Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Environmental disturbances in semi-arid ecosystems have highlighted the need to monitor current and future vegetation conditions across the landscape. Imaging spectroscopy provide the necessary information to derive vegetation characteristics at high-spatial resolutions across large geographic areas. The work of this thesis is divided into two sections focused on using imaging spectroscopy to estimate and classify vegetation cover, and approximate aboveground biomass in a semi-arid ecosystem. The first half of this thesis assesses the ability of imaging spectroscopy to derive vegetation classes and their respective cover across large environmental gradients and ecotones often associated with semi-arid ecosystems. Optimal endmember selection and endmember bundling are coupled with classification and spectral unmixing techniques to derive vegetation species and abundances across Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho at high spatial resolution (1 m). Results validated using field data indicated classification of aspen, Douglas fir, juniper, and riparian classes had an overall accuracy of 57.9% and a kappa coefficient of 0.43. Plant functional type classification, consisting of deciduous and evergreen trees, had an overall accuracy of 84.4% and a kappa coefficient of 0.68. Shrub, grass, and soil cover were predicted with an overall accuracy of 67.4% and kappa coefficient of 0.53. I conclude that imaging spectroscopy can be used to map vegetation communities in semi-arid ecosystems across large environmental gradients at high-spatial resolution and with high accuracy. The second half of this thesis focuses on monitoring the changes of aboveground biomass (AGB) from the 2015 Soda Fire, which burned portions of southwest Idaho and southeastern Oregon. Classifications derived in the first study are used to estimate AGB loss within a portion of RCEW, and these estimates are used to compare to gross estimates made over the full extent of the Soda Fire. I found that there was an AGB loss of 174M kg within RCEW and approximately 1.8B kg lost over the full extent of the Soda Fire. Additionally, a post-fire analysis was performed to provide insight into the amount of AGB that returned to both RCEW and the full extent of the Soda Fire. An estimated 2,100 – 208,000 kg of AGB had returned to the burned portion of RCEW one-year post fire, and approximately 3.2M kg of AGB had returned over the full extent of the Soda Fire. These AGB loss and re-growth estimates can be used by researchers and practitioners to monitor carbon flux across the Soda Fire and as baseline data for wildfires in semi-arid ecosystems

Semi-Arid Ecosystem Plant Functional Type and LAI from Small Footprint Waveform Lidar

Plant functional traits such as vegetation structure, density, and composition are indicators of ecosystem response to climate and human driven disturbances. We used small footprint waveform lidar with an ensemble random forest approach to differentiate the functional traits in a western US semi-arid ecosystem. We introduced a new gap fraction based leaf area index (LAI) estimator using lidar derived parameters. Results showed 60% - 89% accuracies discriminating plant functional types and estimating shrub LAI. These results imply the potential of waveform lidar to quantify plant functional traits in low-stature vegetation which is useful to assess climate impact in semi-arid ecosystems

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Active region extent assessment with X-rays (AREA-X)

The development of semiconductor sensors for new particle tracking detectors places increasing limits on sensor characteristics such as uniformity, size and shape of inefficient areas and size of active compared to inactive sensor areas. Accurately assessing these relatively subtle effects requires either measurements in particle beams or the modification of samples to be used in dedicated laser test setups. Active Region Extent Assessment with X-rays (AREA-X) has been developed as an alternative method for the fast, efficient and precise study of the active area of a semiconductor sensor. It uses a monochromatic, micro-focused X-ray beam with a 10–20 keV energy range as provided by several synchrotron beam lines and uses the photo current induced in the sensor to measure the depth of the responsive sensor volume. It can be used to study local inhomogeneities or inefficiencies, the overall extent of the active sensor volume and its shape and its localised application, which makes the need to gather statistics over a large area unnecessary, allowing for fast readout, which enables studies of the sensor behaviour at a range of external parameters, e.g. temperature or applied bias voltage. This paper presents the measurement concept and technical setup of the measurement, results from initial measurements as well as capabilities and limitations of the method

Using uncrewed aerial vehicles for identifying the extent of invasive phragmites australis in treatment areas enrolled in an adaptive management program

