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    郭正齐, 张晓丽, 王月婷. Sentinel-2A多特征变量反演针叶林地上生物量能力评估[J]. 北京林业大学学报, 2020, 42(11): 27-38. DOI: 10.12171/j.1000-1522.20200097
    引用本文: 郭正齐, 张晓丽, 王月婷. Sentinel-2A多特征变量反演针叶林地上生物量能力评估[J]. 北京林业大学学报, 2020, 42(11): 27-38. DOI: 10.12171/j.1000-1522.20200097
    Guo Zhengqi, Zhang Xiaoli, Wang Yueting. Ability evaluation of coniferous forest aboveground biomass inversion using Sentinel-2A multiple characteristic variables[J]. Journal of Beijing Forestry University, 2020, 42(11): 27-38. DOI: 10.12171/j.1000-1522.20200097
    Citation: Guo Zhengqi, Zhang Xiaoli, Wang Yueting. Ability evaluation of coniferous forest aboveground biomass inversion using Sentinel-2A multiple characteristic variables[J]. Journal of Beijing Forestry University, 2020, 42(11): 27-38. DOI: 10.12171/j.1000-1522.20200097

    Sentinel-2A多特征变量反演针叶林地上生物量能力评估

    Ability evaluation of coniferous forest aboveground biomass inversion using Sentinel-2A multiple characteristic variables

    • 摘要:
        目的  森林生物量是衡量森林碳储量的关键因子,准确估算生物量对掌握森林现状和森林资源合理利用具有重要意义。欧空局发射Sentinel-2A数据因其丰富的光谱信息和较高的空间分辨率为生物量的反演和监测提供了新的机会。本文旨在评估基于Sentinel-2A的各类特征变量反演针叶林地上生物量的能力以及完成区域尺度的针叶林地上生物量定量估测。
        方法  试验以内蒙古赤峰市喀喇沁旗旺业甸林场针叶林为研究对象,以Sentinel-2A为主要数据源,提取了10个波段反射率、20个植被指数和5个生物物理参数共3种类型变量,分别建立基于光谱反射率、植被指数、生物物理参数,以及融合3类变量的多元逐步回归生物量估算模型,同时每组均加入高程因子分析地形对估算精度的影响。
        结果  (1)基于多种类型参数建立的模型估算效果最好,模型决定系数达到0.765,均方根误差为39.49 t/hm2;(2)在3组单类型变量模型中,基于植被指数的预测结果最好,说明相比于波段反射率和生物物理参数,植被指数对针叶林地上生物量的估算贡献更大;(3)无论基于何种类型参数建模,高程信息的加入都会提高针叶林地上生物量的估算精度。
        结论  基于Sentinel-2A植被指数与地形特征的针叶林地上生物量反演模型较好,可用于区域生物量估算。该研究对区域性森林资源监测的实际应用具有指导意义。

       

      Abstract:
        Objective  Forest biomass is the key factor to measure forest carbon reserves. Therefore, accurate estimation of forest biomass is helpful for forest management and resource utilization. The data from Sentinel-2A provide new opportunities for biomass estimation and monitoring due to rich spectral information and high spatial resolution. In this paper, we evaluated the ability of various parameters to estimate aboveground biomass of coniferous forest , and completed regional-scale forest biomass estimation based on Sentinel-2A.
        Method  Selecting the coniferous forest of Wangyedian Forest Farm in Chifeng City, Inner Mongolia of northern China as the research object, this research extracted spectral reflectance, vegetation index and biophysical parameters in Sentinel-2A. Then we set up multiple stepwise regression equations by data types to estimate biomass. In addition, the elevation factor was added to improve the accuracy of model.
        Result  The results showed that: (1) the model built with multiple types of parameters had the highest accuracy, with R2 reaching 0.765 and RMSE was 39.49 t/ha; (2) among the models established by spectral reflectance, vegetation index and biophysical parameters, the accuracy based on the vegetation index was higher, indicating that the vegetation index had a greater impact on coniferous forest aboveground biomass estimation than the spectral reflectance and biophysical parameters; (3) for all the models of our research, elevation always improved accuracy.
        Conclusion  The retrieved aboveground biomass from Sentinel-2A spatial distribution is basically consistent with the actual situation, which shows that the coniferous forest aboveground biomass inversion is meaningful for regional forest resource monitoring.

       

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