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356 Research products

  • Rural Digital Europe
  • 2013-2022
  • Doctoral thesis
  • eScholarship - University of California

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    Authors: Zheng, Zeshi;

    The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE.

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    Authors: Sarigul-Klijn, Yasemin;

    Stroke is one of the leading causes of disability in the United States. After a stroke, patients require both rehabilitation to reduce upper and/or lower body impairments, and assistance to help carry out daily living. The amount of conventional therapy performed is often limited due to cost and lack of intensity, therefore patients do not receive an effective dose to maximize gains. Robotic training devices can promote greater dosage of therapy, but often are expensive and bulky, limiting their use to the clinic. In the home, wheelchairs provide low-cost mobility, but no means of therapy. Emerging hybrid assistive/rehabilitation devices like exoskeletons provide mobility with some clinical benefit but remain infeasible due to complexity and cost. The premise of this dissertation is that simple, low-cost devices can be designed to promote both therapy and mobility, improving patient outcomes. This dissertation explored the design and experimental validation of three such devices, including a new class of hybrid assistive/rehabilitative device called an “exochair”. An exochair couples a rehabilitative exoskeleton to a wheelchair, with the goal of allowing a user’s therapeutic exercise drive around the chair, granting the user both assistance in daily living as well as therapy. First, I report results from experiments that quantified the ability of young nonimpaired users to learn to drive a previously developed upper extremity exochair called LARA. The LARA chair is driven via levers with an arm support (i.e. by a simple exoskeleton) and is intended to help individuals with weak arms to self-propel and exercise the arms. I compared two designs of LARA, one that used a simple one-way bearing mechanism to provide propulsion (a technique that had been previously proposed), and a second that utilized a yoked hand-clutching scheme for driving (a novel technique suitable for people with unilateral arm/hand weakness such as occurs after stroke, but potentially more difficult to learn). Unexpectedly, I found that the participants learned to drive the hand-clutched device with similar learning time constants, and ultimately achieved similar over ground speeds, compared to the one-way bearing model. The hand-clutched approach increased the physiological effect of exercise, cognitive engagement, and maneuverability, showing its potential as a hybrid assistive/rehabilitation device for people with stroke. In a follow-up study, I then quantified the ability of people with severe arm impairment after chronic stroke to learn to drive the hand clutch design. All participants increased their propulsion speed across five days of training. Unexpectedly, they showed a rate of motor learning that was two times higher than the average value reported for learning of a variety of motor tasks by unimpaired individuals, including the rate of learning of LARA by unimpaired users. They also improved on clinical arm movement scores by an amount comparable to previous robotic therapy device studies and showed high levels of motivation and self-ratings of competence that increased to high levels across training. Thus, the exochair unmasked a latent motor learning ability for individuals who have a severe arm impairment after stroke. This is significant because such arm impairment normally prevents such individuals from learning functional tasks with the impaired arm. People can learn to drive an exochair, it is rewarding, it assists in functional ambulation ability, and this learning process improves clinical arm movement scores. Based on these experiments, I explored the application of classical machine learning algorithms to assist people with bilateral hand impairment in driving LARA. I showed the feasibility of an automated clutch system, comparing it with a hand switch approach. This approach could potentially relieve the burden of hand-clutching when using LARA for longer periods of time. Next, I present results of a low-cost hand exoskeleton designed for assisting in hand extension post-stroke, with a demonstration of use by an unimpaired subject. This work explored an alternate way functionality could be increased for patients via wearable devices. Finally, I extended the exochair concept to a hybrid assistive/rehabilitative device for leg movement called GRAM. My design concept for GRAM was that users could manipulate a linkage with their legs, mimicking the natural kinematic pattern of walking, in order to propel the wheelchair. I tested GRAM in a case study with an unimpaired individual, demonstrating that it was possible to learn to propel GRAM using this technique. These results demonstrate the potential of exochairs such as LARA and GRAM to provide hybrid assistance/therapy in a simpler and less costly way than is currently possible with other technologies. Future research will study the long-term functional and therapeutic benefits of exochairs in larger patient population.

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    Authors: Drevno, Ann Gleason;

