After presenting my poster, I wanted to go into more depth with my research and wrote an essay about geospatial technology applications to human rights. This essay expanded on the computer algorithms presented in the poster, described new algorithms and their potential applications, and explained how statistics can be applied to human rights data. Lastly, I cited how a San Francisco start-up called Planet Labs is advancing the geospatial imaging landscape by putting more satellites into orbit for much lower prices. The high costs of satellite imagery and lack of satellite availability can greatly hinder human rights activists’ efforts, but Planet Labs’ endeavors give us hope for the future of human rights monitoring. Here is the essay:
Human rights activists at Non-Governmental Organizations such as the American Association for the Advancement of Science and Amnesty International have employed geospatial imaging to identify human rights abuses around the world. Often, this process involves geospatial analysts searching for human rights abuses in images with their own eyes. Computational processes and new geospatial imaging technology have great potential to aid these analysts in identifying human rights abuses more efficiently and effectively. These processes and technology include algorithms, statistical methods, and affordable satellites.
Change detection analysis is a computational process “used to determine the change between two or more time periods of a particular object of study” (Macleod & Congalton, 1998). When applied to geospatial imagery, change detection analysis methods measure differences in radiance values of the Earth’s surface (Theau, 2012). Currently, most of the applications of change detection analysis concern deforestation. In 2005, a group of scientists using the Moderate Resolution Imaging Spectroradiometer (MODIS) noticed that “once a clearing was the size of three MODIS pixels, change-detection computer programs consistently identified the areas as deforested,” which demonstrates that both advanced change detection algorithms and remote sensors must be developed to effectively track change using geospatial imagery (Morton et al, 2011).
Before change detection analysis can be applied to geospatial imagery, image pre-processing must be conducted. Image pre-processing improves images by making them appear as if they are derived from the same remote sensor (Xie et al, 2008). Common image pre-processing algorithms address radiometric errors, geometric errors, image fusion, and shadow-removal. Radiometric errors refer to differences in the brightness value of pixels (Richards, 2013). Sources of this error are instrumentation, time of day, season, and the atmosphere. Sources of geometric errors are the velocity of the remote sensor and the Earth’s curvature (Richards, 2013). Image fusion algorithms enhance image clarity by overlaying images from different remote sensors (Xie et al, 2008). Lastly, shadow removal algorithms take into account image feature height, resolution, and the angle of the sun to remove shadows from images (Mena & Malpica). The aforementioned pre-processing algorithms enhance image quality and control for variability due to errors. In doing this they allow for more accurate change detection analysis.
Computational algorithms and statistics have already been applied to monitoring human rights abuses, and the results are promising. While conducting research at the University of Maryland, Andrew Marx utilized algorithms to identify burned villages in Darfur (Marx, 2013). Marx used images from the US Geological Survey and employed a NASA algorithm to reduce atmospheric noise, like aeorosols, and create an automatic cloud mask. Using the Interactive Data Language, Marx created an algorithm to analyze near-infrared reflectance to search for villages destroyed by burning (Marx, 2013). The algorithm picked up differences in the geospatial images between pre-burn materials, such as dead wood, and post-burn materials, such as charwood and ash.
Andrew Marx also employed statistical methods in his study to determine the stability of remotely sensed signals and accuracy of certain geospatial images (Marx, 2013). Marx conducted a one-way ANOVA to compare the variability among the images of 179 villages to the variability among the images of a single village. Ideally, there should be more variability among images of different villages than images of the same village, so a greater F-statistic suggests a more stable signal when compared to observations across all villages (Marx, 2013). Marx also used t-tests to determine the accuracy of geospatial images of the same location. He created a baseline of all images he had of a village by creating a “mean” from the images (Marx, 2013). He compared a new image, or “observation,” to this “mean.” The fewer standard deviations it was from the “mean,” the more accurate the image was (Marx, 2013).
Geospatial imaging has already demonstrated its use in monitoring human rights abuses. When combined with computational processes, statistics, and new algorithms, human rights monitoring can be even more effective. While geospatial images may take considerable time and require considerable financial resources to acquire, the geospatial technology landscape is changing. In February of 2014, a San Francisco start-up named Planet Labs released a fleet of 28 small (10 cm x 10 cm x 30 cm) satellites into space (Taylor, 2014). Planet Labs plans to release 131 more of them in the next year. They are less expensive than current satellites and will be able to provide images for analysis within a few hours of request (Taylor, 2014). With ever-advancing and more affordable technology, the future of human rights monitoring holds great promise.
Macleod, Robb D., and Russell G. Congalton. “A Quantitative Comparison of
Change-Detection Algorithms for Monitoring Eelgrass from Remotely Sensed
Data.” Photogrammetric Engineering & Remote Sensing 64.3 (1998): 207.
Marx, Andrew J. “Employing Moderate Resolution Sensors in Human Rights and
International Humanitarian Law Monitoring.” Diss. U of Maryland, 2013.
Marx, AJ. A New Approach to Detecting Mass Human Rights Violations Using Satellite
Imagery. Washington, DC: US Holocaust Memorial Museum, 2013. Print.
Mena, J. B., and J. A. Malpica. A Change Detection Method with Radiometric
Normalization and Shadows Removal in Multispectral Satellite Imagery.
Madrid: Alcala University, n.d. Print.
Morton, D.C., DeFries, R.S., Shimabukuro, Y.E., Anderson, L.O., Del Bon Espírito-Santo, F., Hansen, M., and Carroll, M. 2005. Rapid Assessment of Annual Deforestation in the Brazilian Amazon Using MODIS Data. Earth Interactions. 9: 1-22.
Richards, John A. Remote Sensing Digital Image Analysis. 5th ed. Heidelberg:
Springer, 2013. Print.
Taylor, Richard. “Mini-Satellites Send High-Definition Views of Earth.” BBC 15
May 2014, Technology: n. pag. BBC News. Web. 30 May 2014.
Theau, Jerome. “Change Detection.” Handbook of Geographic Information. Ed.
Wolfgang Kresse and David M. Danko. N.p.: Springer, 2012. 175-84. Print.
Xie, Yichun, Zongyao Sha, and Mei Yu. “Remote Sensing Imagery in Vegetation
Mapping: A Review.” Plant Ecology 1.1 (2008): 9-23. Print.