Elizabeth Tellman

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Scholarly Contributions

Chapters

• Ho, J. C., Vu, W., Tellman, B., Dinga, J. B., N'diaye, P. I., Weber, S., Bauer, J., Schwarz, B., Doyle, C., Demuzere, M., Anderson, T., & Glinskis, E. (2021). From Cloud to Refugee Camp: A Satellite-Based Flood Analytics Case-Study in Congo-Brazzaville. In Earth Observation for Flood Applications(pp 131--146).
• Tellman, B., Sullivan, J. A., & Doyle, C. (2021). Global Flood Observation with Multiple Satellites: Applications in Rio Salado, Argentina, and the Eastern Nile Basin. In Global Drought and Flood: Monitoring, Prediction, and Adaptation. AGU Books Wiley.
• Schwarz, B., Pestre, G., Tellman, B., Sullivan, J., Kuhn, C., Mahtta, R., Pandey, B., & Hammett, L. (2018). Mapping Floods and Assessing Flood Vulnerability for Disaster Decision-Making: A Case Study Remote Sensing Application in Senegal. In Earth Observation Open Science and Innovation(pp 293–300.). Springer, Cham. doi:10.1007/978-3-319-65633-5_16
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  While environmental and social threats to society changes faster than in recent centuries, there is more of a need for faster, globally scalable and locally relevant risk information from developing Banks and the countries they serve. Big Data can range from gigabytes (call details records), to terabytes (satellite data), to petabytes (web traffic), with each magnitude requiring unique algorithms to extract the signal from the noise. This chapter explores how one type of sensor data—satellite imagery—can be made more useful through the development of an application that leverages Cloud Computing—Google Earth Engine—to turn data into insight for decision-makers on the ground.

Journals/Publications

• Crowley, M., Stuhlmacher, M., Trochim, E., Van, D., Pasquarella, V., Szeto, S., Howarth, J., Platt, R., Roy, S., & Tellman, B. (2023). Pillars of Cloud Based Earth Observation Science Education. AGU Advances, 4.
• Murillo-Sandoval, P. J., Kilbride, J., Tellman, E., Wrathall, D., Van, D., & Kennedy, R. E. (2023). The post-conflict expansion of coca farming and illicit cattle ranching in Colombia. Sci Rep, 13(1), 1965.
• Tellman, B., & Eakin, H. (2023). Risk management alone fails to limit hazard impact.
• Thomas, M., Tellman, E., Osgood, D., DeVries, B., Islam, A. S., Steckler, M. S., Goodman, M., & Billah, M. (2023). A framework to assess remote sensing algorithms for satellite-based flood index insurance. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 1-17.
• Colosio, P., Tedesco, M., & Tellman, E. (2022). Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh. Remote Sensing, 14(5), 1180.
• Tellman Sullivan, E. M., Lall, U., Islam, S., & Bhuyan, A. (2022). Regional Index Insurance using Satellite-based Fractional Flooded Area.. Earth's Future, 10(3), 20.
• Tellman, B., Eakin, H., & Turner, B. L. (2022). Identifying, projecting, and evaluating informal urban expansion spatial patterns. Journal of Land Use Science, 17(1), 100-112. doi:10.1080/1747423X.2021.2020919
• Devine, J. A., Wrathall, D., Aguilar-gonzález, B., Benessaiah, K., Tellman, B., Ghaffari, Z., & Ponstingel, D. (2021). Narco-degradation : Cocaine trafficking ’ s environmental impacts in Central America ’ s protected areas. World Development, 144. doi:10.1016/j.worlddev.2021.105474
• Magliocca, N., Torres, A., Margulies, J., Mcsweeney, K., Arroyo-quiroz, I., Carter, N., Curtin, K., Easter, T., Gore, M., Hübschle, A., Massé, F., Rege, A., & Tellman, E. (2021). Comparative Analysis of Illicit Supply Network Structure and Operations : Cocaine , Wildlife , and Sand. Journal of Illicit Economies and Development, 3(1), 50-73.
