이전|다음

3차원 해석 복사 전달 모델 ESRT 원리 및 이질적 산림 수관 스펙트럼 시뮬레이션 응용

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, 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：xiaoyaoli@ifrit.ac.cn
  • Introduction：李骁尧，研究方向为林业遥感机理建模与应用。E-mail： xiaoyaoli@ifrit.ac.cn
LI Xiaoyao12,  
  • Affiliation：

    Northwest Surveying and Planning Institute of National Forestry and Grassland Administration, Xi'an 710048, China

    Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, National Forestry and Grassland Administration, Xi'an 710048, China

RAO Yueming34,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

ZHANG Zhuoli1,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

WEI Shengrong1,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

QIN Pengyao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, 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：tan@caf.ac.cn
  • Introduction：谭炳香，研究方向为森林资源遥感监测、森林信息遥感提取。E-mail： tan@caf.ac.cn
TAN Bingxiang12*,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

FU Liyong1

추상적인

산림 장면의 복잡한 공간 이질성은 수관 반사 특성에 영향을 미치며, 이질성 문제를 다루는 것은 복사 전달 모델링 분야의 난제입니다. 전통적인 해석 모델이 장면을 단순화하여 정확도가 제한되는 문제와 대규모 장면 시뮬레이션에서 컴퓨터 시뮬레이션 모델의 실행 효율 제한 문제를 극복하기 위해, 우리는 확률적 복사 전달 이론을 기반으로 3차원 해석 복사 전달 모델 ESRT를 개발했습니다. 그러나 ESRT 모델의 다양한 복잡한 산림 장면 적용은 아직 탐구가 필요하며, 모델 검증 및 평가를 위한 더 많은 현장 측정 데이터가 필요합니다. 본문에서는 ESRT 모델의 기본 원리와 입력 출력 파라미터를 소개하고, 21개의 혼효림 표본지와 50개의 병해충 스트레스 표본지 조사 데이터를 기반으로 모델이 다양한 유형의 이질적 산림 수관 스펙트럼을 시뮬레이션하는 능력을 탐구하며, 혼효 및 병해 정도가 산림 수관 스펙트럼에 미치는 영향을 분석합니다. 결과는 원본 SRT 모델과 비교해 확장된 ESRT 모델이 혼효림 및 병해충 스트레스 산림 군락의 수관 스펙트럼과 표본지 측정 스펙트럼 간 더 나은 일관성을 보이며, 시뮬레이션 정확도가 3차원 컴퓨터 모델에 더 가깝다는 것을 나타냅니다; 침엽 혼효율과 피해 잎의 수직 분포 모두 수관 스펙트럼 신호에 영향을 미칩니다. ESRT 모델은 다양한 이질적 산림 장면을 위한 복사 전달 시뮬레이션 프레임워크를 제공하며, 3차원 구조 시뮬레이션 정확도와 전통적 해석 모델의 시뮬레이션 효율성을 균형 있게 조화시켜 대규모 복잡 산림 군락 시뮬레이션에 더 적합합니다. 본 연구는 ESRT 모델의 복잡한 산림 장면 모니터링 응용에 일정한 이론적 기반을 제공할 수 있습니다.

