Scythian Landscapes - Prepare Week

First part of Scythian Landscapes module focused on preparing the data. This involved finding and downloading DEMs of the area, and creating a raster mosaic out of them. Then the mosaic raster was clopped to the provided study area shapefile.

Second part of the module was to take an aerial image of the mound site, and georeference it based on coordinates provided. Visual georeferencing was not possible, due to very poor resolution of the background image.

Predictive Modeling

In this module we performed predictive modelling of an area. The model was divided into three site categories, high, medium and low probability. These categories were based on slope and proximity to a waterway.

First I created a raster out of a mosaic of smaller rasters. Then I extrapolated elevation values for the area. I those values to determine slope steepness, and slope facing. In this case South facing slopes were more favorable to slopes facing other directions. Lastly I used a separate shapefile of streams and rivers in the area, created a buffer around it representing optimal settlement distance from the waterway. Lastly I combined the slope and waterway data to determine site probability in the area.

Finding Pyramids

During week 3 we identified possible pyramid locations in densely forested areas. The map included here shows area surrounding Angkor Wat it Cambodia. 

The supervised classification is based on false color infrared base raster image. Considering the base color band combination, any possible pyramid sites will be very difficult to spot, if not impossible. This is in part due to healthy vegetation overgrowing the ruins showing in the same shades and tones as all other healthy vegetation.

NDVI False Color measures greenness of the vegetation. It measures the difference between the Red and Near Infrared bands.

Lyr 451 depicts healthy vegetation in shades of reds, browns, oranges and yellows. Opened, developed areas appear in shades of white and near white colors. Adding mid infrared band allows for detection of stages of plant growth or stress.

Supervised Classification allows us to assign certain pixel values to specific objects. In turn, ArcMap extrapolates those values to the entire image.