• Tellman, B., Eakin, H., Janssen, M. A., Alba, F. D., & Ii, B. (2021). The Role of Institutional Entrepreneurs and Informal Land Transactions in Mexico City’s Urban Expansion. World Development, 140, 1-44.
• Tellman, B., Mcsweeney, K., Manak, L., Devine, J. A., Sesnie, S., Nielsen, E., & Dávila, A. (2021). Narcotrafficking and Land Control in Guatemala and Honduras. Journal of Illicit Economies and Development, 3(1), 132-159. doi:https://doi.org/10.31389/jied.83
• Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., & Slayback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80-86.
• Devine, J. A., Wrathall, D., Currit, N., Tellman, B., & Langarica, Y. R. (2020). Narco‐Cattle Ranching in Political Forests. Antipode, 52(4), 1018-1038. doi:10.1111/anti.12469
• Tellman, B., Magliocca, N. R., Turner, B. L., & Verburg, P. H. (2020). Understanding the role of illicit transactions in land-change dynamics. Nature Sustainability, 3(3), 175-181. doi:10.1038/s41893-019-0457-1
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  Anthropogenic land use has irrevocably transformed the natural systems on which humankind relies. Advances in remote sensing have led to an improved understanding of where, why and how social and economic processes drive globally important land-use changes, from deforestation to urbanization. The role of illicit activities, however, is often absent in land change analysis. The paucity of data on unrecorded, intentionally hidden transactions makes them difficult to incorporate into spatially specific analyses of land change. We present a conceptual framework of illicit land transactions and a two-pronged approach using remotely sensed data to spatially link illicit activities to land uses. Advances in remote sensing have helped to understand the human drivers of land-use change globally, but have neglected the role of illicit transactions. This Perspective presents a framework to identify illicit land transactions, and an approach to link them to land uses using remotely sensed data.
• Tellman, B., Sherbinin, A. D., Schwarz, B., Schank, C. J., & Howe, P. D. (2020). Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA. Sustainability, 12(15), 1-28. doi:10.3390/su12156006
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  Social vulnerability indicators seek to identify populations susceptible to hazards based on aggregated sociodemographic data. Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations. This paper validates social vulnerability indicators using two flood outcomes: death and damage. Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge data. We compare models with (i) socioeconomic variables, (ii) the composite social vulnerability index (SoVI), and (iii) flood intensity variables only. The SoVI explains a larger portion of the variance in death (AIC = 2829) and damage (R2 = 0.125) than flood intensity alone (death—AIC = 2894; damage—R2 = 0.089), and models with individual sociodemographic factors perform best (death—AIC = 2696; damage—R2 = 0.229). Socioeconomic variables correlated with death (rural counties with a high proportion of elderly and young) differ from those related to property damage (rural counties with high percentage of Black, Hispanic and Native American populations below the poverty line). Results confirm that social vulnerability influences death and damage from floods in the USA. Model results indicate that social vulnerability models related to specific hazards and outcomes perform better than generic social vulnerability indices (e.g., SoVI) in predicting non-coastal flood death and damage. Hazard- and outcome-specific indices could be used to better direct efforts to ameliorate flood death and damage towards the people and places that need it most. Future validation studies should examine other flood outcomes, such as evacuation, migration and health, across scales.
