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引用本文:

DOI:

10.11834/jrs.20210156

收稿日期:

2020-05-13

修改日期:

2020-09-01

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应用迁移学习的林火烈度初始评估研究
郑忠1, Jinfei Wang2, 邹滨3, 高阳华4, 杨世琦4, 王永前1
1.成都信息工程大学 资源环境学院;2.the University of Western Ontario;3.中南大学 地球科学与信息物理学院;4.重庆市气象科学研究所
摘要:

林火发生后,开展森林生态系统烈度信息的初始评估,能够为灾后生态修复管理措施的实施提供定量依据。为了改善传统林火烈度评估模型的时效性,利用历史过火区域的实地调查数据,构建基于迁移学习的烈度评估模型,并将其应用于2020年3月30日发生的西昌泸山森林大火烈度初始评估研究中。结果表明:迁移学习算法能够将源区域和目标区域的遥感影像光谱转换为多个新的特征变量,在这些新特征变量构成的投影空间中,源区域和目标区域样本具有相似的分布。在此基础上,基于源区域历史实地调查数据构建的烈度评估模型,能够迁移应用于目标区域的烈度评估。与传统模型相比,基于迁移学习的烈度评估模型精度较高,总体精度为71.20%,Kappa系数为0.64。该研究可为林火灾后管理措施的快速响应,提供一种新的思路和参考。

Initial assessment of burn severity using transfer learning model
Abstract:

Forest fires have broken out frequently in recent years, which severely damages the structure and function of forest ecosystem around the world. Initial assessment of burn severity after forest fires could provide a quantitative basis for rapid implementations of restoration measures in burned areas. In last decades, remote sensing-based models have become an appropriate choice to assess burn severity, which generally requires a certain amount of field survey data. However, this requirement could not be satisfied sufficiently in most cases, since the field survey work would cost a lot of time, labor, and money. Therefore, this would largely limit the efficient application of remote sensing technology to the initial assessment of burn severity. To improve the time-efficiency of traditional remote sensing-based models, a transfer learning algorithm (i.e., SSTCA, semi-supervised Transfer Component Analysis) was employed in this study to build an initial assessment model of burn severity. At first, the SSTCA algorithm was applied to project a series of new features from original spectral features of remotely sensed data. Based on these projected features, a SVR (Support Vector Regression) model was then trained using historical field survey data of source areas (i.e., Bear fire on June 27, 2002 and Mule fire on July 11, 2002). After that, this combined SSTCA-SVR model was transferred to the initial assessment of burn severity of a target area (i.e., Lushan fire on March 30, 2020). Finally, its performance was quantitatively compared with that of some traditional models (i.e., dNDVI-, dLST-, dNBR-, and SVR-based models). Results showed that original spectral features of remote sensing images over source and target areas were quite different. After the projection of SSTCA, projected features of source and target samples have similar distribution patterns in the new features-based space. Meanwhile, in the initial assessment of burn severity, dNDVI- and dNBR-based models have overestimated burn severity levels with the lowest accuracy (i.e., overall accuracy was from 20.80% to 24.80% and Kappa value was between 0.01 and 0.06). Compared to these spectral indices-based models, dLST-based model has a better performance with an overall accuracy of 34.80% and a Kappa value of 0.19. Although SVR-based model has shown a promising performance with an overall accuracy of 58.00% and a Kappa value of 0.48, this machine learning model still has overestimated burn severity in some regions of burned areas. Compared to that of mentioned-above models, assessment results of burn severity levels using the SSTCA-SVR model was more accurate with an overall accuracy of 71.20% and a Kappa value of 0.64. It could be concluded that the application of a transferring learning algorithm would be very helpful for building an assessment model of burn severity with a good transferring ability. In this way, more accurate results could be obtained in the initial assessment of burn severity and the response of post-fire management could thus be speeded up after forest fires.

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