Visualization of Surface Processes over Space and Time using a Long Series of Satellite Based Imagery

Friday, 19 December 2014
Robert Schafer1, Thomas Harris1, David Hulslander2, Amanda S O'Connor1 and Jason Wolfe1, (1)Exelis Visual Information Solutions, Boulder, CO, United States, (2)Exelis VIS, Boulder, CO, United States
With the increasing diversity and long temporal record of satellite-based Earth imagery, we have new opportunities to better understand and predict Earth surface processes and activities. Satellite-based imagery is an increasingly important resource for analyzing changes in vegetation and land use, as well as monitoring the evolution of hazards and environmental conditions. A key requirement for exploitation of this imagery is visualization and extraction of multimodal data over space and time.

Analysis of this imagery requires four primary components: 1) Assignment of acquisition time, spatial reference, and parameter descriptions, 2) Preprocessing including radiometric calibration, generation of derived parameters such as NDVI, and normalization to a common spatial grid, 3) Cataloging and access for discovering and extracting data through space, parameter, and time, and 4) Visualization techniques including animation, parameter-time, space-time, and space-frequency plots.

Using ENVI, we will demonstrate how Landsat, MODIS, and Suomi NPP VIIRS data products can be prepared and visualized for exploring the evolution of processes and activities. Visual animation through a temporal stack of imagery is used to quickly understand trends in urban growth, vegetation, and land use. After exploring the temporal stack of images, spatio-temporal and periodic relationships are visualized using space-time and space-frequency representations of the data.

Satellite-based imagery is a primary source of data for understanding global changes over time. To understand processes and activities, it is now increasingly important for data exploitation tools such as ENVI to easily extract data from multiple satellite-based sensors and visualize this multimodal data in both space and time.