Abstract
This study provides an evaluation of spectral responses of hollow ways in Upper Mesopotamia. Hollow ways were used for the transportation of animals, carts, and other moving agents for centuries. The aim is to show how the success of spectral indices varies in describing topologically simple features even in a seemingly homogeneous geographic unit. The variation is further highlighted under the changing precipitation regime. The methodology begins with an exploration of the relationship between the date of a multispectral scene and the visibility of hollow ways. The next step is to evaluate the impact of rainfall levels on numerous indices and to quantify spectral contrast. The contrast between a hollow way and its background is evaluated with Welch’s t-test and the association between precipitation regime and spectral responses of hollow ways are investigated with Correspondence Analysis and Fisher’s test. Results highlight an intrinsic relationship between the precipitation regime and the ways in which archaeological features reflects and/or emits electromagnetic energy. Next, the categorization of spectral indices based on different rainfall levels can be used as a guidance in future studies. Finally, the study suggests contrast becomes an even more fruitful concept as one moves from the spatial domain to the spectral domain.
Highlights
In remote sensing, the detection of an archaeological feature within its surrounding matrix depends on the level of contrast [1]; a target is easier to decipher—both visually and computationally—when it has higher contrast
Hollow ways are most visible to the naked eye in November, but again, the Normalized Difference Water Index (NDWI) average spectral contrast between hollow ways and their backgrounds is highest during March and April
Available multispectral sensor data with increasingly high spatial resolutions call for a wider use of spectral indices in archaeology
Summary
The detection of an archaeological feature within its surrounding matrix depends on the level of contrast [1]; a target is easier to decipher—both visually and computationally—when it has higher contrast. Within the context of satellite remote sensing, contrast is the difference between the electromagnetic energy emitted and/or reflected from a feature (or its proxy) and the background energy. The level of difference as registered by the sensor determines whether the feature mapping and analysis will be successful. Contrast is an underlying concept of all remote mapping projects. The increasing access to Very High Resolution (VHR) satellite imagery (with sub-meter resolutions in panchromatic bands) and the widespread use of online imagery viewing platforms make it easier to manually or (semi-)automatically detect potential targets [2]. Spectral data in and of themselves contain useful information aside from location and shape
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