Fusion of high-resolution lidar elevation data with hyperspectral data to characterize tree canopies

Craig J. Miller

doi:10.1117/12.437014

20 August 2001 Fusion of high-resolution lidar elevation data with hyperspectral data to characterize tree canopies

Craig J. Miller

Proceedings Volume 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII; (2001) https://doi.org/10.1117/12.437014
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

This paper describes a methodology developed at the Spectral Information Technology Applications Center (SITAC) to combine information derived from high resolution LIDAR elevation data with information derived form hyperspectral data to characterize tree canopies. High resolution elevation data are used to detect abrupt changes in elevation, indicative of man-made structures or certain natural features. The underlying elevation is estimated by first masking out the pertinent structures or features and then interpolating. Structure or feature height is then calculated as the difference between the original elevation and the interpolated elevation. This procedure is applied to a high resolution LIDAR elevation data set of an open forest scene to produce a tree height image. These tree height data are then combined with other tree information to infer trunk diameter. Hyperspectral data are employed to detect as well as characterize man-made and natural structures. Fusion of hyperspectral information with elevation information promises benefits to remote sensing applications.

Citation Download Citation

Craig J. Miller "Fusion of high-resolution lidar elevation data with hyperspectral data to characterize tree canopies", Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); https://doi.org/10.1117/12.437014

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