Understanding and interpreting rapidly changing Earth surface processes across a template of a rapidly urbanizing and increasingly connected world

Understanding and interpreting rapidly changing Earth surface processes across a template of a rapidly urbanizing and increasingly connected world is a major challenge. Our ability to observe and measure features on the Earth surface is increasing in quality, resolution, and temporal repeat. Thus, we have an opportunity to move understanding beyond the assumption of steadiness. It is clear that many surface phenomena occur rapidly (e.g., wildfires and the subsequent drainage network response, volcanic eruptions, earthquakes, mass movements, etc.). Each is part of a cascade of precursory and subsequent processes.

Revolutions in Earth observing and the connectedness of humanity (e.g., internet of things, social media) provide a major opportunity to characterize surface process events across the world. But they also provide a great data discovery, integration, and analysis challenge. How to bring the disparate observations into a common and quantitative 4D framework so they can be examined, and rates of change measured? For example, satellite imagery such as that available from Planet.com provide a daily view of the Earth’s surface at <5m per pixel. This temporal and spatial resolution enables us to observe many phenomena at or near the spatial and temporal scales at which critical processes operate. Discovering these data is relatively straightforward, but their rapid integration with data for context, as well as with ground observations is difficult and time consuming. Integrating the synoptic view from the space-based platform with the typically less intentional but ubiquitous eyewitness views from social media posts or public image sharing platforms can provide essential ground truth, detailed observations of phenomena, and an indication of the human experience of the event.

For many events, a generic workflow can be imagined:

1. Build on the well curated contextual geospatial data (landcover/land use, topography, imagery, 3D structures) to include (with proper geo- and temporal referencing) near real time Earth observations and compute if necessary derived products of interest (e.g., NDVI for vegetation health).
2. Discover, locate in time and space, assess for veracity, and examine user contributed or freely posted images and videos from the ground (and maybe from UAS).
3. Use the high geodetic accuracy of the framework to measure changes using space-based, airborne, and ground-based data. These changes may be spectral or 3D. Use the measurements to contribute to process-based models.
4. Present predicted conditions (e.g., hazard maps and forecasts) potentially in a updating cycle defined by subsequent additional observations.
5. Educate both in the short term (explain the event) and long term (enhance science and engineering literacy).

Several uses cases are evident:

1. Wildfires: Map forest health using space imaging (NDVI) before, during, and after wildfire seasons. Much commercial space imaging is well configured for measuring vegetation vigor. Exposure of the built environment to the fires (and to subsequent debris flows) would be easy to explore, and hazard maps easily visualized. Including user contributed or freely posted images and videos from the ground (and maybe from UAS) during the fires and after would provide a sense of the detailed processes and phenomena.
2. Debris flows in mixed wild-agricultural-urban environments: Flooding, especially by heavy sediment-laden flows, are hazardous and their conveyance highly sensitive to the complex 3D near surface environment which many include natural and built structures. Observations of them include larger watershed scale activation and evolution during storm events (space and airborne observations potentially combined with very high resolution 3D data from as built urban models and with on ground experiences from mobile phone picture and video). Flow simulations are available and may be useful for forecasting hazardous conditions, and also maybe updated and calibrated with detailed observations.
3. Tsunami inundation in complex coastal environments: The 2011 Tohoku Japan earthquake and tsunami showed the very complex and rapid large scale interaction of the rising waters and the coastal Japanese environment. These were observed by some airborne and many haphazard ground-based views. Integrating those observations, and georeferencing imagery to help to measure inundation depths and flow velocities could be done with value for fluid dynamics simulations as well as for tsunamic education for coastal communities.
4. ETC.

I wanted to capture this text that I contributed to a recent proposal and stash it here.

This is part of some ideas that I have been working on with capstone students in our capstone class. Here is a link to a presentation: LINK.