Geography

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Autochthonous and Introduced Stores of Biomass Value: Measuring Resilience Outcomes of Enset and Eucalyptus as Green Assets in Three Representative Smallholder Farm Systems of Ethiopia
(2020) Morrow, Nathan; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Fundamental shifts in the ability to observe our world with synoptic satellite remote sensing and the profusion of trend tracking longitudinal data sources not only better inform us of the mounting trouble our planet is in but also provide completely new perspectives on basic shared understandings, such as how many trees grow on Earth and where they take root. Observing the dispersed pattern of increasing tree cover across a multidecadal satellite mosaic, developed by Matt Hansen and colleagues at University of Maryland at College Park, sparked an interest in the ramifications of this unanticipated change, marked clearly upon the landscape in Ethiopia. The following chapters explore the relation of changing amounts of autochthonous treelike perrenial enset and introduced eucylyptus trees, commonly found on Ethiopian farms, to smallholder resilience, food security, and well-being. Spatially informed longitudinal models for three representative subnational data sets are used to investigate the central thesis of this dissertation—trees and treelike perennials on farms in rural Ethiopia indicate a fundamental store of value in living biomass, building a household’s assets over time through improved biomass management, for resilient small farm livelihoods that ensure food security and related well-being. Green assets acting as biomass stores indicate natural “value,” representing transformed and stored energy of the sun, that Blaikie and Brookfield (1987) considered inadequately captured as a no-cost contribution to the “use value” concept in development economics, economic geography production, and income-focused research, as well as in Marx’s (1887/2013) labor-focused value constructs that only briefly acknowledge workers are helped by the transformative “natural forces” at work on the land. Model results presented in Chapters 3, 4, and 5 reveal a lack of on-farm trees and treelike perennials often indicates biomass poverty and energy insecurity. Chronic biomass poverty, measured with spatially aware hierarchal models, is related to an inability to maintain a sufficient level of essential green assets, thereby contributing to poor resilience and well-being outcomes on small farms. On the other hand, medium and longer term asset accumulation supports improved well-being when livelihood strategies make use of farm forests, other on-farm trees, and treelike perennials.
Characterizing Small-Town Development Using Very High Resolution Imagery within Remote Rural Settings of Mozambique
(MDPI, 2021-08-26) Chen, Dong; Loboda, Tatiana V.; Silva, Julie A.; Tonellato, Maria R.
While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements.
Considerations for AI-EO for agriculture in Sub-Saharan Africa
(Institute of Physics, 2023-03-24) Nakalembe, Catherine; Kerner, Hannah
Adapting to and mitigating climate change while addressing food insecurity are top priorities in SubSaharan Africa that require technologies to improve rural livelihoods with minimal environmental costs [1]. Artificial intelligence (AI) offers great promise for climate-smart solutions that improve food security outcomes. While precision agriculture is often the foremost use case for AI in agriculture (e.g. automation of farm equipment or nutrient application), precision agriculture is out of reach for most African farmers due to the required capital and infrastructure. AI solutions using satellite Earth observations (EOs), which we call AI-EO, are more accessible in the near term. EO enables agricultural analyses and insights at global scales, and many datasets are freely available, making EO-based solutions affordable [2]. AI-EO-derived products such as crop type maps and yield estimates are necessary to forecast food production surpluses or deficits, inform trade, and aid decisions. These products can support policies that accelerate the design and adoption of climate-smart agriculture and impact farmer livelihoods by increasing access to actionable early warning, risk financing or insurance [3], farm inputs, markets, and costreducing interventions [2, 4]. Despite their promise, AI-EO solutions for agriculture in Africa are still limited. Most techniques are not generalizable across heterogeneous landscapes. In this paper, we describe the principal sub-fields of research in AI-EO for agriculture in Africa and discuss examples and limitations of existing work. We also propose ten considerations for future work to help increase the impact of AI-EO research in Africa.
Fire Regions as Environmental Niches: A New Paradigm to Define Potential Fire Regimes in Africa and Australia
(Wiley, 2022-07-07) Zubkova, M.; Boschetti, L.; Abatzoglou, J. T.; Giglio, L.
Despite the widespread use of the “fire regime” concept for describing spatial and temporal patterns and ecosystem impacts of fire, this concept lacks an unambiguous, quantitative definition. By adopting from the ecological literature the concept of climate niche, that is, the environmental conditions that allow a specie to exist, we propose a new framework where variables that promote fuel accumulation and desiccation were used to define the environmental space at the continental level, later divided into regions (“fire regions”) with distinct fire potential. Our proposed approach emphasizes climate controls on fire patterns, distinct from the controls that humans exert on observed fire activity. By applying this framework, we identified nine fire regions in Africa and eight in Australia, distinguishing differences in fire patterns between continents as a result of changes in environmental gradient. Not only did we find that fire size and intensity varied significantly between continents, but biomes at a continental level were also found to be heterogeneous in terms of fire frequency, size, and intensity. For example, within African tropical savannas, the total annual rainfall and tree cover change drastically North and South of the equator, resulting in fire regions with significantly different fire characteristics. Meanwhile, in Australia, a strong gradient of annual temperature and precipitation seasonality was observed within tropical savannas and xeric shrublands, which was recognized by dividing those biomes into five regions with statistically different fire activity. Additionally, human presence led to some heterogeneity of fire patterns within delineated fire regions that also varied across biomes.