    Much of modern-day agriculture relies heavily on fertilizers and pesticides to increase yields, yet when applied in excess or without proper control mechanisms these inputs can wreak havoc on local waterways. This dissertation analyzes policy approaches implemented in Europe, the U.S. and California to abate discharges from farms. The research utilizes a mixed methods approach, integrating qualitative, quantitative and spatial analyses, to investigate regulatory tools, governance structures, policy outcomes, and stakeholder opinions relating to water pollution from agriculture. The dissertation is comprised of four interrelated parts. The first part assesses the range of regulatory approaches employed to control agricultural nonpoint source pollution in the United States and the European Union. Findings suggest that transitioning from the voluntary control mechanisms to more effective instruments based on measurable water quality performance relies predominantly on three factors: (1) more robust quality monitoring data and models; (2) local participation; and (3) political will. Identifying obstacles to and successes of national and international agricultural water pollution policies set the context for delving deeper into this regulatory problem on a regional level. The second, third, and fourth parts of this doctoral research focus on the primary regulatory mechanism for agricultural discharges in California’s Central Coast Region: The Conditional Agricultural Waiver. One of these parts uses the policy tool framework to assess the overall effectiveness of the Conditional Agricultural Waiver and its associated monitoring programs. Research results show that while the regional policy represented a small step forward in implementing appropriate control mechanisms for agricultural pollution, the significance of monitoring programs greatly limited the policy’s success. Another part of this dissertation surveyed 1,000 growers and their opinions on water quality practices and regulations. Results corroborate with prior research—growers’ trust in the majority of regional agricultural groups was closely correlated with communication. However, trust in the Regional Board did not correspond to the relatively high contact frequency with the regulatory agency, most likely due to a divergence of interests and institutional distance. This study also confirms anecdotes of declining trust between farmers and the Regional Board over the course of the two Ag Waivers. A final part of the dissertation focuses on specific provisions aimed at controlling two pesticides in the region. Results from this chapter indicate that the 2012 Central Coast Conditional Agricultural Waiver was a contributing factor in successfully reducing the use of diazinon and chlorpyrifos, but several unintended consequences, such as continued presence of the pollutants in waterways, remain unsettled.

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    Authors: Matthew, Robert Peter;

    This thesis introduces a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy subjects to determine the utility as a clinical tool.The proposed framework was used to augment the five-times sit-to-stand test. This is a clinical test designed to estimate an individual's stability by timing the total time to stand/sit five times. Using the proposed framework, a representative kinematic and dynamic model was obtained which outperformed conventional height/mass scaled models. This allows for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians. These tools are then used to develop a prescriptive model for developing assistive devices. The recovered models can be used to formulate an optimisation to determine the actuator types and parameters to provide augmentation.This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance. The modelling framework is used to analyse the effect this assistance has on compensatory actions of the shoulder.

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    Authors: Laughner, Joshua L;

    Satellite observations of NO2 provide information about the spatial and temporal variability of NO2 column densities that can be used to infer the processes controlling NO2 concentrations. However, satellite observations require a priori information to properly transform the observed radiances into vertical column densities. Previous work has shown that using a priori data at equal or better spatial resolution to the satellite pixels significantly improves the retrieved NO2. Of particular importance are the a priori vertical profiles that represent the vertical distribution of NO2 within each satellite pixel.In this dissertation, I show that the temporal resolution of the a priori NO2 profiles, as well as the spatial resolution, is important to accurately retrieve NO2. I show that using profiles with high spatial resolution, but coarse temporal resolution, overestimates the rate at which NO2 is lost in the outflow from an urban source, and that high spatial and temporal resolution of these profiles is necessary to simultaneously retrieve the NO2 column density and lifetime in an urban plume. To account for this, I design and validate an upgrade to the Berkeley High Resolution (BEHR) NO2 retrieval that incorporates daily, high resolution profiles for over 7 years. With this retrieval, I show direct observations of the relationship between NOx concentration and lifetime by examining the weekend-weekday changes in column density and lifetime from several US cities between 2005 and 2014. Specifically, I show that Chicago, IL and Dallas, TX are undergoing a transition from NOx-suppressed to NOx-limited chemistry, which may indicate that future reductions in NOx emissions will be more effective at controlling O3 production, but have less direct effect on NOx concentrations.

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    Authors: Baldo, Elisabeth;

    Accurately representing the spatial variability of montane snowpack is challenging due to the high degree of complexity in the terrain's topography, and the lack of good quality in-situ data. To explicitly resolve snow processes in montane environment while taking into account the uncertainties in the system's high spatial and temporal resolution snow reanalyses are required. Ensemble-based approaches assimilating Landsat VIS-NIR remote sensing data at spatial resolutions of 100 m or less are, however, prohibitively expensive to run at large scales, and sub-optimal given that only the most complex parts of a montane range require such fine resolution. In addition, the assimilation of remote sensing data from a single source can also be unsatisfactory due to a lack of global coverage, hardware malfunction etc. In order to optimize computational needs while preserving the accuracy of ~ 100 m reanalyses, a raster-based multi-resolution approach was first developed and successfully implemented for a headwater catchment in the Colorado River Basin over the full length of Landsat record (30+ years). The potential use of MODIS-derived snow cover information in addition to Landsat in snow reanalyses was then investigated over three different regions in the Western U.S. and High Mountain Asia in order to make up for Landsat's shortcomings over midlatitude snowpacks. The key findings of this dissertation can be summarized as follows: 1) The physiographic complexity of a terrain can be characterized by its standard deviations of elevation, northness index and forested fraction. Using such a complexity metric to discretize the terrain into different spatial resolutions via a multi-resolution approach can significantly reduce computational needs, while mitigating errors in snow processes representation. 2) The multi-resolution approach did not significantly impact the remote sensing observations assimilated, and the posterior snow water equivalent (SWE) ensemble median and standard deviation matched the 90 m reanalysis, thus leading to a robust implementation in the context of a data assimilation framework. 3) A MODIS-derived snow cover product was found to be a useful complementary source of remote sensing data to be simultaneously assimilated with Landsat. Off-nadir-looking observations first had to be screened out of the reanalysis due to the distorting and snow obscuring effects of the sensor viewing geometry at high zenith angles. Ultimately, the methods developed in this work can be applied to all midlatitude montane ranges over the full lengths of Landsat and MODIS records to generate a fine spatial resolution SWE reanalysis dataset that will be useful to snow hydrologists to solve many unanswered science questions over such challenging regions.

