OpenTopography has released a dataset from the South Island of New Zealand. The data are part of the Marlborough Fault System and were collected by NCALM in March, 2014. Access to the data can be found here.

The Geosensing Systems Engineering & Sciences (GSES) Graduate Program in the Department of Civil & Environmental Engineering at the University of Houston, in conjunction with the NSF National Center for Airborne Laser Mapping (NCALM), is seeking qualified applicants (with backgrounds in engineering, geomatics, geosciences, physics, astronomy, computer sciences, or geography) interested in: Design, development, and testing of lidar, digital image, and hyperspectral sensors; Research applications of lidar observations, including when combined with digital imagery or hyperspectral measurements; Study of satellite altimetry for applications in hydrology, glaciology, geodynamics, and sea levels; Applications of terrestrial laser scanning, airborne lidar, and GPS in natural hazards studies and mitigation; and Applications of airborne remote sensing techniques in geomorphology, climate/weather studies, hydrology, and tectonics.

Additionally, U.S. citizens interested in single photon and Geiger mode lidar analysis are encouraged to apply for opportunities to participate in sensor design and error analysis, geometric and radiometric calibration algorithm development, data filtering and feature extraction, comparison and fusion with traditional active and passive remote sensing products, and multi-sensor fusion design and data exploitation.

Please visit: ncalm.cive.uh.edu/gses/geosensing, for more information.

The application process and forms can be found on the Civil & Environmental Engineering website. Interested students can contact Dr. Craig Glennie at: clglennie [at] uh.edu (subject: GSES%20Graduate%20Program%20Information%20Request) .

Deadline: February 1st, 2017

Zhigang Pan defended his Ph.D. dissertation, "Shallow Water Bathymetry Using Full Waveform Bathymetric LiDAR and Hyperspectral Imagery." High-resolution airborne hyperspectral imagery and high-resolution, low-pulse energy, full waveform lidar were investigated to determine their capabilities for predicting water column characteristics and bathymetry. Using multiple evaluations, significant improvements in point density, multiple return detection, water turbidity, and accuracy were determined for full waveform bathymetric lidar. Additionally, improvements were made to the determination of shallow water bathymetry, water turbidity, and water column constituent concentration using hyperspectral imagery.

Zhigang becomes the fifth Geosensing Systems Engineering & Sciences (GSES) doctoral student to defend in 2016. His advisor was Dr. Craig Glennie. Zhigang will be working closely with Dr. Glennie and Leica Geosystems as a postdoctoral researcher, where he will be concentrating on single photon lidar calibration and filtering.

Congratulations, Zhigang!

Doctoral student Hongzhou Wang, from the Geosensing Systems Engineering & Sciences (GSES) graduate studies program, recently defended his dissertation on, “Fusion of Full Waveform LiDAR and Passive Remote Sensing for Improved Land-Cover Classification.” Land cover classification is a crucial step in interpreting remote sensing data, and the accuracy of the classification determines the reliability of the product for further downstream applications. Hyperspectral sensors have been widely utilized for classification because of the discrimination afforded by their rich spectral information and high resolution in both the spatial and spectral domains. Additionally, lidar data has gained increased interest for use in classification, because it provides precise 3D data with information regarding target backscattering properties. With the introduction of full waveform lidar (FWL), the possibility of using lidar for target discrimination has been enhanced due to the additional structural information acquired. The geometrical information and backscattering properties measured by FWL is complementary to the reflectance characteristics recorded within hyperspectral imagery (HI). The fusion of FWL and HI is, consequently, desirable. Hongzhou’s research aimed to build a framework for fusing FWL and HI while demonstrating the application of combined datasets for land cover classification. Feature extraction methods and classifier designs were proposed after considering characteristics of both datasets, and performance of the proposed methods were evaluated. Experimental results showed that the suggested methods were successful in extracting features from reconstructed FWL data.

Hongzhou has accepted a position at CGG as a Seismic Imager. Dr. Craig Glennie was Hongzhou’s graduate advisor. Congratulations and good luck!