1. Introduction
Building on frozen ground entails many geotechnical implications related to the properties and thermal regime of the ground [
1]. Ground heave and subsidence induced by seasonal freezing and thawing of the active layer (AL) generate stresses on infrastructures that can lead to damage and failures. In permafrost terrain, climate change, in conjunction with anthropogenic disturbances, has furthermore caused an increase in ground temperature and deepening of the active layer [
2,
3]. As a result, loss of bearing capacity [
4,
5,
6,
7], seasonal and long-term ground subsidence, e.g., [
8,
9,
10], have been observed across the Arctic due to melting of the ground ice and soil consolidation. These changes severely threaten the integrity of the built environment and increasingly expose Arctic communities to hazards [
11,
12]. It is therefore paramount to adapt construction designs and mitigation solutions to frost conditions and thermal regime changes. In this context, the monitoring of ground surface deformations can serve as a powerful tool to manage permafrost-induced risks and to support community planning.
The occurrence and magnitude of surface deformations is primarily controlled by the frost susceptibility of the ground, defined as the proneness of the ground (soil or rock) to form segregated ice (causing frost heave) under the required conditions of moisture supply and temperature [
13]. Frost susceptibility, soil particle size and ground ice content thereby strongly interrelate. Yet, assessing the spatial distribution of frost-susceptible soils remains very complex. Determining ground properties and quantifying ground ice content typically require drilling and retrieving soil material for analyses. Such geotechnical investigations are costly, labor intensive, and logistically challenging in Arctic environments. Furthermore, sample extraction only provides punctual information in space and time and hence is limited in portraying the complex spatial heterogeneity and temporal evolution of subsurface properties under climate change [
14].
Remote sensing techniques provide continuous spatial information in areas where the acquisition of in situ data remains challenging. In this context, the potential of Interferometric Synthetic Aperture Radar (InSAR) has been extensively explored in the past years to monitor surface deformations in permafrost environments with a high spatial resolution [
15]. Prior research has primarily been dedicated to develop InSAR techniques for the comprehension of freezing/thawing dynamics of the AL and identification of driving factors explaining the magnitude of ground movements [
9,
16,
17,
18,
19,
20,
21,
22,
23]. It is worth mentioning the approach proposed by Liu et al. [
16] and Schaefer et al. [
24] who developed an active layer thickness (ALT) retrieval algorithm, based on the relationship between the thaw depth and InSAR-derived surface displacement. By using synthetic aperture radar (SAR) scenes covering several thawing seasons, the authors were able to identify a seasonal and a long-term component in the remotely sensed displacement signal. Seasonal subsidence was attributed to thaw settlement, caused by phase and volume changes occurring in the AL upon thawing, while long-term subsidence was explained by permafrost degradation and melting of the ice at the top of permafrost. Under the assumption that the seasonal subsidence can be related to the volume of melted ice in the AL, they finally estimated ALT from the InSAR measurements by modeling the vertical distribution of the pore water within the AL.
Despite these advances, little work has been undertaken to map subsurface properties—such as the frost susceptibility or ground ice content—that are of practical interest to actors from the construction and planning sectors. In their paper, Zwieback and Meyer [
14] notably explained the difficulty of deriving permafrost ground ice from remote sensing data and stressed the limitations of traditional mapping techniques, currently relying on expert knowledge and surface indicators of excess ice [
25]. In an attempt to overcome these challenges, the authors assessed the suitability of late-season subsidence derived from InSAR, to identify the presence of ice-rich materials at the top of permafrost [
14]. They hypothesized that ice melting under the permafrost table occurs when the thaw front penetrates frozen material toward the end of a warm summer season. Consequently, the thawing of ice-rich top-of-permafrost would induce a peculiar acceleration of the InSAR late-season subsidence. Their methodology proved to be most successful in the case of exceptionally warm and wet summers, but the separability of late-season subsidence signals from ice-rich versus ice-poor areas was reduced under cooler conditions. Those findings agree with Bartsch et al. [
20], which documented higher-than-average subsidence in an exceptionally warm summer in regions known for the presence of tabular ground ice at the base of the active layer. Differences between landcover types (representing differences in soil properties) were, however, found.
Overall, InSAR techniques have rarely been used in an engineering context to prevent the occurrence of infrastructure failures [
10,
26] and to help governmental and planning entities make informed decisions. Arctic communities could benefit from multi-disciplinary approaches combining remote sensing and geotechnical data, at a time when adaptation strategies are urgently needed in response to climate change [
27,
28].
