Detecting earthquake-induced bathymetric changes helps to understand the geomorphologic process of tufa lakes. Traditional field measurement methods are difficult for spatially complete and continuous bathymetric mapping. Multi-temporal high-resolution optical satellite images are cost-efficient data used for bathymetric change detection. However, for detecting bathymetric changes in tufa lakes, collecting high-density depth calibration data and constructing highly robust water depth inversion models pose certain challenges. This study takes Huohua Lake before and after the Jiuzhaigou Earthquake as the research object, and carries out the bathymetric change detection based on high-resolution remote sensing data. Initially, the WorldView-2 (WV-2) multispectral images obtained before and after the earthquake under the water-storage state of the lake were used as the data source, and the unmanned aerial vehicle (UAV)-based measurement under the water-free state of the lake after the earthquake was used as the bathymetric calibration and validation data. Then using satellite-derived image reflectance, we constructed two-phase bathymetric models with machine learning methods, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP). The comparison results with classical regression models indicate that machine learning-based water depth inversion models are generally superior. Specifically, the R² (coefficient of determination) of the optimal model RF reach 0.85 and 0.91, with RMSE (root mean square error) of 1.40 m and 1.08 m. The bathymetric difference maps generated from water depth inversion results reveal that during the period from October 2016 to January 2022, the core area of Huohua Lake experienced more erosion than accretion due to the earthquake-induced flooding. The spatial patterns of changes show that the erosion mainly located in the raised tufa mound area, while the accretion was concentrated in the shallow flat area. This study provides a remote sensing approach for quantifying bathymetric changes in tufa lakes after extreme geological disasters.

检测地震引起的水深变化有助于了解凝华湖的地貌过程。传统的现场测量方法很难进行空间完整和连续的测深测绘。多时相高分辨率光学卫星图像是用于测深变化检测的经济高效的数据。然而，为了检测凝灰岩湖泊的水深变化，收集高密度深度校准数据并构建高度稳健的水深反演模型提出了一定的挑战。本研究以九寨沟地震前后火花湖为研究对象，基于高分辨率遥感数据开展水深变化检测。最初以湖泊蓄水状态下震前和震后获取的WorldView-2（WV-2）多光谱影像为数据源，在蓄水状态下基于无人机进行测量。采用地震后湖泊的自由状态作为测深校准和验证数据。然后，利用卫星图像反射率，利用随机森林（RF）、支持向量机（SVM）和多层感知器（MLP）等机器学习方法构建了两阶段测深模型。与经典回归模型的比较结果表明，基于机器学习的水深反演模型总体上更优越。具体而言，最优模型RF的R ²（决定系数）分别达到0.85和0.91，RMSE（均方根误差）分别为1.40 m和1.08 m。水深反演结果生成的水深差值图显示，2016年10月至2022年1月期间，火花湖核心区受地震洪水影响，侵蚀程度大于增生程度。变化的空间格局表明，侵蚀主要集中在凸起的凝灰岩丘区，而增生则集中在浅平坦区。这项研究提供了一种遥感方法来量化极端地质灾害后凝灰岩湖泊的水深变化。