Modelling Arctic Coastal Plain Lake
Depths Using Machine Learning andGoogle Earth Engine

Ram Avtar, Ph.D.

Associate Professor
Graduate School of Environmental Science
Hokkaido University, Japanram@ees.hokudai.ac.jp

Motivation

1

Introduction
 Numerous shallow thermokarst lakes in the Arctic
coastal plains recently shows a change in lake
abundance and area owing to global warming.
 Lakes and ponds can occupy more than 20%–40% of
the landscape in Arctic lowland regions (Grosse et al.,2013; Muster, et al., 2017)
 IPCC and other recent studies reported that the highlatitudes warming is much higher than the global
annual average warming Grosse et al. 2013 estimated that more than half ofthe lakes found in permafrost regions are likely ofthermokarst origin
https://news.uaf.edu/arctic_lakes_july2014/

Introduction
 These lakes undergo various phases of formation, growth,drainage, and reformation (Jone et al., 2015)
 With a rapidly warming in Arctic there is dramatic changes inlake hydrology, lake ice characteristics, and permafrost
degradation (Liljedahl et al., 2016) Therefore, monitoring of lake-rich Arctic regions at hightemporal and spatial resolution is crucial for understanding
their response to climate change (Paltan et al., 2015)
 To better understand the degradation of lake bodies due tothe climate change in the Arctic environment, acomprehensive lake water volume analysis over multiple
time period and a larger spatial scale is necessary.
 However, no quantitative bathymetric studies in a larger
scale has been attempted in the region primarily owing to
the logistic issues, and harsh climatic environment.

Study Location

Data & Methods
 Simpson and Arp, (2017) measured thediscrete depth points of 19 lakes in the study
area using a HumminBird 798ci HD SONAR
instrument mounted on a floating plane during
the summer of 2017.
 The dataset is published as an open inventory
and can be retrieved from Arctic Data Center
portal (https://arcticdata.io/).
 To adequately represent the depth samples,
they selected the lakes which are larger than 1
km2 in surface area. The minimum measured
depth value is 0 m, maximum is 21.3 m, meanis 5.26 m and standard deviation is 4.77 m.

Google Earth Engine Applications & Machine Learning
Regional-scale inventories of lake bathymetric mapping in inaccessible terrains is possible withthe help of satellite remote sensing images (based on Reflectance) We used Landsat-2017 (correspondingto the field dates) to model thebathymetry for the study area.
 As some of the Landsat-8 OLI images ofJuly to September are cloudy and partial
cloudy, we used the ‘median’ image ofLandsat surface reflectance collection(ee.ImageCollection('LANDSAT/LC08/C0
1/T1_SR')) acquired between July andSeptember of 2017.
 Landsat SR product is theatmospherically corrected surface
reflectance from the Landsat 8 OLI/TIRS
sensors having contain 5 visible andnear-infrared (VNIR) bands, 2 shortwave infrared (SWIR) bands, and twothermal infrared (TIR) bands. The clouds
in the image scenes have been masked
based on the ‘pixel_qa’ band of Landsat
8 SR data.

Machine Learning Models

7 CART introduced by Breimanrefer to decision tree (DT)algorithms that are useful forclassification or regression
predictive modeling problems.
 RF is a supervised machinelearning based on the decision
trees but formed by many
number of individual DT’s thatfunction as an ensemble
 The advantage of RF over CART
is that the former decrease thevariance of the resulting output,
and are less prone to overfitting,thus gives an robust results.

Fig 2. Schematics of ML models employed inthis study: (a) classification and regression
tree, (b) random forest, and (c) support
vector machines with different kernels.

Results
Decision Trees

Random Forest

Support Vector Machines
Fig 3. Lake bathymetric maps
derived from three machine learningmodels(a)classificationandregression trees, (b) random forest,and (c) support vector machines.

Accuracy Assessment

Evaluation
MatrixDecision
TreeRandom
ForestSupportVectorMachines

ACC

0.88

0.88

0.32

MAE

0.55

0.53

3.12

RMSE

0.9

0.86

5.22

R20.977

0.979

0.21

The hold out data (5734) of the depth points (validation data)
was used to prepare the ACC, MAE, RMSE and R2.

 RF has the lowest error (RMSE = 0.86),
while the SVM method again showed thelowest performance (RMSE = 5.22). Interms of Accuracy, both CART and RF
shows a similar values of ACC (88%),
whereas ACC for SVM is rated the poorest(32%).
Figure 5. Bathymetric profiles of some studied Arctic lakes:(a) and (f) are the locations of field samples for X-Y and U-V
profiles; (b) and (g) are CART derived bathymetric maps, (c)and (h) are RF derived bathymetric maps, (d) and (i) are
SVM derived bathymetric maps for the two lakes shown in(a) and (f); (e) and (j) are corresponding profile plots for X-Yand U-V profiles respectively.

Future Work One of the potential application of satellite
derived bathymetry is that we can model thebathymetry for any time period as long as cloud
free images are available.
 Figure presents the comparative analysis ofbathymetric maps for 2017 and 2016 derived
from the random forest model. It can be seen that
there is a considerable amount of under
estimation in the depth estimation when applying
the models trained from 2017 to 2016 despite
having used the same time period images andmodel.
 We are exploring the possibility to overcome thislimitation by applying more effective atmosphericcorrection algorithms.
 We will apply this algorithm to other areas ofArctic.
Figure 5. Applying the trained data from 2017 bathymetric model
to 2016 images: (a) bathymetric map of 2017, (b) bathymetric mapof 2016. Note that the reflectance values of same location (redcross) is different for both years.

Conclusions
 High-quality bathymetric data of water bodies in climate sensitive
areas are important for understanding glacier mass balance, water
storage and the pace of warming induced degradation.
 In this study, we employed in situ data from Simpson and Arp [26] toderive the bathymetric information of hundreds of thermokarst lakes in
Arctic coastal plains in the Northern Alaska. Landsat 8-OLI surface
reflectance product of 2017 The best method for bathymetric retrieval from OLI images was RF
method, which out classed both CART and SVM
 Therefore, RF method and their implementation in GEE isrecommended for future lake depth retrievals in shallow coastal lakes,
especially for large scale analysis.

11THANK YOU

for your kind attention