Investigating Satellite Landsat TM and ASTER Multitemporal Data Set to Discover Ancient Canals and Acqueduct Systems

  • Rosa Lasaponara
  • Nicola Masini
Conference paper

DOI: 10.1007/978-3-642-31137-6_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7335)
Cite this paper as:
Lasaponara R., Masini N. (2012) Investigating Satellite Landsat TM and ASTER Multitemporal Data Set to Discover Ancient Canals and Acqueduct Systems. In: Murgante B. et al. (eds) Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg

Abstract

In this paper, we focus on the use of the Landsat and ASTER multitemporal data set for extracting information on ancient irrigation systems and artificial wet agro-ecosystems. The study area is the Nazca basin in Southern Peru selected mainly for its extreme drought. Despite these critical environment conditions, the area was populated since millennia ago thanks to adequate survival strategies developed by ancient Nazca populations. To cope with hostile environmental factors and water scarcity, efficient aqueduct systems, today called puquios, were devised and some of them are still in use today. The main purpose of our investigations was the identification of buried unknown puquios by using satellite multitemporal maps of vegetation indices and moisture content. Results from satellite data were also identified on the ground, checked and confirmed in situ. The successful results obtained in the Nazca Basin suggest that our methodological approach can be efficiently re-used in a number of areas, characterized by similar environmental conditions and long human frequentation.

Keywords

GIS satellite based Analysis ancient irrigation systems Spatial variation Moisture index vegetation index Nazca (Peru) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rosa Lasaponara
    • 1
  • Nicola Masini
    • 1
  1. 1.CNR-IMAATito ScaloItaly

Personalised recommendations