Abstract
Enriching forest resource inventory is important to ensure the sustainable management of forest ecosystems. Obtaining forest inventory data from the field has always been difficult, laborious, time consuming, and expensive. Advances in integrating photogrammetry and computer vision have helped researchers develop some numeric algorithms and methods that can turn 2D (images) into 3D (point clouds) and are highly applicable to forestry. This paper aimed to develop a new, highly accurate methodology that extracts sample plot parameters based on continuous terrestrial photogrammetry. For this purpose, we designed and implemented a terrestrial observation instrument combining real-time kinematic (RTK) and charge-coupled device (CCD) continuous photography. Then, according to the set observation plan, three independent experimental plots were continuously photographed and the 3D point cloud of the plot was generated. From this 3D point cloud, the tree position coordinates, tree DBHs, tree heights, and other plot characteristics of the forest were extracted. The plot characteristics obtained from the 3D point cloud were compared with the measurement data obtained from the field to check the accuracy of our methodology. We obtained the position coordinates of the trees with the positioning accuracy (RMSE) of 0.162 m to 0.201 m. The relative root mean square error (rRMSE) of the trunk diameter measurements was 3.07% to 4.51%, which met the accuracy requirements of traditional forestry surveys. The hypsometrical measurements were due to the occlusion of the forest canopy and the estimated rRMSE was 11.26% to 11.91%, which is still good reference data. Furthermore, these image-based point cloud data also have portable observation instruments, low data collection costs, high field measurement efficiency, automatic data processing, and they can directly extract tree geographic location information, which may be interesting and important for certain applications such as the protection of registered famous trees. For forest inventory, continuous terrestrial photogrammetry with its unique advantages is a solution that deserves future attention in the field of tree detection and ecological construction.
Highlights
Forest inventory determines a forest’s current situation and trend and ensures the sustainable management of forest ecosystems [1,2]
To minimize the forest resource inventory cost with high precision and efficiency, we proposed in this study a new method based on terrestrial photogrammetry and the sample plot 3D point cloud for the accurate extraction of forest parameters
A 3D point cloud was constructed according to the obtained data and the tree coordinates, tree diameter, tree height, and other stand parameters were extracted by designing algorithms
Summary
Forest inventory determines a forest’s current situation and trend and ensures the sustainable management of forest ecosystems [1,2]. Obtaining forest inventory factors data with high precision and minimum cost and time is a key objective in forestry research. Remote sensing images and aerial photogrammetry promptly obtain forest surface data over a large area. These two methods have a limitation because the accuracy of the terrestrial data under the tree crown is compromised [10,11,12,13]. 3D laser scanning technology can efficiently collect high precision 3D data of a forest’s vertical structure. It has useful practical applications in forestry. In the Nordic countries such as Denmark and Finland, forest inventory has been largely developed and managed by the Airborne Laser Scanning (ALS) approach [14,15,16]
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