Finding Angkor's Hidden Sites

While the use of satellite images for identifying potential archaeological sites is successful in several parts of the world, it is less so in others.  In Cambodia, where many monumental stone structures are hidden in the dense tropical vegetation, and where land mines and unexploded bombs pose a threat to those conducting ground survey, it is possible to use training samples to classify images for the identification of previously unknown sites.  However, it is problematic.  This map shows a supervised classification of the area surrounding the core of Angkor's monumental architecture.  The classification does identify several areas where potential sites may be located, and indeed one area, Phnom Kulen, has recently been identified as a previously undiscovered urban landscape associated with early Angkorian settlement (Evans et al, 2013).  The classification is not good at distinguishing the stone monuments from other classes, such as dense forest and water.  However, patterning that points to hydraulic features and geometric lines associated with Angkorian architectures is visible.

It appears that the use of lidar in this situation is far superior to the results that can be achieved using Landsat imagery, as seen here.  The following website and article provide additional information.

http://angkorlidar.org/publications/

Evans, D. H., R. J. Fletcher, C. Pottier, J.-B. Chevance, D. Soutif, B. S. Tan, S. Im, D. Ea, T. Tin, S. Kim, C. Cromarty, S. De Greef, K. Hanus, P. Bâty, R. Kuszinger, I. Shimoda and G. Boornazian. 2013. “Uncovering archaeological landscapes at Angkor using lidar,” Proceedings of the National Academy of Sciences of the United States of America 110: 12595-12600

Band Combinations, Training Samples, and Supervised Classification

Different band combinations can be used to bring out various characteristics in the environment.  The top two maps here use two different combinations of Landsat satellite imagery.  The NVDI map uses a False Color image composite, which joins bands 2,3, and 4.  When the NVDI process is added, the negative ouputs show up as red.  Bare rocks, sand, and snow have an output close to zero, and those with a higher measure of "greenness" have higher values.  This allows dense vegetation, like tropical rainforest, to show up clearly.  The combination of bands 4,5, and 1 in the second map is used to differentiate vegetation that is stressed and sparse with healthy vegetation.  

The map at the bottom shows a map resulting from a supervised classification,  First a training signature file was created by drawing polygons around samples of each class.  Known Mayan pyramid sites were used to create this class's sample.  The classification shows the locations of possible new sites, and creates a tool to be used in survey and ground-truthing.

Maya Pyramids Part I

This series of maps shows several different band combinations that can be useful in locating potential sites.  Landsat imagery from the USGS can be viewed in ways which reveal certain characteristics depending on the combination of bands.  ArcMap Image Analysis and Processing tools are used to create combinations suitable for different purposes.

For example, the Landsat Band 8 is a high resolution panchomatic view, used here by itself to show the location of the Mirador pyramid.  It can also be used to create a sharper composite image, using the Pan-sharpening option. 

The Natural Color map shows a band combination that displays a color image to visualize data like a color photo using Bands 1,2 and 3, which show visible light.  Band 1 distinguishes soil from vegetation, Band 2 is useful for showing which plants are stressed, and which are more healthy, and Band 3 is also used to highlight vegetation.

The False Color map uses Bands 2,3,and 4, adding the Near Infared which emphasizes biomass content.  This band combination is most useful for the dense jungle where the Mirador pyramid is located because the red band (#3) indicates areas where chlorophyll is being absorbed, and the NIR band (#4) indicates areas of high refelectivity of plant materials.  The NDVI(Normalied Difference Vegitation Index) tool is used to show relative biomass in the image.