Multisource remote sensing archaeology of the Ancient Canal in Zhejiang, China

Qian Cheng; Xiuju Wu

doi:10.1117/12.798546

14 October 2008 Multisource remote sensing archaeology of the Ancient Canal in Zhejiang, China

Qian Cheng, Xiuju Wu

Proceedings Volume 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII; 71101T (2008) https://doi.org/10.1117/12.798546
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

This paper was to address an issues of human settlement and environmental interaction on the archaeological Ancient Canal sites in Zhejiang province using Hyperspectral Remote Sensing technology. The reflectance spectra within the visible-near-infrared (VNIR) region of Ancient Canal ground soil samples were measured in the lab. High-resolution satellite remote sensing provides cheap and quick data resources for delineating the Ancient Canal. In this paper, the remote sensing mechanism and spectral characteristics of the Ancient Canal in remote sensing imagery were analyzed. A gray-slope algorithm is introduced, which can extract Ancient Canal information effectively and easily. Due to the extensive destroy to the Ancient Canal, it is only 1 or 2 pixels wide in IKONOS imagery. And the gray level between Ancient Canal and other objects around it is very different, that is, the change of gray level along Ancient Canal is greater than that of road and sand. Our study results showed that the feature absorption band center of Ancient Canal around 2288nm, and the bandwidth was between 2098-2335nm. The absorption features of background soils were at 2180nm and 2350nm, which were caused by clay minerals and carbonate, respectively. The Ancient Canal could be distinguished from the background soils by reflectance spectra. These two characteristics and bands threshold, the gray-slope method makes road and sand separated from the Ancient Canal easily.

Citation Download Citation

Qian Cheng and Xiuju Wu "Multisource remote sensing archaeology of the Ancient Canal in Zhejiang, China", Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71101T (14 October 2008); https://doi.org/10.1117/12.798546

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