سابِق|تال

An improved method for estimating clumping index in mixed coniferous and broadleaved forests using BRDF shape of surface ecotype as constraints

  • role： First author第一作者
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：xierui@mail.bnu.edu.cn
  • Introduction：谢蕊，研究方向为定量遥感，混交林植被聚集指数反演。E-mail： xierui@mail.bnu.edu.cn
XIE Rui12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

  • Email：jiaozt@bnu.edu.cn
  • Introduction：焦子锑，研究方向为多角度光学遥感的建模、反演与应用（土壤—植被—冰雪）。E-mail： jiaozt@bnu.edu.cn
JIAO Ziti12*,  
  • Affiliation：

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DONG Yadong23,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

CUI Lei12,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

YIN Siyang12,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

ZHANG Xiaoning12,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

CHANG Yaxuan12,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

    Skate Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

GUO Jing12

ملخص

The foliage Clumping Index (CI) is an important structural parameter of vegetation canopies. The CI influences radiation interception within canopies and plays an important role in the study of global carbon and water cycles. Currently, the widely used method for deriving satellite-borne CI products is based on a linear model constructed on the basis of the CI and the Normalized Difference between the Hotspot and Dark spot (NDHD) angular indices. As coniferous and broadleaf forests exhibit aggregate differences at the leaf scale, the CI inversion model can be applied to a variety of coefficients to generate different CI-NDHD models. Modelers typically use CI inversion coefficients of broadleaf forests to estimate the CI of coniferous-broadleaf mixed forests for medium-coarse resolution pixels, but this approach can theoretically cause a CI overestimation for this landcover type. Thus, in this study, we propose a novel coniferous-broadleaf Mixed Forest CI (MFCI) estimation method to dynamically select the endmember CIs of mixed forests pixel by pixel. The proposed method was successfully applied to satellite-borne MODIS data. The MFCI of the tree-farm study area on Saihanba was estimated, and the accuracy of the results was validated using ground-measured CIs.The MFCI was estimated by considering land cover classes and the Anisotropy Flatness Index (AFX), which describes the basic Bidirectional Reflectance Distribution Function (BRDF) variation. First, the prior values of the endmember NDHD were extracted pixel by pixel by imposing double constraints on the landcover type of the International Geosphere–Biosphere Program and the surface AFX, which characterize the shape of the BRDF. Then, the high-resolution land cover classification data were used to obtain the proportions of the endmembers in the coniferous-broadleaf mixed forest pixels. An optimization factor f was introduced to eliminate the differences between the NDHD of mixed forest pixels and the NDHD prior values of different vegetation cover types based on the NDHD linear mixing assumption. Then, the endmember CIs were calculated. Finally, the endmember CIs, combined with endmember abundance, were used to estimate the MFCIs based on Beer’s law.First, the existing MODIS CI product algorithm does not consider coniferous-broadleaf mixed forest pixels within mixed forest pixels, which leads to overestimation of coniferous–broadleaf mixed forest CIs. When the proportion of coniferous species reaches 60% in a mixed forest pixel, the overestimation of the CI can exceed 35%. Second, the proposed MFCI estimation method based on the CI-NDHD algorithm can significantly improve the CI estimation accuracy of coniferous-broadleaf mixed forest pixels. When the proportion of coniferous forest in the mixed forest pixels reached 60%, the accuracy improved by 28.03%. The root mean-square error and bias for the enhanced results were reduced by approximately 84% and 175%, respectively. Third, the MFCI method is more sensitive than the current MODIS CI products to changes in coniferous and broadleaf forest structures in mixed forest pixels.The current satellite CI products for mixed forest pixels typically use the modeled coefficients of broadleaf forests in the CI-NDHD model, which theoretically implies increased uncertainty in CI products. In this study, the proposed MFCI estimation method was used for coniferous-broadleaf forest mixed pixels. The CI endmembers were dynamically adjusted. The validation based on ground-measured CIs showed that the proposed method was significantly more accurate than the current MODIS CI products in terms of estimating the CI of mixed coniferous and broadleaved forests. In summary, the MFCI estimation method improved the estimation accuracy of mixed forest CI products in the selected study area. The proposed method is a promising technique for further improving the accuracy of global CI products.

