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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

реферат

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.

ключеви́че слова́

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

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