Früher|Nächster

Observation and analysis of bidirectional and hotspot reflectance of conifer forest canopies with a multiangle hyperspectral UAV imaging platform

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China

  • Email：qiufeng2018@nju.edu.cn
  • Introduction：邱凤，1988年生，女，助理研究员，研究方向为植被定量遥感。E-mail: qiufeng2018@nju.edu.cn
QIU Feng12,  
  • Affiliation：

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

HUO Jingwen23,  
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China

ZHANG Qian12,  
  • Affiliation：

    Sichuan Dualix Spectral Image Technology Co. Ltd, Chengdu 610016, China

CHEN Xinghai4,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China

  • Email：yongguang_zhang@nju.edu.cn
  • Introduction：张永光，1980年生，男，教授，研究方向为植被定量遥感和全球碳循环。E-mail: yongguang_zhang@nju.edu.cn
ZHANG Yongguang12*

Resümee

Multiangle remote sensing observation is critical for the study of the Bidirectional Reflectance Distribution Function (BRDF) of vegetation canopies. However, sampling the bidirectional reflectance of forest canopies at a small fixed angular interval over a certain observation plane is still difficult at present, and capturing hotspot and dark spot images is especially challenging. The objectives of this study are (1) to obtain bidirectional hyperspectral reflectance images, including hotspot and dark spot, at the principal plane for two conifer forest canopies, (2) to analyze the observed canopy BRDF characteristics, and (3) to compare and validate the observation with four-scale geometric optical model simulation.The two coniferous forest sites are located in the Saihanba Mechanical Forest Farm in Hebei Province in northern China. A multiangle hyperspectral observation method was developed on the basis of an Unmanned Aerial Vehicle (UAV) imaging platform. A hyperspectral imaging sensor was mounted on the multirotor UAV with a rotatable gimbal. The View Zenith Angle (VZA) of the hyperspectral sensor was controlled by adjusting the pitch angle of the gimbal at each observation point over the flight of the UAV. The VZAs ranged from 60° backward to 60° forward with an interval of 10°, along with the hotspot and dark spot angles. The observed UAV images were processed from digital numbers to reflectance by using a standard gray panel with known reflectance placed in the observation area. The UAV images were then resampled to 60 m to examine the reflectance at the canopy scale. Canopy reflectance was also simulated with the four-scale model and compared with the observation.Bidirectional reflectance, including hotspot and dark spot, images of conifer forest canopies were effectively observed using the developed multiangle UAV observation method. This method has the advantage of making hyperspectral and multiangular observations at the same time. The canopy reflectance values are higher at backward observation and highest at the hotspot direction compared with those at forward directions. The observed BRDF shapes differ at the two study sites with different canopy structure and leaf optical properties. The bowl effect with increasing reflectance with VZA is observed when the VZAs are larger than 40°. The results are as follows: (1) The BRDF shapes and canopy reflectance simulated using a four-scale model are consistent with the observation, except for a minimal underestimation at the hotspot in the red spectral bands and some deviation in the near infrared (NIR) bands at large VZAs in the forward direction; (2) The BRDF shapes differ in forests with different canopy structure and leaf optical properties; (3) The observed bidirectional reflectance of the two coniferous forests demonstrates distinct spectral variability of BRDF effects. The reflectance anisotropy is highest in the red bands and lowest in the NIR bands; and (4) Anisotropy of vegetation indices, including normalized difference vegetation index, photochemical reflectance index, MERIS terrestrial chlorophyll index, and enhanced vegetation index, is observed due to the spectral variability of BRDF effects.The multiangle UAV platform is able to capture bidirectional reflectance images effectively, especially in the hotspot and dark spot directions. Compared with satellite-based, aerial, and tower- or ground-based multiangle observation methods, the UAV platform is more effective and flexible with a much lower labor and financial cost. However, the application of this UAV platform at a large spatial scale is limited due to the small coverage of UAV images compared with satellite images. The multiangle UAV platform has great potential in the research of bidirectional characteristics of various targets.

