Abe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5
https://doi.org/10.1186/s40645-024-00610-5

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Ground subsidence and polygon
development due to thermokarst
in the Lena‑Aldan interfluve, eastern Siberia,
revealed by satellite remote sensing data
Takahiro Abe1*   and Yoshihiro Iijima2

Abstract
Thermokarst development is a topographic change in the landscape that is commonly associated with permafrost
degradation in ice-rich permafrost regions. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia
has undergone extensive thermokarst development in the last three decades, particularly in the vicinity of settlements. Despite the negative effects of thermokarst development on the inhabitants of these settlements, no quantitative observation methods have been developed to investigate the surface displacement due to thermokarst
development over entire towns. This study utilized interferometric synthetic aperture radar to reveal ground-surface
displacement associated with thermokarst near the settlements of selected towns. The findings showed that significant subsidence was detected in disturbed areas (farming and abundant arable land) near the towns. The magnitude of subsidence in the Tyungyulyu and Mayya areas was less than that in Churapcha and Amga. Polygon density
in a defined area in each town was examined using high-resolution optical images. The polygon density in Churapcha was considerably lower than that in Mayya, whereas polygonal texture in some areas in Tyungyulyu and Amga
was unclear. Spatial frequency analysis using satellite optical images showed clear differences in averaged spectrum
models between well-developed and less-developed polygons, which may reflect trough depths and density of vegetation between polygons. Satellite-based subsidence maps and statistics describing polygon development may be
useful for evaluating both initial and subsequent stages of thermokarst development.
Keywords Permafrost degradation, Thermokarst, Ice wedge polygon, Eastern Siberia, Interferometric synthetic
aperture radar, Satellite optical images, Spatial frequency analysis
1 Introduction
Thermokarst development is an irreversible process associated with landscape changes caused by the melting ofground ice (ice wedges) underlain by ice-rich permafrost*Correspondence:
Takahiro Abe
abe@bio.mie-u.ac.jp
1
Graduate School of Bioresources, Mie University, 1577
Kurimamachiyacho, Tsu, Mie 514‑8507, Japan
2
Graduate School of Urban Environmental Sciences, Tokyo Metropolitan
University, 1‑1 Minami‑Ohsawa, Hachioji, Tokyo 192‑0397, Japan(Czudek and Demek 1970; van Everdingen 2005). An
example of ice-rich permafrost, the Yedoma Ice Complex, is found in eastern Siberia. In particular, the area
between the Lena and Aldan Rivers (Fig. 1) is characterized by abundant ground ice with a volumetric ice content above 60% (Schirrmeister et al. 2013; Shestakova
et al. 2021). Recent climate changes have promoted permafrost degradation in the interfluve (Iijima et al. 2014),
and thermokarst-induced polygonal subsidence patterns,
hereafter referred to as polygons, have started to appear
in residential areas (Crate et al. 2017). Thermokarst
development in the region has had a negative impact on© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 Map of the Lena-Aldan interfluve in eastern Siberia. Black dots indicate the location of representative towns, and dashed rectangles indicate
observation areas of ALOS-2 images

the daily lives of local inhabitants as the changes in thelandscape have been associated with extensive destruction of infrastructure (Shiklomanov et al. 2017; Hjortet al. 2018; 2022).
Thermokarst development can be divided into fourmain stages (Bosikov 1991; Desyatkin et al. 2009). In
the initial stage, called byllar, the ground surface starts
to subside, and polygonal patterns begin to appear at
the surface due to melting of ice wedges. As thermokarst development progresses, the extent of subsidence
increases and the bumpy relief become more apparent,
often forming small lakes. These first two stages of initiation and enlargement typically occur over a period
spanning several decades or up to a few hundred years(Soloviev 1973; Fedorov et al. 2014a). The ice wedge
continues to thaw, and the resulting depression is filledwith the meltwater from the ice wedge and precipitation, which promote further thawing of the permafrost
and the formation of large thermokarst lakes. After water
evaporation, stable thermokarst basins, called alases, areformed. Approximately 16,000 alases exist in the lowlands of Central Yakutia (Bosikov 1991), and alases cover
approximately 30% of the territory’s surface. Although all
the stages of thermokarst development can be observedin the interfluve, thermokarst dynamics are still not fully
understood. For example, during the first two stages ofthermokarst development, the spatiotemporal evolutionof surface subsidence remains unclear. Long-term fieldobservations of thermokarst that have been carried out
in Yukechi (Fig. 2a) since the 1990s have revealed interannual subsidence (Fedorov and Konstantinov 2008) andthermokarst lake expansion (Ulrich et al. 2017). However,the large scale of the areas that have undergone thermokarst subsidence in the interfluve has meant that investigations are logistically difficult, and conventional remotesensing techniques are not capable of detecting subsidence at a level of several to tens of centimeters per year.
As a result, the temporal changes in thermokarst subsidence and prediction of further changes are difficult toassess.
Recent advances in remote sensing techniques haveenabled us to examine ground-surface changes fromspace without any equipment in situ. In particular, interferometric synthetic aperture radar (InSAR) well suitedfor detecting surface displacement (e.g., Hanssen 2001).
InSAR time-series analysis using a number of interferograms can be used to derive spatiotemporal changes insurface displacement (e.g., Berardino et al. 2002; Schmidt

