M-band discrete wavelet transform–based deep learning algorithm for identifying thermokarst lakes in the QinghaiTibetan Plateau

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Andrew Li , Jiahe Liu , Olivia Liu, Xiaodi Wang


Thermokarst lakes serve as key signs of permafrost thaw, and as point sources of CH4 in the present and near future. However, detailed information on the distribution of thermokarst lakes remains sparse across the entire permafrost region on the Qinghai–Tibet Plateau (QTP). In this research, we developed a new discrete wavelet transform (DWT)–based dual-input deep learning (DL) model using a convolutional neural network (CNN) framework to automatically classify and accurately predict thermokarst lakes. We created a new 3-way tensor dataset based on raw image data from more than 500 Sentinel-2 satellite lake images and decomposed those images using state-of-the-art M-band DWTs. We also incorporated non-image feature data for various climate variables. The special data treatment adds additional features and improves validation accuracy by up to 17%. As our data pre-processing does not require any manual polygon tracing, our method is more robust and can be upscaled easily without having to collect field data.




Environmental Sciences


climate change, permafrost thaw, thermokarst lake classification, thermokarst lake identification, discrete wavelet transform, Deep learning, convolutional neural network


Published: 2023-08-11 23:33

Last Updated: 2023-08-12 06:33


CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
Data will be available at https://github.com/jliu2006/pingo.

Conflict of interest statement:
The authors declare no competing interests.