High-Resolution Canopy Height Model Generation and Validation Using USGS 3DEP LiDAR Data in Indiana, USA

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.

Positive association between emerald ash borer residence time and accumulation of invasive plants

“…Forest structural metrics were calculated from 2016 to 2020 3DEP aerial LiDAR collected by U.S. Geological Survey. LAS tiles contained a minimum of two laser pulse returns per square meter (although the average density was ~6 points/m 2 ; Oh et al, 2022) and were projected into one of two state plane coordinate systems: Indiana east (EPSG 2967) or west (EPSG 2968), to prevent overlapping tiles. In ArcGIS Pro (ESRI, Redlands, California, USA), we created a shapefile of CFI plot locations for each of the two projections to ensure that their locations would overlay with point cloud data.…”

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

“…Two main methods were applied to generate CHM from laser point cloud data: one was to obtain the difference between the canopy surface model DSM (digital surface model) and the ground digital elevation model DEM (digital elevation model) and the other is to use the classified ground points to normalize the elevation of the point cloud data, and to then obtain the CHM from the rasterization [45]. In this paper, we directly rasterize the normalized point cloud to generate CHM (as shown in Figure 7), which aims to reduce the loss of canopy information as interpolated only once.…”

U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding

“…Although the data source for the two DEM layers is not the same, we believe the performance difference is primarily due to the finer data resolution. This is because they are both obtained from US federal agencies and have been widely used in previous studies (Oh et al, 2022;Scott et al, 2022;Uuemaa et al, 2020;Wong et al, 2020), and thus even if there are slight vertical differences between these two products, it is less likely that one has significantly higher data accuracy than the other. We discovered that the 10m three-band model without pretrain outperformed the outcomes of the five-band 10m model without pretrain.…”