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Fig. 2 Study area. a Elevation, towns (stars) and an observation site by Melnikov Permafrost Institute (dot) in the study area close to Lena River; b
Map of terraces distinguished by Soloviev (1959); c Elevation along the Amga River around Amga and towns (stars)

and Bürgmann 2003; Biggs et al 2007), and several studies on permafrost dynamics using this method have been
published to date (e.g., Liu et al. 2015; Daout et al. 2017;
Antonova et al. 2018). In eastern Siberia, van Huissteden
et al. (2021) used C-band Sentinel-1 InSAR data to derive
vertical displacement in an area of the Chokurdakh tundra (70.49°N, 147.29°E) during the thawing season. In the
Lena-Aldan interfluve, the average rate of surface subsidence due to permafrost thawing has been examined byL-band InSAR stacking in Mayya and Churapcha (Abeet al. 2020; Iijima et al. 2021; Fig. 2a). Abe et al. (2022)
recently examined the inter-annual and seasonal surface
displacement of a fire scar in the southern Mayya using Land C-band InSAR analysis. They reported that L-band
InSAR was suitable for monitoring long-term surface
displacement in terms of coherence. On the contrary,C-band InSAR with a high temporal resolution is very
useful for examining short-term seasonal displacement,
but the C-band coherence can sometimes be lost, evenin 12 days, particularly in forested taiga areas (Abe et al.
2022). Consequently, L-band InSAR time-series analysis
is better suited to deriving long-term surface subsidence
due to thermokarst. However, the studied areas were very
limited, and the spatiotemporal changes in surface displacement remained unknown.
Polygonal terrain is formed by thermal contraction
cracking, characterizing in terrestrial periglacial regions.
Especially in ice-rich permafrost regions, high-centeredpolygons are resulted from the widening and deepening of the polygon-outlining troughs due to ice-wedge
degradation (e.g., French 2017). Ice-wedge polygons are
the most common type of thermal contraction polygons

Abe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

with 5–20 m in diameter in Arctic regions (Black 1976).
Therefore, the size and density of polygons that form in
the initial stage of thermokarst reflects the local geomorphological characteristics (Ulrich et al. 2011). However,few studies have attempted a statistical analysis of polygons in the Lena-Aldan interfluve. Saito et al. (2018) used
an UAS to obtain high spatial resolution images (i.e., ata spatial resolution of several centimeters), and to reveal
the spatial distribution and size of polygons in anthropogenically disturbed areas in Churapcha (Fig. 2a). Thesestatistical analyses require micro-topographic data,
images with resolutions of less than tens of centimeters,
and field observations together with a UAS and/or aerial
photographss (Ulrich et al. 2011). Although applicationsof UAS in cryosphere studies have been increasing andvery useful (Gaffey and Bhardwaj 2020), the coverage andacquisition timing of UAS images are limited. It is thus
necessary to examine whether it is possible to identify
and evaluate polygon development using high-resolution
satellite images, such as those collected by the WorldView-series optical satellites. It is considered that theextraction and statistical analysis of ice wedge polygon
characteristics from optical satellite images in different
areas will facilitate a deeper understanding of thermokarst dynamics, especially in the initial stages of thermokarst development.
The objective of this study was to characterize thermokarst subsidence in the study area, and to evaluate theextent of permafrost degradation in the Lena-Aldaninterfluve. L-band InSAR data obtained by the Advanced
Land Observing Satellite-2 (ALOS-2) platform were used
to derive the spatiotemporal changes in surface displacement in the study area. WorldView and Pleiades highresolution optical images were used to identify ice wedge
polygons in areas where subsidence has been detected,
and to collect data for statistical analysis. A combinationof InSAR-based subsidence measurements and estimatesof the degree of polygonal texture development wereused to produce an index for evaluating the initial stageof thermokarst development in the disturbed areas.

2 Study area
The Yedoma Ice Complex is widely distributed in the
Lena-Aldan interfluve, which is terraced and mainly
divided into five terraces named after geomorphology(Soloviev 1959). Figure 2 shows map of the terraces andtheir elevation. Typical well-developed alas depressions
and many thermokarst features are identified mainly on
the Tyungyulyu and Abalakh terraces, and both terraces
are characterized by ice-rich deposits over 25 m-thinkin some places (Soloviev 1959; Ulrich et al. 2017). On
the Tyungyulyu terrace from Borogontsy to the Mayyaarea (Fig. 2a and b), many deforested areas (farming andPage 4 of 17

arable land) and alases surrounded by forest are found in
the vicinity of towns. The town of Mayya is representative of a residential area located in a typical thermokarst
landscape. Yukechi, which is 10 km east of Mayya, is on
the Abalakh terrace (Fig. 2a and b) and is the site of fieldobservation station managed by the Melnikov Permafrost Institute in Yakutsk. Thermokarst features were also
observed in this area (Fedorov and Konstantinov 2008;
Fedorov et al. 2014a; Ulrich et al. 2017). Churapcha islocated approximately 200 km to the east of Yakutsk andis located on the same Abalakh terrace as Yukechi. Many
dry grasslands and abandoned arable lands, which may
be at risk of thermokarst development, are widely distributed around the settlement of Churapcha (Iijima et al.
2021). The population of Churapcha has increased in thelast 10 years, which has resulted in expansion of the settlement into the dry grasslands and abandoned arable
lands. Therefore, this town was selected for evaluating
the effect of thermokarst development in anthropogenically disturbed areas.
The Yedoma ice complex is also distributed alongwatersheds and on some terraces in river valleys (Soloviev 1959; Iijima and Fedorov 2019), which may be also
at a risk of thermokarst development. Amga is locatedon the left bank of the Amga River, which is one of thetributaries of the Aldan River (Figs. 1 and 2c). This area islocated within the Amga-Aldan region of gently sloping
central taiga (Fig. 2c), and there are large ice-wedges up
to 20 m thick on the left bank (Lytkin et al. 2021). Lytkin et al. (2021) recently conducted field research andgeographical analysis to map the extensive thermokarst
development in the Amga town. Their findings showedthat areas of byllar are distributed to the northwest andnortheast of the settlement. However, the spatiotemporal variation in the surface subsidence is unknown. Since
numerous arable areas exist on the left bank of the Amga
River, we included this site in our study area.

