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引用本文:

DOI:

10.11834/jrs.20211522

收稿日期:

2021-07-28

修改日期:

2021-11-29

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一种改进的针阔混交林植被聚集指数估算方法(备注:定量遥感专刊)
谢蕊1, 焦子锑1, 董亚冬2, 崔磊1, 尹思阳1, 张小宁1, 常雅轩1, 郭静1
1.北京师范大学;2.中国科学院空天信息创新研究院
摘要:

聚集指数(Clumping Index, CI)是植被冠层的一个重要结构参数,对植被冠层的辐射截获,以及全球碳、水循环的研究均有重要作用。现有星载CI产品的估算主要是基于CI-NDHD (Normalized Difference between Hotspot and Dark spot, NDHD) 线性模型方法,由于针叶林和阔叶林在叶片尺度上存在聚集层级的差异,该模型对它们分别采用了不同的模型系数。但是,该模型对中粗分辨率的针阔混交林像元通常采用阔叶林的CI反演系数,因此,理论上会导致该类型CI的高估。为此,本文提出了一种动态选取混交林像元端元CI组分的方法,以改进针阔混交林植被聚集指数的估算精度。首先,通过国际地圈-生物圈计划(IGBP)的地表类型和描述二向性反射分布函数(BRDF)特征的地表各向异性平整指数(AFX)进行双重约束,逐像元地计算端元CI值;然后,结合高分辨率的土地覆盖分类数据确定端元在像元中的面积比例,并估算MODIS针阔混交林像元的聚集指数(Mixed Forest CI, MFCI) ;最后,将方法应用于研究区MODIS数据的MFCI估算,并通过地面实测数据进行精度评价。结果表明:目前的MODIS产品算法高估了针阔混交林像元的CI值,而MFCI估算方法在CI-NDHD算法的基础上,可以显著改善该类型聚集指数的估算精度,当针叶林树种成数达到60%时,精度改善可达28.03%,其中,改进结果的均方根误差(RMSE)和偏差(Bias)各降低约84%和175%。研究表明,MFCI方法对针阔混合像元的端元组份的变化敏感,在高分辨率地表分类已知的条件下,MFCI方法为针阔混交林CI产品生产和精度提高提供了可行的解决方案。

An improved Clumping Index estimation method for mixed coniferous and broadleaved forests
Abstract:

Foliage Clumping Index (CI) is an important structural parameter within vegetation canopy. CI has an influence on the radiation interception within canopy, and plays an important role in the study of global carbon and water cycle. Currently, widely used method to derive satellite-borne CI product is based on a linear model by building a linear model between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index. Because the coniferous and broadleaf forests have differences in their aggregation levels at the leaf scale, such a CI inversion model uses different coefficients to generate different CI-NDHD models. However, the model usually uses the CI inversion coefficients from broadleaf forests to estimate the CI of conifer-broadleaf mixed forest for medium-coarse resolution pixels, which tends to cause a CI overestimation for this landcover type in theory. In order to solve this problem, we proposed a novel conifer-broadleaf mixed forest CI (MFCI) estimation method, which can select the endmember CIs of mixed forest pixel by pixel in a dynamic way. The proposed method was successfully applied to the satellite-borne MODIS data to estimate the CI for the mixed forest in a study area in the Saihanba tree farm, and was finally validated in accuracy using the ground-measured CIs.

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