키워드

원격 감지;복사 전달;3차원 해석 모델;이질적 산림 수관;혼효림;산림 병해충

References

  1. 1.
    Bugmann H and Seidl R. 2022. The evolution, complexity and diversity of models of long-term forest dynamics. Journal of Ecology, 110(10): 2288-2307
  2. 2.
    Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156
  3. 3.
    Huang D, Knyazikhin Y, Wang W L, Deering D W, Stenberg P, Shabanov N, Tan B and Myneni R B. 2008. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements. Remote Sensing of Environment, 112(1): 35-50
  4. 4.
    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
  5. 5.
    Huang H G, Zhang Z Y, Ni W J, Chai L N, Qin W H, Liu G, Xie D H, Jiang L M and Liu Q H. 2018. Extending RAPID model to simulate forest microwave backscattering. Remote Sensing of Environment, 217: 272-291
  6. 6.
    Knyazikhin Y, Martonchik J V, Diner D J, Myneni R B, Verstraete M, Pinty B and Gobron N. 1998. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data. Journal of Geophysical Research: Atmospheres, 103(D24): 32239-32256
  7. 7.
    Kuusk A. 2001. A two-layer canopy reflectance model. Journal of Quantitative Spectroscopy and Radiative Transfer, 71(1): 1-9
  8. 8.
    Kuusk A and Nilson T. 2000. A directional multispectral forest reflectance model. Remote Sensing of Environment, 72(2): 244-252
  9. 9.
    Leblanc S G, Bicheron P, Chen J M, Leroy M and Cihlar J. 1999. Investigation of directional reflectance in boreal forests with an improved four-scale model and airborne POLDER data. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1396-1414
  10. 10.
    Li X W and Wang J D. 1995. The Vegetation Optical Remote Sensing Models and the Structures Parameters. Beijing: Science Press
  11. 11.
    Li X W, Wang J D, Liu Y, Zhu X Y and Strahler A H. 1993. A hybrid geometric optical and radiative transfer approach for modeling BRDF of discontinuous canopies. Remote Sensing of Environment China, 8(3): 161-172
  12. 12.
    Li X Y, Huang H G, Shabanov N V, Chen L, Yan K and Shi J. 2020. Extending the stochastic radiative transfer theory to simulate BRF over forests with heterogeneous distribution of damaged foliage inside of tree crowns. Remote Sensing of Environment, 250: 112040
  13. 13.
    Li X Y, Shabanov N V, Chen L, Zhang Y G and Huang H G. 2022. Modeling solar-induced fluorescence of forest with heterogeneous distribution of damaged foliage by extending the stochastic radiative transfer theory. Remote Sensing of Environment, 271: 112892
  14. 14.
    Lin Q N, Huang H G, Chen L, Wang J X, Huang K and Liu Y Y. 2021. Using the 3D model RAPID to invert the shoot dieback ratio of vertically heterogeneous Yunnan pine forests to detect beetle damage. Remote Sensing of Environment, 260: 112475
  15. 15.
    Lin Q N, Huang H G, Wang J X, Huang K and Liu Y Y. 2019. Detection of pine shoot beetle (PSB) stress on pine forests at individual tree level using Uav-based hyperspectral imagery and lidar. Remote Sensing, 11(21): 2540
  16. 16.
    Lin Q N, Huang H G, Yu L F and Wang J X. 2018. Detection of shoot beetle stress on Yunnan pine forest using a coupled LIBERTY2-INFORM simulation. Remote Sensing, 10(7): 1133
  17. 17.
    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 (in Chinese), 20(5): 933-945
  18. 18.
    Myneni R B, Maggion S, Iaquinta J, Privette J L, Gobron N, Pinty B, Kimes D S, Verstraete M M and Williams D L. 1995. Optical remote sensing of vegetation: modeling, caveats, and algorithms. Remote Sensing of Environment, 51(1): 169-188
  19. 19.
    Pu J B, Yan K, Gao S, Zhang Y M, Park T, Sun X, Weiss M, Knyazikhin Y and Myneni R B. 2023. Improving the MODIS LAI compositing using prior time-series information. Remote Sensing of Environment, 287: 113493
  20. 20.
    Qi J B, Xie D H, Jiang J Y and Huang H G. 2022. 3D radiative transfer modeling of structurally complex forest canopies through a lightweight boundary-based description of leaf clusters. Remote Sensing of Environment, 283: 113301
  21. 21.
    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
  22. 22.
    Ross J. 1981. The Radiation Regime and Architecture of Plant Stands. Dordrecht: Springer, 391
  23. 23.
    Shabanov N V and Gastellu-Etchegorry J P. 2018. The stochastic Beer-Lambert-Bouguer law for discontinuous vegetation canopies. Journal of Quantitative Spectroscopy and Radiative Transfer, 214: 18-32
  24. 24.
    Shabanov N V, Huang D, Knjazikhin Y, Dickinson R E and Myneni R B. 2007. Stochastic radiative transfer model for mixture of discontinuous vegetation canopies. Journal of Quantitative Spectroscopy and Radiative Transfer, 107(2): 236-262
  25. 25.
    Shabanov N V, Knyazikhin Y, Baret F and Myneni R B. 2000. Stochastic modeling of radiation regime in discontinuous vegetation canopies. Remote Sensing of Environment, 74(1): 125-144
  26. 26.
    Shabanov N V, Wang Y, Buermann W, Dong J, Hoffman S, Smith G R, Tian Y, Knyazikhin Y and Myneni R B. 2003. Effect of foliage spatial heterogeneity in the MODIS LAI and FPAR algorithm over broadleaf forests. Remote Sensing of Environment, 85(4): 410-423
  27. 27.
    Stoyan D, Kendall W S and Mecke J. 2013. Stochastic geometry and its applications. Journal of the Royal Statistical Society, 45(2): 345
  28. 28.
    Wang Q and Li P H. 2013. Canopy vertical heterogeneity plays a critical role in reflectance simulation. Agricultural and Forest Meteorology, 169: 111-121
  29. 29.
    Xie D B, Liu Q W, Lei Y K, Yu H, Yang X P and Fu L Y. 2022. Suitability analysis of single tree segmentation algorithm in the core area of winter olympic games based on airborne lidar data. Scientia Silvae Sinicae, 58(10): 121-130
  30. 30.
    Yan B Y, Xu X R and Fan W J. 2012. A unified canopy bidirectional reflectance (BRDF) model for row ceops. Science China Earth Sciences, 55(5): 824-836
  31. 31.
    Yan K, Zhang Y M, Tong Y Y, Zeng Y L, Pu J B, Gao S, Li L Y, Mu X H, Yan G J, Rautiainen M, Knyazikhin Y and Myneni R B. 2021. Modeling the radiation regime of a discontinuous canopy based on the stochastic radiative transport theory: modification, evaluation and validation. Remote Sensing of Environment, 267: 112728
  32. 32.
    Yang B, Knyazikhin Y, Xie D H, Zhao H M, Zhang J Q and Wu Y. 2018. Influence of leaf specular reflection on canopy radiative regime using an improved version of the stochastic radiative transfer model. Remote Sensing, 10(10): 1632
  33. 33.
    Yu L F, Huang H G, Ze S Z, Ren L L, Zong S X, Lu W J and Luo Y Q. 2017. Research on the spatial distribution patterns of Tomicus sp. in Pinus yunnanensis during the shoot feeding period. Chinese Journal of Applied Entomology, 54(6): 940-946
  34. 34.
    Zeng Y L, Badgley G, Chen M, Li J, Anderegg L D L, Kornfeld A, Liu Q H, Xu B D, Yang B, Yan K and Berry J A. 2020. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment, 240: 111678
  35. 35.
    Zeng Y L, Li J, Liu Q H, Huete A R, Yin G F, Xu B D, Fan W L, Zhao J, Yan K and Mu X H. 2016. A radiative transfer model for heterogeneous agro-forestry scenarios. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4613-4628

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