3 Data and methods
3.1 ALOS‑2 data and InSAR analysis

This study used ALOS-2 L-band HH-polarized SAR
data with a resolution of 10 m (Stripmap mode 3 data)obtained from 2014 to 2021 (Table 1) to examine interannual changes in surface displacement in the LenaAldan interfluve, Central Yakutia. The advantage ofL-band SAR is that it is less affected by surface vegetation
and a small temporal decorrelation (Wang et al. 2017).
Thus, L-band SAR data were used to estimate interannual displacement in areas surrounded by forested
taiga in the interfluve. Our analysis method was essentially the same as that used in previous studies (Strozzi
et al. 2018; Abe et al. 2020, 2022; Iijima et al. 2021). We
used Gamma software to generate Single Look Complex

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Table 1 Detail of ALOS-2 data used in this study
Area

Path-Frame,
Incidence angle

Acquisition date of scenes (dd/mm/yyyy)

Mayya

126–1230, 32.5

03/10/2014, 02/10/2015, 30/09/2016, 31/08/2018, 12/10/2018, 19/07/2019, 30/08/2019, 11/10/2019, 17/07/2020,
21/05/2021, 08/10/2021

Churapcha

124–1240, 28.5

11/08/2015, 22/09/2015, 19/09/2017, 10/07/2018, 21/08/2018, 07/07/2020, 06/07/2021

Tyungyulyu

126–1240, 40.6

05/09/2014, 12/06/2015, 04/09/2015, 02/09/2016, 01/09/2017, 22/06/2018, 03/08/2018, 21/06/2019, 19/06/2020,
18/06/2021

Amga

126–1210, 40.6

05/09/2014, 12/06/2015, 04/09/2015, 02/09/2016, 01/09/2017, 22/06/2018, 03/08/2018, 21/06/2019, 19/06/2020,
18/06/2021(SLC) images from level 1.1 ALOS-2 data (Wegmüller and Werner 1997). After co-registration of two SLC
images, we performed interferometry with subtractionof topographic effects using the ALOS World Digital
Surface Model − 30 m- (Takaku et al. 2020). After adaptive spectral filtering (Goldstein and Werner 1998), a
phase unwrapping procedure was employed following
the method of Costantini (1998). The spatial resolution
after the terrain-corrected data were geocoded and projected onto a UTM map projection was 30 m. Finally, theline of sight (LOS, distance between satellite and ground)change detected by InSAR was converted to a verticaldisplacement by dividing the LOS by the cosine of theincidence angle because vertical displacement is assumed
to be dominant in thermokarst. We generated as many
interferograms within a period of less than four years as
possible.
Tropospheric and ionospheric disturbances can sometimes interfere with extracting the extent of surface displacement in interferograms. To address the troposphericeffect, the GACOS product was used to reduce the noise(Yu et al. 2017; 2018). The impacts of ionospheric disturbances typically manifest as a long-wavelength trend.
Indeed, several unexpected signals were sometimes
observed, particularly in forested areas, which may affectthe fitting of the long-wavelength trend. To address thisissue, we first clipped the targeted areas in each interferogram to exclude forested areas where possible. We
then modeled the trend by fitting a 2D polynomial function and subtracted from original interferogram. A manual quality check of the unwrapped interferograms was
also performed to remove any pixels affected by major
unwrapping errors.
In InSAR analysis, selecting a phase reference point ininterferograms has been demonstrated to be importantfor deriving surface displacement. However, identifying astable point that is unlikely to be displaced in permafrost
regions is difficult. In a study conducted to the southeastof Churapcha, Iijima et al. (2021) used an alas that was
not located in a wetland landscape as a reference point,

mainly because alases are representative of the climax
geomorphological stage of thermokarst development(Bosikov 1991). In this study, averaged InSAR phases atthree reference points were used to correct the phasein each interferogram. The reference points used in thisstudy were also in the bottom of alases and in settlements
outside wetlands that were free of any polygonal development. This manipulation was performed to reduce localized tropospheric effects, which sometimes appear andcould not be reduced by the GACOS due to their spatial
scale. The phase differences between each reference point
and the averaged one in each region was examined toevaluate that they did not displace excessively relative tothe averaged reference. The averaged deviation of phasedifferences in areas of Mayya, Churapcha, Tyungyulyu,
and Amga was 0.17, 0.11, 0.18 and 0.22 cm, respectively.
After generating and correcting interferograms following these procedures, we performed InSAR timeseries analysis as described in previous studies (Schmitt
and Bürgmann 2003; Biggs et al. 2007, Liu et al. 2015;
Yanagiya and Furuya 2020; Abe et al. 2022). Selected
InSAR pairs in each site are shown in Figure S1 in Additional file 1. In the time-series analysis, pixels affected bynoise were eliminated using a coherence filter (i.e., coherence above 0.35 in 50% of the interferograms, Rouyet
et al. 2019). To estimate the propagation errors in thetime-series analysis, we assumed that each interferogram
contains an error of 0.2 cm (Yanagiya and Furuya 2020;
Abe et al. 2022) and created an InSAR data covariancematrix following the method of Biggs et al. (2007). The
2σ propagation error reached up to approximately 2 cm,
which depends on the number of epochs in the timeseries analysis.
3.2 High‑resolution satellite optical images and extractionof polygon statistics

