## 4 Materials and Methods
### Satellite Spectral Analysis  
#### 4.3.1 Spectral Analysis of Vegetation  
Since a dense layer of vegetation, primarily camel thorn and coarse grass, covers the archaeological ruins of Hermopolis due to the high-water table (Figure 2), it was imperative to explore in detail the general vegetation greenness and health at the site. Traditionally, the flora growing atop an archaeological feature will be very stressed, while the vegetation on an undistributed area will be much more robust and greener [43]. Previous studies have found that the near-infrared (NIR) spectral channel is more effective in revealing the robustness or stress of vegetation than the bands in the visible portion of the electromagnetic spectrum [14]. Thus, the NIR spectral band is most frequently used for the prospection of new archaeological features. In the infrareds (near and middle wavelengths), the pigments found in healthy vegetation have a very high reflectance, while those in stressed vegetation exhibit a much lower level of reflectance [44]. This variance in reflectance between healthy and stressed vegetation is an instrumental tool for the detection and identification of potential archaeological structures.  
By applying different vegetation indices to raw satellite data, scholars can extract significant information such as vegetation type, quantity, and condition. The collection of spectral-enhancement indices that analyze vegetation greenness and health is commonly  
Figure 2: Different vegetation growth at the site of Hermopolis (photo taken by author).  
known as the \"Broadband Greenness Vegetation\" indices. All the indices in this group measure the amount and quality of photosynthetic material (chlorophyll) in vegetation. For the present study, several spectral indices were employed, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and the Infrared Percentage Vegetation Index (IPVI).  
\[\sum\frac{\rho\text{NIR}-\rho\text{Red}}{\rho\text{NIR}+\rho\text{Red}}=\text{ NDVI} \tag{1}\]  
\[\sum\frac{\rho\text{NIR}-\rho\text{Green}}{\rho\text{NIR}+\rho\text{Green}}= \text{GNDVI} \tag{2}\]  
\[\sum\frac{\rho\text{NIR}}{\rho\text{NIR}+\rho\text{Red}}=\text{IPVI} \tag{3}\]  
By using the NIR and red/green spectral bands, subtle physical differences in stressed versus healthy vegetation can be easily recognized. In the resulting processed image, the stressed vegetation appears brighter than the neighboring pixels. This contrast in colors allows for visual ease in detecting linear, circular, or rectilinear archaeological features. In addition to qualitative information, the vegetation indices also produce important quantitative information for categorizing the condition of the flora. The most vital information produced is a value ranging from \(-1\) or \(0\) to \(+1\), which gives a numerical assessment of the state of robustness of the vegetation. The closer to \(+1\), the healthier the vegetation is, while the closer to \(0\) (IPVI) or \(-1\) (NDVI and GNDVI), the more stressed it is [45]. To further enhance the results, a \(3\times 3\) ENVI directional filter mask at \(45\) degrees was applied to the processed IPVI image. As an edge detector, directional filtering allows for the amplification and/or enhancement of selective linear features, which is beneficial for a more detailed analysis of the satellite imagery [46]. In addition, directional filtering can also be utilized as a light source to generate a 3D-shaded relief-image effect, which, when merged with the original processed satellite image (in this case, the IPVI image), allows for subtle features to be easily recognized and delineated [46].