Abstract
Forest canopy height model (CHM) is useful for analyzing forest stocking and its spatiotemporal variations. However, high-resolution CHM with regional coverage is commonly unavailable due to the high cost of LiDAR data acquisition and computational cost associated with data processing. We present a CHM generation method using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data for tree height measurement capabilities for entire state of Indiana, USA. The accuracy of height measurement was investigated in relation to LiDAR point density, inventory height, and the timing of data collection. A simple data exploratory analysis (DEA) was conducted to identify problematic input data. Our CHM model has high accuracy compared to field-based height measurement (R2 = 0.85) on plots with relatively accurate GPS locations. Our study provides an easy-to-follow workflow for 3DEP LiDAR based CHM generation in a parallel processing environment for a large geographic area. In addition, the resulting CHM can serve as critical baseline information for monitoring and management decisions, as well as the calculation of other key forest metrics such as biomass and carbon storage.
Highlights
Tree height is one of the most important attributes that can be used to assess the ecological status and economic value of a forest system [1] and to estimate ecosystem services it provides, such as carbon sequestration and productivity [2,3]
The visualization and download of light detection and ranging (LiDAR) data products are available as a web service [30,31]
This study presented a canopy height model (CHM) generation workflow using U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) LiDAR data which is characterized by a low-density point cloud and a large spatial coverage
Summary
Tree height is one of the most important attributes that can be used to assess the ecological status and economic value of a forest system [1] and to estimate ecosystem services it provides, such as carbon sequestration and productivity [2,3]. Fieldbased height measurement is labor-intensive and time-consuming, and infeasible when a field site is inaccessible due to terrain conditions, dense vegetation, or man-made barriers. To address these limitations, various research has been conducted to test the feasibility of applying the light detection and ranging (LiDAR) technology as a complement for forest inventory [4,5,6]. LiDAR and broader areal coverage than ground-based LiDAR, airborne LiDAR has been widely used to measure tree height for forest inventory applications [9,10,11,12,13,14].
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