WorldView and Pleiades images (panchromatic: 0.5 m
resolution, multispectral: 2 m resolution) were used to
identify polygons caused by thermokarst. Details of thedatasets used in this study are shown in Table 2. We firstAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

Table 2 Detail of high-resolution satellite optical images used inthis study
Area

Satellite

Acquisition date of scenes (dd/mm/yyyy)

Mayya

WorldView-2
Pleiades-1B

12/09/2019
04/06/2021

Churapcha

WorldView-2
WorldView-3

11/09/2011, 04/06/2021
14/06/2021

Tyungyulyu Pleiades-1B

26/07/2013, 05/08/2013, 18/06/2019

Amga

05/08/2017, 20/06/2021
12/06/2019

WorldView-2
Pleiades-1B

performed visual comparisons between an UAS orthorectified image taken in 2017 by Saito et al. (2018) anda WorldView-2 pansharpened image taken in 2021 toconfirm whether number of polygons were identical inboth image types (Fig. 3a and 3b). Next, we counted thenumber of polygons in a defined area (white inset) andcalculated the density of polygons (Fig. 3b). Note that the

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selected area should contain only polygons and exclude
features such as forests, lakes, and buildings, as much as
possible.
In addition to calculating the number of polygons,
we attempted to estimate the degree of polygon development by spatial frequency analysis. Polygons weregenerally circular in shape with a diameter of approximately 10 m, and the borders are represented by theshading in the image (Fig. 3). When the polygons are
well-developed, their borders can be easily identified,because we consider the amplitude spectrum in spatial
frequencies corresponding to the diameter of polygons
becomes large. The spatial frequency analysis was conducted as follows: 1) clipped panchromatic images containing only polygons in an area measuring 100 × 100 ­m2
were prepared (Fig. 3c); 2) discrete Fourier transformation was applied to the clipped panchromatic images toobtain the relationship between spatial frequency andamplitude spectrum in the two-dimensional (2D) spatial
domain (Fig. 3d); and 3) the 2D amplitude spectrum wasFig. 3 Visual comparison of a an orthorectified UAS image, and b a WorldView pansharpened image. Each red symbol indicates an identified
polygon, and the white rectangle indicates the area in which the density of polygons was calculated. c, d, and e Procedure of spatial frequency
analysis. An example of c a clipped panchromatic image, d an amplitude spectrum in 2D spatial domain, and e a 1D amplitude spectrum

Abe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

converted to a one-dimensional (1D) amplitude spectrum
for fitting to an exponential function (except for spatial
frequencies below 0.05 ­m−1) to obtain a model representing the degree of polygonal texture development (Fig. 3e).
The average of the models between well-developed andless-developed polygons was compared to discuss thedegree of polygon development.

4 Result
4.1 Ground displacement derived from ALOS‑2 InSAR
time‑series analysis

Figure 4a shows the WorldView-2 image of Mayya town.
The settlement is in the top center of the image. Mayya issituated on almost flat ground at an elevation of approximately 140 m above sea level (a. s. l.); the southern part of
the town is higher and has an elevation of up to 200 m a.
s. l. (Fig. 4b). Alas depressions can be identified by theirslightly lower elevation compared to the surrounding
inter-alas meadows (Fig. 4b). Figure 4c shows the spatial distribution of the cumulative vertical displacement
around Mayya town, with positive and negative signals
indicating uplift and subsidence, respectively. Significant
subsidence of up to 10 cm was observed in the deforested
inter-alas areas. Temporal changes in the displacement at
the four selected subsidence sites (M1–M4, Fig. 4d) were

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almost linear, except near the end of the observations
when they appear to have accelerated somewhat.
Figure 5a shows WorldView-2 images of Churapcha
town. The settlement of Churapcha is located to thewest and the center of the large lakes in the center of thesatellite image. In the south, dry grasslands and localized areas of abundant arable land were distributed. The
elevation of Churapcha ranges from 170 to 220 m a. s. l.
(Fig. 5b), with areas to the north, west, and southeast having a slightly higher elevation, while areas in the centerare lower than the surrounding areas. Figure 5c shows
the spatial distribution of the cumulative surface subsidence, and that maximum subsidence of up to 16 cm wasobserved in an area of abundant arable land in the north.
Other areas of arable land and grassland subsided bymore than 10 cm. Interestingly, temporal changes in thefour selected subsidence sites (C1–C4, Fig. 5d) showed
little inter-annual variations and the rates of subsidence
were almost linear.
Figure 6a shows the spatial distribution of cumulative
surface displacement in the Tyungyulyu area from Borogontsy (north) to Tyungyulyu (south) (see also Fig. 2a),showing that areas where large subsidence was observed(representative areas are indicated by white rectangles) close to local town settlements. Time-series plots