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    Authors: Norton, Juliet;

    Scientists widely accept that modern agriculture is unsustainable, but the best methods for addressing unsustainability are still contested (Constance, Konefal, and Hatanaka 2018). Grassroots sustainable agriculture communities have long participated in the exploration of solutions for agriculture unsustainability, and their momentum continues to grow in the technical age. Practitioners of grassroots sustainable agriculture use many information systems that were not originally built to support the design of agricultural systems. Based on ethnographic research with two grassroots sustainable agriculture communities, I show that participants’ personal and community values frequently clashed with those embedded in information systems, including ones used to look for and manage plant information. Furthermore, I demonstrate a range of information challenges that participants faced in the absence of tools designed to support their specific work. I argue that practitioners of grassroots sustainable agriculture need information systems tailored to their goals and values in order to productively address barriers to designing and building agroecosystems for their communities.This dissertation provides an example of how to involve communities in the development of information technology artifacts and strengthen efforts to support sustainability via technological interventions. First, I engaged in two grassroots sustainable agriculture communities as a participant, experiencing their practices, values, and information challenges first hand. Then, I worked with the communities to create a plant database web application (SAGE Plant Database) that supports agroecosystem design in local contexts. Members of the communities participated in the design, development, and data population stages so that the SAGE Plant Database supports their design context and upholds their technological and holistic sustainability values. At the foundation of the database is a plant ontology grounded in the participants’ practice of designing agroecosystems. My comparative analysis of the design of the SAGE Plant Database to other databases demonstrates its relevance due to its emphasis on agroecological relationships among plants and between plants and the environment, the inclusion of ethnobotanical data, and the embedded community values. By engaging in this research, I seek to make progress towards transforming the technology-supported food system into one that furthers food security, food sovereignty, and holistic sustainability.

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    Authors: Solera, Melissa Viola Eitzel;

    In this work, I answer timely questions regarding tree growth, tree survival, and community change in California tree species, using a variety of sophisticated statistical and remote sensing tools.In Chapter 1, I address tree growth for a single tree species with a thorough explanation of hierarchical state-space models for forest inventory data.Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation (spatial plot effects, temporal repeated measures, and a mosaic of sampling intervals) make these data challenging to use for growth estimation. I account for these complexities and incorporate potential limiting factors (tree size, competition, and resource supply) into a hierarchical state-space model. I estimate the diameter growth of white fir (Abies concolor) in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables (representing light, water, and nutrient availability) do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth.In Chapter 2, I expand my state-space modeling to examine survival in seven tree species, as well as investigating the results of modeling them in aggregate (at the community level) and comparing with the individual species models.Declining tree survival is a complex, well-recognized problem, but studies have been largely limited to relatively rare old-growth forests or low-diversity systems, and to models which are species-aggregated or cannot easily accommodate yearly climate variables. I estimate survival models for a relatively diverse second-growth forest in the Sierra Nevada of California using a hierarchical state-space framework. I account for a mosaic of measurement intervals and random plot variation, and I directly include yearly stand development variables alongside climate variables and topographic proxies for nutrient limitation. My model captures the expected dependence of survival on tree size. At the community level, stand development variables account for decreasing survival trends, but species-specific models reveal a diversity of factors influencing survival. Species time trends in survival do not always conform to existing theories of Sierran forest dynamics, and size relationships with survival differ for each species. Within species, low survival is concentrated in susceptible subsets of the population and single estimates of annual survival rates do not reflect this heterogeneity in survival. Ultimately only full population dynamics integrating these results with models of recruitment can address the potential for community shifts over time.In Chapter 3, I combine statistical modeling with remote sensing techniques to investigate whether topographic variables influence changes in woody cover.In the North Coast of California, changes in fire management have resulted in conversion of oak woodland into coniferous forest, but the controls on this slow transition are unknown. Historical aerial imagery, in combination with Object-Based Image Analysis (OBIA), allows us to classify land cover types from the 1940s and compare these maps with recent cover. Few studies have used these maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation (ideally without throwing away data) and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. I study the change in woody cover in California's North Coast using historical (1948) and recent (2009) high-spatial-resolution imagery. I classify the imagery using eCognition Developer and aggregate the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. I use Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. I explore the relative roles of elevation, topographic slope, aspect (Northness/Eastness), topographic wetness index, profile curvature, historical percent woody cover, and geographical coordinates in influencing current percent woody cover. I estimate these models for scales of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. I find that historical woody cover has a consistent positive effect on current woody cover, and that the spatial term in the model is significant even after controlling for historical cover. Specific topographic variables emerge as important for different sites at different scales, but no overall pattern emerges across sites or scales for any of the topographic variables I tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.I conclude that these techniques are promising but a framework is needed for sensitivity analysis, as modeling results can depend strongly on variable selection and model structure.

