Abstract
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m 2 , corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m 2 . Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m 2 , and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m 2 , our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
Highlights
Accurate estimation, mapping and monitoring of the amount of carbon stored in terrestrial vegetation is crucial to reliable analysis, understanding and projection of the global carbon cycle and its interactions with land use and climate change
Errors associated with the aboveground biomass estimates decreased as the lidar point density increased (p = 0.03, Figure 2). 5-fold cross validated RMSEs were slightly larger than the model RMSEs as we expected, yet they followed the similar trend along the point density gradient (Table 2)
Out of all the metrics derived from the airborne lidar dataset, skewness and tree density were the two predictors that were consistently selected for high point densities including 4 and 8 points/m2; whereas standard deviation and kurtosis were the two metrics that were consistently used in the models derived from low density lidar data including 0.5, 1, and 2 points/m2 (Table 2)
Summary
Accurate estimation, mapping and monitoring of the amount of carbon stored in terrestrial vegetation is crucial to reliable analysis, understanding and projection of the global carbon cycle and its interactions with land use and climate change. Lidar is an active remote sensing system that can measure the three-dimensional (3-D) structural characteristics of trees and other vegetation which are not directly captured by passive optical land imaging systems such as Landsat Such 3-D structural information is critical for improved aboveground biomass estimation with greater accuracy (Dubayah and Drake, 2000; Gregoire et al, 2016). Given lidar data with sufficiently high pulse density, determination of these fundamental biometric variables (tree height, tree density, fractional cover, and stand structure) with a low level of uncertainty is relatively straightforward Using these variables to estimate aboveground biomass introduces potentially large errors due to additional complexities and uncertainties, such as field sampling design, model selections and the accuracy of the allometric equations that are used in the estimation procedure (Gregoire et al, 2016)
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