Title
Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series.
Abstract
Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect crop growth changes to reveal subtle surface marks in large areas. This paper aims to study the classical Chinese settlements of Taosi and Erlitou over large areas using Landsat NDVI time series crop phenology to determine the optimum periods for detection and monitoring of crop anomalies. Burial areas (such as the palace area and the sacrificial area) were selected as the research area while the surrounding empty fields with a low density of ancient features were used as reference regions. Landsat NDVI covering two years' growth periods of wheat and maize were computed and analyzed using Pearson's correlation coefficient and Euclidean distance. Similarities or disparities between the burial areas and their empty areas were computed using the Hausdorff distance. Based on the phenology of crop growth, the time series NDVI images of winter wheat and summer maize were generated to analyze crop anomalies in the archeological sites. Results show that the Hausdorff distance was high during the critical stages of water for both crops and that the images of high Hausdorff distance can provide more obvious subsurface archeological information.
Year
DOI
Venue
2017
10.3390/rs9040374
REMOTE SENSING
Keywords
Field
DocType
Landsat NDVI,time series,large prehistoric sites,Hausdorff distance,crops phenology
Correlation coefficient,Crop,Remote sensing,Euclidean distance,Normalized Difference Vegetation Index,Hausdorff distance,Geology,Prehistory,Phenology,Low density
Journal
Volume
Issue
Citations 
9
4
3
PageRank 
References 
Authors
0.50
5
5
Name
Order
Citations
PageRank
Yuqing Pan130.50
Yueping Nie262.42
Chege Watene330.50
Jianfeng Zhu4217.02
Fang Liu58720.09