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From Space to Place: Utilizing Dual-Remote Sensing Systems to Characterize Maya Lowland Landscapes

Authors: Danielle Ruffe*, The University of Texas at Austin, Manda Adam, The University of Texas at Austin, Timothy Beach, The University of Texas at Austin
Topics: Remote Sensing, Human-Environment Geography, Anthropocene
Keywords: multispectral imaging, lidar, Maya tropical lowlands, remote sensing
Session Type: Paper
Presentation File: No File Uploaded

In the last decade, research utilizing remote sensing imaging platforms has revolutionized investigations in the Maya lowlands. In particular, airborne light detection and ranging (lidar) and multispectral imaging have aided research efforts to characterize and identify environmental and natural features. With the ability to penetrate tropical forest canopy, researchers have explored multiple uses of lidar for analyzing and characterizing anthropogenic modifications to the landscape. This study will build upon previous studies that have explored the use of lidar in computer training methods to extract anthropogenic features from Maya landscapes. The training area is a 4 km-squared portion the Maya site of Gran Cacao in the Rio Bravo Conservation and Management Area located in northwest Belize. This project will focus on the feasibility of dual-remote sensing technologies, in particular lidar and multispectral imaging, to discern anthropogenic and natural modifications to the landscape that were previously obscured in approaches with lidar-alone.

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