• Tshimanga, R., Tellman, B., Sullivan, J. A., Schumann, G., Savage, J., Neal, J. C., Liang, J., Hawker, L., & Doyle, C. (2020). Comparing earth observation and inundation models to map flood hazards. Environmental Research Letters, 15(12), 124032. doi:10.1088/1748-9326/abc216
• Wrathall, D. J., Tellman, B., Sesnie, S. E., Nielsen, E. A., Mcsweeney, K., Magliocca, N. R., Jain, M., Devine, J. A., Davila, A., Benessaiah, K., & Aguilar-gonzalez, B. (2020). Illicit Drivers of Land Use Change: Narcotrafficking and Forest Loss in Central America. Global Environmental Change-human and Policy Dimensions, 63, 102092. doi:10.1016/j.gloenvcha.2020.102092
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  Abstract Illegal activity, such as deforestation for illicit crops for cocaine production, has been inferred as a cause of land change. Nonetheless, illicit activity is often overlooked or difficult to incorporate into causal inference models of land change. Evidence continues to build that narcotrafficking plays an important, yet often unreported, role in forest loss. This study presents a novel strategy to meet the challenge of estimating the causal effect of illicit activity in land change using consolidated news media reports to estimate the relationship between drug trafficking and accelerated forest loss in Central America. Drug trafficking organizations engage in illegal land transactions, money laundering, and territorial control that can manifest as forest conversion to agriculture or pasture land uses. Longitudinal data on 50 sub-national units over a period of 16 years (2001-2016) are used in fixed effects regressions to estimate the role of narcotrafficking in forest loss. Two narcotrafficking activity proxies were developed as explanatory variables of forest loss: i) an “official” proxy from drug seizures data within 14 sub-national units; and, ii) an “unofficial” proxy developed from georeferenced news media accounts of narcotrafficking events. The effect of narcotrafficking was systematically compared to the other well-known causes of forest loss, such as rural population growth and other conventional drivers. Both proxies indicate narcotrafficking is a statistically significant (p
• Kugler, T. A., Grace, K., Wrathall, D. J., Aubrecht, C., Adamo, S. B., Cervone, G., Engstrom, R., Hultquist, C., Gaughan, A. E., Gaughan, A. E., Linard, C., Tatem, A. J., Tellman, B., Stevens, F. R., Stevens, F. R., Sherbinin, A. D., Riper, D. V., Moran, E. F., Hoek, J. V., & Comer, D. E. (2019). People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data. Population and Environment, 41(2), 209-234. doi:10.1007/s11111-019-00326-5
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  In 1998, the National Research Council published People and Pixels: Linking Remote Sensing and Social Science. The volume focused on emerging research linking changes in human populations and land use/land cover to shed light on issues of sustainability, human livelihoods, and conservation, and led to practical innovations in agricultural planning, hazard impact analysis, and drought monitoring. Since then, new research opportunities have emerged thanks to the growing variety of remotely sensed data sources, an increasing array of georeferenced social science data, including data from mobile devices, and access to powerful computation cyberinfrastructure. In this article, we outline the key extensions of the People and Pixels foundation since 1998 and highlight several breakthroughs in research on human–environment interactions. We also identify pressing research problems—disaster, famine, drought, war, poverty, climate change—and explore how interdisciplinary approaches integrating people and pixels are being used to address them.
• Manuel-navarrete, D., Tellman, B., Eakin, H., Siqueiros-garcia, J. M., Morehart, C. T., & Aguilar, B. H. (2019). Intentional disruption of path-dependencies in the Anthropocene: Gray versus green water infrastructure regimes in Mexico City, Mexico. Anthropocene, 26, 100209. doi:10.1016/j.ancene.2019.100209
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  Abstract Cities are urged to promote green infrastructures to reduce their global environmental impacts, while simultaneously adapting to the global climatic variability that such impacts generate. Human Niche Construction theory, however, predicts evolutionary pressures acting over social groups that, this paper contends, tend to favor gray over green infrastructure. This conflict is due to competitive advantages of gray infrastructure, such as their higher capacity to concentrate and intensively use natural resources. Cities need to intentionally override these evolutionary pressures to allow emergence of regimes where green solutions are the normal way of responding to infrastructure-related challenges and development. The override calls for understanding how infrastructure regime dynamics are historically constructed, and cultivating collective intentions capable of harnessing such dynamics. After conceptualizing the roles of evolution versus collective intentionality in infrastructure regime dynamics, we apply Foucauldian genealogical analysis to critically understand how and why the superiority of gray infrastructure became naturalized as truth in Mexico City, Mexico. The analysis explores how gray infrastructure momentum was built over history and the sporadic emergence of collective intentions to break it. Findings show that shifting to a green- infrastructure-dominated regime in Mexico City today would benefit from strategies that simultaneously promote technical, political and subjective changes. Purely technical efforts to promote regime shifts are doomed to fail. Decentralization, democratization, and cultivating certain collective intentions are key factors to override evolutionary pressures. In the context of the anthropocene and growing recognition of the importance of artificially created environments, we remphasize subjectivity and intentionality.
