DOI: 10.6084/m9.figshare.2007723.v2
title: Fusion of LiDAR and Hyperspectral Data

description: The dataset is captured over <i>Samford Ecological Research Facility</i> (SERF), which is located within the Samford valley in south east Queensland, Australia. The central point of the dataset is located at coordinates: 27.38572<sup>o</sup>S, 152.877098<sup>o</sup>E. The <i>Vegetation Management Act 1999</i> protects the vegetation on this property as it provides a refuge to native flora and fauna that are under increasing pressure caused by urbanization.The hyperspectral image was acquired by the <i>SPECIM AsiaEAGLE II</i> sensor on the second of February, 2013. This sensor captures 252 spectral channels ranging from 400.7<i>nm</i> to 999.2<i>nm</i>. The last five channels, i.e., channels 248 to 252, are corrupted and can be excluded. The spatial resolution of the hyperspectral data was set to 1<i>m</i>.The airborne light detection and ranging (LiDAR) data were captured by the <i>ALTM Leica ALS50-II</i> sensor in 2009 composing of a total of 3716157 points in the study area: 2133050 for the first return points, 1213712 for the second return points, 345.736 for the third return points, and 23659 for the fourth return points.The average flight height was 1700 meters and the average point density is two points per square meter. The laser pulse wavelength is 1064<i>nm</i> with a repetition rate of 126 <i>kHz</i>, an average sample spacing of 0.8<i>m</i> and a footprint of 0.34<i>m</i>. The data were collected up to four returns per pulse and the intensity records were supplied on all pulse returns.The nominal vertical accuracy was ±0.15<i>m</i> at 1 sigma and the measured vertical accuracy was ±0.05<i>m</i> at 1 sigma. These values have been determined from check points contrived on an open clear ground. The measured horizontal accuracy was ± 0.31<i>m</i> at 1 sigma.The obtained ground LiDAR returns were interpolated and rasterized into a 1<i>m</i>×1<i>m</i> digital elevation model (DEM) provided by the LiDAR contractor, which was produced from the LiDAR ground points and interpolated coastal boundaries.The first returns of the airborne LiDAR sensor were utilized to produce the normalized digital surface model (nDSM) at 1<i>m</i> spatial resolution using <i>Las2dem</i>.The 1<i>m</i> spatial resolution intensity image was also produced using <i>Las2dem</i>. This software interpolated the points using triangulated irregular networks (TIN). Then, the TINs were rasterized into the nDSM and the intensity image with a pixel size of 1<i>m</i>. The intensity image with 1<i>m</i> spatial resolution was also produced using <i>Las2dem</i>.The LiDAR data were classified into ``ground" and ``non-ground" by the data contractor using algorithms tailored especially for the project area. For the areas covered by dense vegetation, less laser pulse reaches the ground. Consequently, fewer ground points were available for DEM and nDSM surfaces interpolation in those areas. Therefore, the DEM and the nDSM tend to be less accurate in these areas.In order to use the datasets, please fulfill the following three requirements: 1) Giving an acknowledgement as follows: The authors gratefully acknowledge <i>TERN AusCover</i> and Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, QLD for providing the hyperspectral and LiDAR data, respectively. Airborne lidar are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+LidarAirborne hyperspectral are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+Hyperspectral 2) Using the following license for LiDAR and hyperspectral data: http://creativecommons.org/licenses/by/3.0/3) This dataset was made public by Prof. Stuart Phinn from the University of Queensland and Dr. Pedram Ghamisi from German Aerospace Center (DLR). Please cite: In WORD:Pedram Ghamisi and Stuart Phinn, Fusion of LiDAR and Hyperspectral Data, December 2015, 10.6084/m9.figshare.2007723.v2In LaTex:@article{Ghamisi2015,author = "Pedram Ghamisi and Stuart Phinn",title = "{Fusion of LiDAR and Hyperspectral Data}",year = "2015",month = "12",url = "10.6084/m9.figshare.2007723.v2", }