Fig. 4 (a) WorldView-2 image captured in 2019 over Mayya; (b) Elevation around Mayya; (c) Spatial distribution of cumulative surface displacement.
M1–M4 denote sites used for extracting time-series displacement data shown in (d). Stars indicate the reference points used to calibrate InSAR
phases; (d) Time-series plots of the four selected sites shown in (c)Abe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 5 (a) WorldView-2 image captured in 2011 over Churapcha; (b) Elevation around Churapcha; (c) Spatial distribution of cumulative surface
displacement. C1–C4 denote sites used for extracting time-series displacement data shown in (d). Stars indicate the reference points used
to calibrate InSAR phases; (d) Time-series plots of the four selected sites shown in (c)of subsidence at three of the selected sites (T1–T3) are
shown in Fig. 6b; the plots show that while there weresome inter-annual variations, the overall trends between
2014 and 2021 were generally linear. The magnitude ofsubsidence was up to 8 cm. Figure 6c–e shows enlarged
views of the subsidence map for the three areas withsatellite optical images (Fig. 6f–h) for comparison. In
Syrdakh, areas of subsidence are distributed to the southof the settlement, as well as to the west and east (Fig. 6c
and f ). The magnitude of subsidence in the area to thesouth reached up to 8 cm (T1). In Oltyokhsky, areas of
subsidence are distributed to the northeast of the settlement (Fig. 6d and g), which corresponds to the distribution of arable land (Fig. 6g). The magnitude of subsidence
in these areas was up to 6 cm (T2). In Balyktakh, areasof subsidence are distributed to the northeast of the settlement (Fig. 6e and h), and the magnitude of subsidence
was up to 7 cm (T3).

Figure 7a shows the spatial distribution of cumulative surface displacement in the Amga area from Amga
(northeast) to Bolugur (southwest) along the Amga River.
Four areas (A1–A4 in white rectangles) showing significant surface subsidence were examined in detail andtime-series plots for these areas are shown in Fig. 7b. Thechanges in subsidence rates were almost linear between
2015 and 2019, followed by a slight increase in the last
two years. Enlarged views of the subsidence maps in theseareas (Fig. 7c–e) are compared to optical satellite images(Fig. 7f–h). The areas of subsidence in Amga (Fig. 7c)are distributed to the northwest and northeast of thesettlement (Fig. 7f ), and subsidence of up to 10 cm was
detected. In Pokrovka, a large area of subsidence (~ 0.6­km2) was observed to the north of the settlement, andsubsidence of 16 cm was detected. Around Bolugur, clear
evidence of subsidence was observed around the settlement (Fig. 7a) and significant subsidence was detected inAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 6 a Spatial distribution of inter-annual vertical displacement in the Tyungyulyu area from Borogontsy to Tyungyulyu. T1–T3 denote sites
selected for extracting the time-series displacement data shown in b. Stars indicate the reference points used to calibrate InSAR phases; bTime-series plots for the three selected sites shown in a; c, d, and e Enlarged view of inter-annual vertical displacement in c Syrdakh, d Oltyokhsky
and e Balyktakh, respectively; f, g, and h Pleiades-1 images captured f in 2013 over Syrdakh, g in 2013 over Oltyokhsky, and h in 2019 over Balyktakh,
respectively

two areas of arable land located to the southwest of thesettlement (Fig. 7e and h) in the upstream reaches of theAmga River (Figs. 2c and 7a). The magnitude of subsidence was over 25 cm, which was largest observed in thisstudy.
4.2 Density of polygons and comparison with subsidence
and elevation

The top panel of Fig. 8 shows the locations where the polygon density was counted in the four towns; the box plots
shown in the bottom panel show the number of polygons
in each town. The median polygon density in Mayya was

292 ­ha−1, which was considerably higher than that inother towns (165 ­ha−1 in Syrdakh, 76 ­ha−1 in Churapcha,
and 63 ­ha−1 in Amga). These results imply that the diameter of polygons in Mayya is considerably smaller thanthose at the other three sites. Examples of polygonal terrain were shown in Figure S2 in Additional file 1.
Figure 9 shows comparisons between subsidence andelevation, subsidence and polygon density, and elevation and polygon density in areas with well-developed
polygons shown in Fig. 8. Results of linear regressions
with all data (dashed line) and all data excluding Amga
(solid line) were also presented. The R2 values for theAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 7 a Spatial distribution of inter-annual vertical displacement in the Amga area along the Amga River. A1–A4 denote sites selected
for extracting time-series displacement data shown in b. Stars indicate the reference points used to calibrate InSAR phases; b Time-series plotsof the four selected sites shown in a; c, d, and e Enlarged view of inter-annual vertical displacement in c Amga, d Pokrovka and e the western partof Bolugur, respectively; f WorldView-2 image captured in 2021 over Amga; g Pleiades-1 image captured in 2013 over Pokrovka; h WorldView-2
image captured in 2017 over the western part of Bolugur, respectively

InSAR-based subsidence and elevation were 0.32 and0.31 for all data and all-except-Amga data, respectively(Fig. 9a). The R2 values for the subsidence and polygon
density for all data and all-except-Amga data were muchsmall, 0.07 and 0.17, respectively (Fig. 9b). The R2 value
for elevation and polygon density for all data was 0.18,
but that for all-except-Amga data was 0.45 (p < 0.001,
Fig. 9c), which was the highest correlation of all combinations using the three factors (subsidence, elevation, andpolygon density).
4.3 Normalized amplitude spectra in area with polygons