• Schumann, G., Tellman, B., & Kettner, A. J. (2019). The Push Toward Local Flood Risk Assessment at a Global Scale. Eos, 100. doi:10.1029/2019eo113857
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  Flood Risk Workshop; Boulder, Colorado, 1–3 October 2018
• Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Georgescu, M., Wang, C., Serrano-candela, F., Khandelwal, A. K., Cheng, W., Balling, R. C., & Hanson, G. H. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253-275. doi:10.1016/j.rse.2017.11.026
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  Abstract Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.
• Tellman, B., Bausch, J. C., Eakin, H., Anderies, J. M., Mazari-hiriart, M., Manuel-navarrete, D., & Redman, C. L. (2018). Adaptive pathways and coupled infrastructure: seven centuries of adaptation to water risk and the production of vulnerability in Mexico City. Ecology and Society, 23(1). doi:10.5751/es-09712-230101
• Tellman, B., Mcdonald, R. I., Goldstein, J. H., Vogl, A. L., Florke, M., Shemie, D., Dudley, R., Dryden, R., Petry, P., Karres, N., Vigerstol, K., Lehner, B., & Veiga, F. (2018). Opportunities for natural infrastructure to improve urban water security in Latin America.. PloS one, 13(12), e0209470. doi:10.1371/journal.pone.0209470
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  Governments, development banks, corporations, and nonprofits are increasingly considering the potential contribution of watershed conservation activities to secure clean water for cities and to reduce flood risk. These organizations, however, often lack decision-relevant, initial screening information across multiple cities to identify which specific city-watershed combinations present not only water-related risks but also potentially attractive opportunities for mitigation via natural infrastructure approaches. To address this need, this paper presents a novel methodology for a continental assessment of the potential for watershed conservation activities to improve surface drinking water quality and mitigate riverine and stormwater flood risks in 70 major cities across Latin America. We used publicly available geospatial data to analyze 887 associated watersheds. Water quality metrics assessed the potential for agricultural practices, afforestation, riparian buffers, and forest conservation to mitigate sediment and phosphorus loads. Flood reduction metrics analyzed the role of increasing infiltration, restoring riparian wetlands, and reducing connected impervious surface to mitigate riverine and stormwater floods for exposed urban populations. Cities were then categorized based on relative opportunity potential to reduce identified risks through watershed conservation activities. We find high opportunities for watershed activities to mitigate at least one of the risks in 42 cities, potentially benefiting 96 million people or around 60% of the urbanites living in the 70 largest cities in Latin America. We estimate water quality could be improved for 72 million people in 27 cities, riverine flood risk mitigated for 5 million people in 13 cities, and stormwater flooding mitigated for 44 million people in 14 cities. We identified five cities with the potential to simultaneously enhance water quality and mitigate flood risks, and in contrast, six cities where conservation efforts are unlikely to meaningfully mitigate either risk. Institutions investing in natural infrastructure to improve water security in Latin America can maximize their impact by focusing on specific watershed conservation activities either for cleaner drinking water or flood mitigation in cities identified in our analysis where these interventions are most likely to reduce risk.