Many Greenland settlements are facing the challenges previously mentioned. Due to limited resources and logistical constraints, detailed site investigations are rarely performed prior to construction projects. Available geotechnical information is hence scarce or not easily accessible, and construction designs are not sufficiently adapted to local sub-surface conditions [
27]. As a result, stability issues frequently affect existing infrastructure and particularly roads crossing ice-rich sedimentary basins. Like in other regions of the Arctic, stakeholders have expressed their interest in reliable decision support tools to guide urban planning on sensitive permafrost terrain and to identify suitable mitigation solutions.
In order to address Greenlandic stakeholders’ needs, we revisited the methodology of Liu et al. [
16] and used InSAR data to map surface deformation trends in Ilulissat, West Greenland. Previous tests for Illulisat indicated applicability for seasonal as well as for long-term change monitoring applications [
6,
15]. We combine the InSAR results with local information to prepare a frost susceptibility index (FSI) map of the region.
2. Study Area
Our study area is the settlement of Ilulissat (
Figure 1a), which is centrally located on the west coast of Greenland (69.2198
N, 51.0986
W) and is the seat of the Avannaata municipality. Ilulissat is the third largest town in Greenland and is an attractive tourist destination due to its proximity to the UNESCO World Heritage Site Ilulissat Icefjord. For these reasons and due to the current construction of an international airport, an extensive expansion strategy has been planned for the town [
29]. Reliable and lasting facilities will therefore be needed in the coming years to sustain the development of economic activities and possible demographic growth.
Over the past 20 years, mean annual air temperatures (MAAT) have increased by 4–5
, reaching −1.8
in the particularly warm year of 2019 [
30]. The area of Ilulissat is underlain by continuous and relatively warm permafrost, characterized by average ground temperatures around −3
[
31,
32]. ALTs are highly variable spatially, ranging from 30
to more than 2
, and follow an increasing trend (
Figure 1c). During the Holocene deglaciation, fine-grained marine sediments were deposited as a result of marine transgression. The sedimentary deposits gradually became subaerial and exposed to precipitation and percolation due to isostatic uplift. Very ice-rich material (
Figure 1b), depleted of salts, is typically found at the permafrost table today. The ground ice content decreases with depth as the pore water salinity increases [
32], and for these reasons, permafrost is highly sensitive to climatic changes and surface disturbances.
The landscape is characterized by the presence of these fine-grained marine deposits, lying between gentle bedrock outcrops and interspersed with a system of lakes and small drainage channels. Periglacial features are relatively homogeneous across the area and are primarily dominated by frost boils, which are indicative of the presence of frost-susceptible sediments [
25]. Natural water drainage channels and microtopographic depressions form mire zones, becoming wet to inundated during the thawing season, and are colonized by graminoids, bryophytes, and low Salix shrubs. In contrast, frost boil patches are vegetated by dwarf-shrub cryptogam tundra or remain mainly barren, being more exposed to winds.
In this scenery, bedrock outcrops often offer a stable substrate for construction. However, roads and other linear infrastructure extending through sedimentary basins tend to be more heavily affected by seasonal frost/thaw surface deformations and permafrost thaw-induced damages.
Figure 1.
Study area and local permafrost conditions. (
a) Map of Ilulissat (69.2198
N, 51.0986
W) town area. The map contains an orthophoto and infrastructure spatial data from Asiaq, Greenland Survey, Basemap, 2022 [
33]. (
b) Permafrost core retrieved in the main sedimentary basin, showing ice-rich fine-grained sediments. (
c) Temporal evolution in average active layer thicknesses (ALT), measured at the Ilulissat Circumpolar Active Layer Monitoring (CALM) grid. The red dotted line represents the linear increase in the annually averaged ALT.
Figure 1.
Study area and local permafrost conditions. (
a) Map of Ilulissat (69.2198
N, 51.0986
W) town area. The map contains an orthophoto and infrastructure spatial data from Asiaq, Greenland Survey, Basemap, 2022 [
33]. (
b) Permafrost core retrieved in the main sedimentary basin, showing ice-rich fine-grained sediments. (
c) Temporal evolution in average active layer thicknesses (ALT), measured at the Ilulissat Circumpolar Active Layer Monitoring (CALM) grid. The red dotted line represents the linear increase in the annually averaged ALT.
