold value can be calculated by using a ground truth data set. Furthermore, species specific leaf senescence type and the existence of flowering affect the accuracy of NDVI-based estimates, indicating that we should confirm the composition of tree species when evaluating the NDVI-based phenology data of cool temperate deciduous forests.