Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: a case study in Fujian Province, China

Bamboo forest, especially moso bamboo forest, is very important to human society. However, our ability to detect large-scale moso bamboo with optical remote sensing is still limited due to the spectral similarity with other forest species and the influence of cloud occurrence. In this study, we examined the capability of dense Landsat time series for moso bamboo forest mapping by comparing three different interpretation schemes. For each scheme, two experimental groups were further conducted to investigate the usefulness of gray-level co-occurrence matrix (GLCM) textures. Considering classification accuracy, the full-season compositing strategy was regarded as the most efficient. It was generally beneficial to include GLCM textures as input features, although their usefulness would be partially offset due to noise/correlation issues. We also investigated the roles of 15 types of auxiliary covariates in extracting moso bamboo and found some of them could enhance the classification performance significantly. With the full-season compositing scheme and crucial auxiliary covariates, an improved moso bamboo mapping performance (93.21% in overall accuracy and 73.97% in minimum accuracy) was observed within the study area. Our evaluation results are promising to provide robust guidelines for remote mapping of moso bamboo forest over large areas.