RSVP: Remote Sensing Visualization Platform for Data Fusion

Remote sensing involves the acquisition of data in terms of images, point clouds and so on. One of the major challenges with remote sensing datasets is managing and understanding the massive amounts of data that is collected. In many instances, scientists acquire data for the same region using varied sensing devices. Scientists would like to fuse and examine this data acquired from different sensing devices to further explore the region under investigation. Immersive visualization has emerged as an ideal solution for three-dimensional exploration of multimodal remote sensing data. The ability to manipulate data interactively in true 3D (using stereo) with interfaces designed specifically for the immersive environment can significantly speed up the exploration process. We have developed a visualization platform that facilitates the fusion of multiple modalities of remote sensing data and allows a scientist to learn more about the data obtained from different sensing devices. It is currently being used in research labs at Idaho State University and at the Idaho National Labs

To Eat or Not to Eat? Developing Biomarkers for Diet Selection by Herbivores

A major goal in conservation biology is to explain habitat use by animals. Remote sensing has been used for landscape-scale analysis of habitat features. However, studies that directly link specific parameters of habitat quality to selection by wildlife are needed at the microsite-scale before landscape-scale mapping can be validated. We used the sagebrush-pygmy rabbit system to develop spectral biomarkers that can predict how the quality of food influences habitat use

Aboveground Total and Green Biomass of Dryland Shrub Derived from Terrestrial Laser Scanning

Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R2 = 0.92 and R2 = 0.83, respectively) than the voxel-based method (R2 = 0.86 and R2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p \u3c 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems

Fearscapes: Mapping Functional Properties of Cover for Prey with Terrestrial LiDAR

Heterogeneous vegetation structure can create a variable landscape of predation risk—a fearscape—that influences the use and selection of habitat by animals. Mapping the functional properties of vegetation that influence predation risk (e.g., concealment and visibility) across landscapes can be challenging. Traditional ground-based measures of predation risk are location specific and limited in spatial resolution. We demonstrate the benefits of terrestrial laser scanning (TLS) to map the properties of vegetation structure that shape fearscapes. We used TLS data to estimate the concealment of prey from multiple vantage points, representing predator sightlines, as well as the visibility of potential predators from the locations of prey. TLS provides a comprehensive data set that allows an exploration of how habitat changes may affect prey and predators. Together with other remotely sensed imagery, TLS could facilitate the scaling up of fearscape analyses to promote the management and restoration of landscapes

Scaling Up Sagebrush Chemistry with Near-Infrared Spectroscopy and UAS-Acquired Hyperspectral Imagery

Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 \u3c 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 \u3c 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors

Spectral Fingerprints Predict Functional Chemistry of Native Plants Across Sagebrush-Steppe Landscapes

Landscapes are changing and under threat from anthropogenic activities, decreasing land cover, contaminated air and water quality, and climate change. These changes impact native communities and their functions at all spatial scales. A major functional trait being affected across these communities is nitrogen. Nitrogen supports plant nutrient cycling and growth, serves as an indicator for crude protein and productivity, and offers quality forage for wild and domestic herbivores. We need better ways to monitor nitrogen across space and time. Current monitoring is elaborate, time-consuming, and expensive. We propose drawing from agricultural methodologies to incorporate near-infrared spectroscopy as a technique in detecting and monitoring nitrogen concentrations across a threatened shrub-steppe ecosystem. We are currently developing calibration equations for nitrogen in sagebrush across four species (Artemisia tridentata wyomingensis, A. tripartita, A. arbuscula, A. nova), three study sites and two seasons. Preliminary results suggest that nitrogen can be accurately predicted across all sites, species, and seasons, explaining 75-90% of the variation in nitrogen. These results indicate that near infrared spectroscopy offers a rapid, noninvasive diagnostic tool for assessing nitrogen in wild systems. This advancing technology is important because it economizes the collection of ecological data in rapidly changing landscapes and provides land managers and researchers with valuable information about the health and sustainability of their lands

Remotely-Sensing Chemical Diversity and Function of Native Plants Across Sagebrush-Steppe Landscapes

Plant chemical diversity provides ecosystem services by supporting wildlife diversity and offering sources for novel medicines. Current mapping of phytochemicals can be expensive, time-intensive and provides only a snapshot of available diversity. To overcome this, I will use handheld and airborne instruments collecting near infrared spectra and hyperspectral imagery to remotely sense chemical diversity within plants and ecosystems. I hypothesize that greater plant chemical diversity will be correlated with greater habitat use by wildlife and greater bioactivity of plant extracts. This research provides a powerful tool to map chemical diversity, target wildlife conservation and direct the discovery of novel medicines

Habitat mapping and multi-scale resource selection of pygmy rabbits with unmanned aerial systems

Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to functional habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) bridge the gap between relatively high spatial resolution, low spatial extent, field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. In chapter one, I mapped structural habitat quality (i.e., shrub height, shrub volume, and aerial concealment) at two study sites with UAS and compared that with estimates derived from terrestrial laser scanning, a highly accurate ground-based sensor. I demonstrated that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. In chapter two, I classified sagebrush structural morphotypes and mapped forage quality including both nutrients (i.e., crude protein) and plant secondary metabolites (i.e., coumarins and monoterpenes) with UAS in both summer and winter. With these maps, I generated foodscapes representing high and low forage quality for herbivores that use sagebrush landscapes such as the sagebrush-obligates, pygmy rabbits (Brachylagus idahoensis) and greater sage-grouse (Centrocercus urophasianus), and generalist herbivore mountain cottontails (Sylvilagus nuttallii). In chapter three, I analyzed habitat selection by pygmy rabbits at multiple scales. I found that pygmy rabbits select for food variables (i.e., crude protein and total monoterpenes) while foraging (bite marks) at the plant scale, and selected for both security cover (i.e., aerial concealment and distance to burrow) and food variables at the patch scale. While moving and resting (fecal pellets), pygmy rabbits selected for a combination of food, security, and thermal properties, with thermal variables appearing more frequently in top models at the plant scale, and primarily for security at the patch scale. Finally, I applied my patch-scale model of pygmy rabbit habitat selection across the landscape using UAS-derived maps of security cover and food quality. These results show scale-dependent habitat selection in a central-place browsing herbivore. Making explicit predictions of trade-offs affecting habitat use, and assessing those predictions at distinct scales, will be necessary for improving knowledge of habitat selection patterns in herbivores

Combining Hyperspectral and LiDAR Data in the CAVE\u3csup\u3eTM\u3c/sup\u3e

The Remote Sensing Visualization Platform (RSVP) is a new software application which was created in order to display hyperspectral and LiDAR data sets together in an immersive visualization environment known as the Cave Automated Virtual Environment (CAVETM). The goal was to display a hyperspectral band of a specified wavelength alongside a LiDAR point cloud of the same geographic region. Single hyperspectral bands can be shown as a greyscale image or three bands can be selected where each band represents red, green, or blue. The user can switch between different band combinations within one session. Future work will include analysis capabilities within the immersive session. The user will be able to create plots detailing the spectral response for all bands of a selected location. This application will provide a new method for remote sensing experts to display and analyze their data