• Sesnie, S. E., Tellman, B., Wrathall, D., Mcsweeney, K., Benessaiah, K., Wang, O., Rey, L., & Nielsen, E. A. (2017). A spatio-temporal analysis of forest loss related to cocaine trafficking in Central America. Environmental Research Letters, 12(5), 054015. doi:10.1088/1748-9326/aa6fff
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  A growing body of evidence suggests that criminal activities associated with drug trafficking networks are a progressively important driver of forest loss in Central America. However, the scale at which drug trafficking represents a driver of forest loss is not presently known. We estimated the degree to which narcotics trafficking may contribute to forest loss using an unsupervised spatial clustering of 15 spatial and temporal forest loss patch metrics developed from global forest change data. We distinguished anomalous forest loss from background loss patches for each country exhibiting potential 'narco-capitalized' signatures which showed a statistically significant dissimilarity from other patches in terms of size, timing, and rate of forest loss. We also compared annual anomalous forest loss with the number of cocaine shipments and volume of cocaine seized, lost, or delivered at country- and department-level. For Honduras, results from linear mixed effects models showed a highly significant relationship between anomalous forest loss and the timing of increased drug trafficking (F = 9.90, p = 0.009) that also differed significantly from temporal patterns of background forest loss (t-ratio = 2.98, p = 0.004). Other locations of high forest loss in Central America showed mixed results. The timing of increased trafficking was not significantly related to anomalous forest loss in Guatemala and Nicaragua, but significantly differed in patch size compared to background losses. We estimated that cocaine trafficking could account for between 15% and 30% of annual national forest loss in these three countries over the past decade, and 30% to 60% of loss occurred within nationally and internationally designated protected areas. Cocaine trafficking is likely to have severe and lasting consequences in terms of maintaining moist tropical forest cover in Central America. Addressing forest loss in these and other tropical locations will require a stronger linkage between national and international drug interdiction and conservation policies.
• Strickland, L. R., Alia, E., Blonder, B., Kohl, M. T., Mcgee, E., Quintana, M., Ridley, R. E., Tellman, B., Gerber, L. R., Puritty, C. E., & Klein, E. S. (2017). Without inclusion, diversity initiatives may not be enough.. Science (New York, N.Y.), 357(6356), 1101-1102. doi:10.1126/science.aai9054
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  Diversity among scientists can foster better science ( 1 , 2 ), yet engaging and retaining a diversity of students and researchers in science has been difficult ( 3 ). Actions that promote diversity are well defined ( 4 ), organizations are increasingly focused on diversity ( 5 ), and many institutions are developing initiatives to recruit and enroll students from underrepresented minority (URM) groups (racial, ethnic, gender, sexual identity, or persons with disabilities). Yet representation of URM groups in science, technology, engineering, and math (STEM) fields lag behind demographics in society at large ( 3 – 5 ), and many URM students feel unwelcome in academic departments and in scientific fields. Why is progress so limited ( 6 , 7 )? We see a widespread and under-acknowledged disconnect between initiatives aimed at increasing diversity in academic and professional institutions and the experience of URM students (including many of us authors) ( 6 , 7 ). We argue that failure to grasp foundations of this disconnect is the crux of why diversity initiatives fail to reach the students that they were made to recruit. We believe that addressing this will resonate with other individuals and groups and help advance discussion in the scientific community.
• Vogl, A. L., Goldstein, J. H., Daily, G. C., Vira, B., Mcdonald, R. I., Shemie, D., Tellman, B., Cassin, J., & Bremer, L. L. (2017). Mainstreaming investments in watershed services to enhance water security: Barriers and opportunities. Environmental Science & Policy, 75, 19-27. doi:10.1016/j.envsci.2017.05.007
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  Abstract Watersheds are under increasing pressure worldwide, as expanding human activities coupled with global climate change threaten the water security of people downstream. In response, some communities have initiated investments in watershed services (IWS), a general term for policy-finance mechanisms that mitigate diverse watershed threats and promote ecosystem-based adaptation. Here, we explore the potential for increasing the uptake and impact of IWS, evaluating what limits its application and how institutional, financial, and informational barriers can be overcome. Our analysis complements the growing literature on individual programs by identifying levers at regional and global scales. We conclude that mainstreaming IWS as a cost-effective strategy alongside engineered approaches will require advances that (i) lower institutional barriers to implementation and participation in IWS; (ii) introduce structural market changes and standards of practice that account for the value of watersheds’ natural capital; (iii) develop practical tools and metrics of IWS costs and benefits; and (iv) share success stories of replicable institutional and financial models applied in varied contexts.
