Understory Bamboo Discrimination Using a Winter Image

Abstract

In this study, a new approach is presented that combines forest phenology and Landsat vegetation indices to estimate evergreen understory bamboo coverage in a mixed temperate forest. It was found that vegetation indices, especially the normalized difference vegetation index (NDVI) derived from leaf-off (winter) images were significantly correlated with percent understory bamboo cover for both deciduous and mixed coniferous/deciduous forests. Winter NDVI was used to map bamboo coverage using a binary decision tree classifier. A high mapping accuracy for understory bamboo presence/absence was achieved with an overall accuracy of 89 percent ( k5 0.59). In addition, for the first time, we successfully classified three density classes of bamboo with an overall accuracy of 68 percent ( k5 0.48). These results were compared to three traditional multispectral bandsbased methods (Mahalanobis distance, maximum likelihood, and artificial neural networks). The highest mapping accuracy was again obtained from winter images. However, the kappa z-test showed that there was no statistical difference in accuracy between the methods. The results suggest that winter is the optimal season for quantifying the coverage of evergreen understory bamboos in a mixed forest area, regardless of the classification methods use.