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The first non-colonial genocide of the twentieth-century was the Armenian catastrophe in the Ottoman Empire during World War I. It started in early 1915, when the Young Turk regime rounded up hundreds of Armenians and hanged many of them in the streets of Istanbul, before beginning the genocidal deportation of most of the Armenian population to the desert, in which up to a million died or were murdered en route.

The Armenian minority in Ottoman Turkey had been subject to sporadic persecutions over the centuries. In 1894-96, these were stepped up with pogrom-like massacres. With the outbreak of the First World War, the Young Turk government proceeded far more radically against the Armenians. From 1915, inspired by rabid nationalism and secret government orders, Turks drove the Armenians from their homes and massacred them in such numbers that outside observers at the time described what was happening as ‘a massacre like none other,’ or ‘a massacre that changes the meaning of massacre.’ Although we do not have reliable figures on the death toll, many historians accept that between 800,000 and one million people were killed, often in unspeakably cruel ways, or marched to their deaths in the deserts to the south. Unknown numbers of others converted to Islam or in other ways survived but were lost to the Armenian culture. At the time a number of influential people spoke out against these atrocities, most notably the distinguished historian Arnold J. Toynbee, but it has only been since the 1970s that scholars have devoted anything like sustained attention to this human catastrophe. There is more than enough evidence to suggest that the mass murder of the Armenians was a case of genocide, as that crime was subsequently defined in the United Nations Genocide Convention of 1948. Surviving perpetrators of the Armenian genocide could certainly have been held to account in an international criminal court. 

The primary benefit of Geographic Information Systems (GIS) is the ability to interrelate spatially multiple types of information assembled from a range of sources. These data do not necessarily have to be visual. GIS “shape files” are helpful for interpolating and visualizing many other types of data, e.g. demographic data. Many research models rely on the ability to analyze and extract information from images by using a variety of computer-available research tools and then express these findings as part of a project with images in a variety of layers and scenes.

Remote sensing is the measurement of object properties on Earth’s surface using data acquired from aircraft and satellites. It attempts to measure something at a distance, rather than in situ, and, for research purposes, displays those measurements over a two-dimensional spatial grid, i.e. images. Remote-sensing systems, particularly those deployed on satellites, provide a repetitive and consistent view of Earth facilitating the ability to monitor the earth system and the effects of human activities on Earth. There are many electromagnetic (EM) band-length ranges that Earth’s atmosphere absorbs. The EM band ranges transmittable through Earth’s atmosphere are sometimes referred to as atmospheric windows.

The human eye detects, through the reflective solar radiance humans actually see, only that part of the EM scale in the band length range 0.4 – 0.7 μm. But remote sensing technology allows for the detection of other reflective and radiant (e.g. thermal) energy band-length ranges that reach or are emitted by Earth’s surface, and even some that Earth’s atmosphere reflects, e.g. the EM reflective qualities of clouds. Hence, for viewing purposes, red, green, and blue (RGB) false color assignments are used to express the reflective qualities of objects in these EM band-length groups, and the combination and mixing of these false color assignments express the true physical reflective qualities of all objects present in an image.

When utilizing satellite images to assess most types of land-cover change, primarily those involving change in vegetation coverage, variations in climate must be considered. For better control and accuracy in these analyses, comparing images acquired during the same month or season is advisable. But due to the limited availability of satellite images, obtaining materials corresponding both spatially and temporally to the location and period under research are not always possible. Furthermore, annual and seasonal climate data are not always available for the region or temporal period being researched. Sometimes, changes in average rainfall, temperature, etc. must be inferred using more macro regional or global data.

One standard remote sensing application for detecting temporal change in land cover, especially vegetation, is the Normalized Difference Vegetation Index (NDVI). The NDVI application involves a ratio formula between the visual red and NIR EM bands. This ratio application helps to distinguish healthy and stronger vegetation reflection from other materials with similar reflective qualities in those EM band wavelength groups. NDVI applications are useful because two images can be processed into a false color composite, which allows for visual temporal change detection in vegetation coverage.

Moreover, by applying standardized thresholds to multiple NDVI manipulated images, one can create classification training regions and execute supervised computer-generated classifications of multiple images. From these resulting images, area summary reports are calculated. These empirical data enable a more accurate assessment of change in area of the corresponding land-cover classes. More information on some of the above topics, as well as a more comprehensive description of some remote sensing technologies, and a glossary of terms, may be found in the 2009 GSP document, “An Introduction to Remote Sensing and GIS,” compiled by Russell Schimmer.