Abstract
In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study, we proposed a novel method called region-based hierarchical cross-section analysis (RHCSA), which combined the two procedures together based on a canopy height model (CHM) derived from airborne LiDAR data for ITCD. This method considers the CHM as a three-dimensional topological surface, simulates stereoscopic scanning from top to bottom using an iterative process, and utilizes the individual crown and vertical structure of crowns to progressively detect individual treetops and delineate crown boundaries. The proposed method was tested in natural forest stands with high canopy densities in Liangshui National Nature Reserve and Maoershan Forest Farm, Heilongjiang Province, China. Its performance was evaluated by an accuracy procedure that considered both the relative position of treetops and overlapped area of crowns. The average overall accuracy achieved was 85.12% for coniferous plots, 83.86% for deciduous plots and 86.44% for coniferous and broad-leaved mixed forest plots. The results revealed that the RHCSA method can detect and delineate individual tree crowns with little influence from forest types and crown size. It could provide technical support for individual tree crown delineation in coniferous, deciduous and mixed forests with high canopy densities.
Highlights
Forests are an important natural resource which play a significant role in modulating stores and fluxes of water and carbon, maintaining ecological diversity, and regulating climate on the Earth’s surface, and provides timber and other forest products constantly which are closely related to humans [1,2,3]
Data preprocessing in our study consisted of three steps: (1) classify the raw Light Detection and Ranging (LiDAR) point data into ground and above-ground points using TerraSolid’s TerraScan software (Terrasolid Ltd., Helsinki, Finland); (2) derive canopy height model (CHM) using the classified point clouds, that is the difference between Digital Surface Model (DSM) and Digital Terrain Model (DTM) interpolated from surface points and ground points, respectively; (3) correct and smooth the CHM by using a pit-filling algorithm [57] and Gaussian filtering with a window size of 3 × 3 pixels
This study conducted the proposed region-based hierarchical cross-section analysis (RHCSA) algorithm in nine plots of 100 × 100 m, which belonged to coniferous forest, coniferous-broadleaved mixed forest, and deciduous forest
Summary
Forests are an important natural resource which play a significant role in modulating stores and fluxes of water and carbon, maintaining ecological diversity, and regulating climate on the Earth’s surface, and provides timber and other forest products constantly which are closely related to humans [1,2,3]. Since the early 1960s, with the extensive application of remote sensing data in forest inventory [14,15,16] a variety of image processing techniques were developed for automatically detecting and delineating individual tree crowns, such as local maxima filtering with fixed or variable window size [17,18,19], valley-following [20,21,22], region-growing [23,24], and watershed segmentation [25,26] These methods make it possible to obtain individual tree-based attributes that can be directly used as input parameters for environmental modeling without the limitations of the sample sizes and inaccessible areas [27,28]. Laser-based individual tree crown delineation can reflect accurate geometrical properties of trees, such as tree height and crown diameter which cannot be affected by illumination angle and shadows on multispectral imageries [34], and become attractive techniques for both forestry and remote sensing communities [13]
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