• Eakin, H., Lerner, A. M., Manuel-navarrete, D., Martinez-canedo, A., Tellman, B., Charli-joseph, L., Bojorquez-tapia, L. A., Alvarez, R. F., & Aguilar, B. H. (2016). Adapting to risk and perpetuating poverty: Household's strategies for managing flood risk and water scarcity in Mexico City. Environmental Science & Policy, 66, 324-333. doi:10.1016/j.envsci.2016.06.006
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  Abstract Adaptation is typically conceived uniquely in positive terms, however for some populations, investments in risk management can entail significant tradeoffs. Here we discuss the burden for households of coping with, and adapting to, adverse water conditions in economically marginal areas of Mexico City. We argue that households’ efforts to adapt in conditions of marginality can come at the expense of households’ investment in other aspects of human welfare, reinforcing poverty traps. Both economic theory and social-ecological systems analysis point to the importance of cross-scalar investments and institutional support in breaking down persistent poverty traps. Using data from twelve focus groups conducted in Mexico City, we illustrate how such cross-scale connectivity is failing as a result of lack of trust and transparency, the difficulty of collective action, and the devolution of some responsibilities for risk management from the public sector to the household level. We conclude our analysis by arguing for greater attention to these tradeoffs in public policy to help ensure that adaptation does not come at the cost of more generic welfare gains among the most vulnerable populations.
• Tellman, B., Saiers, J. E., & Cruz, O. A. (2016). Quantifying the impacts of land use change on flooding in data-poor watersheds in El Salvador with community-based model calibration. Regional Environmental Change, 16(4), 1183-1196. doi:10.1007/s10113-015-0841-y
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  Urbanization can decrease the flood mitigation capacity of a catchment, and these impacts can be measured with hydrologic modeling. Models are typically calibrated against observed discharge and satellite data, but in a developing country context like El Salvador, these data are often unavailable. Even if a model is well calibrated and tested, its ability to influence land use plans requires additional stakeholder engagement. This study uses a participatory modeling approach to calibrate a watershed model and estimate flood impacts of land use scenarios in two urbanizing catchments in El Salvador with a linked land use–catchment hydrology–hydraulic model calibrated on flood height observed by community members. This paper explores both the value of household flood observation in model calibration and differences of flood extent estimates for land use scenarios with an uncalibrated versus community-calibrated model. We find that calibration using household surveys improves model performance. Results of scenario modeling suggest that while past urbanization has significantly increased household flood exposure in one catchment, future land use scenarios that further urbanize or reforest large areas of either catchment have little effect on the number of houses at risk for flooding. The success of the participatory methodology to increase model accuracy and link results to local land use planning makes clear the contribution of social science to traditional hydrological methods to understand land use–flood links in data-poor catchments.
• Tellman, B., Alaniz, R., Rivera, A., & Contreras, D. (2014). Violence as an obstacle to livelihood resilience in the context of climate change. UNU-EHS Working paper series.
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  Central America continues to be a violent region and is prone to increasing climatic shocks and environmental degradation. This paper explores the non-linear feedback loop between violence and climate shocks on livelihood resilience in El Salvador and Honduras, two countries experiencing high rates of violence. The nature of this complex feedback loop is examined by analysing case studies on the community scale, which include challenges in reconstructing community social capital post-Hurricane Mitch (1998) in Honduras and the importance of social capital in community resilience to Hurricane Ida (2009) in El Salvador. We conclude that social capital is central in communities facing violence in order to enhance livelihood resilience to climate change impacts in Central America.
