Abstract
Detailed up-to-date ground reference data have become increasingly important in quantitative forest inventories. Field reference data are conventionally collected at the sample plot level by means of manual measurements, which are both labor-intensive and time-consuming. In addition, the number of attributes collected from the tree stem is limited. More recently, terrestrial laser scanning (TLS), using both single-scan and multi-scan techniques, has proven to be a promising solution for efficient stem mapping at the plot level. In the single-scan method, the laser scanner is placed at the center of the plot, creating only one scan, and all trees are mapped from the single-scan point cloud. Consequently, the occlusion of stems increases as the range of the scanner increases, depending on the forest's attributes. In the conventional multi-scan method, several scans are made simultaneously inside and outside of the plot to collect point clouds representing all trees within the plot, and these scans are accurately co-registered by using artificial reference targets manually placed throughout the plot. The additional difficulty of applying the multi-scan method is due to the point-cloud registration of several scans not being fully automated yet. This paper proposes a multi-single-scan (MSS) method to map the sample plot. The method does not require artificial reference targets placed on the plot or point-level registration. The MSS method is based on the fully automated processing of each scan independently and on the merging of the stem positions automatically detected from multiple scans to accurately map the sample plot. The proposed MSS method was tested on five dense forest plots. The results show that the MSS method significantly improves the stem-detection accuracy compared with the single-scan approach and achieves a mapping accuracy similar to that achieved with the multi-scan method, without the need for the point-level registration.
Highlights
The reference data collected from sample plots are fundamental parameters for forest-related studies
The MSS method significantly improved the stem-detection accuracy compared with the single-scan approach
This paper presents a fully automated solution for mapping stand attributes at the plot level using terrestrial laser scanning (TLS) and the multi-single-scan (MSS) method
Summary
The reference data collected from sample plots are fundamental parameters for forest-related studies. They are indispensable inputs to the biological, ecological, and geospatial modeling of standing trees for purposes such as the development of nationwide allometric models to estimate forest parameters [1], the calibration of estimation models developed from remote sensing techniques in national forest inventories [2] and the study of ecological characteristics [3]. Calipers, measuring tapes and hypsometers are the most common measuring tools used for the retrieval of stem attributes. They are low-cost and relatively reliable, but labor-intensive [4,5]. More automated and more cost-effective techniques are needed to provide field inventory data
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