Mapping CHM and LAI for Heterogeneous Forests Using Airborne Full-Waveform LiDAR Data

Abstract

Canopy height model (CHM) and leave area index (LAI) are essential forest structure attributes that are estimated to understand the ecological states and processes occurring in forest ecosystems. Airborne light detection and ranging (LiDAR) systems have proven efficient in producing both CHM and LAI maps for heterogeneous forests at the regional scale. The unique advantage of airborne LiDAR over optical and radar sensors is its vegetation penetration capability. Although the LiDAR penetration capability decreases in dense, complex forests, full-waveform LiDAR systems are currently available to provide critical point observations under the forest canopy. This research developed and tested methods to map CHM and LAI in heterogeneous forests using airborne waveform LiDAR datasets acquired using two different LiDAR systems and flight altitudes. Since using waveform data significantly increases the laser penetration rate, the test results strongly recommend using waveform data for the estimation of both CHM and LAI. These experiments also revealed that the flight data collection altitude will not affect LAI estimation. Through the analysis of CHMs and LAI data variations derived from 4 different datasets, CHM estimation may be good to 0.8 m and LAI estimation may be as precise as 0.5.

An investigation into remote sensing techniques and field observations to model hydraulic roughness from riparian vegetation

“…The accuracy of vegetation height can be marginally improved by bias correction (Fradette, Leboeuf, Riopel, & Bégin, 2019). Similarly, several other studies (Bao et al, 2018;Kamoske et al, 2019;Richardson et al, 2009;Tseng et al, 2016) have experimented to improve the LAI using remote sensing approaches. The modified Beer-Lambert law used here to estimate LAI also introduces uncertainties related to k, which depends on the vegetation species and zenithal angle.…”

“…The height of vegetation was represented using the CHM (Mielcarek, Stereńczak, & Khosravipour, 2018). The modified Beer–Lambert equation (Bao et al, 2018; Kamoske, Dahlin, Stark, & Serbin, 2019; Richardson et al, 2009; Saitoh, Nagai, Noda, Muraoka, & Nasahara, 2012; Tseng et al, 2016), a widely used equation to determine the LAI of the forest canopy, was applied in this study to estimate LAI. The modified Beer–Lambert equation (Equation ()) relates LAI to the number of ground points ( N g ), the number of total points ( N T ), and an extinction coefficient ( k ). k is given by 0.5/ cos θ , where θ is the zenithal angle.…”

“…Remotely sensed data provide benefits over field‐based methods with respect to data collection efficiency, spatial extent, and spatial resolution (Breda, 2003; Chen, McDermid, Castilla, & Linke, 2017). Airborne light detection and ranging (LiDAR) data are widely used in forestry for determining vegetation height (Li et al, 2016; Sullivan, Ducey, Orwig, Cook, & Palace, 2017), LAI (Richardson, Moskal, & Kim, 2009; Solberg, Næsset, Hanssen, & Christiansen, 2006; Tseng, Lin, & Wang, 2016), phenology, and classification (Tomsett & Leyland, 2019). Some studies have utilized LiDAR to characterize vegetation and implement resistance equations to calculate vegetation‐induced roughness (Abu‐Aly et al, 2014; Antonarakis et al, 2010; Antonarakis, Richards, Brasington, Bithell, & Muller, 2008; Casas, Lane, Yu, & Benito, 2010; Mason, Cobby, Horritt, & Bates, 2003; Prior, Aquilina, Czuba, Pingel, & Hession, 2021; Straatsma & Baptist, 2008; Wang & Zhang, 2019).…”

“…The height of vegetation was represented using the CHM(Mielcarek, Stere nczak, & Khosravipour, 2018). The modified Beer-Lambert equation(Bao et al, 2018;Kamoske, Dahlin, Stark, & Serbin, 2019;Richardson et al, 2009;Saitoh, Nagai, Noda, Muraoka, & Nasahara, 2012;Tseng et al, 2016), a widely used equation to determine the LAI of the forest canopy, was applied in this study to estimate LAI. The modified Beer-Lambert equation (Equation (…”

Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

“…Jan [16] applied a CHM model to estimate canopy height in forest stands and found LiDAR performed better than aerial photos in forest canopy structure investigations. Tseng et al [17] developed a method to map CHM in heterogeneous forests using airborne waveform LiDAR datasets and the CHM estimation is with an estimation error of approximately 0.8 m. Due to the laser penetration ability and steep slopes of terrains, some abnormal changes of elevation exist in the derived CHM regularly [18][19][20]. In order to accurately characterize the top canopy surface, Ben-Arie et al [21] developed a semiautomated pit filling algorithm to create smooth CHMs.…”