Welcome!
I am excited to share with you what my students, department, and I are up to in the Department of Geography at Binghamton University (State University of New York). I am a broadly-trained geographer that specializes in geospatial tools and techniques including geographic information systems (GIS), remote sensing (including use of unoccupied aircraft systems--UAS and light detection and ranging--lidar), and the global navigation satellite systems (GNSS).
RECENT CONTRIBUTIONS
Land use/land cover (and change) remote sensing analyses have long been restricted to two-dimensional datasets and evaluations (e.g., namely, satellite imagery) due to the expense (and therefore lack of coverage) of three-dimensional data such as light detection and ranging (lidar). My coauthored chapter in Urban Remote Sensing: Monitoring, Synthesis, and Modeling in the Urban Environment (2nd edition edited by X. Yang) introduces readers to three-dimensional geospatial approaches within urban environments. Primarily this means use of active remote sensing data; drawing from previous work comparing spaceborne radar data (QuikSCAT) to airborne lidar over nine U.S. cities (as well as lidar to SAR), this work describes how these active remote sensing technologies help to more comprehensively model urban environments with the hope to improve future land cover/land use change analyses.
Drones have drastically changed remote sensing by offering a low-cost and relatively easy-to-use platform for aerial data collection. Fundamentals of Capturing and Processing Drone Imagery and Data (edited by A. Frazier and K. Singh) provides both foundational knowledge in drones and drone data collection, processing, and analysis as well as practical guidance on using drones for scientific analysis. I contributed two chapters to the book on the conceptual basics of drone-based photogrammetric modeling (i.e. Structure from Motion-Multiview Stereo computer vision) and an applied exercise taking readers through a three-dimensional analysis of drone generated point cloud data of vegetation.
