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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

Resümee

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.

Schlüsselwort

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

References

  1. 1.
    Airbus, 2015. 产品手册. 2015) [2020-11-04]
  2. 2.
    Asner G P, Knapp D E, Kennedy-Bowdoin T, Matthew O, Jones, Martin R E, Boardman J and Field C B. 2007. Carnegie Airborne Observatory:in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems. Journal of Applied Remote Sensing, 1(013536): 1-21
  3. 3.
    Asner G P, Knapp D E, Boardman J, Green RO, Kennedy-Bowdoin T, Eastwood M, Martin R E, Anderson C and Field C B. 2012. Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion. Remote Sensing of Environment, 124(2012): 454-465
  4. 4.
    ASPRS. 2013. LAS Specification version 1.4-R13. USA: ASPRS: https://www.asprs.org/wp-content/uploads/2010/12/LAS_1_4_r13.pdf [2020-11-04]
  5. 5.
    Cao L, Dai J S, Xu J X, Xu Z Q and She G H. 2014. Optimized extraction of forest parameters in subtropical forests based on airborne small footprint LiDAR technology. Journal of Beijing forestry university, 36(5): 13-21
  6. 6.
    Cao L, Coops N C, Innes J L, Stephen R J, Fu L Y, Ruan H H and She G H. 2016. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data. Remote Sensing of Environment, 178(2016): 158-171
  7. 7.
    Cook B, Corp L A, Nelson R F, Middleton E M, Morton D C, McCorkel J T, Ranson K J, Ly V and Mountesano P M. 2013. NASA Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) airborne imager. Remote Sensing, 5(8): 4045-4066
  8. 8.
    Dian Y Y, Pang Y, Dong Y F and Li Z Y. 2016. Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral Data. Journal of the Indian Society of Remote Sensing, 44(4): 595-603.
  9. 9.
    Dong Y F, Pang Y, Xu L N and Chen L Z. 2014. Feature extraction based on fusion of hyperspectral image and airborne LiDAR data. Remote Sensing Information, 29(6): 73-76+83
  10. 10.
    Du Y K, Wang J, Liu Z J, Yu H Y, Li Z H and Cheng H. 2018. Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1): 200-209
  11. 11.
    Du Y K, Wang J, Lin Z J, Yu H Y, Li Z H and Cheng H. 2018. Estimating the Aboveground Biomass of Phragmites Australis (Common Reed) Based on Multi-Source Data. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE: 9241-9244
  12. 12.
    Hong Y F, Zhang S G, Chen W, Chen D S, Xiang W B and Pang Y. 2019. Inversion of biomass components for Larix olgensis plantation using airborne LiDAR. Forest Research, 32(5): 83-90
  13. 13.
    Krause K S, Kuester M A, Johnson B R, McCorkel J and Kampe T U. 2011. Early algorithm development efforts for the National Ecological Observatory Network Airborne Observation Platform imaging spectrometer and waveform LiDAR instruments//Imaging Spectrometry XVI. International Society for Optics and Photonics, 8158: 81580D
  14. 14.
    Jia W, Pang Y, Yue C R and Li Z Y. 2015. Mountain forest classification based on AISA Eagle II hyperspectral data. Forest Inventory and Planning, 40(1): 9-14
  15. 15.
    Jia W, Nicholas C C, Tortini R, Pang Y and Black T A. 2018. Remote sensing of variation of light use efficiency in two age classes of Douglas-fir. Remote Sensing of Environment, 219(15): 284-97
  16. 16.
    Jia W, Pang Y, Tortini R, Schläpfer D, Li Z Y and Roujean J L. 2020. A kernel-driven BRDF approach to correct airborne hyperspectral imagery over forested areas with rugged topography. Remote Sensing, 12(3): 432
  17. 17.
    Li D, Gu X F, Pang Y, Chen B W and Liu L X. 2018. Estimation of forest aboveground biomass and leaf area index based on digital aerial photograph data in Northeast China. Forests, 9(5): 275
  18. 18.
    Li J L, Pang Y, Li Z Y and Jia W. 2019. Tree species classification using airborne hyperspectral data in a temperate natural forest. Journal of Northeast Forestry University, 47(5): 72-76
  19. 19.
    Li J L, Pang Y, Li Z Y and Jia W. 2019. Tree species classification by airborne hyperspectral image of forest in cloud shadow area. Forest Research, 32(5): 136-141
  20. 20.
    Li Z Y and Chen E X. 2021. Development course of forestry remote sensing in China. Journal of Remote Sensing， 25（1）：292-301
  21. 21.
    Li Z Y, Liu Q W and Pang Y . 2016. Review on forest parameters in version using LiDAR. Journal of Remote sensing, 2051:1138-1150
  22. 22.
    Liu L X, Pang Y, Li Z Y, Si L and Liao S X. 2017. Combining Airborne and Terrestrial Laser Scanning Technologies to Measure Forest Understory Volume. Forests, 8(4): 111
  23. 23.
    Liu L X, Nicholas C C, Neal W A and Pang Y. 2017. Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sensing of Environment, 200 (2017): 170-182
