Authors: Colin Doyle*, University of Texas - Austin, Sheryl Luzzadder-Beach, University of Texas at Austin, Timothy Beach, University of Texas at Austin
Topics: Geomorphology, Remote Sensing
Keywords: machine learning, LiDAR, soil, geoarchaeology
Session Type: Virtual Paper
Start / End Time: 11:10 AM / 12:25 PM
Room: Virtual 36
Presentation File: No File Uploaded
Recent research using LiDAR and geoarchaeological techniques unveiled the rediscovery of massive stretches of ancient Maya wetland raised fields in Northwest Belize (Beach et al., 2019). The largest area of these fields lies along the Rio Bravo and has never been studied before, only being recognized through airborne LiDAR survey in 2016. This paper outlines the first evidence for the chronology and use of these newly identified fields. First, we develop a machine learning model to extract the canals and raised fields from the LiDAR data to quantitatively estimate their size and shapes. Next, we present initial chronologies from AMS radiocarbon dating of charcoal and geochemical analysis of multiple raised fields across the floodplain. These excavations revealed two buried soils, which suggest multiple significant changes in floodplain conditions through the time of ancient Maya occupation. The lowest buried soil, dating to Preclassic Maya, is covered by over a meter of erosional clay before returning to typical floodplain layered stratigraphy. This sequence suggests early Maya occupation caused significant erosion in the watershed and aggradation of clay in the floodplain, but quickly reduced erosion and annual flood layers aggrade. Later, during the Classic to Postclassic periods, the second buried soil was covered by the raised fields’ construction. The timing and geochemistry suggest the fields were likely an adaptation to environmental conditions and high resource demands. This study presents a multi-method approach to understanding the chronology and extent of rediscovered ancient Maya wetland fields, and what we can learn about long-term floodplain management.