Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item PEOPLE AND PIXELS: INTEGRATING REMOTELY-SENSED AND HOUSEHOLD SURVEY DATA FOR FOOD SECURITY AND NUTRITION(2020) Cooper, Matthew William; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)For several decades now, the study of environmental impacts on human well-being has been informed by what are called "People and Pixels'' methods: the combining of remotely sensed data about environmental conditions with geolocated data from household surveys about health and nutrition. However, much of this work has been conducted at the scale of individual countries and often relies on only one or two survey waves, which creates substantial issues around spatial autocorrelation and endogeneity. Furthermore, much of this work uses simple linear regression as its analysis technique, which is limited in its ability to describe spatial variation as well as non-linearities in the relationship between the environment and human well-being. Thus, this dissertation uses several insights from the emerging field of data science to advance these methods. First, this analysis draws on large, multinational datasets from dozens of surveys, making it possible to better estimate the non-linear effects of climate extremes on human well-being as well as examine spatial heterogeneities in vulnerability. Secondly, this analysis uses techniques at the boundary between traditional econometric regression models and more complex machine learning models, such as using Generalized Additive Models (GAMs) as well as LASSO estimation. This permits the creation of spatially-varying terms as well as nonlinear effects. Applying these techniques, the dissertation has yielded several insights that could be beneficial to policymakers in governments, non-profits, and multinational organizations. The initial chapters analyze the effects of rainfall anomalies on food security and malnutrition, finding that the effect of an anomaly varies considerably depending on the local socioeconomic and environmental contexts, with low-income, poorly-governed, and arid countries, such as Somalia and Yemen, being the most vulnerable. The latter chapters look at the role of ecosystem services in improving human livelihoods, as well as how land cover is associated with dependence on local provisioning ecosystem services.Item AGRICULTURAL LAND USE, DROUGHT IMPACTS AND VULNERABILITY: A REGIONAL CASE STUDY FOR KARAMOJA, UGANDA(2017) Nakalembe, Catherine Lilian; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The increasing frequency of extreme climate events brings into question the sustainability of agriculture in marginal lands, especially those already experiencing drought such as the Karamoja region in northeastern Uganda. A significant amount of research often qualitative has been conducted documenting drought and its impact on Karamoja. Taking a mixed methods approach, this study combined remotely-sensed satellite data, national agricultural surveys, census, and field data to expand on empirical knowledge on agricultural drought, land use and human perceptions of drought necessary for comprehensive drought forecasting, monitoring, and management. Results from this study showed that Karamoja is at least twice more vulnerable to drought than any other region in Uganda. This is because of its very low adaptive capacity in part due to high poverty rates and a higher dependency on the natural environment for livelihood. Analysis of satellite data quantified a 229 percent increase in cropland area in Karamoja between 2000 and 2011/12, driven largely by agricultural development programs. Underlying forces (e.g., cropland expansion programs and controlled grazing) originating from land use policy and development programs, more than proximate causes (direct local level actions) remain the major drivers of this expansion. Although the cultivated area has dramatically increased, there is no quantifiable overall increase in yield or per-capita production as evidenced by the recurrent poor food security. This status quo, (poor yields and dependence on food aid) is likely to continue as more land is put to crop cultivation by poor households and meager investments are made in livestock-based livelihood opportunities. The cropland area mask developed in this research facilitated the characterization of drought within agricultural areas. The drought information developed by this study is spatially and temporally explicit, showing differences in severity between years and between districts. Overall Abim District showed the least variation and is the least impacted while, Moroto District had the highest inter-annual variability and was often the most severely impacted. This research presents an approach to predict the number of people who would require food aid during the lean season in Karamoja (December to March) within a reasonable margin of error (less than 10\%) at the peak of the growing season (August/September), although the need for more extensive testing is recognized. The method takes advantage of readily available satellite data and can contribute to planning for a timely and appropriate response. A case study of farmer's perceptions of drought in Moroto District found that many farmers feel helpless and have no control of their future. For the majority of farmers in the district, past experiences of drought do not necessarily impact on future expectations of drought and many have no long-term adjustment plans. Quite often the majority of the population depends on emergency food assistance, building a culture of dependency. The analysis indicates that factors such as; conflict (insecurity) and interventions by government and international agencies intermingle with culture to have a profound direct influence on farmers' perception of drought amongst communities in Moroto district. This research shows that satellite data can provide the much-needed information to fill the gaps that inhibit long-term drought monitoring, at a significantly lower cost than traditional climate station-based monitoring in data scarce regions like Karamoja. It also points to a way forward for proactive assessment, planning, and response.