سابِق|تال

The comprehensive airborne remote sensing experiment in Saihanba forest farm

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：pangy@ifrit.ac.cn
  • Introduction：庞勇，1976年生，男，研究员，研究方向为林业遥感机理模型、激光雷达信号处理及林业应用、森林碳汇计量、森林变化监测。E-mail: pangy@ifrit.ac.cn
PANG Yong12,  
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

LIANG Xiaojun12,  
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

JIA Wen12,  
  • Affiliation：

    Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China

    Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

SI Lin12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

YAN Guangjian3,  
  • Affiliation：

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

SHI Jiancheng4

ملخص

This paper introduces the “Saihanba comprehensive airborne remote sensing experiment of forest resources” (The carbon cycle airborne experiment), which is part of “The comprehensive experiment of carbon cycle, water cycle and energy balance”. This paper described the purpose, design scheme, flight mission execution, data processing and products of airborne remote sensing experiment of forest resources, respectively. The experiment focused on Saihanba Forest Farm, launched out with the monitoring of forest resources and energy balance of carbon-water cycle. 10 flights were flown from August 31 to September 20, 2018 using the Chinese Academy of Forestry’s LiDAR，CCD and Hyperspectral airborne observation system (CAF-LiCHy). The raw data volume was about 1568 GB. High-level remote sensing products were produced after further data processing. The POS position has a difference within 2 cm in both horizontal and vertical directions. The LiDAR point density is larger than 4 pts/m2. The horizontal and vertical differences of LiDAR point cloud data are within 0.2 m. The spatial resolution of digital elevation model product is 2 m. The spectral resolution of hyperspectral image is better than 10 nm with the spatial resolution of 1 m. The spatial resolution of CCD image is 0.2 m. The overall geolocation accuracy is about 1 m among these three type sensors. This study provided high-quality datasets for carbon-water cycle and forest resources monitoring, reflected the advantages of active and passive integrated observation system in collecting forest resources simultaneously.

مفهوم

airborne remote sensing;lidar;hyperspectral;CCD;forest resources monitoring;saihanba forest farm

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