Ten simple rules for working with high resolution remote sensing data

Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data

Drought adaptation and development: small-scale irrigated agriculture in northeast Brazil

Water scarcity has intensified in northeast Brazil over the past decade. The same period has brought economic growth, aggressive government-funded social support programmes, and technological advancements. These latter factors have led to widespread, successful, and largely unintended adaptation to increasing climatic stress. With specific focus on the experience of irrigated farmers in Pernambuco during the 2011-2013 drought, the worst in a half century, in this article, we examine how Brazil's societal changes have led to the emergence of unique climate adaptation strategies. To put this into context, income diversification, particularly in the form of employment in clothing production, provides a stable back-up income for farmers amidst environmental uncertainty. Aggressive poverty alleviation programmes, foundational to the presidential administrations of Lula da Silva and Dilma Rousseff, have had the spillover benefit of decreasing climate vulnerability. Efficient irrigation technology, which farmers have adopted primarily in an effort to decrease erosion and labour needs, saves water and decreases drought vulnerability. In summary, we find that the study area serves as a global example that economic, political, and social developments not aimed at climate adaptation can inadvertently facilitate it and decrease drought vulnerability.12 month embargo; published online: 27 March 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we designed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on learners’ comfort and confidence with technical and non-technical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM