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Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5 m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2 = 0.87, p

Fine-scale variation of vegetation structure in dryland systems, such as the Great Basin in the western US, is critical to understanding ecosystem responses to changing land-use conditions. High resolution airborne hyperspectral (HyMap) and LiDAR datasets acquired across independent collection sites can reduce uncertainty in predictive ecosystem modeling and provide a basis for regional upscaling to satellite observations of structural metrics such as cover and height. In the first part of our study, we combined ground reference and airborne data collected at three sagebrush-steppe locations and used the statistical data mining tool random forests to identify remote sensing variables most relevant to estimating shrub cover. In the second part of our study, we hypothesized that vegetation indices derived from hyperspectral satellite observations would not only reliably predict shrub cover but also be relatable to shrub height; thereby augmenting the collection of vertical structure estimates from future satellite platforms such as ICESAT-2. To test this hypothesis, we simulated HyspIRI observations to derive variables to relate to LiDAR-based estimates of shrub cover and height. We generated the same hyperspectral variables as in the first part of this study but at coarser resolution (60 m) and we again used random forests to model shrub cover and height and identify predictors of greatest importance. Overall, combining LiDAR and HyMap datasets at the airborne scale improved shrub cover model results (r2 = 0.58) compared to LiDAR alone (r2 = 0.49). Primary shrub cover variables of importance were HIQR (the interquartile range of height of all LiDAR vegetation returns), HMAD (median absolute deviation from median height of all LiDAR vegetation returns), a narrowband index sensitive to anthocyanins, the ratio of LiDAR vegetation returns to total returns, and a red to green ratio. In addition, HyspIRI-simulated narrowband vegetation indices were relatable to LiDAR-derived shrub cover and height variables (r2 ranging from 0.63 to 0.71) with relatively low root mean square error.

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicate that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.

Viewshed and line-of-sight are spatial analysis functions used in applications ranging from urban design to archaeology to hydrology. Vegetation data, a difficult variable to effectively emulate in computer models, is typically omitted from visibility calculations or unrealistically simulated. In visibility analyzes performed on a small scale, where calculation distances are a few hundred meters or less, ineffective incorporation of vegetation can lead to significant modeling error. Using an aerial LiDAR light detection and ranging data set of a lodgepole pine Pinus contorta dominant ecosystem in Idaho, USA, tree obstruction metrics were derived and integrated into a short-range visibility model. A total of 15 visibility plots were set at a micro-scale level, with visibility modeled to a maximum of 50 m from an observation point. Digital photographs of a 1 m2 target set at 5 m increments along three sightline paths for each visibility plot were used to establish control visibility values. Trunk obstructions, derived from mean vegetation height LiDAR data and processed through a series of tree structure algorithms, were factored into visibility calculations and compared to reference data. Results indicate the model calculated using trunk obstructions with LiDAR demonstrated a mean error of 8.8% underestimation of target visibility, while alternative methods using mean vegetation height and bare-earth models have an underestimation of 65.7% and overestimation of 31.1%, respectively.

[1] Woody encroachment is a globally occurring phenomenon that contributes to the global carbon sink. The magnitude of this contribution needs to be estimated at regional and local scales to address uncertainties present in the global- and continental-scale estimates, and guide regional policy and management in balancing restoration activities, including removal of woody plants, with greenhouse gas mitigation goals. The objective of this study was to estimate carbon stored in various successional phases of woody encroachment. Using lidar measurements of individual trees, we present high-resolution estimates of aboveground carbon storage in juniper woodlands. Segmentation analysis of lidar point cloud data identified a total of 60,628 juniper tree crowns across four watersheds. Tree heights, canopy cover, and density derived from lidar were strongly correlated with field measurements of 2613 juniper stems measured in 85 plots (30 × 30 m). Aboveground total biomass of individual trees was estimated using a regression model with lidar-derived height and crown area as predictors (Adj. R2 = 0.76, p

Research presented here explores the feasibility of leveraging vegetation data derived from airborne light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) for visibility modeling. Using LiDAR and TLS datasets of a lodgepole pine (Pinus contorta) dominant ecosystem, tree canopy and trunk obstructions were isolated relevant to a discrete visibility beam in a short-range line-of-sight model. Cumulative obstruction factors from vegetation were compared with reference visibility values from digital photographs along sightline paths. LiDAR-derived tree factors were augmented with single-scan TLS data for obstruction prediction. Good correlation between datasets was found up to 10 m from the terrestrial scanner, but fine scale visibility modeling was problematic at longer distances. Analysis of correlation and regression results reveal the influence of obstruction shadowing inherent to discrete LiDAR and TLS, potentially limiting the feasibility of modeling visibility over large areas with similar technology. However, the results support the potential for TLS-derived subcanopy metrics for augmenting large amounts of aerial LiDAR data to significantly improve models of forest structure. Subtle LiDAR processing improvements, including more accurate tree delineation through higher point density aerial data, combined with better vegetation quantification processes for TLS data, will advance the feasibility and accuracy of data integration.

A method is presented for the co-registration of overlapping elevation surfaces based on local slope analysis. Comparison and statistical analysis of local slope versus local elevation difference between overlapping surfaces allows for the estimation of both vertical and horizontal offsets between the two surfaces. This method is then used to re-align the flightlines of a Light Detection and Ranging (lidar) dataset collected in southern Idaho resulting in a dataset with significantly higher accuracy than the original. The relative horizontal accuracy is doubled, with a final value of approximately 25 to 30 cm, while the relative vertical accuracy is improved by several centimeters to a final value of approximately 6 to 7 cm.

With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

Quantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely lacking for individual trees in urban landscapes. Prediction models to estimate biophysical parameters such as height, crown area, diameter at breast height, and biomass for over two thousand individual trees were developed using best subsets multiple linear regression for a study area in central Oklahoma, USA using point cloud distributional metrics from an Optech ALTM 2050 lidar system. A high level of accuracy was attained for estimating individual tree height (R2 = 0.89), dbh (R2 = 0.82), crown diameter (R2 = 0.90), and biomass (R2 = 0.67) using lidar-based metrics for pooled data of all tree species. More variance was explained in species-specific estimates of biomass (R2 = 0.68 for Juniperus virginiana to 0.84 for Ulmus parviflora) than in estimates from broadleaf deciduous (R2 = 0.63) and coniferous (R2 = 0.45) taxonomic groups-or the data set analysed as a whole (R2 = 0.67). The metric crown area performed particularly well for most of the species-specific biomass equations, which suggests that tree crowns should be delineated accurately, whether manually or using automatic individual tree detection algorithms, to obtain a good estimation of biomass using lidar-based metrics. United States. National Aeronautics and Space Administration – NNS06AA57G National Institute of Food and Agriculture (U.S.) - NIFA PINEMAP Project Published version