Estimation of volume resources for planted forests using an advanced LiDAR and hyperspectral remote sensing

Abstract

Planted forests play a key role in alleviating the stress of harvesting woods and carbon emission on natural forests. Accurate estimation of forest volume resources in planted forests is crucial for balancing relationships between ecological benefits from timber production and ecological benefits from biomass energy. In this study, we explored the use of spectral indices (SIs) and wavelet features (WFs) derived from hyperspectral imaging (HSI) data, as well as Light Detection And Ranging (LiDAR) metrics, with three multivariate regression methods for estimating volume resources in subtropical forests. The results showed that using combined LiDAR and HSI metrics generally outperformed LiDAR-only models. HSI metrics exhibited stronger relationships with volume in lower volume plots (volume < 320 m3/ha) but had saturation problem in higher volume plots, which could be well alleviated by LiDAR metrics. Specifically, coupling WFs and SIs as HSI metrics can further reinforce the synergetic use of LiDAR and HSI metrics, especially with the approach of backward elimination. In comparing three regression methods, Gaussian Processes Regression models (CV-R2 = 0.87, rRMSE = 16.12% for the best model) mostly outperformed Partial Linear Squares Regression and Random Forest regression models. The common important LiDAR metrics for three regression methods were height-related (H50) and density-related (D3 and D9) metrics, while the common optimal spectral metrics (WF615, 2, WF685, 5 and REIP) were related to the chlorophyll absorption features. These findings demonstrated the significance of the wavelet approach in strengthening the synergetic use of LiDAR and HSI metrics for enhancing forest parameter estimations.