مفهوم

remote sensing;clumping index;Mixed Forest;MODIS;AFX;NDHD;MFCI

References

  1. 1.
    Baldocchi D D and Harley P C. 1995. Scaling carbon dioxide and water vapour exchange from leaf to canopy in a deciduous forest. II. Model testing and application. Plant, Cell & Environment, 18(10): 1157-1173
  2. 2.
    Bartelink H H. 1998. Radiation interception by forest trees: a simulation study on effects of stand density and foliage clustering on absorption and transmission. Ecological Modelling, 105(2/3): 213-225
  3. 3.
    Breece III H T and Holmes R A. 1971. Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo. Applied Optics, 10(1): 119-127
  4. 4.
    Chang Y X, Jiao Z T, Dong Y D, Zhang X N, He D D, Yin S Y, Cui L and Ding A X. 2019. Parameterization and correction of hotspot parameters of Ross-Li kernel driven models on POLDER dataset. Journal of Remote Sensing (in Chinese), 23(4): 661-672
  5. 5.
    Chang Y X, Jiao Z T, Zhang X N, Mei L L, Dong Y D, Yin S Y, Cui L, Ding A X, Guo J, Xie R, Zhu Z D and Li S J. 2021. Assessment of improved Ross-Li BRDF models emphasizing albedo estimates at large solar angles using POLDER data. IEEE Transactions on Geoscience and Remote Sensing, 59(12): 9968-9986
  6. 6.
    Chen J M and Black T A. 1991. Measuring leaf area index of plant canopies with branch architecture. Agricultural and Forest Meteorology, 57(1/3): 1-12
  7. 7.
    Chen J M and Cihlar J. 1995. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Transactions on Geoscience and Remote Sensing, 33(3): 777-787
  8. 8.
    Chen J M and Cihlar J. 1997. A hotspot function in a simple bidirectional reflectance model for satellite applications. Journal of Geophysical Research: Atmospheres, 102(D22): 25907-25913
  9. 9.
    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
  10. 10.
    Chen J M, Liu J, Leblanc S G 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
  11. 11.
    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
  12. 12.
    Chen J M, Mo G, Pisek J, Liu J, Deng F, Ishizawa M and Chan D. 2012. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global Biogeochemical Cycles, 26(1): GB1019
  13. 13.
    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
  14. 14.
    Dong Y D, Jiao Z T, Zhang H, Li J Y, Jiao G P, Shi H Y. 2014. Efficient algorithm for improving the hotspot effect of the operational MODIS BRDF product. Journal of Remote Sensing (in Chinese), 18(4): 804-825
  15. 15.
    Friedl M A, Mciver D K, Hodges J C F, Zhang X Y, Muchoney D, Strahler A H, Woodcock C E, Gopal S, Schneider A, Cooper A, Baccini A, Gao F and Schaaf C. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1/2): 287-302
  16. 16.
    He L M, Chen J M, Pisek J, Schaaf C B and Strahler A H. 2012. Global clumping index map derived from the MODIS BRDF product. Remote Sensing of Environment, 119: 118-130.
  17. 17.
    Huang T, Fan W Y, Mao X G and Yu Y. 2017. Foliage clumping index of main vegetation types in Daxing’an Mountains, Northeast China. Chinese Journal of Applied Ecology, 28(3): 757-762
  18. 18.
    Jiao Z T, Dong Y D, Schaaf C B, Chen J M, Román M, Wang Z S, Zhang H, Ding A X, Erb A, Hill M J, Zhang X N and Strahler A. 2018. An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model. Remote Sensing of Environment, 209: 594-611.
  19. 19.
    Jiao Z T, Hill M J, Schaaf C B, Zhang H, Wang Z S and Li X W. 2014. An anisotropic flat index (AFX) to derive BRDF archetypes from MODIS. Remote Sensing of Environment, 141: 168-187
  20. 20.
    Jiao Z T, Li X W, Wang J D and Zhang H. 2011. Assessment of MODIS BRDF shape indicators. Journal of Remote Sensing (in Chinese), 15(3): 432-456
  21. 21.
    Jiao Z T, Schaaf C B, Dong Y D, Román M, Hill M J and 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
  22. 22.
    Kucharik C J, Norman J M and Gower S T. 1999. Characterization of radiation regimes in nonrandom forest canopies: theory, measurements, and a simplified modeling approach. Tree Physiology, 19(11): 695-706
  23. 23.
    Li X and Strahler A H. 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
  24. 24.
    Liu J, Chen J M and Cihlar J. 2003. Mapping evapotranspiration based on remote sensing: an application to Canada's landmass. Water Resources Research, 39(7): 1189
  25. 25.
    Luo D, Shi Z M, Wang W X, Liu S R, Liu L H, Ming A G and Yu H L. 2015. Carbon and nitrogen storage in monoculture and mixed young plantation stands of Erythrophleum fordii and Pinus massoniana in subtropical China. Acta Ecologica Sinica, 35(18): 6051-6059
  26. 26.
    Ma L L, Li C R, Tang B H, Tang L L, Bi Y Y, Zhou B Y and Li Z L. 2008. Impact of spatial LAI heterogeneity on estimate of directional gap fraction from SPOT-satellite data. Sensors, 8(6): 3767-3779
  27. 27.
    Ma Q M, Li Y J, Li J and Liu Q H. 2019. Modeling of mixed-pixel clumping index from remote sensing data and its evaluation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7): 2320-2331
  28. 28.
    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
