Remote sensing, landscape and archaeology tracing ancient tracks and roads between Palmyra and the Euphrates in Syria

Abstract

ABSTRACT:The present paper concentrates on the use of remote sensing by satellite imagery for detecting ancient tracks and roads in the area between Palmyra and the Euphrates in Syria. The Syrian desert was traversed by caravans already in the Bronze Age, and during the Greco-Roman period the traffic increased with the Silk Road and trade as well as with military missions annexing the areas into empires. SYGIS -the Finnish archaeological survey and mapping project traced, recorded and documented ancient sites and roads in the region of Jebel Bishri in Central Syria in 2000-2010 before the outbreak of the civil war in Syria. Captured data of ancient roads and bridge points bring new light to the study of ancient communication framework in the area. Archaeological research carried out by the project on the ground confirmed the authenticity of many road alignments, new military and water harvesting sites as well as civilian settlements, showing that the desert-steppe area was actively used and developed probably from the second century AD. The studies further demonstrated that the area between Palmyra and the Euphrates was militarily more organised already in the second and third centuries AD than earlier believed. Chronologically, the start of this coincided with the "golden age" of the Palmyrene caravans in the second century AD. Topography and landscape were integral parts of the construction of graves/tumuli as sign-posts guiding in the desert, as well as roads and all kinds of settlements whether military or civilian.

Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy

“…In addition, the differences that indicate the presence of possible structures or effects of anthropogenic transformation of landscape were well delineated using the spectral separability (see Figures 5 and 6) applied to the ROIs within the individual bands and the spectral indices. The most performing indices are those involving the Green, Red, and NIR bands, such as (i) crop-band, (ii) soil-band, (iii) RN, (iv) NDVI (Table 2), as expected according to the past investigations [7,8,[11][12][13][14][70][71][72][73][74][75][76]. This is certainly due to the high resolution that the Sentinel-2 satellites have in these bands (10 m).…”

“…Normalized Difference Water Index (NDWI) [71][72][73][74][75][76] These spectral combinations are useful for the enhancement of crop-marks, soil-marks, and anomalies generated by the presence of buried structures of potential archaeological interest [60][61][62][76][77][78]. The NDVI (Normalised Difference Vegetation Index) is one of the most widely used indices also in archaeological applications.…”

Remote Sensing Analyses on Sentinel-2 Images: Looking for Roman Roads in Srem Region (Serbia)

“…There are several techniques to map buried archaeological sites through the use of different typologies of satellite images, which have been broadly explained in many different publication that constitute a solid scientific literature [34][35][36][37][38]. Agriculture is the main economic activity in Srem and cereals are the main crop, covering around 70% of its extension [14]: for this reason, the research team considered that analyzing multi-spectral satellite images would have ensured better results in comparison with other kinds of data.…”

Potential Impact of Using ChatGPT-3.5 in the Theoretical and Practical Multi-Level Approach to Open-Source Remote Sensing Archaeology, Preliminary Considerations

“…In recent years, RS studies in archaeology have focused on the use of different systems to improve the visibility of features of archaeological interest. The most common practices are (i) spectral enhancement via the creation of indices (mathematical combination between bands) such as indices derived from the use of NIR, Red, and Green (e.g., NDVI, GNDVI, and SAVI) [63,[75][76][77][78][79] or indices based on SWIR (e.g., NDMI and MSI) [80][81][82]; (ii) radiometric enhancement obtained using linear and non-linear stretching or equalisation of the histogram to increase the contrast between pixel classes [83,84]; (iii) transformation, aggregation or reduction in data using various techniques such as TCT (Tasseled Cap Transformation) [85], PCA (Principal Component Analysis) and SPCA (Selective PCA) [86][87][88][89], local and global spatial autocorrelation indices (e.g., Anselin Local Moran's I, Getis-Ord's index and Geary's index); and (iv) classification (e.g., K-Means, Isodata, and machine-and deep-learning based classification) [90,91].…”