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    Authors: Sampson, Devon D.;

    Biodiversity conservation and food security are often assumed to be separate or conflicting issues. In the municipality of Tzucacab, in a rural corner of Mexico’s Yucatan peninsula, I found that crop diversity and food security are deeply intertwined. I measured food security and home garden agrobiodiversity on a randomized selection of sixty smallholder farms in the municipality, conducted ethnographic interviews over a period of four years, and collaborated with six high school students on a participatory photography project documenting local food culture. From the quantitative data, I found that crop diversity is the strongest predictor of household food security during a drought in the rural municipality I surveyed. This finding indicates that maintaining high levels of agrobiodiversity can be an important strategy for subsistence farmers to buffer their food supply against the risk of crop failure. Additionally, I found evidence that diversification of the home garden is one important strategy managing risk among a complex of several approaches to livelihood diversification. The finding helps to explain why some farmers conserve diversity while others do not, and suggests that the goals of agrobiodiversity conservation and rural food security might be better addressed together. These results point to the ability of small-scale, diverse farms run by campesino farmers to feed themselves, challenging the dominant discourse and practice of development that prioritizes increasing yields above all other properties of agroecosystems.

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    Authors: Pickard, Michael R.;

    Much of the Sierra Nevada forests are overstocked with too many trees, are at risk of burning in stand-replacing wildfires due to the fuel loading, contain almost complete canopy coverage over the landscape and intercept a substantial portion of the snowpack, and in need of thinning. New research shows that forest thinning patterns allowing for forest structures comprising of clumps of varying density, single individual trees, and forest gaps are what the historic stands in Sierra Nevada were comprised of. In addition to returning the forest to historic stand structure, forest thinning will also allow for greater amounts of snowpack to reach the forest floor and persist longer into the season before complete meltout. This study set out to see if this new thinning spacing pattern would affect snow accumulation or ablation rates as compared with the current standard even-tree spacing. Data from four snow surveys, distributed light/temperature sensors, hemispherical photographs, and a small distributed network of snow-depth sensors were analyzed from three different forest treatments in the Sierra Nevada. The first treatment consisted of variable thinning, which reduced the stems per acre by 80%, the basal area per acre by 40%, and created a heterogeneous forest structure with small gaps and clumps of trees of varying density. The second forest treatment reduced the stems per acre and the basal area per acre stand down to similar levels as the variable thinning, however the residual trees were left to replicate a homogenous, even spacing. An unthinned control provided a comparison to pre-treatment conditions. Snow-survey data showed that approximately 10-20% more snow accumulated in the two thinning treatments than in the control. Differences in snow depth between the two thinning treatments were statistically insignificant. Snow-survey results also indicated that the variable treatments kept snow on the ground longer than the even treatments in the 2013 water year, which had a peak snow water equivalent (SWE) of 17.9 cm, and melted out at approximately the same time as the control units. In 2014 the peak SWE recorded was only 6.3 cm, both the variable and even treatments melted out at the same time, and the control units kept a steady snowpack for approximately 1-3 days longer. Data from snow-surveys, distributed light/temperature sensors and snow-depth sensors showed that more snow accumulated in the open than at the drip edge and under the forest canopy, and that melt rates are faster in the open than under the canopy. Our data showed that the variable treatment held the snowpack longer and exhibited a slower melt rate than did the even treatments indicating a possibility to retain substantial amounts of snow in the mountains longer if this thinning structure were to be implemented on a landscape level.

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    Authors: Zheng, Zeshi;

    The Sierra Nevada winter snowpack is the major water resource for the state of California. To better quantify the input of the water system, we deployed wireless-sensor networks across several basins in the Sierra Nevada. Together with operational and scientific research agencies, we also collected numerous scans of snow-on and snow-off lidar data over several basins in the high Sierra. We mined the lidar data and found how spatial patterns of snow depth are affected by topography and vegetation while elevation is the primary variable, other lidar-derived attributes slope, aspect, northness, canopy penetration fraction explained much of the remaining variance. By segmenting the vegetation into individual trees using lidar point clouds, we were able to extract tree wells from the high resolution snow-depth maps and we found the spatial snow distribution to be affected by the interactions of terrain and canopies. The snowpack is deeper at the downslope direction from the tree bole, however the snowpack at upslope direction being deeper. On sub-meter to meter scales, non-parametric machine-learning models, such as the extra-gradient boosting and the random-forest model, were found to be effective in predicting snow depth in both open and under-canopy areas. At spatial scales that are larger than 100 × 100 m2, we developed a novel approach of using the k-NN algorithm to combine the real-time wireless-sensor-network data with historical spatial products to estimate snow water equivalent spatially. The results suggest only a few historical snow-water-equivalent maps are needed if the historical maps can accurately represent the spatial distribution of snow water equivalent. The residual from the k-NN estimates can be distributed spatially using a Gaussian-process regression model. The entire estimation process can explain 90% of the variability of the spatial SWE.

