Adjudication and the Adaptive Capacity of Pecan Farmers in the Lower Rio Grande

Despite growing uncertainty of water availability in the future and popular understandings of water conservation in agriculture, a growing number of farmers in the Lower Rio Grande Basin are rapidly transitioning to pecan orchards, a long-term and highly water-dependent crop. Drivers of landscape change can be environmental, historic, socioeconomic, or institutional. Adaptation to change is understood as responses to external stimuli and is limited to a threshold by which an actor can meet their goals. Much of the current scholarship focuses on a given population’s adaptive capacity toward global climate change, however, most water policy in the western United States is part and parcel a response to burgeoning climate crises. By framing agrarian change in terms of the capacity to adapt to water policy, adjudication, and litigation, I explore what externalities and mechanisms of uncertainty influence agricultural management decisions. I use multiple regression models to unpack some of the interplay between physical and institutional factors, spatial relationships, and cropping patterns. The analysis is evaluated based on qualitative surveys of farmers’ perceptions of the drivers of change. I argue that the ability to acquire water rights to meet or exceed adjudicated irrigation amounts has disproportionately ensured the livelihood and environmental resilience of farmers with higher property value and larger farms in less rural areas. Current markets and ongoing adjudication has given advantage to pecan orchards over other crops

The Detection of Ionizing Radiation by Plasma Panel Sensors: Cosmic Muons, Ion Beams and Cancer Therapy

The plasma panel sensor is an ionizing photon and particle radiation detector derived from PDP technology with high gain and nanosecond response. Experimental results in detecting cosmic ray muons and beta particles from radioactive sources are described along with applications including high energy and nuclear physics, homeland security and cancer therapeuticsComment: Presented at SID Symposium, June 201

Plasma Panel Sensors for Particle and Beam Detection

The plasma panel sensor (PPS) is an inherently digital, high gain, novel variant of micropattern gas detectors inspired by many operational and fabrication principles common to plasma display panels (PDPs). The PPS is comprised of a dense array of small, plasma discharge, gas cells within a hermetically-sealed glass panel, and is assembled from non-reactive, intrinsically radiation-hard materials such as glass substrates, metal electrodes and mostly inert gas mixtures. We are developing the technology to fabricate these devices with very low mass and small thickness, using gas gaps of at least a few hundred micrometers. Our tests with these devices demonstrate a spatial resolution of about 1 mm. We intend to make PPS devices with much smaller cells and the potential for much finer position resolutions. Our PPS tests also show response times of several nanoseconds. We report here our results in detecting betas, cosmic-ray muons, and our first proton beam tests.Comment: 2012 IEEE NS

Development of a plasma panel radiation detector: recent progress and key issues

A radiation detector based on plasma display panel technology, which is the principal component of plasma television displays is presented. Plasma Panel Sensor (PPS) technology is a variant of micropattern gas radiation detectors. The PPS is conceived as an array of sealed plasma discharge gas cells which can be used for fast response (O(5ns) per pixel), high spatial resolution detection (pixel pitch can be less than 100 micrometer) of ionizing and minimum ionizing particles. The PPS is assembled from non-reactive, intrinsically radiation-hard materials: glass substrates, metal electrodes and inert gas mixtures. We report on the PPS development program, including simulations and design and the first laboratory studies which demonstrate the usage of plasma display panels in measurements of cosmic ray muons, as well as the expansion of experimental results on the detection of betas from radioactive sources.Comment: presented at IEEE NSS 2011 (Barcelona

Understanding public perspectives on fracking in the United States using social media big data

People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics

Plasma panel‐based radiation detectors

The plasma panel sensor (PPS) is a gaseous micropattern radiation detector under current development. It has many operational and fabrication principles common to plasma display panels. It comprises a dense matrix of small, gas plasma discharge cells within a hermetically sealed panel. As in plasma display panels, it uses nonreactive, intrinsically radiation‐hard materials such as glass substrates, refractory metal electrodes, and mostly inert gas mixtures. We are developing these devices primarily as thin, low‐mass detectors with gas gaps from a few hundred microns to a few millimeters. The PPS is a high gain, inherently digital device with the potential for fast response times, fine position resolution (<50‐µm RMS) and low cost. In this paper, we report on prototype PPS experimental results in detecting betas, protons, and cosmic muons, and we extrapolate on the PPS potential for applications including the detection of alphas, heavy ions at low‐to‐medium energy, thermal neutrons, and X‐rays.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98325/1/jsid151.pd

73.1: Large‐Area Plasma‐Panel Radiation Detectors for Nuclear Medicine Imaging to Homeland Security and the Super Large Hadron Collider

A new radiation sensor derived from plasma panel display technology is introduced. It has the capability to detect ionizing and non‐ionizing radiation over a wide energy range and the potential for use in many applications. The principle of operation is described and some early results presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92058/1/1.3499840.pd

Workflow for hydrologic modelling with sUAS-acquired aerial imagery

Operating with a conceptual workflow for the appropriate processing of high-spatial resolution small unmanned aircraft system (sUAS) data for hydrologic modelling of floodplains during flood events, this research investigated the effects of input data fidelity on hydrologic model generation. A digital surface model (DSM) and co-registered orthophoto mosaic of a stretch of the Drau River in southern Austria was generated. A digital terrain model (DTM) was then approximated from the generated DSM to within a vertical root-mean-square error (RMSE) of 4.65 cm. Horizontal and random metrics of roughness were calculated based on the DSM and then used to determine spatially-varying values of Manning’s n coefficient across an observed floodplain. A distributed two-dimensional hydrologic model of a river channel and simulated floodplain was performed using open source hydraulic modelling software. The effectiveness of each roughness metric at multiple pixel resolutions to model significant variations in reported velocity, water surface elevation and depth during a flood event was tested in a controlled sensitivity analysis. Results indicated that accuracy in hydrologic modelling is impacted by image spatial resolution. Research results conclude by emphasizing the importance of context in hydrologic modelling scenarios to effectively meet the needs of end users

Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework

Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram&ndash;Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application