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Bayesian analysis for uncertainty of forest height inversed by polarimetric interferometric SAR data

  • role： First author第一作者
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

  • Email：zhangtingwei@swfu.edu.cn
  • Introduction：张庭苇， 研究方向为林业微波遥感。E-mail： zhangtingwei@swfu.edu.cn
ZHANG Tingwei,  
  • role： Corresponding author通信作者
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

  • Email：zhangwf@swfu.edu.cn
  • Introduction：张王菲， 研究方向为林业微波遥感。E-mail： zhangwf@swfu.edu.cn
ZHANG Wangfei*,  
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

ZHANG Yongxin,  
  • Affiliation：

    Forest College, Southwest Forestry University, Kunming 650224, China

HUANG Guoran

resumen

Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) has been widely used in forest height inversion. Accurate evaluation of the uncertainty caused by model input parameters, model assumptions, stand structure, and site conditions can improve the accuracy of forest height inversion with PolInSAR technology. In practical application, the study on uncertainty of forest height inversion is as important as of forest height estimation methods. Quantification of global carbon stocks based on forest biomass calculations usually requires reducing the error in biomass estimates through forest height. The uncertainty of forest height may be attributed to model input parameters, model assumptions, observed data, and forest scene factors. However, comprehensive collaborative impact analyses on the uncertainty of forest height inversion results are few. On this basis, the uncertainty of forest height inversion should be studied using PolInSAR technique. We initially analyze the uncertainty caused by the input parameters of the RVoG (Random Volume over Ground) model based on the Bayesian model using the simulated L-band full PolInSAR data, and then prior knowledge (value of the forest height in the imaging) is applied to fix the extinction of the RVoG model. Subsequently,we inversed the forest height. The results show that a priori knowledge can greatly reduce canopy height uncertainties in some cases. On this basis, we combine the RVoG model and Bayesian framework, use L-band simulated PolInSAR data, and comprehensively explore the uncertainties that result from the input parameters of the RVoG model, model hypothesis, observation value, changes in forest tree species, forest density, surface properties, ground moisture content, and other factors in the process of forest height inversion. The research results indicated that: (1) prior knowledge can reduce the uncertainty of the forest height inversion (by fix the extinction value) with RVoG model and L-band PolInSAR data. (2) The forest height inversion results are greatly affected by forest tree species, and the inversion results on the uncertainty of coniferous forest are lower than those of broad-leaved forest. (3) The change in forest stand density has a significant influence on the uncertainty of the forest height inversion results. The higher density indicates lower uncertainty, especially in the pure coniferous forest. When the forest density is small, the uncertainty of the forest height retrieved by the RVoG model is large. When the forest stand density increases from 150 plants/hm² to 1200 plants/hm², the uncertainty decreased to approximately 67.5%. (4) The change in surface roughness has a positive correlation with the uncertainty of the forest height inversion results, the greater roughness indicates higher uncertainty. (5) The uncertainty caused by ground moisture content is smaller than those by the other factors and can be ignored.

palabra clave

PolInSAR;RVoG;forest height;Tree Species;Forest Stand Density;Surface roughness;Ground Moisture Content

