Preface Acknowledgments Introduction Workflow Module 1 - Enhancing images Project 1: Displaying and enhancing Landsat imagery of the Chesapeake Bay Project 2: Displaying and enhancing Landsat imagery of Las Vegas, Nevada Project 3: On your own Module 2 - Composite images Project 1: Creating multispectral imagery of the Chesapeake Bay Project 2: Creating multispectral imagery of Las Vegas, Nevada Project 3: On your own Module 3 - Spectral signatures Project 1: Investigating spectral signatures of the Chesapeake Bay Project 2: Investigating spectral signatures of Las Vegas, Nevada Project 3: On your own Module 4 - Land cover Project 1: Comparing digitized land cover to USGS land-cover classes in Loudoun County, Virginia Project 2: Comparing digitized land cover to USGS land-cover classes near Lake Mohave Project 3: On your own Module 5 - Unsupervised classification Project 1: Calculating unsupervised classification of the Chesapeake Bay Project 2: Calculating unsupervised classification of Las Vegas, Nevada Project 3: On your own Module 6 - Supervised classification Project 1: Calculating supervised classification of the Chesapeake Bay Project 2: Calculating supervised classification of Las Vegas, Nevada Project 3: On your own Module 7 - Classification accuracy Project 1: Assessing the accuracy of land-cover data and different types of classification of the Chesapeake Bay Project 2: Assessing the accuracy of land-cover data and different types of classifications of Las Vegas, Nevada Project 3: On your own Module 8 - Urban change Project 1: Measuring urban change in Loudoun County, Virginia Project 2: Measuring urban change in Las Vegas, Nevada Project 3: On your own Module 9 - Water Project 1: Measuring the impact of drought on Texas reservoirs Project 2: Measuring Minnesota lake temperature using thermal infrared data Project 3: On your own Module 10 - Normalized Difference Vegetation Index Project 1: Using NDVI to study drought conditions in Texas Project 2: Using NDVI to study seasonal change in Minnesota Project 3: On your own Appendix A: Downloading Landsat imagery Appendix B: References Appendix C: Image and data credits Appendix D: Data license agreement Appendix E: Installing the data and software
The United Nations Sustainable Development Goals (UNSDGs) represent the consensus of the global community on the most important issues facing our planet. A major challenge is embedding the UNSDGs in primary and secondary education and providing the tools needed for students to explore and analyse data relevant to the UNSDGs. The Geospatial Semester is a secondary school class in the United States that is focused on mastering geospatial technologies and using them to examine key problems of student interest, including the UNSDGs. Research studies show that the extended use of geographic information systems augments student problem solving and spatial thinking skills, particularly for females. Spatial thinking skills are a key gateway to science, technology, engineering and mathematics careers and an avenue to addressing the UNSDGs.
Current debate surrounds the promise of neuroscience for education, including whether learning-related neural changes can predict learning transfer better than traditional performance-based learning assessments. Longstanding debate in philosophy and psychology concerns the proposition that spatial processes underlie seemingly nonspatial/verbal reasoning (mental model theory). If so, education that fosters spatial cognition might improve verbal reasoning. Here, in a quasi-experimental design in real-world STEM classrooms, a curriculum devised to foster spatial cognition yielded transfer to improved verbal reasoning. Further indicating a spatial basis for verbal transfer, students’ spatial cognition gains predicted and mediated their reasoning improvement. Longitudinal fMRI detected learning-related changes in neural activity, connectivity, and representational similarity in spatial cognition–implicated regions. Neural changes predicted and mediated learning transfer. Ensemble modeling demonstrated better prediction of transfer from neural change than from traditional measures (tests and grades). Results support in-school “spatial education” and suggest that neural change can inform future development of transferable curricula.
In an effort to understand the observed complex form of Saturn's F ring, we have used Gauss' perturbation equations to numerically model the short-term, three-dimensional dynamics of narrow rings. We consider ring particles that are perturbed by local moonlets orbiting with small (i ≤ 0°.1) inclination; collisions are ignored. We confirm that, as expected, the distance of closest approach determines the strength of the interaction; this distance depends on the orientation of the orbits, as well as the orbital eccentricity and the separation in semimajor axes of the ring particle from the perturbing satellite. Furthermore the exact geometry of encounter, particularly the relative orientation of the longitude of ascending node to the argument of pericenter, plays an important role in the vertical response of any satellite-ring particle perturbation. We find that, with an appropriate choice of parameters, ring particles can be perturbed to substantial inclinations. We present simulations of a model narrow ring consisting initially of three strands typically with 800 particles apiece, which show the short-term effects of the neighboring moons and the influence of different orientations at encounter. Finally, we demonstrate that a combination of modest vertical and horizontal distortions to three narrow strands, as induced by out-of-plane satellite perturbations comparable to those present in the real ring, produces a structure that looks much like the “braided” F ring.
The use of location-based services and mobile technologies is increasing in K-12 classrooms. In this article, we describe the history and the current use of these tools in the innovative Geospatial Semester project in Virginia. We share a number of examples where students are creating projects of their own interest that use editable feature services, mobile data collection and other cutting-edge technologies. These projects help students build their spatial thinking and problem-solving skills, and help teachers build conceptual understanding in a variety of domains.
