Detection of forest growing stock volume changes in a subtropical region with multitemporal UAV LiDAR data

Abstract

Detection of forest growing stock volume (GSV) change is crucial for carbon assessment and sustainable forest management. Conventional multitemporal optical or SAR-based approaches are limited by data saturation problem and poor model transferability. Multitemporal UAV LiDAR provides new opportunities to detect GSV dynamics, but how different tree species, age stages and temporal periods affect the GSV detectability is poorly understood. Therefore, this study takes eucalyptus, Masson pine and Chinese fir with different ages and disturbance densities in subtropical regions as case studies. Canopy height model data from LiDAR were used to extract structural metrics, and species-specific GSV estimation models were developed using multiple linear and power-law segmented regression. GSV changes were calculated by differencing estimates for each tree species across two time points. The results indicate that: (1) for natural growth, longer detection intervals improved GSV change accuracy. Eucalyptus GSV changes showed a relative root mean square error of 12.36% at a 3.5-year interval, reductions of 29.7% and 24.2% compared to 1- and 2-year intervals. Nevertheless, young eucalyptus and Masson pine, and middle-aged Chinese fir exhibited detectable GSV changes (16.3–37.3 m³ ha^–1) even within one year; (2) multitemporal UAV LiDAR data effectively captured disturbance-induced GSV changes, with shorter intervals preferable for fast-growing species. Within a one-year interval, thinning thresholds of ~15% in Masson pine and Chinese fir produced negative GSV changes of at least 33.12 m³ ha^–1. Eucalyptus showed growth compensation effects, and only when thinning intensity exceeded 50% did GSV changes consistently become negative across age classes. This study advances the application of using multitemporal UAV LiDAR data for detecting forest GSV dynamics and provides quantitative guidance for optimizing detection strategies in subtropical plantations.

Keywords

Growing stock volume / Change detection / UAV LiDAR / Subtropical region / Different forest types

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