Ms. Holly W. Leigh Profile

Holly W. Leigh

Engineering Scientist at Univ of Texas at Austin

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SPIE Journal Paper | 6 October 2016

Using dual-wavelength, full-waveform airborne lidar for surface classification and vegetation characterization

Holly Leigh, Lori Magruder

JARS, Vol. 10, Issue 04, 045001, (October 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.045001

KEYWORDS: LIDAR, Near infrared, Vegetation, Feature extraction, Sensors, Pulsed laser operation, Reflectivity, Remote sensing, Signal processing, Ecosystems

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This study examines the utility of cocollected, dual-wavelength, full-waveform lidar data to characterize vegetation and landscapes through the extraction of waveform features, such as total waveform energy, canopy energy distribution, and foliage penetration metrics. Assessments are performed using data collected in May 2014 over Monterey, California, using the Chiroptera dual-laser lidar mapping system from Airborne Hydrography AB. Both full-waveform and discrete return data were collected simultaneously at green (532 nm) and near-infrared (NIR) (1064 nm) wavelengths; however, the two channels are operated independently at different pulse repetition frequencies, thus measurements are not spatially coincident. A voxelization approach is employed to generate pseudowaveforms for each wavelength along vertical columns in a regularly spaced grid, such that spectral waveform properties can be evaluated independently of spatial variations resulting from instrumentation configuration and collection scenario. The pseudowaveforms are parameterized and extracted parameters are mapped to raster layers, which are then used as inputs to a random forest classifier to predict land cover classifications across the survey area. In comparison to independent classification results for the two wavelength channels, the combination of the NIR and green response provided an improvement in overall classification accuracy of up to 6%. This effort presents the methodology associated with the voxelization approach and the exploitation of the pseudowaveform features, while illustrating a potential utility for geospatial classification using multiple wavelengths.

Proceedings Article | 13 May 2016 Paper

Automated feature extraction for 3-dimensional point clouds

Lori Magruder, Holly Leigh, Alexander Soderlund, Bradley Clymer, Jessica Baer, Amy Neuenschwander

Proceedings Volume 9832, 98320F (2016) https://doi.org/10.1117/12.2223845

KEYWORDS: LIDAR, Feature extraction, Clouds, Data modeling, RGB color model, Image segmentation, Vegetation, Raster graphics, Algorithm development, Visualization

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Light detection and ranging (LIDAR) technology offers the capability to rapidly capture high-resolution, 3-dimensional surface data with centimeter-level accuracy for a large variety of applications. Due to the foliage-penetrating properties of LIDAR systems, these geospatial data sets can detect ground surfaces beneath trees, enabling the production of highfidelity bare earth elevation models. Precise characterization of the ground surface allows for identification of terrain and non-terrain points within the point cloud, and facilitates further discernment between natural and man-made objects based solely on structural aspects and relative neighboring parameterizations. A framework is presented here for automated extraction of natural and man-made features that does not rely on coincident ortho-imagery or point RGB attributes. The TEXAS (Terrain EXtraction And Segmentation) algorithm is used first to generate a bare earth surface from a lidar survey, which is then used to classify points as terrain or non-terrain. Further classifications are assigned at the point level by leveraging local spatial information. Similarly classed points are then clustered together into regions to identify individual features. Descriptions of the spatial attributes of each region are generated, resulting in the identification of individual tree locations, forest extents, building footprints, and 3-dimensional building shapes, among others. Results of the fully-automated feature extraction algorithm are then compared to ground truth to assess completeness and accuracy of the methodology.

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