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    参数优选支持的光学与SAR数据森林地上生物量反演研究

    Inversion exploration on forest aboveground biomass of optical and SAR data supported by parameter optimization method

      摘要
    • 摘要:
        目的  森林是整个陆地碳循环系统中最大的有机碳贮库，准确地估测森林地上生物量影响着全球碳源与碳储量的分析与评价。本文旨在评价利用Landsat8 OLI、高分一号光学数据、ALOS-1 PALSAR-1SAR 3组不同源遥感数据估测森林AGB的潜力，进而剖析光学数据和SAR数据在估测森林AGB方面的差异。
        方法  首先对Landsat8 OLI、高分一号光学数据、ALOS-1 PALSAR-1SAR数据分别提取波段比值、植被指数、纹理信息，对ALOS-1 PALSAR-1SAR数据同时提取极化分解信息；然后，利用随机森林算法对不同数据提取的特征参数进行重要性排序，选择排序靠前的特征进行建模；最后，利用KNN-FIFS算法分析不同特征组合，对4组数据建立4个模型估测森林AGB，并使用留一交叉验证法对4个模型估测森林AGB值进行精度评价。
        结果  使用植被因子、波段比值、纹理因子、极化分解信息4种特征参数分别对3组数据进行建模估测森林AGB，基于Landsat8 OLI数据反演森林AGB的精度评价结果为R2 = 0.50，RMSE = 33.34 t/hm2；基于高分一号数据估测精度为R2 = 0.36，RMSE = 37.60 t/hm2；基于PALSAR纹理特征估测精度为R2 = 0.45，RMSE = 35.40 t/hm2；基于PALSAR全极化分解信息估测精度为R2 = 0.63，RMSE = 28.84 t/hm2。
        结论  参数提取方法相同时，即基于植被因子、波段比值、纹理信息3种特征参数估测森林AGB，其光学数据和SAR数据的反演潜力基本一致；参数提取方法不同时，即SAR数据加入极化分解信息估测森林AGB，与光学数据相比，SAR数据对森林AGB的反演潜力较好。

       

      Abstract:
        Objective  Forest is the largest storage of organic carbon in the whole terrestrial carbon cycle system. Accurate estimation of forest aboveground biomass (AGB) is essential for global carbon storage analysis and estimation. This paper aims to explore the potential of optical and synthetic aperture radar (SAR) data for forest AGB inversion. In this study, Landsat8 OLI, GF-1 data were selected as optical data and advanced land observing satellite (ALOS)-1 phased array type L-band synthetic aperture radar (PALSAR)-1 data was selected as SAR data.
        Method  Firstly, band ratio parameters, vegetation index parameters, and texture information were extracted from Landsat8OLI, gaofen-1 optical data and ALOS-1 PALSAR-1 SAR data, respectively. While polarization decomposition information was also extracted from ALOS-1 PALSAR-1 SAR data. Then these parameters extracted from different remote sensing data were sorted according to their importance by random forest (RF) algorithm. Finally, fast iterative feature selection method for k-nearest neighbor (KNN-FIFS) algorithm was used to analyze different feature combinations, and four models were constructed to estimate forest AGB and a cross-validation method was applied for result validation.
        Result  These remote sensing data were modeled to estimate forest AGB using four characteristic parameters: vegetation factor, band ratio, texture factor and polarization decomposition information. For parameters extracted from vegetation factor, band ratio, texture factor, Landsat8 OLI data performed best than GF-1 and PALSAR-1 data with R2 = 0.50, RMSE = 33.34 t/ha. For GF-1 data, R2 was 0.36, RMSE was 37.60 t/ha, R2 was 0.45, RMSE was 35.40 t/ha for PALSAR-1 data. However, for parameters extracted from polarization decomposition, PALSAR-1data showed better performance with R2 = 0.63 and RMSE = 28.84 t/ha.
        Conclusion  When the parameter extraction methods are the same, the forest AGB inversion potentials of the optical and SAR data are similar. However, when the parameter extraction method is different, for example, using polarization decomposition to extract parameters, the SAR data show better performance for forest AGB inversion.

       

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