5. Discussion
5.1. InSAR Surface Displacement Model and Maps
In this study, we modified the algorithm of Liu et al. [
8] and applied it to Sentinel-1 SAR scenes acquired between 2015 and 2019. Maps of average seasonal displacement (S) and long-term displacement rate (R) were successfully produced for the community of Ilulissat with a nominal resolution of 10
. The magnitude of the seasonal deformations was found to be predominantly related to the soil type and moisture conditions. Compared to fine-grained sedimentary deposits that exhibited severe surface displacements, less subsidence was observed in coarser and drier areas. These observations, which were substantiated by soil properties measured at borehole locations [
59], corroborate the findings of Schaefer et al. [
24]. The R map also highlighted zones of larger downward displacement trends that were interpreted as degrading permafrost areas. Our results generally confirmed the measured increase in AL over the study time frame but underestimated the magnitude of the long-term subsidence.
Overall, our surface displacement model was able to explain up to 25% of the InSAR data variation over the sedimentary basins. The model performance was considerably reduced over high shrubs, inundated areas, and in the close vicinity of bedrock. In their studies, Jia et al. [
75], Strozzi et al. [
6] and Zwieback and Meyer [
14] experienced similar errors characteristic of the use of InSAR over low-land permafrost, densely vegetated, barren, and rocky areas. As discussed by the authors and by Ansari et al. [
76], changes in ionospheric, vegetation, and soil moisture conditions exert an influence on radar penetration and may introduce biases in the InSAR signal (notably drop of coherence) and measured surface displacement. This phenomenon partly explains our inversion results and the positive R values specifically observed at a few locations and indicating a decreasing trend in ALT. These areas typically align with wetlands, where ponds and water accumulation may have affected the coherence of the InSAR signal. Anthropogenic modifications may also have resulted in heave or mixed signals in the derived S and R maps at specific locations. Future work could investigate and quantify the impacts of vegetation density and surface drainage conditions on the coherence of the InSAR signal.
The main contribution of our approach lied in the implementation of a moving-windowed constrained linear least-squares inversion. In order to compensate for the lack of points in the temporal InSAR dataset and assuming that spatial variations were smooth, several pixel values were used in the inversion, providing more information from neighboring areas and reducing the noise in the data. However, this technique may not have been sufficient to entirely balance out the fact that only five years of InSAR images could be processed. For this reason, the R component, which represented the “long-term” trend in surface deformations and was interpreted as changes in the permafrost table depth, should still be considered cautiously. The robustness of the inversion and estimation accuracy of the R component could be improved if longer InSAR time series of surface displacements were available, therefore providing more points to constrain the inversion and to reduce the uncertainty in the estimated parameters. The inversion could additionally be weighted based on the land cover classes.
Secondly, the generation of the displacement time series and variations in the temporal coverage of the InSAR stacks (and therefore in the measured thaw season vertical displacement) may have also lessened the inversion capability of our algorithm. Our model notably relied on the Stefan equation to reproduce the evolution of the thaw front. By validating the evolution of the thawing front with ground temperature measurements, we demonstrated that the Stefan approximation is well suited to model the onset of thawing but that predicted thaw depths are somewhat underestimated. The correction factor applied to the InSAR amplitudes was therefore also underestimated, and the final S and R products should thus be considered as conservative estimates. In our dataset, the 2015 time series notably started very late in the thawing season, which may have contributed to a larger variation in the observations and to poorer regression fits. Such effects especially impact relatively short stacks (study periods) such as ours.
In our model, we also assumed that vertical displacements of the ground surface would only be caused by volume changes induced by the freezing and thawing of the AL. Assuming that there were no lateral water exchanges was appropriate for the area of Ilulissat where the topography is flat. Nonetheless, as mentioned by Liu et al. [
8,
16], secondary driving mechanisms such as erosion, clay contraction, inundation, and other changes in soil properties may contribute to ground movements but were not accounted for in our model.
In comparison, sinusoidal models have been tested by Li et al. [
17] and Jia et al. [
75] and have generally proved performant. However, many physical processes influencing the occurrence and magnitude of surface deformations do not follow sinusoidal trends [
17,
75]. Further modeling efforts are required to improve the representation of ground movements occurring in permafrost regions.
