Refining Airborne Laser Scanning Data to See Through Mayapán's Dense Vegetation

Summary

This is an abstract from the "SAA 2024: Individual Abstracts" session, at the 89th annual meeting of the Society for American Archaeology.

I present a workflow for optimizing the classification of airborne laser scanning point data and the selection of appropriate surface visualization techniques to improve the identification of archaeological and environmental features at the Postclassic city of Mayapán. The initial 2013 digital elevation model enabled the identification of thousands of structures in and around the city. Still, ground-checking revealed that the dense canopy and low vegetation obscured many structures and walls in parts of the study area. Starting with the raw laser scanning data, I developed a systematic workflow for testing different point classification methods and visualization techniques to improve feature identification in areas with different surface vegetation types. No single ground point classification or surface visualization method is best. Specific combinations of point classification methods and visualization techniques can be tailored to different surface conditions to improve feature identification. I found that three different point classification methods and a set of visualization techniques produced the best results across the varied surface conditions of the study area. The results of this project are the identification of hundreds of previously hidden features that, after additional field-checking, will contribute to answering broader research questions and support additional research in and around Mayapán.

This Resource is Part of the Following Collections

• SAA 2024: Individual Abstracts   •
• Society for American Archaeology 89th Annual Meeting, New Orleans, LA (2024)

Keywords

Spatial Coverage

Record Identifiers

Abstract Id(s): 37967.0