## 1 Introduction  
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]. However, field-based 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 has been used to map a continuous three-dimensional (3D) structure of the forestland [7] and biomass estimations [8]. With greater point densities than spaceborne 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]. Figure 1 shows areal coverage and LiDAR point density of previous forest application research. Previous studies demonstrated that airborne LiDAR could provide a high accuracy height measure with an R\({}^{2}\) over 0.9 [15; 16] and low measurement error within 1.12 \(\pm\) 0.56 m from an actual height [10; 17]. A similar level of accuracy was observed when LiDAR-based height measurement was applied to deciduous and conifer trees when LiDAR density was above 7 points/m\({}^{2}\)[3]. However, the accuracy of LiDAR-based height measurement was shown to decrease when LiDAR point density is reduced below 2 points/m\({}^{2}\)[18].  
To date, most studies have been focused on local-level applications of high-density LiDAR data and their spatial extent rarely exceed 1000 km\({}^{2}\) (Figure 1). Research on validation of tree LiDAR-based height measurement on a regional scale has been limited due to the cost of LiDAR data acquisition and logistics associated with on-the-ground forest measurement. Although many organizations and agencies have used LiDAR-derived data products for diverse forest management applications, limited peer-reviewed publications are available to formally test the accuracy of LiDAR-derived data products.  
Recently, low-density airborne LiDAR data products have become publicly available to support forest management decisions [19; 20; 21]. In particular, the U.S. Geological Survey (USGS) 3D Elevation Program (3 DEP) LiDAR provides wall-to-wall coverage for many states in the USA [22]. Several studies have been conducted to generate forest attributes using the 3 DEP LiDAR data [23; 24; 25]. However, the application of 3 DEP LiDAR for large-scale canopy height measurement is still limited due to the large volume of data and relatively insufficient computing power.  
We present a canopy height mapping workflow with 3 DEP LiDAR across the state of Indiana, USA. To address the issue related to large data size and data processing requirements of 3 DEP data, we proposed a stepwise CHM generation framework to produce high-resolution CHMs in an efficient manner. We investigated the accuracy of CHM-based tree height by using tree height collected as part of a field-based forest inventory effort and assessed the accuracy in relation to LiDAR point density and year of data acquisition. The resulting CHMs could be widely applied in the management of timber, fiber, wildlife, and many other disciplines.