2013 Morley Nelson Snake River Birds of Prey National Conservation Area RapidEye 7m Landcover Classification

Boise State University conducted an area-wide vegetation classification of the Orchard Combat Training Center (OCTC) for the Idaho National Guard/IDARNG and expanded the classification to cover areas of the Morley Nelson Snake River Birds of Prey National Conservation Area for the Bureau of Land Management. This report documents the field data collection and processing, image acquisition and processing, and image classification. Work was performed between January 2012 – October 2015

Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R2 of 0.76 and RMSE of 125 g/m2 for shrub biomass and a pseudo R2 of 0.74 and RMSE of 141 g/m2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem

Investigating the Relationships Between Canopy Characteristics and Snow Depth Distribution at Fine Scales: Preliminary Results from the SnowEX TLS Campaign

In temperate, mountainous regions across the world, upwards of 60% of seasonal surface water is stored in the snowpack. In forested areas, characterizing the effect of forest structure on the spatial distribution of snow can provide insight into hydrological modelling efforts, and forest management decisions. Just as snow drifts and scours correspond to underlying topography, wind redistribution can create patterns in snow distribution which reflect the surrounding canopy structure. Using variables derived from terrestrial laser scans collected in Grand Mesa, Colorado, the effect of forest structure and topography on snow depth is analyzed statistically

Estimating Vegetation Biomass and Cover Across Large Plots in Shrub and Grass Dominated Drylands Using Terrestrial Lidar and Machine Learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2 = 0.77, RMSE = 7%), annual grasses (R2 = 0.70, RMSE = 21%), perennial grasses (R2 = 0.36, RMSE = 12%), forbs (R2 = 0.52, RMSE = 6%), bare earth or litter (R2 = 0.49, RMSE = 19%), and the biomass of shrubs (R2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space

Methodological Considerations of Terrestrial Laser Scanning for Vegetation Monitoring in the Sagebrush Steppe

Terrestrial laser scanning (TLS) provides fast collection of high-definition structural information, making it a valuable field instrument to many monitoring applications. A weakness of TLS collections, especially in vegetation, is the occurrence of unsampled regions in point clouds where the sensor’s line-of-sight is blocked by intervening material. This problem, referred to as occlusion, may be mitigated by scanning target areas from several positions, increasing the chance that any given area will fall within the scanner’s line-of-sight from at least one position. Because TLS collections are often employed in remote regions where the scope of sampling is limited by logistical factors such as time and battery power, it is important to design field protocols which maximize efficiency and support increased quantity and quality of the data collected. This study informs researchers and practitioners seeking to optimize TLS sampling methods for vegetation monitoring in dryland ecosystems through three analyses. First, we quantify the 2D extent of occluded regions based on the range from single scan positions. Second, we measure the efficacy of additional scan positions on the reduction of 2D occluded regions (area) using progressive configurations of scan positions in 1 ha plots. Third, we test the reproducibility of 3D sampling yielded by a 5-scan/ha sampling methodology using redundant sets of scans. Analyses were performed using measurements at analysis scales of 5 to 50 cm across the 1-ha plots, and we considered plots in grass and shrub-dominated communities separately. In grass-dominated plots, a center-scan configuration and 5 cm pixel size sampled at least 90% of the area up to 18 m away from the scanner. In shrub-dominated plots, sampling at least 90% of the area was only achieved within a distance of 12 m. We found that 3 and 5 scans/ha are needed to sample at least ~ 70% of the total area (1 ha) in the grass and shrub-dominated plots, respectively, using 5 cm pixels to measure sampling presence-absence. The reproducibility of 3D sampling provided by a 5 position scan layout across 1-ha plots was 50% (shrub) and 70% (grass) using a 5-cm voxel size, whereas at the 50-cm voxel scale, reproducibility of sampling was nearly 100% for all plot types. Future studies applying TLS in similar dryland environments for vegetation monitoring may use our results as a guide to efficiently achieve sampling coverage and reproducibility in datasets

Combining Airborne Hyperspectral and LiDAR Data Across Local Sites for Upscaling Shrubland Structural Information: Lessons for HyspIRI

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

Spatial Pattern of Soil Organic Carbon Acquired from Hyperspectral Imagery at Reynolds Creek Critical Zone Observatory (RC-CZO)

Soil Organic Carbon (SOC) is a key soil property and is important for understanding carbon storage and soil-vegetation dynamics. Hyperspectral imagery (imaging spectroscopy) providing detailed spectral signatures of vegetation and soil make it possible to continuously map SOC content over a watershed scale. In this paper, the Next Generation Airborne Visible / Infrared Imaging Spectrometer (AVIRISng) was used with an unmixing algorithm, the Multiple Endmember Spectral Mixture Analysis, to differentiate fractional cover of healthy vegetation, stressed vegetation and soil at the Reynolds Creek Critical Zone Observatory (RC-CZO). The fractional cover information was used to remove noisy spectra and the resulting residual spectra were used to predict SOC by Partial Least Squares Regression (PLSR). The results showed that the root mean standard error and mean bias of the predicted SOC (%) are 0.75 and 2.4, respectively. We found the best relationship between SOC and spectra after filtering out the influence of green vegetation from mixed spectra. The resulting residual, spectra comprised of stressed vegetation and soil, contained enough information for mapping SOC distribution within the shrub dominated regions of the watershed. This may provide a method to better understand the interaction of soil and vegetation in semiarid ecosystems

Characterizing Peatland Microtopography Using Gradient and Microform-Based Approaches

Peatlands represent an important component of the global carbon cycle, storing 180–621 Gt of carbon (C). Small-scale spatial variations in elevation, frequently referred to as microtopography, influence ecological processes associated with the peatland C cycle, including Sphagnum photosynthesis and methane flux. Microtopography can be characterized with measures of topographic variability and by using conceptual classes (microforms) linked to function: most commonly hummocks and hollows. However, the criteria used to define these conceptual classes are often poorly described, if at all, and vary between studies. Such inconsistencies compel development of explicit quantitative methods to classify microforms. Furthermore, gradient-based characterizations that describe spatial variability without the use of microforms are lacking in the literature. Therefore, the objectives of this study were to (1) calculate peatland microtopographical elevation gradients and measures of spatial variability, (2) develop three microform classification methods intended for specific purposes, and (3) evaluate and contrast classification methods. Our results suggest that at spatial scales much larger than microforms, elevation distributions are unimodal and are well approximated with parametric probability density functions. Results from classifications were variable between methods and years and exhibited significant differences in mean hollow areal coverages of a raised ombrotrophic bog. Our results suggest that the conceptualization and classification of microforms can significantly influence microtopographic structural metrics. The three explicit methods for microform classification described here may be used and built upon for future applications