References

  1. 1.
    Ballester-Berman J D, Vicente-Guijalba F and Lopez-Sanchez J M. 2015. A simple RVoG test for PolInSAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1028-1040
  2. 2.
    Cao B, Yang X M, Xiao L, Lu P, Wang Y Q, Yue C R and Yu W L. 2016. Study on forest tree height inversion based on polarization interference SAR data. Forest Resources Management, (6): 100-105
  3. 3.
    Chen L W, Han L, Qin X B and Zhang W. 2017. A new method for constructing land surface combined roughness parameter in the process of soil moisture retrieval by microwave remote sensing. Geography and Geo-Information Science, 33(6): 37-43
  4. 4.
    Chen S. 2019. Inversion of Soil Surface Roughness by Combining Optical and SAR Data. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences
  5. 5.
    Cloude S R and Papathanassiou K P. 1998. Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1551-1565
  6. 6.
    Fan Y X. 2019. Forest Height Estimation Method Using Space-Borne X-Band SAR Interferometry. Beijing: Chinese Academy of Forestry
  7. 7.
    Fu Y, Lei Y C and Zeng W S. 2014. Uncertainty assessment in regional-scale above ground biomass estimation of Chinese fir. Scientia Silvae Sinicae, 50(12): 79-86
  8. 8.
    Fu Y. 2015. Above-Ground Biomass Estimates Uncertainty Assessment for Regional-level Forest. Beijing: Chinese Academy of Forestry
  9. 9.
    Gao Y K. 2016. Detecting the Moisture Content of Forest Surface Soil Base on the Microwave Remote Sensing Technology. Harbin: Northeast Forestry University
  10. 10.
    Hao L T and Yu H D. 2005. Standard deviation and standard error of arithmetic mean. Acta Editologica, 17(2): 116-118
  11. 11.
    Huang Y, Yang X R and Geng H B. 1986. The relationship of the microwave reflective characteristic to soil moisture. Remote Sensing of Environment, 1(2): 101-106
  12. 12.
    Iversen G R and Gergen M. 2000. Statistics: THE CONCEPTUAL APPROACH. Beijing：Higher Education Press
  13. 13.
    Jackson R D and Pinter P J. 1981. Detection of water stress in wheat by measurement of reflected solar and emitted thermal IR radiation. Versailles, France
  14. 14.
    Jiang Y Y, Cheng J Z, Li X and Zeng Z. 2020. Phase and amplitude of joint inversion based on forest density. Remote Sensing Information, 35(2): 8-13
  15. 15.
    Kugler F, Lee S K, Hajnsek I and Papathanassiou K P. 2015. Forest height estimation by means of Pol-InSAR data inversion: the role of the vertical wavenumber. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5294-5311
  16. 16.
    Li B K. 2007. Standard Error Representation in Forest Resource Sampling Survey. Forest Resources Management, (4): 62-63
  17. 17.
    Liao Z M. 2019. Forest Aboveground Biomass Estimation Using PolInSAR Data. Chengdu: University of Electronic Science and Technology of China
  18. 18.
    López-Martínez C and Alonso-González A. 2014. Assessment and estimation of the RVoG model in polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3091-3106
  19. 19.
    Luo H M, Chen E X, Cheng J and Li X W. 2010. Forest height estimation methods using polarimetric SAR interferometry. Journal of Remote Sensing, 14(4): 806-821
  20. 20.
    Luo S Y, Tong L and Chen Y. 2017. Soil moisture estimation for mountainous areas covered with low vegetation using fully polarimetric SAR images. Journal of Remote Sensing, 21(6): 907-916
  21. 21.
    Martin O. 2018. Bayesian Analysis with Python. Tian J, trans. Beijing: Posts and Telecom Press
  22. 22.
    Papathanassiou K P S. R. Cloude. 1998. Phase decomposition in polarimetric SAR interferometry. IGARSS Sensing and Managing the Environment, 2184-2186 vol.4
  23. 23.
    Papathanassiou K P and Cloude S R. 2001. Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(11): 2352-2363
  24. 24.
    Pottier E and Ferro-Famil L. 2012. PolSARPro V5.0: an ESA educational toolbox used for self-education in the field of POLSAR and POL-INSAR data analysis//Proceedings of 2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 7377-7380
  25. 25.
    Riel B, Denbina M and Lavalle M. 2018. Uncertainties in forest canopy height estimation from polarimetric interferometric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10): 3478-3491
  26. 26.
    Simard M and Denbina M. 2018. An assessment of temporal decorrelation compensation methods for forest canopy height estimation using airborne L-band same-day repeat-pass polarimetric SAR interferometry. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1): 95-111
  27. 27.
    Treuhaft R N, Madsen S N, Moghaddam M and van Zyl J J. 1996. Vegetation characteristics and underlying topography from interferometric radar. Radio Science, 31(6): 1449-1485
  28. 28.
    Wang C C, Wang L, Fu H Q, Xie Q H and Zhu J J. 2016. The impact of forest density on forest height inversion modeling from polarimetric InSAR data. Remote Sensing, 8(4): 291
  29. 29.
    Wu W, Clark J S and Vose J M. 2010. Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling. Journal of Hydrology, 394(3/4): 436-446
  30. 30.
    Wu X D, Xiao Q, Wen J G, Liu Q, Peng J J and Li X W. 2014. Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example. Journal of Remote Sensing, 18(5): 1011-1023
  31. 31.
    Zhang T W，Ji Y J and Zhang W F. 2022. The analysis on uncertainty resulting from method and wavelength selecting in forest height inversion using simulated polarimetric interferometric SAR data. National Remote Sensing Bulletin， 26(10)：1963-1975
  32. 32.
    Zhang W F, Chen E X, Li Z Y, Zhao L and Ji Y J. 2017. Development of forest height estimation using InSAR/PolInSAR technology. Remote Sensing Technology and Application, 32(6): 983-997
  33. 33.
    Zhou Z B. 2013. Research of Forest Height Extraction and Data Simulation Polarimetric SAR Interferometry. Qingdao: Shandong University of Science and Technology
  34. 34.
    Zhu X L. 2009. Review of Markov chain Monte Carlo method. Statistics and Decision, (21): 151-153

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