Abstract
Currently, existing methods for single-tree detection based on airborne laser scanning (ALS) data usually require some thresholds and parameters to be set manually. Manually setting threshold or parameters is laborious and time-consuming, and for dense forests, the high commission and omission rate make most existing single-tree detection techniques inefficient. As a solution to these problems, this paper proposed an automatic single-tree detection method in ALS data through gradient orientation clustering (GOC). In this method, the rasterized Canopy Height Model (CHM) was derived from ALS data using surface interpolation. Then, potential trees were assumed as approximate conical shapes and extracted based on the GOC. Finally, trees were identified from the potential trees based on the compactness of the crown shape. This method used the gradient orientation information of rasterized CHM, thus increasing the generalization of single-tree detection method. In order to verify the validity and practicability of the proposed method, twelve 1256 m2 circular study plots of different forest types were selected from the benchmark dataset (NEWFOR), and the results from nine different methods were presented and compared for these study plots. Among nine methods, the proposed method had the highest root mean square matching score (RMS_M = 43). Moreover, the proposed method had excellent detection (M > 47) in both single-layer coniferous and single-layered mixed stands.
Highlights
Remote sensing (RS) is a synthesized, efficient, and rapid means of obtaining earth surface information, which can help forestry management departments achieve high-precision forest resource surveys, timber production estimation, and other applications [1,2,3]
We provide a new idea for single tree detection, clustering by gradient orientation information in canopy height model (CHM), which is named gradient orientation clustering (GOC)
This paper proposed an automatic method for single-tree detection in airborne laser scanning data through gradient orientation clustering
Summary
Remote sensing (RS) is a synthesized, efficient, and rapid means of obtaining earth surface information, which can help forestry management departments achieve high-precision forest resource surveys, timber production estimation, and other applications [1,2,3]. Light detection and ranging (LiDAR) is an RS method that uses light in the form of a pulsed laser to measure ranges (i.e., variable distances) to the Earth. These light pulses, combined with other data recorded by the airborne system, generate precise, three-dimensional information about the shape of the Earth and its surface characteristics. Airborne laser scanning (ALS) is a widely used LiDAR technology in forestry, which has a unique advantage in 3D terrain and forest height detection. It can quickly and accurately obtain a digital surface model (DSM) and forest height information for forested land [4,5]. Single-tree detection algorithms that use active RS data, such as ALS, have grown rapidly in recent years, accounting for 80% of the total increase in RS area, at the same time, those algorithms using passive data or a fusion of passive and active data accounts for 8% and 12% respectively [6]
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