Figure 10a and b shows examples of pan-sharpened
images in the well-developed and less-developed polygons in C4 and A1, respectively. Polygons representing
circular convexities were clearly identified in C4, but not
in A1. We selected each seven areas as well-developed
and less-developed polygons like C4 and A1 (see also
Additional file 1: Fig. S3) and used the spatial frequency
analysis to derive an averaged model that could be fitted

to a 1-D amplitude spectrum (Fig. 3e, see also Additional
file 1: Fig. S4). Figure 10c shows a comparison of averaged
models in 1-D normalized amplitude spectra between
clearly- and unclearly- identified polygons. The difference between the two averaged models was pronounced
at spatial frequencies in the range 0.05 to 0.2 ­m−1, whichcorresponds to a spatial wavelength of 5–20 m. As acomparison with other land covers, two other amplitude
spectrum models of water surface and forested areas was
also presented (Fig. 10c). The amplitude spectrum at forested areas was much stronger at any spatial frequencies
than those polygonal areas, and the frequency dependence at water surface was quite different from thosepolygonal areas.

5 Discussion
5.1 Impacts of land use changes on thermokarst
development

Inter-annual subsidence maps of the four areas (Figs. 4,
5, 6 and 7) showed that significant subsidence occurredAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 8 (Top panel) Location of sampling points in the selected towns (Syrdakh, Churapcha, Mayya and Amga) for calculating density of ice wedge
polygons. White-circle dots indicate the location of displacement time series shown in Figs. 4, 5, 6 and 7, and the rectangle dots indicate the others;(Bottom panel) Box plots of polygon density in the four towns. Median value of polygon density (M) and number of samples (N) are also shownin the figure

in farming areas and/or in areas of arable land, comparedwith other land use areas. These open areas were originally covered by forest taiga before the collapse of theSoviet Union in the 1990s; a period of large-scale changes
in land use (Fedorov et al. 2014b). The combination ofclimate change and increased precipitation in the 2000s

resulted in warming of permafrost, deepening of theactive layer, and melting of ground ice (Iijima et al. 2014),
all of which markedly increased the progress of thermokarst development in deforested areas (Iijima et al. 2021).
Changes in land use history and subsidence have been
investigated previously in Mayya and Churapcha (AbeAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 9 Relationship between a subsidence and elevation, b subsidence and polygon density, and c elevation and polygon density. Coloredsymbols indicate the different towns. Dashed and solid lines indicate linear regressions using all of the markers and all of the markers
excluding those from Amga, respectively

et al. 2020; Iijima et al. 2021), and the other studied areas(Tyungyulyu and Amga) are also in the vicinity of towns,
implying that they are almost in the same situations.
These drastic changes have also impacted cattle andhorse farming, as well as residential areas that are highly
dependent on the local ecosystem (Crate et al. 2017;
Takakura et al. 2021). On the contrary to the deforested
areas mentioned above, ground in forest areas is considered to be still stable (Fedorov et al. 2019). However,further forest cutting, and climate changes could increase
the area affected by thermokarst more than ever before.
Therefore, there need to live in a way that does not disturb the ground surface to protect permafrost.
5.2 Relationship among subsidence, elevationand polygon density and its implications

Relationship between InSAR-based subsidence and elevation in areas with well-developed polygons showedthat the subsidence seems to be greater at higher elevations (Fig. 9a). However, only data in Churapcha arelocated at higher elevation than others and they are scattered ranging in a few to several tens of centimeters.
Therefore, we consider little correlation between subsidence and elevation. Relationship between subsidence andpolygon density (Fig. 9b) showed that the polygon density in Churapcha were scattered regardless of magnitudeof subsidence and that the subsidence in other towns
were also scattered regardless of polygon density. Thesesuggest that subsidence occurred regardless of polygon
density. However, Saito et al. (2018) derived averaged
subsidence rates of two sites with different polygon densities in Churapcha and mentioned that the difference ofsubsidence might be explained by that of volumetric ice

contents. We speculate that the difference of relationship between subsidence and polygon density might becaused by that of spatial resolution and analysis method
between satellite and the UAS images. The UAS-based“cumulative” subsidence were calculated on the assumption of straight original surface before the thermokarst
initiation. The UAS images have high spatial resolutions
with a few centimeters, which could detect the bottomof narrow troughs between polygons in both sites. However, the InSAR-based subsidence is a magnitude of that“over a given period of time” and the spatial resolution is
30 m, which might not detect the subsidence distribution
in troughs. In initial stage of thermokarst, ground surface gradually subsides particularly in troughs (Bosikov
1991; Desyatkin et al. 2009) and the spatial heterogeneity of subsidence distribution and the difference stagesof thermokarst might make the relationship between
observed subsidence and polygon density more complicated. The subsidence rate itself is also time-variant due
to the stages and meteorological factors, which may be
associated with the relationship (Fedorov et al. 2014a).
There was no strong correlation between elevation andpolygon density, but with the exception of the Amga data,
the correlation was the strongest (Fig. 9c). The elevationand polygon density are time-invariant at least within ourobservation period, different from subsidence, and they
may reflect ground ice structure in the interfluve. Basedon those results, we speculate that the higher elevation
has older topographic landform and would remain older
ice wedges. The studied area is terraced by the Lena andAldan River, with newer terrace faces closer to the rivers.
Therefore, it is likely that newer and smaller ice wedges
formed on the Tyungyulyu terrace, which is lower andAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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Fig. 10 Examples of WorldView pansharpened images indicating a well-developed and b less-developed polygons; c Averaged 1-D spectrum
models of (red) well-developed, (blue) less-developed polygons, (yellow green) forested areas, and (cyan) water surface with the standard deviation(shaded areas)

