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    童彤, 林思美, 李林源, 罗涛, 黄华国. 联合微波与光学时间序列影像的马尾松林松材线虫病遥感识别[J]. 北京林业大学学报, 2024, 46(3): 40-52. DOI: 10.12171/j.1000-1522.20220453
    引用本文: 童彤, 林思美, 李林源, 罗涛, 黄华国. 联合微波与光学时间序列影像的马尾松林松材线虫病遥感识别[J]. 北京林业大学学报, 2024, 46(3): 40-52. DOI: 10.12171/j.1000-1522.20220453
    Tong Tong, Lin Simei, Li Linyuan, Luo Tao, Huang Huaguo. Remote sensing recognition of pine wilt disease in Pinus massoniana forest combined with microwave and optical time-series images[J]. Journal of Beijing Forestry University, 2024, 46(3): 40-52. DOI: 10.12171/j.1000-1522.20220453
    Citation: Tong Tong, Lin Simei, Li Linyuan, Luo Tao, Huang Huaguo. Remote sensing recognition of pine wilt disease in Pinus massoniana forest combined with microwave and optical time-series images[J]. Journal of Beijing Forestry University, 2024, 46(3): 40-52. DOI: 10.12171/j.1000-1522.20220453

    联合微波与光学时间序列影像的马尾松林松材线虫病遥感识别

    Remote sensing recognition of pine wilt disease in Pinus massoniana forest combined with microwave and optical time-series images

    • 摘要:
      目的 大范围准确监测林区松材线虫病感染情况对森林疫情防治和经营管理具有重要作用。现有研究往往采用单时相或少量时相数据,松材线虫病遥感监测易受森林背景和非寄主树木影响,导致监测精度存在较大的不确定性。此外,单一数据源往往对病害特征刻画不足,例如被动光学数据侧重描述森林冠层水平结构信息,但易受云雨影响造成数据缺失,而主动微波数据对森林垂直结构和水分含量敏感,但存在噪声高、色素敏感性低以及地形影响大等问题。因此,联合主动微波与被动光学时间序列遥感影像数据,有望在降低环境因素影响的同时追踪同一林分的时序变化特征,进而提升松材线虫病探测的准确性与鲁棒性。
      方法 利用厘米级分辨率无人机影像标记样本,联合Sentinel-1 C波段微波和Sentinel-2光学时间序列数据,构建基于极端梯度提升算法的松材线虫病害监测模型。分别评估微波模型、光学模型和微波与光学联合模型在松材线虫病监测方面的性能,以及最优模型在不同环境因子下的表现。
      结果 (1)联合了微波和光学的模型精度(总体精度为80.62%,Kappa 系数为0.61)略高于单一光学模型的精度(总体精度为79.58%,Kappa系数为0.59),并明显高于单一微波模型的精度(总体精度为68.87%,Kappa系数为0.36),说明了微波与光学时间序列联合数据在松材线虫病害监测中具有优势;(2)模型通常在缓坡、阳坡、低海拔、高覆盖度条件下展现出更高精度。
      结论 本研究充分利用多源遥感卫星数据,为松材线虫病大范围准确监测提供了新的技术支撑。

       

      Abstract:
      Objective Large-scale and accurate monitoring of pine wilt disease (PWD) plays an important role in forest epidemic prevention and management. Existing studies often use single-phase data, resulting in greater uncertainty in the accuracy of monitoring PWD, which is easily affected by forest background and non-host trees. In addition, single data have the limitation of insufficient characterization of disease characteristics. For example, passive optical data focus on describing the horizontal structure of forest canopy, and are easily affected by cloud and rain, resulting in missing data; Active microwave data are sensitive to forest vertical structure and moisture content, but there are problems such as high noise, low pigment sensitivity and large terrain impact. Therefore, the combination of active microwave and passive optical time-series images is expected to reduce the impact of environmental factors while tracking the time-series change characteristics of the same forest stand, which helps to improve the accuracy and robustness of PWD monitoring.
      Method First, using centimeter-level resolution drone images to obtain samples. Based on extreme gradient boosting algorithm, PWD monitoring models were constructed by combining Sentinel-1 C-band microwave and Sentinel-2 optical time-series images. The performance of microwave model, optical model, combined microwave and optical model in the monitoring of PWD was evaluated respectively. At the same time, compare the performance of the optimal model under different environment conditions.
      Result (1) The accuracy of combined microwave and optical model (overall accuracy = 80.62%, Kappa coefficient = 0.61) was slightly higher than that of the single optical model (overall accuracy = 79.58%, Kappa coefficient = 0.59), but significantly higher than that of the single microwave model (overall accuracy = 68.87%, Kappa coefficient = 0.36), and its showed the value of combined microwave and optical time-series data in the monitoring of PWD. (2) The analysis of different environment conditions showed that the model generally exhibited higher accuracy under gentle slope, sunny slope, low altitude, and high coverage conditions.
      Conclusion This study makes full use of multi-source remote sensing satellite data and provides new technical support for large-scale accurate monitoring of PWD.

       

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