基于计算机模拟模型LESS的落叶松林木质元素影响分析
Influence of woody elements on nadir reflectance of forest canopy based on simulations by using the LESS model
- 2021年25卷第5期 页码:1138-1151
纸质出版日期: 2021-05-07
DOI: 10.11834/jrs.20210100
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纸质出版日期: 2021-05-07 ,
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许月,谢东辉,漆建波,阎广建,穆西晗,张吴明.2021.基于计算机模拟模型LESS的落叶松林木质元素影响分析.遥感学报,25(5): 1138-1151
Xu Y,Xie D H,Qi J B,Yan G J,Mu X H and Zhang W M. 2021. Influence of woody elements on nadir reflectance of forest canopy based on simulations by using the LESS model. National Remote Sensing Bulletin, 25(5):1138-1151
目前大部分植被辐射传输模型在模拟太阳辐射与植被之间的相互作用过程时,将植物结构进行了简化,只保留了叶片的结构和空间分布特征,而忽略了木质元素(枝干等)对冠层反射特性的影响。计算机模拟模型LESS能够充分考虑植被的多种组分光谱和结构特征,精确模拟植被冠层内部的光散射和辐射过程。本文以地面实测数据为基础,发展了以单木为基本单元的复杂森林三维场景重建基本流程;并在重建森林场景基础上,利用计算机模拟模型LESS模拟森林三维场景反射率,对比分析了森林木质元素对冠层反射率的影响。结果表明,忽略木质元素会引起植被冠层反射率模拟的偏差,特别是在近红外波段,在不同叶面积指数(LAI)下,其相对偏差都在40%以上。高空间分辨率是一个会突出木质元素影响的重要因素,随着空间分辨率提高,偏差也随之增大。不同等级枝干结构均对冠层反射率产生影响,即使忽略最小分枝也会引起17.7%(近红外)的估计误差。因此,在进行定量遥感研究中,必须考虑到忽略木质元素引起的偏差,即使是高LAI森林也无法忽视木质元素的影响。特别是在高分辨率遥感图像中,传统的以统计特征代替三维结构分布的辐射传输模型已经无法满足精度的要求。
At present
Most vegetation radiative transfer models were developed on the basis of a simplified canopy structure when simulating the interaction between solar radiation and vegetation. They retain the structure and spatial distribution characteristics of leaves but ignore the influences of wood elements (such as branches) on the reflection characteristics of a canopy. LESS
as one of the computer simulation models
can fully consider the spectral and structural characteristics of various components (leaves and branches) of vegetation and accurately simulate the process of light scattering and radiation in the canopy. Thus
it can be applied to analyze the effects of wood elements on the reflectance of a forest canopy on the basis of a reconstructed realistic three dimensional (3D) forest scene.
On the basis of field data
we developed a basic framework to reconstruct a 3D scene of a complex forest with single tree as basic unit. Diameter at Breast Height (DBH) was selected as the main variable to divide trees into six levels (T1—T6). The mean DBH
mean tree height
mean crown width
and mean height of branches at level were used as typical parameters to build a tree model by using OnyxTREE. When a near-real 3D forest scene was constructed
the appropriate model in the constructed single-tree library was selected with the DBH level as the standard. The computer simulation model LESS was used to simulate the reflectance of 3D scenes of forests with and without wood elements. The effects of forest wood elements on canopy reflectance were analyzed quantitatively.
Ignoring the wood elements will lead to the deviation of vegetation canopy reflectance
especially in the NIR band. The relative deviation of reflectance in the NIR band is more than 40% for all scenes with different LAIs. High spatial resolution is another important factor highlighting the influences of wood elements. As the spatial resolution increases
the deviation increases. Different grades of woody structure affect canopy reflectance; even ignoring a twig will cause an estimation error of 17.7% (NIR band). The use of wooden area instead of leaf area can partially alleviate the difference in canopy reflectance caused by completely ignoring wooden elements
but it still leads to overestimation (NIR) or underestimation (visible light) of canopy reflectance.
The vegetation radiative transfer models that use statistical features to replace 3D structure distribution can no longer meet the accuracy requirements of quantitative remote sensing. Hence
the deviation caused by ignoring wood elements should be considered
specially for high-resolution remote sensing images.
辐射传输模型森林反射率木质元素三维重建LESS
radiative transfer modelforestreflectancewood elements3D reconstructionLESS (LargE-Scale remote sensing data and image Simulation framework)
Chen J M and Leblanc S G. 1997. 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]
Daily G, Postel S, Bawa K and Kaufman L. 1997. Nature’s Services: Societal Dependence on Natural Ecosystems. Washington DC: Island Press.