• Tellman, B., Gray, L. C., & Bacon, C. M. (2011). Not Fair Enough: Historic and Institutional Barriers to Fair Trade Coffee in El Salvador. Journal of Latin American Geography, 10(2), 107-127. doi:10.1353/lag.2011.0037
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  Why do relatively few Salvadoran farmers sell to Fair Trade certified markets? This article examines the proximate and root causes that limit the participation of coffee smallholders in Fair Trade markets. Drawing upon a historical analysis of rural coffee society in El Salvador as well as Fair Trade value chains and the empirical evidence from two case studies, one in El Salvador’s Eastern mountains, and the second in the Western coffee growing region, this study illustrates the practical obstacles to participation in Fair Trade. It also shows how farmers are developing alternative marketing solutions such as direct trade and selling organic coffee domestically. The findings suggest that smallholders currently face at least five barriers to accessing Fair Trade, including: certification costs, economies of scale to cover coffee exports operations, stringent quality requirements and altitude constraints. However, the root causes of smallholder coffee farmers’ limited access to Fair Trade are rooted in decades of state-based policies and politics that have undermined rural civil society, discouraged education, perpetuated uneven access to land and debt forgiveness, and repressed the development of dynamic cooperative unions with capacity to export smallholder coffee.

Proceedings Publications

• Giezendanner, J., Mukherjee, R., Purri, M., Thomas, M., Mauerman, M., Islam, A. S., & Tellman, B. (2023, 2023/08/28/16:30:41). Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2155-2165.
• Saunders, A., Giezendanner, J., Tellman, B., Islam, A., Bhuyan, A., & Islam, A. (2023, 2023/12/19/22:07:45). A Comparison Of Remote Sensing Approaches To Assess The Devastating May-June 2022 Flooding In Sylhet, Bangladesh. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 452-455.
• Leach, N. R., Popien, P., Goodman, M. C., & Tellman, E. (2022). Leveraging convolutional neural networks for semantic segmentation of global floods with PlanetScope imagery. In IEEE International Geoscience and Remote Sensing Symposium, 4.
• Karimzadeh, M., Han, H., Tellman, B., & Nielsen, E. (2021, 2021///). Classifying Narcotrafficking Spatial Event Documents using Transformers. In Classifying Narcotrafficking Spatial Event Documents using Transformers.
• Yague-Martinez, N., Leach, N. R., Dasgupta, A., Tellman, E., & Brown, J. S. (2021, 2021///). Towards Frequent Flood Mapping with the Capella Sar System. The 2021 Eastern Australia Floods Case. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6174-6177.
• Akiva, P., Purri, M., Dana, K., Tellman, B., & Anderson, T. (2021, 2021). H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement. In Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)..
• Tellman, B., Issenberg, E., Bonafilia, D., & Anderson, T. (2020). Sen1Floods11: A Georeferenced Dataset to Train and Test Deep Learning Flood Algorithms for Sentinel-1. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 835-845.
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  Accurate flood mapping at global scale can support disaster relief and recovery efforts. Improving flood relief efforts with more accurate data is of great importance due to expected increases in the frequency and magnitude of flood events due to climate change. To assist efforts to operationalize deep learning algorithms for flood mapping at global scale, we introduce Sen1Floods11, a surface water data set including raw Sentinel-1 imagery and classified permanent water and flood water. This dataset consists of 4,831 512x512 chips covering 120,406 km 2 and spans all 14 biomes, 357 ecoregions, and 6 continents of the world across 11 flood events. We used Sen1Floods11 to train, validate, and test fully convolutional neural networks (FCNNs) to segment permanent and flood water. We compare results of classifying permanent, flood, and total surface water from training a FCNN model on four subsets of this data: i) 446 hand labeled chips of surface water from flood events; ii) 814 chips of publicly available permanent water data labels from Landsat (JRC surface water dataset); iii) 4,385 chips of surface water classified from Sentinel-2 images from flood events and iv) 4,385 chips of surface water classified from Sentinel-1 imagery from flood events. We compare these four models to a common remote sensing approach of thresholding radar backscatter to identify surface water. Results show the FCNN model trained on classifications of Sentinel-2 flood events performs best to identify flood and total surface water, while backscatter thresholding yielded the best result to identify permanent water classes only. Our results suggest deep learning models for flood detection of radar data can outperform threshold based remote sensing algorithms, and perform better with training labels that include flood water specifically, not just permanent surface water. We also find that FCNN models trained on plentiful automatically generated labels from optical remote sensing algorithms perform better than models trained on scarce hand labeled data. Future research to operationalize computer vision approaches to mapping flood and surface water could build new models from Sen1Floods11 and expand this dataset to include additional sensors and flood events. We provide Sen1Floods11, as well as our training and evaluation code at: https://github.com/cloudtostreet/Sen1Floods11.

