Abstract
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 (~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.
Highlights
LiDAR, hyperspectral and thermal remote sensing are core areas of current and planned NASA remote sensing capability (e.g., Landsat Enhanced Thematic Mapper Plus, ETM+, and OperationalLand Imager, OLI; Earth Observing-1, Hyperion; Earth Observing System’s Advanced SpaceborneThermal Emission and Reflection Radiometer, ASTER, Multi-angle Imaging SpectroRadiometer, MISR, and Moderate Resolution Imaging Spectroradiometer, MODIS; Suomi National Polar-orbitingPartnership Visible Infrared Imaging Radiometer Suite, NPP VIIRS; Ice, Cloud, and Land ElevationSatellite-2 Advanced Topographic Laser Altimeter System, ICESat-2; and Hyperspectral InfraredImager, HyspIRI), and fusion of complementary data from different sensors offers the potential for improved global remote sensing of terrestrial ecosystems
Level 0 (L0) data products include unprocessed instrument data; Level 1 (L1) products are time-referenced data that have been processed to at-sensor radiometric units; Level 2 (L2) products are geophysical variables derived from L1 products; and Level 3 (L3) products are geophysical variables mapped on a space-time grid scale
G-LiHT data will enhance our ability to validate data from existing satellite missions, design new missions and produce data products related to biodiversity and climate change
Summary
LiDAR, hyperspectral and thermal remote sensing are core areas of current and planned NASA remote sensing capability HyspIRI), and fusion of complementary data from different sensors offers the potential for improved global remote sensing of terrestrial ecosystems. Airborne platforms are more flexible than satellite missions for developing and testing data fusion at fine spatial (1 to 10 m) and spectral resolutions, which have prompted a new generation of LiDAR and imaging spectrometer instrument packages [2,3,4]. FOV, field of view; NETD, Noise Equivalent Temperature Difference The goal of this multi-sensor and data fusion effort is to characterize ecosystem form and function using remote sensing data, with a particular emphasis on the data products needed to develop a new generation of high resolution ecosystem and radiative transfer models. Provide new insight into photosynthetic functionality and vegetation productivity, including new, spatially-explicit remote sensing indicators of key dynamic biological processes; characterize fine-scale spatial and temporal heterogeneity in ecosystem structure and function under diverse environmental and climate conditions; and create new methods for data fusion to monitor ecosystem health and the effects of climate and human-induced changes on these ecosystems
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