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    Authors: Sarigul-Klijn, Yasemin;

    Stroke is one of the leading causes of disability in the United States. After a stroke, patients require both rehabilitation to reduce upper and/or lower body impairments, and assistance to help carry out daily living. The amount of conventional therapy performed is often limited due to cost and lack of intensity, therefore patients do not receive an effective dose to maximize gains. Robotic training devices can promote greater dosage of therapy, but often are expensive and bulky, limiting their use to the clinic. In the home, wheelchairs provide low-cost mobility, but no means of therapy. Emerging hybrid assistive/rehabilitation devices like exoskeletons provide mobility with some clinical benefit but remain infeasible due to complexity and cost. The premise of this dissertation is that simple, low-cost devices can be designed to promote both therapy and mobility, improving patient outcomes. This dissertation explored the design and experimental validation of three such devices, including a new class of hybrid assistive/rehabilitative device called an “exochair”. An exochair couples a rehabilitative exoskeleton to a wheelchair, with the goal of allowing a user’s therapeutic exercise drive around the chair, granting the user both assistance in daily living as well as therapy. First, I report results from experiments that quantified the ability of young nonimpaired users to learn to drive a previously developed upper extremity exochair called LARA. The LARA chair is driven via levers with an arm support (i.e. by a simple exoskeleton) and is intended to help individuals with weak arms to self-propel and exercise the arms. I compared two designs of LARA, one that used a simple one-way bearing mechanism to provide propulsion (a technique that had been previously proposed), and a second that utilized a yoked hand-clutching scheme for driving (a novel technique suitable for people with unilateral arm/hand weakness such as occurs after stroke, but potentially more difficult to learn). Unexpectedly, I found that the participants learned to drive the hand-clutched device with similar learning time constants, and ultimately achieved similar over ground speeds, compared to the one-way bearing model. The hand-clutched approach increased the physiological effect of exercise, cognitive engagement, and maneuverability, showing its potential as a hybrid assistive/rehabilitation device for people with stroke. In a follow-up study, I then quantified the ability of people with severe arm impairment after chronic stroke to learn to drive the hand clutch design. All participants increased their propulsion speed across five days of training. Unexpectedly, they showed a rate of motor learning that was two times higher than the average value reported for learning of a variety of motor tasks by unimpaired individuals, including the rate of learning of LARA by unimpaired users. They also improved on clinical arm movement scores by an amount comparable to previous robotic therapy device studies and showed high levels of motivation and self-ratings of competence that increased to high levels across training. Thus, the exochair unmasked a latent motor learning ability for individuals who have a severe arm impairment after stroke. This is significant because such arm impairment normally prevents such individuals from learning functional tasks with the impaired arm. People can learn to drive an exochair, it is rewarding, it assists in functional ambulation ability, and this learning process improves clinical arm movement scores. Based on these experiments, I explored the application of classical machine learning algorithms to assist people with bilateral hand impairment in driving LARA. I showed the feasibility of an automated clutch system, comparing it with a hand switch approach. This approach could potentially relieve the burden of hand-clutching when using LARA for longer periods of time. Next, I present results of a low-cost hand exoskeleton designed for assisting in hand extension post-stroke, with a demonstration of use by an unimpaired subject. This work explored an alternate way functionality could be increased for patients via wearable devices. Finally, I extended the exochair concept to a hybrid assistive/rehabilitative device for leg movement called GRAM. My design concept for GRAM was that users could manipulate a linkage with their legs, mimicking the natural kinematic pattern of walking, in order to propel the wheelchair. I tested GRAM in a case study with an unimpaired individual, demonstrating that it was possible to learn to propel GRAM using this technique. These results demonstrate the potential of exochairs such as LARA and GRAM to provide hybrid assistance/therapy in a simpler and less costly way than is currently possible with other technologies. Future research will study the long-term functional and therapeutic benefits of exochairs in larger patient population.

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    Authors: Drevno, Ann Gleason;

