Remote Sensing (May 2016)

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

  • Yong Pang,
  • Zengyuan Li,
  • Hongbo Ju,
  • Hao Lu,
  • Wen Jia,
  • Lin Si,
  • Ying Guo,
  • Qingwang Liu,
  • Shiming Li,
  • Luxia Liu,
  • Binbin Xie,
  • Bingxiang Tan,
  • Yuanyong Dian

DOI
https://doi.org/10.3390/rs8050398
Journal volume & issue
Vol. 8, no. 5
p. 398

Abstract

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We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5–7 pixels for large off-nadir observations of 55° with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy’s sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy’s data characteristics, current coverage, and potential vegetation applications.

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