Schlüsselwort

anisotropy;BRDF;multi-angle remote sensing;hotspot;Unmanned Aerial Vehicle (UAV);hyperspectral remote sensing;conifer forest

References

  1. 1.
    Chen J M and Leblanc S G. 1997b. A four-scale bidirectional reflectance model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1316-1337
  2. 2.
    Chen J M and Leblanc S G. 2001. Multiple-scattering scheme useful for geometric optical modeling. IEEE Transactions on Geoscience and Remote Sensing, 39(5): 1061-1071
  3. 3.
    Chen J M, Liu J, Leblanc S G, Lacaze R and Roujean J L. 2003. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sensing of Environment, 84(4): 516-525
  4. 4.
    Chen J M, Menges C H and Leblanc S G. 2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environment, 97(4): 447-457
  5. 5.
    Chopping M, Moisen G G, Su L H, Laliberte A, Rango A, Martonchik J V and Peters D P C. 2008. Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer. Remote Sensing of Environment, 112(5): 2051-2063
  6. 6.
    Croft H, Chen J M, Wang R, Mo G, Luo S, Luo X, He L, Gonsamo A, Arabian J, Zhang Y, Simic-Milas A, Noland T L, He Y, Homolová L, Malenovský Z, Yi Q, Beringer J, Amiri R, Hutley L, Arellano P, Stahl C and Bonal D. 2020. The global distribution of leaf chlorophyll content. Remote Sensing of Environment, 236: 111479
  7. 7.
    De Coca F C, Gilabert M A and Meliá J. 2001. Bidirectional reflectance factor analysis from field radiometry and HyMap data. The Digital Airborne Spectrometer Experiment (DAISEX), 499: 163-175
  8. 8.
    Dong Y D, Jiao Z T, Cui L, Zhang H, Zhang X N, Yin S Y, Ding A X, Chang Y X, Xie R and Guo J. 2019. Assessment of the hotspot effect for the PROSAIL model with POLDER hotspot observations based on the hotspot-enhanced kernel-driven BRDF model. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 8048-8064
  9. 9.
    Gamon J A, Peñuelas J and Field C B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1): 35-44
  10. 10.
    Gatebe C K and King M D. 2016. Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications. Remote Sensing of Environment, 179: 131-148
  11. 11.
    Hall F G, Hilker T, Coops N C, Lyapustin A, Huemmrich K F, Middleton E, Margolis H, Drolet G and Black T A. 2008. Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction. Remote Sensing of Environment, 112(7): 3201-3211
  12. 12.
    Hilker T, Coops N C, Hall F G, Black T A, Wulder M A, Nesic Z and Krishnan P. 2008. Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment, 112(6): 2777-2788
  13. 13.
    Huang J X, Li X and Qian J Y, 1996. Flora Saihanbaensis. Beijing, China: Science and Technology of China Press
  14. 14.
    Jay S, Maupas F, Bendoula R and Gorretta N. 2017. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210: 33-46
  15. 15.
    Jiao Z T, Schaaf C B, Dong Y D, Román M, Hill M J, Chen J M, Wang Z S, Zhang H, Saenz E, Poudyal R, Gatebe C, Bréon F M, Li X W and Strahler A. 2016. A method for improving hotspot directional signatures in BRDF models used for MODIS. Remote Sensing of Environment, 186: 135-151
  16. 16.
    Jiao Z T, Wang J D, Xie L O, Zhang H, Yan G J, He L M and Li X W. 2005. Initial validation of MODIS albedo product by using field measurements and airborne multiangular remote sensing observations. Journal of Remote Sensing, 9(1): 64-72
  17. 17.
    Kennedy B E. 2018. Multi-Angle Spectroscopic Remote Sensing of Arctic Vegetation Biochemical and Biophysical Properties. Ottawa: Carleton University
  18. 18.
    Li X W, Strahler A, Zhu Q J, Liu Y, Liu G C, Zhang R H, Yu X P and Li J Q. 1991. Geometric-optical bidirectional reflectance modelling of ground objects and its progress in measurement. Remote Sensing for Land and Resources, 3(1): 9-19
  19. 19.
    Liang S L, Fang H L, Chen M Z, Shuey C J, Walthall C, Daughtry C, Morisette J, Schaaf C and Strahler A. 2002. Validating MODIS land surface reflectance and albedo products: methods and preliminary results. Remote Sensing of Environment, 83(1/2): 149-162
  20. 20.
