Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS

Shaobo Xia,Cheng Wang,Feifei Pan,He Liu,Hongcheng Zeng,Xiaohuan Xi

doi:10.3390/f6113923

Abstract

Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense foliage and thin structure could intensify the shadowing effect and pose a series of problems in identifying stems, distinguishing neighboring stems, and merging disconnected stem parts in point clouds. This paper presents a new method to automatically detect stems in dense and homogeneous forest using single-scan TLS data. Stem points are first identified with a two-scale classification method. Then a clustering approach is used to group the candidate stem points. Finally, a direction-growing algorithm based on a simple stem curve model is applied to merge stem points. Field experiments were carried out in two different bamboo plots with a stem density of about 7500 stems/ha. Overall accuracy of the stem detection is 88% and the quality of detected stems is mainly affected by the shadowing effect. Results indicate that the proposed method is feasible and effective in detection of bamboo stems using TLS data, and can be applied to other species of single-stem plants in dense forests.

Highlights

A stem or culm refers to the main structural axis of a plant above ground
Because the clear space for Terrestrial laser scanning (TLS) placement was limited by the dense canopy, we found it was quite safe and convenient to setup the scanner adjacent to the plots instead of within them
Benefits of this method are clear: (i) Points from branches will be excluded after the first scale classification, and will improve the robustness of feature classification at the second scale. (ii) The density of non-stem points drops quickly which increases the difference between stem and no-stem points. (iii) The two-scale classification algorithm can reduce data volume as well as improve the computation efficiency

Summary

Introduction

A stem or culm refers to the main structural axis of a plant above ground. It transports fluids, supports the whole plant and stores the energy. Diameter at the breast height (DBH) and plant density are key parameters for estimating biomass productivity and carbon storage [1,2,3]. The traditional survey of vegetation characteristics (e.g., DBH, plant density) is usually done by on-site manual measurement, which is time consuming, labor intensive, and often subject toerrors associated with manual works [5,6,7]. It is usually difficult to gather some stem properties (e.g., diameters at different heights, stem curvature) using the manual survey, unless destructive methods are employed

Results

Discussion

Conclusion