    Much of modern-day agriculture relies heavily on fertilizers and pesticides to increase yields, yet when applied in excess or without proper control mechanisms these inputs can wreak havoc on local waterways. This dissertation analyzes policy approaches implemented in Europe, the U.S. and California to abate discharges from farms. The research utilizes a mixed methods approach, integrating qualitative, quantitative and spatial analyses, to investigate regulatory tools, governance structures, policy outcomes, and stakeholder opinions relating to water pollution from agriculture. The dissertation is comprised of four interrelated parts. The first part assesses the range of regulatory approaches employed to control agricultural nonpoint source pollution in the United States and the European Union. Findings suggest that transitioning from the voluntary control mechanisms to more effective instruments based on measurable water quality performance relies predominantly on three factors: (1) more robust quality monitoring data and models; (2) local participation; and (3) political will. Identifying obstacles to and successes of national and international agricultural water pollution policies set the context for delving deeper into this regulatory problem on a regional level. The second, third, and fourth parts of this doctoral research focus on the primary regulatory mechanism for agricultural discharges in California’s Central Coast Region: The Conditional Agricultural Waiver. One of these parts uses the policy tool framework to assess the overall effectiveness of the Conditional Agricultural Waiver and its associated monitoring programs. Research results show that while the regional policy represented a small step forward in implementing appropriate control mechanisms for agricultural pollution, the significance of monitoring programs greatly limited the policy’s success. Another part of this dissertation surveyed 1,000 growers and their opinions on water quality practices and regulations. Results corroborate with prior research—growers’ trust in the majority of regional agricultural groups was closely correlated with communication. However, trust in the Regional Board did not correspond to the relatively high contact frequency with the regulatory agency, most likely due to a divergence of interests and institutional distance. This study also confirms anecdotes of declining trust between farmers and the Regional Board over the course of the two Ag Waivers. A final part of the dissertation focuses on specific provisions aimed at controlling two pesticides in the region. Results from this chapter indicate that the 2012 Central Coast Conditional Agricultural Waiver was a contributing factor in successfully reducing the use of diazinon and chlorpyrifos, but several unintended consequences, such as continued presence of the pollutants in waterways, remain unsettled.

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    Authors: Matthew, Robert Peter;

    This thesis introduces a kinematic and dynamic framework for creating a representative model of an individual. Building on results from geometric robotics, a method for formulating a geometric dynamic identification model is derived. This method is validated on a robotic arm, and tested on healthy subjects to determine the utility as a clinical tool.The proposed framework was used to augment the five-times sit-to-stand test. This is a clinical test designed to estimate an individual's stability by timing the total time to stand/sit five times. Using the proposed framework, a representative kinematic and dynamic model was obtained which outperformed conventional height/mass scaled models. This allows for rapid, quantitative measurements of an individual, with minimal retraining required for clinicians. These tools are then used to develop a prescriptive model for developing assistive devices. The recovered models can be used to formulate an optimisation to determine the actuator types and parameters to provide augmentation.This framework is then used to develop a novel system for human assistance. A prototype device is developed and tested. The prototype is lightweight, uses minimal energy, and can provide an augmentation of 82% for providing hammer curl assistance. The modelling framework is used to analyse the effect this assistance has on compensatory actions of the shoulder.

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    Authors: Laughner, Joshua L;

    Satellite observations of NO2 provide information about the spatial and temporal variability of NO2 column densities that can be used to infer the processes controlling NO2 concentrations. However, satellite observations require a priori information to properly transform the observed radiances into vertical column densities. Previous work has shown that using a priori data at equal or better spatial resolution to the satellite pixels significantly improves the retrieved NO2. Of particular importance are the a priori vertical profiles that represent the vertical distribution of NO2 within each satellite pixel.In this dissertation, I show that the temporal resolution of the a priori NO2 profiles, as well as the spatial resolution, is important to accurately retrieve NO2. I show that using profiles with high spatial resolution, but coarse temporal resolution, overestimates the rate at which NO2 is lost in the outflow from an urban source, and that high spatial and temporal resolution of these profiles is necessary to simultaneously retrieve the NO2 column density and lifetime in an urban plume. To account for this, I design and validate an upgrade to the Berkeley High Resolution (BEHR) NO2 retrieval that incorporates daily, high resolution profiles for over 7 years. With this retrieval, I show direct observations of the relationship between NOx concentration and lifetime by examining the weekend-weekday changes in column density and lifetime from several US cities between 2005 and 2014. Specifically, I show that Chicago, IL and Dallas, TX are undergoing a transition from NOx-suppressed to NOx-limited chemistry, which may indicate that future reductions in NOx emissions will be more effective at controlling O3 production, but have less direct effect on NOx concentrations.

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    Authors: Baldo, Elisabeth;

