Fig 1.
QTP maps made with Natural Earth (https://www.naturalearthdata.com), whose data is in the public domain (https://www.naturalearthdata.com/about/terms-of-use/). Lake image by Alotrobo from Pexels (https://www.pexels.com/photo/top-view-of-lake-surrounded-by-trees-1559074/)
Fig 2.
Distribution of thermokarst and glacial lakes.
The geographic area shown is the QTP, with degrees latitude and longitude marked on the sides of these figures. (A) Distribution of glacial lakes. Lakes in the Altay and Sayan region are in brown, and lakes not in that region are in blue. The lakes in the Altay and Sayan region are clearly outside the QTP and are far from the other glacial lakes. There are also lakes in the High Asia region outside the QTP included in this inventory. (B) Distribution of thermokarst (red) and glacial (blue) lakes that meet our location criteria. (C) Distribution of thermokarst and glacial lakes that meet our location and size criteria. (D) Distribution of sample thermokarst and glacial lakes that were used to train and evaluate our model. The sample lakes we selected have a similar spatial distribution as the population of lakes meeting our size criteria, which also has a similar spatial distribution as the population of all lakes in the inventories. Made with Natural Earth (https://www.naturalearthdata.com), whose data is in the public domain (https://www.naturalearthdata.com/about/terms-of-use/).
Fig 3.
Image of a Tibetan yak before and after 2D DWT.
(A) Original image of thermokarst lake. (B) The full result of wavelet decomposition using the 3-band 2-regular wavelet with scaled color. Original image by Alexandr Frolov from Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Sarlyk_Yak2.jpg)
Table 1.
Non-image variables used in our model.
Table 2.
A summary of the seven types of input data we trained our model on.
Table 3.
p-values for thermokarst lakes. Suface runoff, total precipitation, snow albedo, 2 meter temperature, soil temperature level 1, and high vegetation leaf area index all produced statistically significant p-values. We explore their distributions and explain these results later in this section.
Table 4.
p-values for glacial lakes. None of these values are significant, indicating that our sample is representative of our population.
Table 5.
p-values for thermokarst lakes. The p-values for elevation and latitude are significant.
Table 6.
p-values for glacial lakes. None of the p-values are significant.
Table 7.
A table summary of five metrics of evaluation calculated based on each model’s testing performance. The highest values in each category are in bold.
Fig 4.
ROC curves and confusion matrices for 4-band 2-regular model vs. true control.
(A) The ROC curve of the selected model using the 4-band 2-regular (wv42) DWT. (B) The confusion matrix of the selected model using the 4-band 2-regular (wv42) DWT. (C) The ROC curve of the true control model with the input of image-only non–wavelet-decomposed satellite data. (D) The confusion matrix of the true control model with the input of image-only non–wavelet-decomposed satellite data.
Fig 5.
Significance of feature variables: lake bottom temperature, snowmelt, and level 1 soil temperature.
(A) Lake bottom temperature for thermokarst and glacial lakes. (B) Snowmelt for thermokarst and glacial lakes. (C) Level 1 soil temperature for thermokarst and glacial lakes.