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites

Mapping the depleted area of silicon diodes using a micro-focused X-ray beam

For the Phase-II Upgrade of the ATLAS detector at CERN, the current ATLAS Inner Detector will be replaced with the ATLAS Inner Tracker. The ATLAS Inner Tracker will be an all-silicon detector, consisting of a pixel tracker and a strip tracker. Sensors for the ITk strip tracker are required to have a low leakage current up to bias voltages of -700 V to maintain a low noise and power dissipation. In order to minimise sensor leakage currents, particularly in the high-radiation environment inside the ATLAS detector, sensors are foreseen to be operated at low temperatures and to be manufactured from wafers with a high bulk resistivity of several k{\Omega} cm. Simulations showed the electric field inside sensors with high bulk resistivity to extend towards the sensor edge, which could lead to increased surface currents for narrow dicing edges. In order to map the electric field inside biased silicon sensors with high bulk resistivity, three diodes from ATLAS silicon strip sensor prototype wafers were studied with a monochromatic, micro-focused X-ray beam at the Diamond Light Source. For all devices under investigation, the electric field inside the diode was mapped and its dependence on the applied bias voltage was studied. The findings showed that the electric field in each diode under investigation extended beyond its bias ring and reached the dicing edge

The Salivary Secretome of the Tsetse Fly Glossina pallidipes (Diptera: Glossinidae) Infected by Salivary Gland Hypertrophy Virus

Tsetse fly (Diptera; Glossinidae) transmits two devastating diseases to farmers (human African Trypanosomiasis; HAT) and their livestock (Animal African Trypanosomiasis; AAT) in 37 sub-Saharan African countries. During the rainy seasons, vast areas of fertile, arable land remain uncultivated as farmers flee their homes due to the presence of tsetse. Available drugs against trypanosomiasis are ineffective and difficult to administer. Control of the tsetse vector by Sterile Insect Technique (SIT) has been effective. This method involves repeated release of sterilized males into wild tsetse populations, which compete with wild type males for females. Upon mating, there is no offspring, leading to reduction in tsetse populations and thus relief from trypanosomiasis. The SIT method requires large-scale tsetse rearing to produce sterile males. However, tsetse colony productivity is hampered by infections with the salivary gland hypertrophy virus, which is transmitted via saliva as flies take blood meals during membrane feeding and often leads to colony collapse. Here, we investigated the salivary gland secretome proteins of virus-infected tsetse to broaden our understanding of virus infection, transmission and pathology. By this approach, we obtain insight in tsetse-hytrosavirus interactions and identified potential candidate proteins as targets for developing biotechnological strategies to control viral infections in tsetse colonies

The ABC130 barrel module prototyping programme for the ATLAS strip tracker

For the Phase-II Upgrade of the ATLAS Detector, its Inner Detector, consisting of silicon pixel, silicon strip and transition radiation sub-detectors, will be replaced with an all new 100 % silicon tracker, composed of a pixel tracker at inner radii and a strip tracker at outer radii. The future ATLAS strip tracker will include 11,000 silicon sensor modules in the central region (barrel) and 7,000 modules in the forward region (end-caps), which are foreseen to be constructed over a period of 3.5 years. The construction of each module consists of a series of assembly and quality control steps, which were engineered to be identical for all production sites. In order to develop the tooling and procedures for assembly and testing of these modules, two series of major prototyping programs were conducted: an early program using readout chips designed using a 250 nm fabrication process (ABCN-25) and a subsequent program using a follow-up chip set made using 130 nm processing (ABC130 and HCC130 chips). This second generation of readout chips was used for an extensive prototyping program that produced around 100 barrel-type modules and contributed significantly to the development of the final module layout. This paper gives an overview of the components used in ABC130 barrel modules, their assembly procedure and findings resulting from their tests.Comment: 82 pages, 66 figure

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Measurement of the inclusive isolated-photon cross section in pp collisions at √s = 13 TeV using 36 fb−1 of ATLAS data

The differential cross section for isolated-photon production in pp collisions is measured at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 36.1 fb. The differential cross section is presented as a function of the photon transverse energy in different regions of photon pseudorapidity. The differential cross section as a function of the absolute value of the photon pseudorapidity is also presented in different regions of photon transverse energy. Next-to-leading-order QCD calculations from Jetphox and Sherpa as well as next-to-next-to-leading-order QCD calculations from Nnlojet are compared with the measurement, using several parameterisations of the proton parton distribution functions. The predictions provide a good description of the data within the experimental and theoretical uncertainties. [Figure not available: see fulltext.