Optimal integration of forest inventory data and aerial image-based canopy height models for forest stand management

Abstract

Accurate, reliable, and regularly updated information is necessary for targeted management of forest stands. This information is usually obtained from sample-based field inventory data. Due to the time-consuming and costly procedure of forest inventory, it is imperative to generate and use the resulting data optimally. Integrating field inventory information with remote sensing data increases the value of field approaches, such as national forest inventories. This study investigated the optimal integration of forest inventory data with aerial image-based canopy height models (CHM) for forest growing stock estimation. For this purpose, fixed-area and angle-count plots from a forest area in Austria were used to assess which type of inventory system is more suitable when the field data is integrated with aerial image analysis. Although a higher correlation was observed between remotely predicted growing stocks and field inventory values for fixed-area plots, the paired t-test results revealed no statistical difference between the two methods. The R² increased by 0.08 points and the RMSE decreased by 7.7 percentage points (24.8 ​m³·ha⁻¹) using fixed-area plots. Since tree height is the most critical variable essential for modeling forest growing stock using aerial images, we also compared the tree heights obtained from CHM to those from the typical field inventory approach. The result shows a high correlation (R² ​= ​0.781) between the tree heights extracted from the CHM and those measured in the field. However, the correlation decreased by 0.113 points and the RMSE increased by 4.2 percentage points (1.04 ​m) when the allometrically derived tree heights were analyzed. Moreover, the results of the paired t-test revealed that there is no significant statistical difference between the tree heights extracted from CHM and those measured in the field, but there is a significant statistical difference when the CHM-derived and the allometrically-derived heights were compared. This proved that image-based CHM can obtain more accurate tree height information than field inventory estimations. Overall, the results of this study demonstrated that image-based CHM can be integrated into the forest inventory data at large scales and provide reliable information on forest growing stock. The produced maps reflect the variability of growth conditions and developmental stages of different forest stands. This information is required to characterize the status and changes, e.g., in forest structure diversity, parameters for volume, and can be used for forest aboveground biomass estimation, which plays an important role in managing and controlling forest resources in mid-term forest management. This is of particular interest to forest managers and forest ecologists.