  29. 29.
    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
  30. 30.
    Myneni R B, Ross J, Asrar G. 1989. A review on the theory of photon transport in leaf canopies. Agricultural and Forest Meteorology, 45(1/2): 1-153
  31. 31.
    Nilson T. 1971. A theoretical analysis of the frequency of gaps in plant stands. Agricultural Meteorology, 8: 25-38
  32. 32.
    Nilson T and Peterson U. 1991. A forest canopy reflectance model and a test case. Remote Sensing of Environment, 37(2): 131-142
  33. 33.
    Pisek J, Chen J M and Nilson T. 2011. Estimation of vegetation clumping index using MODIS BRDF data. International Journal of Remote Sensing, 32(9): 2645-2657
  34. 34.
    Román M O, Gatebe C K, Schaaf C B, Poudyal R, Wang Z S and King M D. 2011. Variability in surface BRDF at different spatial scales (30m—500m) over a mixed agricultural landscape as retrieved from airborne and satellite spectral measurements. Remote Sensing of Environment, 115(9): 2184-2203
  35. 35.
    Ross J. 1981. The radiation regime and architecture of plant stands[M]. Dordrecht: Springer
  36. 36.
    Roujean J L and Lacaze R. 2002. Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation. Journal of Geophysical Research: Atmospheres, 107(D12): 4150
  37. 37.
    Ryu Y, Sonnentag O, Nilson T, Vargas R, Kobayashi H, Wenk R and Baldocchi D D. 2010. How to quantify tree leaf area index in an open savanna ecosystem: A multi-instrument and multi-model approach. Agricultural and Forest Meteorology, 150(1): 63-76.
  38. 38.
    Schaaf C B, Gao F, Strahler A H, Lucht W, Li X W, Tsang T, Strugnell N C, Zhang X Y, Jin Y F, Muller J P, Lewis P, Barnsley M, Hobson P, Disney M, Roberts G, Dunderdale M, Doll C, D’entremont R P, Hu B X, Liang S L, Privette J L and Roy D. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1/2): 135-148
  39. 39.
    Stenberg P. 1998. Implications of shoot structure on the rate of photosynthesis at different levels in a coniferous canopy using a model incorporating grouping and penumbra. Functional Ecology, 12(1): 82-91
  40. 40.
    Strahler A H. 1997. Vegetation canopy reflectance modeling—recent developments and remote sensing perspectives. Remote Sensing Reviews, 15(1/4): 179-194
  41. 41.
    Strahler A H and Jupp D L B. 1990. Modeling bidirectional reflectance of forests and woodlands using Boolean models and geometric optics. Remote Sensing of Environment, 34(3): 153-166.
  42. 42.
    Sun Q, Jiao Q J, Liu L Y, Liu X J, Qian X J, Zhang X and Zhang B. 2021. Improving the retrieval of forest canopy chlorophyll content from MERIS dataset by introducing the vegetation clumping index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 5515-5528
  43. 43.
    Tang S, Chen J M, Zhu Q, Li X, Chen M, Sun R, Zhou Y, Deng F and Xie D. 2007. LAI inversion algorithm based on directional reflectance kernels. Journal of Environmental Management, 85(3): 638-648
  44. 44.
    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
  45. 45.
    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
  46. 46.
    Wu H, Tang B H and Li Z L. 2013. Impact of nonlinearity and discontinuity on the spatial scaling effects of the leaf area index retrieved from remotely sensed data. International Journal of Remote Sensing, 34(9/10): 3503-3519
  47. 47.
    Yin G F, Li J, Liu Q H, Zeng Y L, Xu B D, Yang L and Zhao J. 2015. Improving leaf area index retrieval over heterogeneous surface by integrating textural and contextual information: a case study in the Heihe river basin. IEEE Geoscience and Remote Sensing Letters, 12(2): 359-363
  48. 48.
    Yin G F, Liu Q H, Li J, Zeng Y L and Xu B D. 2014. Effect of crown shape on the estimation of gap probability and leaf area index. Journal of Remote Sensing (in Chinese), 18(4): 752-759
  49. 49.
    Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and surface directional reflectance. Journal of Remote Sensing (in Chinese), 19(3): 355-367
  50. 50.
    Zhang X, Liu L Y, Chen X D, Xie S and Gao Y. 2019. Fine land-cover mapping in China using Landsat Datacube and an operational SPECLib-based approach. Remote Sensing, 11(9): 1056
  51. 51.
    Zhang X, Liu L Y, Wu C S, Chen X D, Gao Y, Xie S and Zhang B. 2020. Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data, 12(3): 1625-1648
  52. 52.
    Zhou Y, Zhang Z W, Wen S, Sun H and Liu Q. 2021. Changes of light response indicators and its impact factors of Larix principis-rupprechtii on Saihanba forest farm, China. Chinese Journal of Applied Ecology, 32(5): 1690-1698
  53. 53.
    Zhu G L. 2016. Spatial-temporal characteristics of foliage clumping index in China during 2000-2013. Chinese Science Bulletin, 61(14): 1595-1603
  54. 54.
    Zhu G L, Ju W M, Chen J M, Gong P, Xing B L and Zhu J F. 2012. Foliage clumping index over China's landmass retrieved from the MODIS BRDF parameters product. IEEE Transactions on Geoscience and Remote Sensing, 50(6): 2122-2137

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