    Accurately representing the spatial variability of montane snowpack is challenging due to the high degree of complexity in the terrain's topography, and the lack of good quality in-situ data. To explicitly resolve snow processes in montane environment while taking into account the uncertainties in the system's high spatial and temporal resolution snow reanalyses are required. Ensemble-based approaches assimilating Landsat VIS-NIR remote sensing data at spatial resolutions of 100 m or less are, however, prohibitively expensive to run at large scales, and sub-optimal given that only the most complex parts of a montane range require such fine resolution. In addition, the assimilation of remote sensing data from a single source can also be unsatisfactory due to a lack of global coverage, hardware malfunction etc. In order to optimize computational needs while preserving the accuracy of ~ 100 m reanalyses, a raster-based multi-resolution approach was first developed and successfully implemented for a headwater catchment in the Colorado River Basin over the full length of Landsat record (30+ years). The potential use of MODIS-derived snow cover information in addition to Landsat in snow reanalyses was then investigated over three different regions in the Western U.S. and High Mountain Asia in order to make up for Landsat's shortcomings over midlatitude snowpacks. The key findings of this dissertation can be summarized as follows: 1) The physiographic complexity of a terrain can be characterized by its standard deviations of elevation, northness index and forested fraction. Using such a complexity metric to discretize the terrain into different spatial resolutions via a multi-resolution approach can significantly reduce computational needs, while mitigating errors in snow processes representation. 2) The multi-resolution approach did not significantly impact the remote sensing observations assimilated, and the posterior snow water equivalent (SWE) ensemble median and standard deviation matched the 90 m reanalysis, thus leading to a robust implementation in the context of a data assimilation framework. 3) A MODIS-derived snow cover product was found to be a useful complementary source of remote sensing data to be simultaneously assimilated with Landsat. Off-nadir-looking observations first had to be screened out of the reanalysis due to the distorting and snow obscuring effects of the sensor viewing geometry at high zenith angles. Ultimately, the methods developed in this work can be applied to all midlatitude montane ranges over the full lengths of Landsat and MODIS records to generate a fine spatial resolution SWE reanalysis dataset that will be useful to snow hydrologists to solve many unanswered science questions over such challenging regions.

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    Authors: Norton, Juliet;

    Scientists widely accept that modern agriculture is unsustainable, but the best methods for addressing unsustainability are still contested (Constance, Konefal, and Hatanaka 2018). Grassroots sustainable agriculture communities have long participated in the exploration of solutions for agriculture unsustainability, and their momentum continues to grow in the technical age. Practitioners of grassroots sustainable agriculture use many information systems that were not originally built to support the design of agricultural systems. Based on ethnographic research with two grassroots sustainable agriculture communities, I show that participants’ personal and community values frequently clashed with those embedded in information systems, including ones used to look for and manage plant information. Furthermore, I demonstrate a range of information challenges that participants faced in the absence of tools designed to support their specific work. I argue that practitioners of grassroots sustainable agriculture need information systems tailored to their goals and values in order to productively address barriers to designing and building agroecosystems for their communities.This dissertation provides an example of how to involve communities in the development of information technology artifacts and strengthen efforts to support sustainability via technological interventions. First, I engaged in two grassroots sustainable agriculture communities as a participant, experiencing their practices, values, and information challenges first hand. Then, I worked with the communities to create a plant database web application (SAGE Plant Database) that supports agroecosystem design in local contexts. Members of the communities participated in the design, development, and data population stages so that the SAGE Plant Database supports their design context and upholds their technological and holistic sustainability values. At the foundation of the database is a plant ontology grounded in the participants’ practice of designing agroecosystems. My comparative analysis of the design of the SAGE Plant Database to other databases demonstrates its relevance due to its emphasis on agroecological relationships among plants and between plants and the environment, the inclusion of ethnobotanical data, and the embedded community values. By engaging in this research, I seek to make progress towards transforming the technology-supported food system into one that furthers food security, food sovereignty, and holistic sustainability.

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    Authors: Solera, Melissa Viola Eitzel;

    In this work, I answer timely questions regarding tree growth, tree survival, and community change in California tree species, using a variety of sophisticated statistical and remote sensing tools.In Chapter 1, I address tree growth for a single tree species with a thorough explanation of hierarchical state-space models for forest inventory data.Understanding tree growth as a function of tree size is important for a multitude of ecological and management applications. Determining what limits growth is of central interest, and forest inventory permanent plots are an abundant source of long-term information but are highly complex. Observation error and multiple sources of shared variation (spatial plot effects, temporal repeated measures, and a mosaic of sampling intervals) make these data challenging to use for growth estimation. I account for these complexities and incorporate potential limiting factors (tree size, competition, and resource supply) into a hierarchical state-space model. I estimate the diameter growth of white fir (Abies concolor) in the Sierra Nevada of California from forest inventory data, showing that estimating such a model is feasible in a Bayesian framework using readily available modeling tools. In this forest, white fir growth depends strongly on tree size, total plot basal area, and unexplained variation between individual trees. Plot-level resource supply variables (representing light, water, and nutrient availability) do not have a strong impact on inventory-size trees. This approach can be applied to other networks of permanent forest plots, leading to greater ecological insights on tree growth.In Chapter 2, I expand my state-space modeling to examine survival in seven tree species, as well as investigating the results of modeling them in aggregate (at the community level) and comparing with the individual species models.Declining tree survival is a complex, well-recognized problem, but studies have been largely limited to relatively rare old-growth forests or low-diversity systems, and to models which are species-aggregated or cannot easily accommodate yearly climate variables. I estimate survival models for a relatively diverse second-growth forest in the Sierra Nevada of California using a hierarchical state-space framework. I account for a mosaic of measurement intervals and random plot variation, and I directly include yearly stand development variables alongside climate variables and topographic proxies for nutrient limitation. My model captures the expected dependence of survival on tree size. At the community level, stand development variables account for decreasing survival trends, but species-specific models reveal a diversity of factors influencing survival. Species time trends in survival do not always conform to existing theories of Sierran forest dynamics, and size relationships with survival differ for each species. Within species, low survival is concentrated in susceptible subsets of the population and single estimates of annual survival rates do not reflect this heterogeneity in survival. Ultimately only full population dynamics integrating these results with models of recruitment can address the potential for community shifts over time.In Chapter 3, I combine statistical modeling with remote sensing techniques to investigate whether topographic variables influence changes in woody cover.In the North Coast of California, changes in fire management have resulted in conversion of oak woodland into coniferous forest, but the controls on this slow transition are unknown. Historical aerial imagery, in combination with Object-Based Image Analysis (OBIA), allows us to classify land cover types from the 1940s and compare these maps with recent cover. Few studies have used these maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation (ideally without throwing away data) and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. I study the change in woody cover in California's North Coast using historical (1948) and recent (2009) high-spatial-resolution imagery. I classify the imagery using eCognition Developer and aggregate the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. I use Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. I explore the relative roles of elevation, topographic slope, aspect (Northness/Eastness), topographic wetness index, profile curvature, historical percent woody cover, and geographical coordinates in influencing current percent woody cover. I estimate these models for scales of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. I find that historical woody cover has a consistent positive effect on current woody cover, and that the spatial term in the model is significant even after controlling for historical cover. Specific topographic variables emerge as important for different sites at different scales, but no overall pattern emerges across sites or scales for any of the topographic variables I tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.I conclude that these techniques are promising but a framework is needed for sensitivity analysis, as modeling results can depend strongly on variable selection and model structure.