closer to the rivers, compared with on higher terraces.
On the other hand, ice wedges in Churapcha are likely tobe older, and have more grown up as times goes by. Inview of polygon density, polygons with an average diameter of more than 10 m are distributed in Churapcha. Notethat some polygonal patterns with smaller diameters arealso distributed within Churapcha, which is thought tobe in localized wetter areas (Saito et al. 2018). This situation may suggest that the lower terraces (i.e., Tyungyulyu
terrace), the much water it converges, which may form
newer and smaller ice wedges. When such ice wedges
melt due to recent temperature increases, smaller polygons will be formed on the lower terraces, as observedin Mayya. Thermokarst development is not restricted tosediments with massive ice-wedges; it can occur in areas
with smaller ice structures, such as ice lenses in alluvial deposits, and in biogenic deposits (Shestakova et al.
2021). Thus, these differences of polygon density might
be affected by the period and soil structure at the time
ice wedges developed. Satellite data can reveal groundsurface conditions, not in ground, and cryostratigraphic
observations such as sampling and analyzing ice wedges
are desirable as a future research to better understanddifferences of polygon sizes and timing of their formation as well as soil conditions at that time (e.g., Opel et al.
2019).
Depositional environment of ice wedges in Amga differs from that at other locations, and that may reflect asAbe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

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the outliers of data in Fig. 9c. This is because Amga is awatershed area that is in close proximity to the Amga
River. The subsidence observed in Bolugur along theAmga River was the greatest of all areas studied, and thedensity of polygons in Amga was small for its elevation(Fig. 9c). Our observed large subsidence may suggest thatAmga was underlain by largely developed ice wedges andthose melting induced entire subsidence before developing polygonal surface. The presence of high ground ice
content along the Amga River was reported by Shestakova et al. (2021) and the extremely wet area along theAmga River is likely to enhance larger ice wedges due toits high ground ice content and/or ground structure. In
addition, Amga has experienced occasional spring floods(Tananaev et al. 2021), which may provide excess watersupply to the frozen ground and to have grown groundice and ice lens up possibly that can cause significant
subsidence.

structures of all size, from small to large such as leaves,branches, trunks and trees, all of which reflect strongly.
Conversely. the frequency dependence at water surface
was quite different from those polygonal areas (Fig. 10c),where the spectrum was weak regardless of frequency.
These results may suggest the possibility of evaluating
polygon development could be performed using satellite optical images. However, our study only presented aqualitative discussion of the degree of polygon development. For a quantitative discussion, a comparison withmicrotopographic data captured by UAVs is needed;such an approach will be the subject of future works. The
physics of polygon development and model-based studies(e.g., Abolt et al. 2020) will also help to develop the evaluation method.

5.3 Evaluation of polygonal pattern development basedon spatial frequency analysis

InSAR can detect ground subsidence with a spatial resolution of tens of meters, which is useful for investigations
focusing on regional-scale thermokarst subsidence. Our
study succeeded in clarifying the spatial extent of subsidence in areas of arable land and farmland at a scale ofseveral-hundred meters. On the other hand, the subsidence associated with thermokarst development can bestrongly heterogeneous at the scale of several meters,
which can make it difficult to compare InSAR-based datawith field measurement data. Abe et al. (2020) compared
InSAR-based subsidence data with field-leveling data
in Mayya in the Lena-Aldan interfluve. They found that
localized thermokarst development covered in a-few-tens
meters, and they concluded that the discrepancy between
the two techniques was due to differences in spatial resolution between InSAR and leveling. To estimate the distribution of thermokarst subsidence at a higher spatial
resolution, a combination of InSAR data and UAS datais considered preferable. Iijima et al. (2021) compared
InSAR-based subsidence with that obtained using UASbased digital surface models (DSMs) and found that thetwo methods were roughly in agreement. However, due
to the low vertical accuracy of DSMs and the UAS dataspanned only 1 year, producing an accurate subsidence
map from UAS data was not possible. We consider that amore detailed study of the subsidence distribution could
be produced using InSAR and UAS-DSM data that havebeen collected over a period of more than 5 years, as the
expected subsidence will exceed the level of uncertainty
associated with UAS-DSM data (− 10 cm).
To better understand the role of freeze–thaw cycles inground displacement, simultaneous extraction of seasonal and inter-annual changes is necessary. In this study,
we could not estimate seasonal displacement directly due

There were some areas where the polygonal patterns
were unclear, but where clear subsidence was detected(e.g., Bolugur in Fig. 7e). These findings indicate that suchpolygons are in developmental stage (i.e., byllar) and thatthe troughs between polygons may be shallow. These
conditions prevented us from counting number of polygons everywhere using satellite optical images. Microtopographic data and super-high-resolution images
derived from UAVs or airplanes may facilitate a moredetailed statistical analysis of these polygons (Ulrich et al.
2011; Saito et al. 2018; Lytkin et al. 2021). In recent years,
analytical techniques have been also improved; for example, a study employed machine learning to identify polygons from the terrain (e.g., Zhang et al. 2020). However,since obtaining training and/or validation data for suchapproaches can be difficult in our study area, it would
be good to have some indices of polygon development
derived solely from satellite images.
Our study performed spatial frequency analysis using
WorldView and Pleiades panchromatic images to reveal
differences in the mean values obtained from models at
spatial frequencies and how these correspond to polygon development. We consider that the differences ofamplitude spectra between well-developed and lessdeveloped areas are relevant to those of trough depths,
which may be also related to the density of vegetation.
The water content tends to be high, especially in theregions between troughs, and vegetation may grow as
troughs get deeper (C4 in Fig. 10a), which may affectthe amplitude spectrum. The spectrum at forested areas
was stronger than that at deforested areas regardless of
spatial frequencies (Fig. 10c). This means that forest has