Dawson T P, Curran P J and Plummer S E. 1998. LIBERTY—modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment, 65(1): 50-60 [DOI: 10.1016/s0034-4257(98)00007-8http://dx.doi.org/10.1016/s0034-4257(98)00007-8]
Fearnside P M. 1997. Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management, 90(1): 59-87 [DOI: 10.1016/s0378-1127(96)03840-6http://dx.doi.org/10.1016/s0378-1127(96)03840-6]
Huang H G, Qin W H and Liu Q H. 2013. RAPID: a Radiosity Applicable to Porous IndiviDual Objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132: 221-237 [DOI: 10.1016/j.rse.2013.01.013http://dx.doi.org/10.1016/j.rse.2013.01.013]
Lau A, Bentley L P, Martius C, Shenkin A, Bartholomeus H, Raumonen P, Malhi Y, Jackson T and Herold M. 2018. Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling. Trees, 32(5): 1219-1231 [DOI: 10.1007/s00468-018-1704-1http://dx.doi.org/10.1007/s00468-018-1704-1]
Li X W and Strahler AH. 1992. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Transactions on Geoscience and Remote Sensing, 30(2): 276-292 [DOI: 10.1109/36.134078http://dx.doi.org/10.1109/36.134078]
Li X W and Wang J D. 1995. Plant Photon Remote Sensing Model and Parameterizing of Plant Structure. Beijing: Science Press (李小文. 王锦地. 1995. 植被光学遥感模型与植被结构参数化. 北京: 科学出版社)
Liang S L, Bai R, Chen X N, Cheng J, Fan W J, He T, Jia K, Jiang B, Jiang L M, Jiao Z T, Liu Y B, Ni W J, Qiu F, Song L L, Sun L, Tang B H, Wen J G, Wu G P, Xie D H, Yao Y J, Yuan W P, Zhang Y G, Zhang Y Z, Zhang Y Z, Zhang Y T, Zhang X T, Zhao T J and Zhao X. 2020. Review of China’s land surface quantitative remote sensing development in 2019. Journal of Remote Sensing, 24(6): 618-671
梁顺林, 白瑞, 陈晓娜, 程洁, 范闻捷, 何涛, 贾坤, 江波, 蒋玲梅, 焦子锑, 刘元波, 倪文俭, 邱凤, 宋柳霖, 孙林, 唐伯惠, 闻建光, 吴桂平, 谢东辉, 姚云军, 袁文平, 张永光, 张玉珍, 张云腾, 张晓通, 赵天杰, 赵祥. 2020. 2019年中国陆表定量遥感发展综述. 遥感学报, 24(6): 618-671 [DOI: 10.11834/jrs.20209476http://dx.doi.org/10.11834/jrs.20209476]
Liu Q H, Cao B, Zeng Y L, Li J, Du Y M, Wen J G, Fan W L, Zhao J and Yang L. 2016. Recent progresses on the remote sensing radiative transfer modeling over heterogeneous vegetation canopy. Journal of Remote Sensing, 20(5): 933-945
柳钦火, 曹彪, 曾也鲁, 李静, 杜永明, 闻建光, 范渭亮, 赵静, 杨乐. 2016. 植被遥感辐射传输建模中的异质性研究进展. 遥感学报, 20(5): 933-945[DOI: 10.11834/jrs.20166280http://dx.doi.org/10.11834/jrs.20166280]
Malenovský Z, Martin E, Homolová L, Gastellu-Etchegorry J P, Zurita-Milla R, Schaepman M E, Pokorný R, Clevers J G P W and Cudlín P. 2008. Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution. Remote Sensing of Environment, 112(1): 1-18 [DOI: 10.1016/j.rse.2006.02.028http://dx.doi.org/10.1016/j.rse.2006.02.028]
Myneni R B, Ramakrishna R, Nemani R and Running S W. 1997. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Transactions on Geoscience and Remote Sensing, 35(6): 1380-1393 [DOI: 10.1109/36.649788http://dx.doi.org/10.1109/36.649788]
Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221: 695-706 [DOI: 10.1016/j.rse.2018.11.036http://dx.doi.org/10.1016/j.rse.2018.11.036]
Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16(2): 125-141 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]
Verrelst J, Schaepman M E, Malenovský Z and Clevers J G. 2010. Effects of woody elements on simulated canopy reflectance: implications for forest chlorophyll content retrieval. Remote Sensing of Environment, 114(3): 647-656 [DOI: 10.1016/j.rse.2009.11.004http://dx.doi.org/10.1016/j.rse.2009.11.004]
Wang C K. 2006. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 222(1/3): 9-16 [DOI: 10.1016/j.foreco.2005.10.074http://dx.doi.org/10.1016/j.foreco.2005.10.074]
Wang L, Luo Y Q, Huang H G, Shi J, Keliövaara K, Teng W X and Qi G X. 2009. Reflectance features of water stressed Larix gmelinii needles. Forestry Studies in China, 11(1): 28-33 [DOI: 10.1007/s11632-009-0012-7http://dx.doi.org/10.1007/s11632-009-0012-7]
Widlowski J L, Côté J F and Béland M. 2014. Abstract tree crowns in 3D radiative transfer models: impact on simulated open-canopy reflectances. Remote Sensing of Environment, 142: 155-175 [DOI: 10.1016/j.rse.2013.11.016http://dx.doi.org/10.1016/j.rse.2013.11.016]
Zhao F, Gu X F, Liu Q, Yu T, Chen L F and Gao H L. 2006. Modeling of 3D Canopy’s radiation transfer in the VNIR and TIR domains. Journal of Remote Sensing, 10(5): 670-675
赵峰, 顾行发, 刘强, 余涛, 陈良富, 高海亮. 2006. 基于3D真实植被场景的全波段辐射传输模型研究. 遥感学报, 10(5): 670-675 [DOI: 10.11834/jrs.20060599http://dx.doi.org/10.11834/jrs.20060599]
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