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Published： 07 April 2021 ，
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邱凤，霍婧雯，张乾，陈兴海，张永光.2021.针叶林冠层热点及多角度无人机遥感观测及特征分析.遥感学报，25（4）： 1013-1024
Qiu F，Huo J W，Zhang Q，Chen X H and Zhang Y G. 2021. Observation and analysis of bidirectional and hotspot reflectance of conifer forest canopies with a multiangle hyperspectral UAV imaging platform. National Remote Sensing Bulletin， 25（4）：1013-1024
多角度遥感观测是研究植被冠层BRDF（Bidirectional Reflectance Distribution Function）特性的重要手段，但目前对森林冠层进行连续间隔采样的多角度遥感观测及数据较少，热点方向的观测尤为缺乏。本研究基于无人机多角度高光谱成像系统，在主平面上对针叶林冠层以等角度连续间隔采样进行多角度观测，获取了主平面上多角度（包括热点和暗点）高光谱影像，并将观测结果与四尺度几何光学模型模拟结果进行对比分析。多角度观测获取的植被冠层反射率呈现出典型的植被方向反射特征，后向大部分角度观测的冠层反射率高于前向，在热点处出现峰值，在暗点附近方向出现最低值，观测天顶角VZA（View Zenith Angle）较大时表现出明显的“碗边效应”。结果表明：（1）观测的针叶林冠层反射率及BRDF特性与四尺度模型模拟基本一致，但红光波段模拟的热点反射率稍低于观测，前向观测VZA较大时模拟与观测结果差异稍大；（2）冠层结构及叶片光学特性的差异会导致观测到的BRDF特征不同；（3）观测的针叶林冠层BRDF呈现明显的光谱效应，不同波段呈现的各向异性特性不同，红光波段各向异性最强，近红外波段最弱；（4）BRDF的光谱效应差异导致观测到的植被指数也表现出各向异性，NDVI（Normalized Difference Vegetation Index）、PRI（Photochemical Reflectance Index）和MTCI（MERIS Terrestrial Chlorophyll Index）在热点方向最低，EVI（Enhanced Vegetation Index）在热点方向最高。本研究中无人机多角度成像观测提供的角度和高光谱信息，尤其是热点和暗点信息，在地物识别及分类、植被冠层结构反演及碳循环关键参数获取等研究方面具有较好的应用前景，在其它地物反射或热辐射等方向性特性研究中也具有较大的潜力。
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.
二向反射特性BRDF多角度遥感热点无人机高光谱遥感针叶林
anisotropyBRDFmulti-angle remote sensinghotspotUnmanned Aerial Vehicle (UAV)hyperspectral remote sensingconifer forest
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 [DOI: 10.1109/36.628798http://dx.doi.org/10.1109/36.628798]
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 [DOI: 10.1109/36.921424http://dx.doi.org/10.1109/36.921424]
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 [DOI: 10.1016/S0034-4257(02)00150-5http://dx.doi.org/10.1016/S0034-4257(02)00150-5]
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 [DOI: 10.1016/j.rse.2005.05.003http://dx.doi.org/10.1016/j.rse.2005.05.003]
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 [DOI: 10.1016/j.rse.2007.07.024http://dx.doi.org/10.1016/j.rse.2007.07.024]
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 [DOI: 10.1016/j.rse.2019.111479http://dx.doi.org/10.1016/j.rse.2019.111479]
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
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 [DOI: 10.1109/TGRS.2019.2917923http://dx.doi.org/10.1109/TGRS.2019.2917923]
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 [DOI: 10.1016/0034-4257(92)90059-Shttp://dx.doi.org/10.1016/0034-4257(92)90059-S]
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 [DOI: 10.1016/j.rse.2016.03.029http://dx.doi.org/10.1016/j.rse.2016.03.029]
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 [DOI: 10.1016/j.rse.2008.03.015http://dx.doi.org/10.1016/j.rse.2008.03.015]
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 [DOI: 10.1016/j.rse.2008.01.011http://dx.doi.org/10.1016/j.rse.2008.01.011]
Huang J X, Li X and Qian J Y, 1996. Flora Saihanbaensis. Beijing, China: Science and Technology of China Press
黄金祥, 李信, 钱进源, 1996. 塞罕坝植物志. 北京: 中国科学技术出版社
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 [DOI: 10.1016/j.fcr.2017.05.005http://dx.doi.org/10.1016/j.fcr.2017.05.005]
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 [DOI: 10.1016/j.rse.2016.08.007http://dx.doi.org/10.1016/j.rse.2016.08.007]
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
焦子锑, 王锦地, 谢里欧, 张颢, 阎广建, 何立明, 李小文. 2005. 地面和机载多角度观测数据的反照率反演及对MODIS反照率产品的初步验证. 遥感学报, 9(1): 64-72 [DOI: 10.3321/j.issn:1007-4619.2005.01.010http://dx.doi.org/10.3321/j.issn:1007-4619.2005.01.010]