5.4 Effectiveness of InSAR‑based subsidence
and polygonal texture degree estimationfor evaluating thermokarst development

Abe and Iijima ﻿Progress in Earth and Planetary Science(2024) 11:5

to the limited number of observation data by ALOS-2
over the study area. Future L-band SAR missions, suchas the ALOS-4 by JAXA and NISAR by NASA and ISRO,
are expected to markedly increase the frequency ofobservations over the Lena-Aldan interfluve, which will
help to reveal temporal changes in the ground displacement more detail. In addition, geophysical models for
explaining InSAR-based displacements in permafrost
regions are also required in order to evaluate ground ice
conditions (Abe et al. 2022). If we can use these models
to estimate the amount of ground ice melting, then we
will be able to evaluate permafrost degradation morequantitatively and improve our capability for future predictions and adaptation.
Our study shows that the use of satellite optical images
to extract statistical characteristics of polygons and estimate their degree of development has the potential to
optical satellite images is useful for evaluating thermokarst conditions in the interfluve. Increasing the numberof optical satellite images in conjunction with validation
data will surely advance the visualization and statisticalanalysis of polygons and better illustrate the extent ofdegradation in permafrost regions.

6 Conclusions
Thermokarst development has been investigated intensively in the Lena-Aldan interfluve in eastern Siberia,
which is underlain by continuous ice-rich permafrost.
However, there were no methods for evaluating thermokarst development over entire towns so far. This study
used satellite radar images to characterize the extent
of subsidence near settlements in the interfluve. Our
results clearly showed that subsidence of up to 25 cm in2014–2021 had occurred in region, but the magnitude ofsubsidence varied among towns. The analysis of satellite
optical images that were used to estimate the densities
of ice wedge polygons in those areas revealed significant
differences at each area, possibly due to differences inground structure and geomorphologic characteristics.
Spatial frequency analysis of the optical images showed
clear differences in averaged spectrum models describing well-developed and less-developed polygons, which
may reflect trough depths and the density of vegetationin the troughs. Our findings showed that InSAR-based
subsidence maps combined with the optical image analysis of polygon development may be useful for evaluating
thermokarst development in permafrost regions.
Abbreviations
GAGOS	Generic atmospheric correction online service for insar
ISRO	Indian Space Research Organization
JAXA	Japan Aerospace eXploration Agency
NASA	National Aeronautics and Space Administration
NISAR	Nasa Isro Syntheric Aperture Radar

Page 15 of 17

UAS	Unmanned Aerial System
UAV	Unmanned Aerial Vehicle

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​024-​00610-5.
Additional file 1. Figure S1: Interferometric pairs used for InSAR timeseries analysis; Figure S2: Examples of polygonal texture at each town;
Figure S3: Examples of well-developed and less-developed polygonal
texture; Figure S4: Examples of one-dimensional normalized amplitude
spectrum and the fitted exponential function model of each image
shown in Figure S3.
Acknowledgements
ALOS-2 data are shared among PIXEL (PALSAR Interferometry Consortium toStudy our Evolving Land surface) and provided by JAXA under a cooperative research contract with PIXEL. The ownership of ALOS-2/PALSAR-2 data
belongs to JAXA. The WorldView and Pleiades images are copyrighted by
Maxar Technologies and Centre national d’études spatiales (CNES). We thankHitoshi Saito for providing an UAS image in Churapcha. We also thank FORTE
Science Communications for proofreading the English used in this manuscript.
Kazuhito Ichii (the Editor) and two anonymous referees provided thoughtfulreview comments and suggestions, which have improved the manuscript.
Author contributions
T.A. and Y.I. designed the study. T.A. analyzed all data and drafted the initialmanuscript. Y.I. reviewed and edited the manuscript. T.A. and Y.I. acquired
funding. All authors have read and approved the final manuscript.
Funding
This study was supported by four Japanese grants: Arctic Challenge for
Sustainability II Program Grant JPMXD1420318865, JSPS KAKENHI Grant
JP18H03353, the Grant for Joint Research Program of the Japan Arctic
Research Network Center (FS04), and ERI JURP 2021-B-03 in Earthquake
Research Institute, the University of Tokyo.
Availability of data and materials
ALOS-2 data can be searched using G-Portal (https://​gport​al.​jaxa.​jp/​gpr/?​lang=​en) by JAXA and purchased from either PASCO (http://​en.​alos-​pasco.​com) and RESTEC (https://​www.​restec.​or.​jp/​en/). AW3D30 data are available
from the ALOS Research and Application Project at the EORC on the JAXA
website (https://​www.​eorc.​jaxa.​jp/​ALOS/​en/​datas​et/​aw3d30/​aw3d30_​e.​htm).

Declarations
Competing interests
Y.I. is a Section Chief Editor of PEPS editorial board, and one of editorial task
team in SPEPS entitled “Water-carbon cycles and terrestrial changes in the
Arctic and subarctic regions,” which this manuscript was submitted to. However, he was not involved in any of peer-review in this manuscript. The authors
declare that they have no other competing interest.
Received: 27 August 2023 Accepted: 4 February 2024

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