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    Authors: Sampson, Devon D.;

    Biodiversity conservation and food security are often assumed to be separate or conflicting issues. In the municipality of Tzucacab, in a rural corner of Mexico’s Yucatan peninsula, I found that crop diversity and food security are deeply intertwined. I measured food security and home garden agrobiodiversity on a randomized selection of sixty smallholder farms in the municipality, conducted ethnographic interviews over a period of four years, and collaborated with six high school students on a participatory photography project documenting local food culture. From the quantitative data, I found that crop diversity is the strongest predictor of household food security during a drought in the rural municipality I surveyed. This finding indicates that maintaining high levels of agrobiodiversity can be an important strategy for subsistence farmers to buffer their food supply against the risk of crop failure. Additionally, I found evidence that diversification of the home garden is one important strategy managing risk among a complex of several approaches to livelihood diversification. The finding helps to explain why some farmers conserve diversity while others do not, and suggests that the goals of agrobiodiversity conservation and rural food security might be better addressed together. These results point to the ability of small-scale, diverse farms run by campesino farmers to feed themselves, challenging the dominant discourse and practice of development that prioritizes increasing yields above all other properties of agroecosystems.

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    Authors: Pickard, Michael R.;

    Much of the Sierra Nevada forests are overstocked with too many trees, are at risk of burning in stand-replacing wildfires due to the fuel loading, contain almost complete canopy coverage over the landscape and intercept a substantial portion of the snowpack, and in need of thinning. New research shows that forest thinning patterns allowing for forest structures comprising of clumps of varying density, single individual trees, and forest gaps are what the historic stands in Sierra Nevada were comprised of. In addition to returning the forest to historic stand structure, forest thinning will also allow for greater amounts of snowpack to reach the forest floor and persist longer into the season before complete meltout. This study set out to see if this new thinning spacing pattern would affect snow accumulation or ablation rates as compared with the current standard even-tree spacing. Data from four snow surveys, distributed light/temperature sensors, hemispherical photographs, and a small distributed network of snow-depth sensors were analyzed from three different forest treatments in the Sierra Nevada. The first treatment consisted of variable thinning, which reduced the stems per acre by 80%, the basal area per acre by 40%, and created a heterogeneous forest structure with small gaps and clumps of trees of varying density. The second forest treatment reduced the stems per acre and the basal area per acre stand down to similar levels as the variable thinning, however the residual trees were left to replicate a homogenous, even spacing. An unthinned control provided a comparison to pre-treatment conditions. Snow-survey data showed that approximately 10-20% more snow accumulated in the two thinning treatments than in the control. Differences in snow depth between the two thinning treatments were statistically insignificant. Snow-survey results also indicated that the variable treatments kept snow on the ground longer than the even treatments in the 2013 water year, which had a peak snow water equivalent (SWE) of 17.9 cm, and melted out at approximately the same time as the control units. In 2014 the peak SWE recorded was only 6.3 cm, both the variable and even treatments melted out at the same time, and the control units kept a steady snowpack for approximately 1-3 days longer. Data from snow-surveys, distributed light/temperature sensors and snow-depth sensors showed that more snow accumulated in the open than at the drip edge and under the forest canopy, and that melt rates are faster in the open than under the canopy. Our data showed that the variable treatment held the snowpack longer and exhibited a slower melt rate than did the even treatments indicating a possibility to retain substantial amounts of snow in the mountains longer if this thinning structure were to be implemented on a landscape level.

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