  24. 24.
    Liu Y J, Pang Y, Liao S X, Jia W, Chen B W and Liu L X. 2016. Merged airborne LiDAR and hyperspectral data for tree species classification in Puer’s mountainous area. Forest Research, 29(3): 407-412
  25. 25.
    Lu H, Pang Y, Li Z Y and Chen B W. 2015. An Automatic Range Ambiguity Solution in High-Repetition-Rate Airborne Laser Scanner Using Priori Terrain Prediction. IEEE Geoscience and Remote Sensing Letters. 12(11): 1-5
  26. 26.
    Lu H，Pang Y，Li Z Y，Wang D，Chen B W and Ma Z Y. 2020. Uncertainty analysis of the absolute radiometric calibration of full waveform airborne LiDAR. Journal of Remote Sensing，24(11)：1353-1362
  27. 27.
    Ma Z Y, Pang Y, Wang D, Liang X J, Chen B W, Lu H, Weinacker H and Koch B. 2020. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sensing, 12: 1078
  28. 28.
    Ma Z Y, Chen B W, Pang Y, Liao S X, Qin X L and Zhang H Q. 2020. Forest Fire potential forecast based on FCCS model. Remote Sensing for Land and Resources, 32(1): 43-50
  29. 29.
    Mu X H, Yan G J, Zhou H M, Pang Y, Qiu F, Zhang Q, Zhang Y G, Xie D H, Zhou Y J, Zhao T J, Zhong B, Song J L, Sun R, Jiang L M, Yin S Y, Li F, Jiao Z T, Qu Y H, Zhang W M, Cheng S and Cui T X. 2021. Airborne comprehensive remote sensing experiment of forest and grass resources in Xiaoluan River Basin. National Remote Sensing Bulletin， 25(4): 888-903
  30. 30.
    Pang Y，Jia W，Qin X L，Si L，Liang X J，Lin X and Li Z Y. 2020. Forest fire monitoring using airborne optical full spectrum remote sensing data. Journal of Remote Sensing， 24（10）：1280-1292
  31. 31.
    Pang Y, Li Z Y, Ju H B, Lu H, Jia W, Si L, Guo Y, Liu Q W, Li S M, Liu L X, Xie B B, Tan B X and Dian Y Y. 2016. LiCHy: The CAF’s LiDAR, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8(5): 398
  32. 32.
    Pang Y, Li Z Y, Meng S L, Lu H, Jia W, Liu Q W, Li H K and Lei Y C. 2016. China typical forest aboveground biomass estimation by fusion of multi-platform data. Geoscience and Remote Sensing Symposium (IGARSS), IEEE 2016: 3557-3560
  33. 33.
    Pang Y, Li Z Y, Jia W, Lu H, Chen B W, Xia Y J, Zheng G, Gao X L and Wang Q. 2016. An airborne multi-angle hyperspectral experiment in a boreal forest of Northeast China. Geoscience and Remote Sensing Symposium (IGARSS), IEEE: 1217-1220
  34. 34.
    Pang Y and Li Z Y. 2016. Subtropical forest biomass estimation using airborne LiDAR and hyperspectral data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic, XLI-B8, 747-749
  35. 35.
    Salas E A L. 2020. Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review. Geo-Spatial Information Science, 2020: 1-22.
  36. 36.
    Sharma R C, Kajiwara K and Honda Y. 2013. Estimation of forest canopy structural parameters using kernel-driven bi-directional reflectance model based multi-angular vegetation indices. ISPRS journal of photogrammetry and remote sensing. 78(1): 50-57
  37. 37.
    Tong X D. 2016. Development of China high-resolution earth observation system. Journal of Remote Sensing, 20(5): 775-780
  38. 38.
    Wang Z S, Schaaf C B, Lewis P, Knyazikhin Y, Schull M A, Strahler A H, Yao T, Myneni R B, Chopping MJ and Blair B J. 2011. Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data. Remote Sensing of Environment, 115(6): 1595-1601
  39. 39.
    Wang Q, Pang Y, Li Z Y, Sun G Q, Chen E X and Ni M W. 2016. The potential of forest biomass inversion based on vegetation indices using multi-angle CHRIS/PROBA data. Remote Sensing, 8(11): 891
  40. 40.
    Xia Y J, Pang Y, Liu L X, Chen B W, Dong B and Huang Q F. 2019. Forest height growth monitoring of Cunninghamia lanceolata plantation using multi-temporal aerial photography with the support of high accuracy DEM. SCIENTIA SILVAE SINICAE, 55(4): 108-121
  41. 41.
    Xiao P, Xie L R, Deng G Q, Sui L C, Ma H C, Zhang S D, Chu Z W and Chu Z H. 2011. Specifications for data processing of airborne LiDAR. CH/T 8023-2011,
  42. 42.
    Xiao P, Sui L C, Deng G Q, Xie L R, Ma H C, Zhang S D, Chu Z W and Xu Z H. 2011. Specifications for data acquisition of airborne LiDAR. CH/T 8024-2011,
  43. 43.
    Xiong H, Pang Y, Li C G and Dai H B. 2020. Sub-compartment division and interpretation of tree species based on high-resolution aerial photos. Forest Resources Management, 31(1): 143-150
  44. 44.
    Yang H J, Yu P, Che X Y, Yu S T, Fan D D and Huang X R. 2016. Study on Scenic Beauty of Forest Interior Landscape in Saihanba Mechanized Forest Farm. Forest Resources Management, 2016(3), 98-103
  45. 45.
    Zhao D, Pang Y, Liu L J and Li Z Y. 2020. Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China. Forests, 11(3): 303

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