Mapping aboveground carbon density of subtropical subalpine dwarf bamboo (Yushania niitakayamensis) vegetation using UAV-lidar

Abstract

Bamboo, a fast-growing vegetation with high carbon sequestration efficiency, is widely distributed across Asia, Central and South America, and Africa. However, mapping aboveground carbon (AGC) density (kgC m−2) in bamboo can be challenging due to the changing composition of old and new culms or the phenology of the canopy. In this study, we conducted a UAV-lidar survey on 120 ha of subalpine dwarf bamboo (Yushania niitakayamensis) vegetation in Central Taiwan. We destructively collected dwarf bamboo plants from seventy-four 1 × 1 m plots and derived 64 spatially corresponding lidar height and density distribution metrics to model dwarf bamboo AGC density. We applied five regression models (stepwise linear regression, principal component regression, partial least squares regression, elastic net, and multivariate adaptive regression splines [MARS]) to model dwarf bamboo AGC density. MARS outperformed other models by referring to model residuals. The metrics zmax (maximum of lidar return height distribution), zq95 (95th percentile), and zq65 (65th percentile) were salient variables (p < 0.001), especially zq65, suggesting that the conventional model specification of height percentiles of the canopy top might overlook that near the canopy bottom or might be due to insufficient point density. Finally, we used MARS to map the dwarf bamboo AGC density of the study area. We found that AGC spatial variation in dwarf bamboo may be related to topographic characteristics and/or microclimate. This study proposes a regression model to integrate UAV-lidar metrics for precise subalpine dwarf bamboo carbon density mapping, aiding regional spatial carbon-cycle monitoring.

Keywords

• Aboveground biomass
• Biomass-carbon conversion
• Canopy lidar metrics
• multivariate adaptive regression splines (MARS)
• Topography