The S and R values retrieved from the inversion were assessed against AL and top-of-permafrost soil properties determined at borehole locations. Even though our results were coherent with subsurface conditions at these locations, measurements of the surface displacements are still needed to quantitatively validate seasonal and long-term deformation trends. To this aim, subsidence sticks could be deployed in the study area, as described in Antonova et al. [
9] and Bartsch et al. [
20].
5.2. ALT Extrapolation Techniques
We investigated the correlations between a set of remotely sensed environmental predictors and ALT probed in 2020 and 2021 (
). Field measurements conducted in Ilulissat evidenced the strong spatial variability of ALT across the study domain. Our observations are in line with previous studies that reported large variations in ALT within study sites across the Arctic, e.g., [
16,
45,
75].
Weak linear correlations were found between
and a subset of predictors. Therefore, we first attempted to extrapolate
with a GLM whose predictive capability was evaluated by cross-validation. The model performed poorly and was unstable when fitted and validated on randomly generated samples. With this approach, we were not able to reproduce the variability in ALT across the study domain. Other researchers have experienced similar challenges when statistically predicting ALT from geospatial datasets. Karjalainen et al. [
12], who used an ensemble model at the pan-Arctic scale, notably reported relatively large uncertainties associated with the predictions of present and past ALT (adjusted
of, respectively, 0.37 and 0.57). The geographically weighted regression approach implemented by Mishra and Riley [
45] over the state of Alaska was also characterized by a moderate predictive capability.
The poor performance of our model could be attributed to the combination of ALT measurements, predictors, and algorithms selected for this study. Before 2020, ALT measurements were scarce and relatively localized. Averaging ALT values over the study period of 2015–2019 (cf. Equation (
14)) was not possible in our case since the dataset would not have been representative of the natural spatial variability of ALT. In order to maximize the spatial distribution of our dataset and the robustness of the extrapolation procedure, ALT measured in 2020 and 2021 was used instead. However, as field protocols changed with our understanding of the area throughout the project, ALT probing was conducted inconsistently across vegetation and landform units in 2020 and 2021. Final sample sizes were therefore larger among certain vegetation classes, while others remained underrepresented. For the same reason, our dataset did not entirely and evenly span the spectrum of values of the environmental predictor rasters, which were sampled at the ALT probing locations only. We recommend that the density and distribution of ALT measurements for validation purposes are carefully considered to appropriately represent different terrain units in future studies.
Furthermore, the area of Ilulissat is characterized by a gentle relief and by homogenous periglacial features (frost boils). Floristic, hydrological, and geomorphological disparities evidently exist, but their spatial gradients are relatively small. Within a homogeneous terrain unit, intra-variations in vegetation composition, soil moisture, microtopography, and ALT additionally occur. However, the resolution of the remotely sensed datasets acquired for this study was likely insufficient in grasping such nuances. Statistical models applied at smaller scales (hundreds of meters) and using high-resolution surface elevation and multi-spectral data were generally successful in predicting ALT [
43]. In this context, Anderson et al. [
77] showed that hyperspectral imaging can be more suitable in relation to statistical extrapolations. Acquiring predictors with a higher resolution could contribute to improving our
predictions.
Finally, a different statistical model could be tested on our datasets, provided that more observations are collected. In our case, a GLM was chosen due to the reduced number of measurements and ease of interpretability [
69]. Relationships between the ALT and surface characteristics may not all be linear. For this reason, generalized additive models (GAM), which present the advantage of accounting for non-linear effects, could be a more flexible alternative to the GLM.
To overcome these difficulties, we exploited the correlation between the thaw depths and vegetation zonation revealed by ALT probing along transects. ALT measurement sites were categorized based on their sampled floristic composition and averaged per-spectral vegetation class ensuing from the supervised land cover classification map. This method was substantially more successful and representative of the ALT spatial distribution than the statistical model. Prediction errors were tied to vegetation misclassifications and class intra-variability in ALT. Floristic surveys were conducted relatively late (September) compared to the vegetation growing season peak (mid-July to mid-August). It is plausible that the species richness was not fully captured in our data, but species abundance and PFT percent covers were expected to represent distinct vegetation types. The RFC-supervised algorithm produced satisfactory classification results (82.51% overall accuracy). Nonetheless, vegetation classes identified from ground truth data were not always spectrally separable, resulting in pixel confusion and misclassifications. These errors may have led to the wrong allocation of ALT values in some locations. Lastly,
t tests revealed overlaps and statistical similarities in ALT averages computed for two pairs of vegetation classes. These results were coherent with the natural variability of thaw depths measured within these units. Our study confirms the conclusions of Mishra and Riley [
45], stating that using vegetation zonation as an indicator of ALT does involve uncertainties but is applicable when more complex statistical models can not be implemented.
