NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

By Bruce Douglas Cook, L. Corp, Ross Nelson, E. Middleton, Douglas C. Morton, J. McCorkel, J. Masek, K. Ranson, V. Ly, P. Montesano. Remote Sensing, 2013

357 citations recorded by the source corpus.

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

Subfields: Ecology, Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/bd4a2a8106847d214ee49ce26872f3a2051b0632

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Citations in the corpus

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, methodology): “Lidar data were collected using a flight-line strip sampling approach with NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager in the summer of 2014 (Cook et al., 2013).”
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows (2018, background): “While most of the UAV sensors still cannot compete with their bigger counterparts carried on airplanes such as the CASI [181], airborne prism experiment (APEX; [182]), HyPlant [183], the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; [184]) or NASA Goddard’s LiDAR, Hyperspectral, and Therma”
Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes (2015, neutral): “New multi-sensor airborne RS systems, such as the Carnegie Airborne Observatory (CAO) [67,68] and the Goddard’s LiDAR, Hyperspectral & Thermal Imager (G-LiHT) [69], are carrying on-board LIDAR and imaging spectrometer instruments simultaneously.”
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, methodology): “Goddard Lidar Hyperspectral Thermal platform (G-LiHT) [41] (Figure 2e,f).”