    Liang S L, Strahler A H, Barnsley M J, Borel C C, Gerstl S A W, Diner D J, Prata A J and Walthall C L. 2000. Multiangle remote sensing: past, present and future. Remote Sensing Reviews, 18(2/4): 83-102
  21. 21.
    Liao Q H, Zhang D Y, Wang J H, Yang G J, Yang H, Craig C, Wong Z J and Wang D C. 2014. Assessment of chlorophyll content using a new vegetation index based on multi-angular hyperspectral image data. Spectroscopy and Spectral Analysis, 34(6): 1599-1604
  22. 22.
    Lucht W, Schaaf C B and Strahler A H. 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 977-998
  23. 23.
    Maignan F, Bréon F M and Lacaze R. 2004. Bidirectional reflectance of Earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot. Remote Sensing of Environment, 90(2): 210-220
  24. 24.
    Milton E J, Schaepman M E, Anderson K, Kneubühler M and Fox N. 2009. Progress in field spectroscopy. Remote Sensing of Environment, 113(S1): S92-S109
  25. 25.
    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
  26. 26.
    Pisek J and Chen J M. 2009. Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data. Remote Sensing of Environment, 113(11): 2412-2423
  27. 27.
    Qin W H and Xiang Y Q. 1996. Influence of vegetation structure and sun/view geometry on NDVI. Remote sensing of environment China, 11(4): 285-290
  28. 28.
    Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data. Journal of Geophysical Research: Atmospheres, 97(D18): 20455-20468
  29. 29.
    Russell C A, Irons J R and Dabney P W. 1997. Bidirectional reflectance of selected BOREAS sites from multiangle airborne data. Journal of Geophysical Research: Atmospheres, 102(D24): 29505-29516
  30. 30.
    Sandmeier S and Deering D W. 1999. Structure analysis and classification of boreal forests using airborne hyperspectral BRDF data from ASAS. Remote Sensing of Environment, 69(3): 281-295
  31. 31.
    Sandmeier S, Müller C, Hosgood B and Andreoli G. 1998. Physical mechanisms in hyperspectral BRDF data of grass and watercress. Remote Sensing of Environment, 66(2): 222-233
  32. 32.
    Su L H, Chopping M J, Rango A, Martonchik J V and Peters D P C. 2007. Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery. Remote Sensing of Environment, 107(1/2): 299-311
  33. 33.
    Wang L X, Xiao P F and Feng X Z. 2012. Retrieving snow information in typical forest zone of Tianshan mountains from multi-angle imaging spetroradiometer data. Journal of Remote Sensing, 16(5): 1035-1053
  34. 34.
    Wang Q, Pang Y, Li Z Y, Sun G Q, Chen E X and Ni-Meister W. 2016. The potential of forest biomass inversion based on vegetation indices using multi-angle CHRIS/PROBA data. Remote Sensing, 8(11): 891
  35. 35.
    Wang S, Garcia M, Bauer-Gottwein P, Jakobsen J, Zarco-Tejada P J, Bandini F, Paz V S and Ibrom A. 2019. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System. Remote Sensing of Environment, 229: 14-31
  36. 36.
    Wanner W, Li X and Strahler A H. 1995. On the derivation of kernels for kernel-driven models of bidirectional reflectance. Journal of Geophysical Research: Atmospheres, 100(D10): 21077-21089
  37. 37.
    Wei S S and Fang H L. 2016. Estimation of canopy clumping index from MISR and MODIS sensors using the normalized difference hotspot and darkspot (NDHD) method: the influence of BRDF models and solar zenith angle. Remote Sensing of Environment, 187: 476-491
  38. 38.
    Yan G J， Zhao T J, Mu X H, Wen J G, Pang Y, Jia L, Zhang Y G, Chen D Q, Yao C B, Cao Z Y, Lei Y H, Ji D B, Chen L F, Liu Q H, Lyu L Q, Chen J M and Shi J C. 2021. Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Luan River Basin. National Remote Sensing Bulletin， 25(4): 856-870
  39. 39.
    Zhang Y Q, Chen J M, Miller J R and Noland T L. 2008. Retrieving chlorophyll content in conifer needles from hyperspectral measurements. Canadian Journal of Remote Sensing, 34(3): 296-310
  40. 40.
    Zhu G L, Ju W M, Chen J M, Liu Y B, Zhu J F and Xing B L. 2011. Validation of MODIS BRDF model parameters product and the Ross-Li model with POLDER data. Journal of Remote Sensing, 15(5): 875-894

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website