The distribution of ALT is influenced by many factors, the relative importance of which is scale-dependent. Previous studies have shown that air temperature is the primary control of ALT over large scales [
43]. Land cover types and topography also exert a strong influence on ALT. At microscales, Gangodagamage et al. [
43] and Anderson et al. [
77] demonstrated that microtopography, vegetation, and soil moisture become predominant driving factors. In our study areas, land cover units proved to be the best predictor of ALT. Due to the lack of strong topographic gradients, the effects of terrain parameters investigated in this study could not be asserted. The significant spatial variability of ALT is still not fully understood. The suitability of different extrapolation techniques and predictors remains considerably site-dependent. More robust approaches that could be extended to different permafrost environments must be developed. Additional monitoring, remote sensing, and modeling efforts remain needed to bridge the gap between micro- and regional scales.
5.3. Frost Susceptibility Mapping
Mapping ground ice traditionally relies on geomorphological expertise and the identification of periglacial features [
25]. Using remote sensing techniques would be highly advantageous in Arctic regions where drilling and soil sampling are logistically challenging and costly. However, to this day, the possibilities to derive ice content from remotely sensed signals are limited [
14]. Liu et al. [
16] were able to link changes in surface subsidence to the thawing of the AL. Using a similar approach, we estimated the AL ice content from 2015 to 2019 average seasonal displacements (S) and extrapolated field observations (
). Since thaw depths follow an increasing trend in Ilulissat, the
dataset we applied may be overestimating the ALT averaged over the study period 2015–2019. Referring to Equation (
14), we can infer that the resulting AL ice content represents a conservative estimate (underestimated). Furthermore, homogeneous porosity and saturation of the AL had to be assumed. These simplifications and errors intrinsic to the retrieval of the S component and predicted
introduced additional uncertainty in our results. We named the final product a frost susceptibility index (FSI) to underpin that it does not represent an exact quantification.
Despite reaching an accurate quantification of the AL ice content, we qualitatively compared obtained FSI values to frost susceptibility classification of sediment samples based on the FDSCS [
60]. The frost susceptibility of AL and permafrost samples corroborated the InSAR-derived FSI, thereby supporting the validity of our mapping approach.
Our efforts are presented here as a first step toward developing remote sensing techniques for ground ice retrieval. Further research could consist in investigating the relevance of extreme year analyses [
14,
20] for our study area. Using late-season subsidence signals to distinguish ice-rich from ice-poor areas proved effective under the following conditions: (i) exceptionally warm summers (ii) and initial melting of excess ice at the top of permafrost. In the case of Ilulissat, the derived R map indicated locations that may be subject to ongoing permafrost degradation. Secondly, 2019 was a particularly warm year, with mean annual air temperature reaching −2
and thawing degree days exceeding 1000. Testing this method for this year, in particular, could provide complementary information with respect to the localization of vulnerable ice-rich areas.
5.4. Potential of InSAR-Derived Maps to Support Infrastructure Maintenance and Planning
In remote Arctic areas, geotechnical data are relatively rare and challenging to acquire. However, site investigations remain essential to adapt construction practices to permafrost conditions and to prevent failures. In this context, remote sensing techniques provide high-resolution and continuous spatial information that can be validated with relatively reduced ground-truth datasets.
InSAR measurements notably provide insightful information regarding AL dynamics and permafrost degradation where surface deformations severely or repeatedly affect the built environment. Used as a complement to site investigations and local knowledge, InSAR maps, therefore, have the potential to support the construction and planning sectors. Such tools are especially valuable in the context of risk management and Arctic urban sprawl. Possible causes of infrastructure deterioration can first be identified, and maintenance operations be more judiciously prioritized. In this study, we implemented a multidisciplinary mapping framework with the aim to map the frost susceptibility of the ground at the community scale. Our work contributed to identifying hazardous frost-susceptible areas currently subject to large seasonal surface displacements and/or long-term subsidence. FSI maps derived from our approach may secondly be helpful in informing construction planning in unbuilt areas with limited geotechnical data, such as in Ilulissat (
Figure 14).