Lidar-based topography metrics indicate environmental change in the Maya Lowlands

Authors: Sara Eshleman*, University of Texas - Austin, Timothy Beach, University of Texas - Austin
Topics: Remote Sensing, Geomorphology, Latin America
Keywords: subtropical ecosystems, Central America, Belize, geomorphic assessment
Session Type: Virtual Paper
Day: 4/11/2021
Start / End Time: 9:35 AM / 10:50 AM
Room: Virtual 36
Presentation File: No File Uploaded


Lidar has revolutionized human-environment interaction research in the Maya Lowlands. Lidar imagery in this region consists of both distinct and unclear indications of the human impact on the environment. The ancient Maya built extensive and intensive constructions which modify the environment. Simultaneously their actions altered hillslopes, wetlands, and waterways, among other topographic features. These latter interactions are often indistinct in imagery and field surveys but become more certain with geomorphic assessment with the lidar data and multi-proxy field studies. Researchers in other geographic regions have developed a suite of topographic metrics that can be utilized here towards better understanding natural and anthropogenic landscape change. These metrics calculate topographic heterogeneity, slope curvature, and hillslope transience, in addition to the categorical characterization of landforms. Within these categories, many methods exist to evaluate landscapes. Currently, it is unknown which methods are the most useful when assessing Maya Lowland environments with lidar data. To determine their potential use for northern Central American landscapes, we assess each metric quantitatively, evaluating their correlations and comparing them to field observations. The effective use of topographic characterization can help explain the lasting human impact in the Maya Lowlands; including, assisting in describing both distinct and indistinct Maya alterations, better depicting the environments where humans have persisted for thousands of years, and lending more accurate data for future quantitative studies of human-environment interactions.

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