Presentations

• Russell, J. L., & Tellman Sullivan, E. M. (2023). Climate Data is Power- to the People. SXSW.
• Tellman Sullivan, E. M. (2023). Addressing and understanding compound flood risk - from floodplain development to flood injustice- with satellites and machine learning. American Geophysics Union..
• Tellman Sullivan, E. M. (2023). Building a Product: A Scientist's journey to creating and building products. Propeller Ocean MBA.
• Tellman Sullivan, E. M. (2023). How to get a job in climate tech: tips from a start-up founder!. Data Science Industry Career Speaker Series, University of Arizona.
• Tellman Sullivan, E. M. (2023). Socializing the Pixel: Leveraging Remote Sensing and Machine Learning to Study the Causes and Address the Consequences of Global Environmental Change. Seminar- Department of Geography, Dhaka University.
• Tellman Sullivan, E. M. (2023). Understanding MORE Flood Risk from Space!. Google Flood Forecasting Meets Machine Learning Workshop.
• Tellman Sullivan, E. M. (2023). Understanding flood risk from space: opportunities to adapt to changing risk and catalyze climate justice. AYA Research Institute. Technology in a Just Transition Symposium.
• Tellman Sullivan, E. M. (2023). Understanding flood risk from space: opportunities to adapt to changing risk and catalyze climate justice. Colloquium- Department of Geography and Geospatial Science, Oregon State University Marston.
• Tellman Sullivan, E. M. (2023). Understanding flood risk from space: opportunities to adapt to changing risk and catalyze climate justice. Department of Geographical Sciences and Urban Planning, Arizona State University.
• Tellman Sullivan, E. M. (2023). Working with Lawyers on Flood Justice in the Rio Grande Valley of Texas with AI and Satellite Imagery. American Geophysics Union..
• Tellman Sullivan, E. M., & Lara-Valencia, F. (2023). Aproximaciones a la justicia Ambiental y a la resiliencia urbana a las inundaciones en la frontera México-Estados Unidos. Colegio de la Frontera, Noglaes..
• Tellman Sullivan, E. M. (2021, February). The consequences of adaptation: mitigating and producing vulnerability in Mexico City. University of Illinois Engineering Department Seminar.
• Tellman Sullivan, E. M. (2021, November). The consequences of adaptation: mitigating and producing vulnerability in Mexico City. Santa Clara University Environmental Studies Seminar. virtual: Santa Clara University Environmental Studies.
• Tellman Sullivan, E. M. (2021, November). Understanding flood risk from space: opportunities to adapt to changing risk with improved monitoring and index-based insurance. Hydrologic and Atmospheric Sciences Colloquium Seminar Speaker. University of Arizona: Department of Hydrology and Atmospheric Sciences.
• Tellman Sullivan, E. M. (2021, November). Understanding flood risk from space: opportunities to adapt to changing risk with improved monitoring and index-based insurance. University of Maryland Geography Department Seminar. virtual: University of Maryland Geography Department.
• Tellman Sullivan, E. M., Muhkerjee, R., Molthan, A., Gurung, I., Melancon, A., Barnard, J. J., & Lall, U. (2021). High resolution imagery to train and validate deep learning models of inundation extent for multiple satellite sensors. American Geophysical Union.