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纸质出版日期： 2021-04-07 ，
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庞勇，梁晓军，荚文，斯林，阎广建，施建成.2021.塞罕坝林场机载综合遥感实验.遥感学报，25（4）： 904-917
Pang Y，Liang X J，Jia W，Si L，Yan G J and Shi J C. 2021. The comprehensive airborne remote sensing experiment in Saihanba forest farm. National Remote Sensing Bulletin， 25（4）：904-917
本文介绍了“碳循环、水循环和能量平衡遥感综合试验”中“碳循环航空试验”的小滦河流域塞罕坝森林机载综合遥感实验。阐述了森林机载遥感实验的目的、实验设计方案，观测实施过程以及数据的处理和产品生产等方面内容。实验利用中国林业科学研究院机载遥感系统CAF-LiCHy（Chinese Academy of Forestry's LiDAR，CCD and Hyperspectral airborne observation system）于2018-8-31—9-20在河北承德塞罕坝机械林场开展，共飞行10架次，获取了高精度、高空间分辨率的机载数据共1568 GB，POS位置误差2 cm，激光雷达点云密度每平方米4个以上，点云水平与垂直方向差均在0.2 m之内，数字高程模型产品空间分辨率2 m；高光谱数据的光谱分辨率优于10 nm，空间分辨率优于1 m；CCD影像空间分辨率优于0.2 m；3种传感器数据产品间的几何位置偏差在1 m内。本实验为碳水循环及森林资源监测提供了高质量的遥感数据集，体现出主、被动集成观测系统同步信息获取在森林资源监测中的优势。
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/m
2
. 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.
机载遥感激光雷达高光谱CCD森林资源监测塞罕坝林场
airborne remote sensingLiDARhyperspectralCCDforest resources monitoringsaihanba forest farm
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