Kennedy B E. 2018. Multi-Angle Spectroscopic Remote Sensing of Arctic Vegetation Biochemical and Biophysical Properties. Ottawa: Carleton University
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
李小文, Strahler A, 朱启疆, 刘毅, 刘广成, 张仁华, 虞献平, 李继泉. 1991. 地物二向性反射几何光学模型和观测的进展. 国土资源遥感, 3(1): 9-19 [DOI: 10.6046/gtzyyg.1991.01.02http://dx.doi.org/10.6046/gtzyyg.1991.01.02]
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 [DOI: 10.1016/S0034-4257(02)00092-5http://dx.doi.org/10.1016/S0034-4257(02)00092-5]
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 [DOI: 10.1080/02757250009532386http://dx.doi.org/10.1080/02757250009532386]
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
廖钦洪, 张东彦, 王纪华, 杨贵军, 杨浩, Craig C, Wong Z J, 王大成. 2014. 基于多角度成像数据的新型植被指数构建与叶绿素含量估算. 光谱学与光谱分析, 34(6): 1599-1604) [DOI: 10.3964/j.issn.1000-0593(201406-1599-06]
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 [DOI: 10.1109/36.841980http://dx.doi.org/10.1109/36.841980]
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 [DOI: 10.1016/j.rse.2003.12.006http://dx.doi.org/10.1016/j.rse.2003.12.006]
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 [DOI: 10.1016/j.rse.2007.08.001http://dx.doi.org/10.1016/j.rse.2007.08.001]
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
穆西晗, 阎广建, 周红敏, 庞勇, 邱凤, 张乾, 张永光, 谢东辉, 周盈吉, 赵天杰, 仲波, 宋金玲, 孙睿, 蒋玲梅, 尹思阳, 李凡, 焦子锑, 屈永华, 张吴明, 程顺, 崔同祥. 2021. 小滦河流域复杂地表碳循环遥感综合试验. 遥感学报, 25(4): 888-903 [DOI：10.11834/jrs.20210305http://dx.doi.org/10.11834/jrs.20210305]
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 [DOI: 10.1016/j.rse.2009.07.003http://dx.doi.org/10.1016/j.rse.2009.07.003]
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
覃文汉, 项月琴. 1996. 植被结构及太阳/观测角度对NDVI的影响. 环境遥感, 11(4): 285-290
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 [DOI: 10.1029/92JD01411http://dx.doi.org/10.1029/92JD01411]
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 [DOI: 10.1029/96JD03880http://dx.doi.org/10.1029/96JD03880]
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 [DOI: 10.1016/S0034-4257(99)00032-2http://dx.doi.org/10.1016/S0034-4257(99)00032-2]
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 [DOI: 10.1016/S0034-4257(98)00060-1http://dx.doi.org/10.1016/S0034-4257(98)00060-1]
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 [DOI: 10.1016/j.rse.2006.05.023http://dx.doi.org/10.1016/j.rse.2006.05.023]
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
汪凌霄, 肖鹏峰, 冯学智. 2012. 天山典型林带积雪的多角度遥感识别. 遥感学报, 16(5): 1035-1053 [DOI: 10.11834/jrs.20121102http://dx.doi.org/10.11834/jrs.20121102]
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 [DOI: 10.3390/rs8110891http://dx.doi.org/10.3390/rs8110891]
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 [DOI: 10.1016/j.rse.2019.03.040http://dx.doi.org/10.1016/j.rse.2019.03.040]
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 [DOI: 10.1029/95JD02371http://dx.doi.org/10.1029/95JD02371]
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 [DOI: 10.1016/j.rse.2016.10.039http://dx.doi.org/10.1016/j.rse.2016.10.039]
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
阎广建, 赵天杰, 穆西晗, 闻建光, 庞勇, 贾立, 张永光, 陈德清, 姚崇斌, 曹志宇, 雷永荟, 姬大彬, 陈良富, 柳钦火, 吕利清, 陈镜明, 施建成. 2021. 滦河流域碳、水循环和能量平衡遥感综合试验总体设计. 遥感学报, 25(4): 856-870） [DOI: 10.11834/jrs.20210341http://dx.doi.org/10.11834/jrs.20210341]
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 [DOI: 10.5589/m08-030http://dx.doi.org/10.5589/m08-030]
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
朱高龙, 居为民, 陈镜明, 柳艺博, 朱敬芳, 邢白灵. 2011. 利用POLDER数据验证MODIS BRDF模型参数产品及Ross-Li模型. 遥感学报, 15(5): 875-894 [DOI: 10.11834/jrs.20110247http://dx.doi.org/10.11834/jrs.20110247]
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