Estimating structure of understory bamboo for giant panda habitat by developing an advanced vertical vegetation classification approach using UAS-LiDAR data

• Optimized demarcation point identification model for stratifying multilayer forests. • Novel vertical classification approach proposed to extract understory bamboo by LiDAR. • Voxel size significantly affects vertical classification with optimal value of ten. • Bamboo structures were estimated by the understory LiDAR metrics with high accuracy. • Understory bamboo for giant panda habitat first isolated from multilayer forests. Bamboo forests are natural habitat for the giant panda which is one of the most vulnerable mammal species. In structurally complex natural forests, bamboos are normally located under the canopy of taller trees, which makes them difficult to be quantified accurately. Although Light Detection and Ranging (LiDAR) technologies have been well established as the effective tool for forest structure assessment, the use of LiDAR to assess understory bamboo in structurally complex natural forests is less well known. We present a novel vertical vegetation classification (VVC) approach to map the structure of understory bamboos for giant panda forage in natural forests. An optimized demarcation point identification (DPI) model was developed for stratifying different vertical layers from coarse to fine scales. Three-dimensional understory bamboo point clouds were successfully isolated from the forest point cloud, then bamboo structure predictive models were developed through understory bamboo point cloud metrics and applied over the entire study area to generate spatially continuous maps of understory bamboo structure. Our results indicate that the isolation of the understory bamboo point cloud using the developed VVC approach performs well and has small bias, the extracted maximum height is close to field-measured maximum height (R 2 = 0.77, rRMSE = 15.02 %). Height-related metrics have higher correlations with bamboo structure (mean natural and true height, basal diameter, and total aboveground biomass) than other metrics ( r > 0.8), and understory bamboo structures are estimated with relatively high accuracy (R 2 = 0.84 – 0.91, rRMSE = 10.87 – 29.41 %). We also find varying effects of topography on the spatial distribution of different understory bamboo species. This study demonstrates the benefits of utilizing LiDAR data to ascertain fine-scale understory bamboo resources, providing critical supports for giant panda habitat assessment and conservation.