سابِق|تال

Influence of woody elements on nadir reflectance of forest canopy based on simulations by using the LESS model

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：201821051067@mail.bnu.edu.cn
  • Introduction：许月，1995年生，女，硕士研究生，研究方向植被参数反演。E-mail: 201821051067@mail.bnu.edu.cn
XU Yue1,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：xiedonghui@bnu.edu.cn
  • Introduction：谢东辉，1976年生，女，副教授，研究方向计算机模拟模型研究及植被参数反演。E-mail： xiedonghui@bnu.edu.cn
XIE Donghui1*,  
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

    State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, China

QI Jianbo12,  
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

YAN Guangjian1,  
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

MU Xihan1,  
  • Affiliation：

    State Key Laboratory of Romote Sensing Science, Beijing Normal University, Beijing 100875, China

ZHANG Wuming1

ملخص

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.

مفهوم

radiative transfer model;forest;reflectance;wood elements;3D reconstruction;LESS (LargE-Scale remote sensing data and image Simulation framework)

References

  1. 1.
    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
  2. 2.
    Daily G, Postel S, Bawa K and Kaufman L. 1997. Nature’s Services: Societal Dependence on Natural Ecosystems. Washington DC: Island Press.
  3. 3.
    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
  4. 4.
    Fearnside P M. 1997. Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management, 90(1): 59-87
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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)
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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

قراءة النص الكامل

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