Stand parameter extraction based on video point cloud data

Abstract

Monitoring sample plots is important for the sustainable management of forest ecosystems. Acquiring resource data in the field is labor-intensive, time-consuming and expensive. With the rapid development of hardware technology and photogrammetry, forest researchers have turned two-dimensional images into three-dimensional point clouds to obtain resource information. This paper presents a method of sample plot analysis using two charge-coupled device (CCD) cameras based on video photography. A handheld CCD camera was used to shoot the sample plot by surrounding a central tree. Video-based point clouds were used to detect and model individual tree trunks in the sample plots and the DBH of each was estimated. The experimental results were compared with field measurement data. The results show that the relative root mean squared error (rRMSE) of the DBH estimates of individual trees was 2.1–5.7%, acceptable for practical applications in traditional forest inventories. The rRMSE of height estimates was 2.7–36.3%. Average DBH and heights, and tree density and volume were calculated. Video-based methods require compact observation instruments, involve low costs during field investigations, acquire data with high efficiency, and point cloud data can be processed automatically. Furthermore, this method can directly extract information on the relative position of trees, which is important to show distribution visually and provides a basis for researchers to regulate stand density. Additionally, video photography with its unique advantages is a technology warranting future attention for forest inventories and ecological construction.

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