Rapid Saline Permafrost Thaw Below a Shallow Thermokarst Lake in Arctic Alaska

Permafrost warming and degradation is well documented across the Arctic. However, observation- and model-based studies typically consider thaw to occur at 0°C, neglecting the widespread occurrence of saline permafrost in coastal plain regions. In this study, we document rapid saline permafrost thaw below a shallow arctic lake. Over the 15-year period, the lakebed subsided by 0.6 m as ice-rich, saline permafrost thawed. Repeat transient electromagnetic measurements show that near-surface bulk sediment electrical conductivity increased by 198% between 2016 and 2022. Analysis of wintertime Synthetic Aperture Radar satellite imagery indicates a transition from a bedfast to a floating ice lake with brackish water due to saline permafrost thaw. The regime shift likely contributed to the 65% increase in thermokarst lake lateral expansion rates. Our results indicate that thawing saline permafrost may be contributing to an increase in landscape change rates in the Arctic faster than anticipated

Détection des cycles de gel/dégel de la couche active du sol en toundra arctique à partir d’imageries radar à synthèse d’ouverture (RSO) multicapteur en bande C

L’augmentation de la température de l’air moyenne annuelle, chiffrée à +2,3 °C pour les régions de l’arctique Canadien entre 1948 et 2016, a des impacts considérables sur le couvert nival arctique et sur la végétation en place. Ces deux paramètres influencent le régime thermique du sol et donc, les cycles de gel/dégel de sa couche active dans l’écosystème arctique. L’importance du suivi de ces cycles réside dans leur influence sur plusieurs paramètres de la cryosphère tels que le cycle hydrologique et du carbone, la saison de croissance de la végétation, l’état du pergélisol sous-jacent ainsi que l’épaisseur de sa couche active. L’utilisation de données ponctuelles ou provenant de capteurs micro-onde passive à basse résolution présente un enjeu pour le suivi spatial et temporel de ces cycles. Le projet vise à développer un algorithme de détection des cycles de gel/dégel du sol en toundra arctique à partir d’imageries RSO multicapteur (i.e., Sentinel-1 et RADARSAT-2) ayant une couverture temporelle quasi journalière en bande C, afin d’évaluer l’impact de la variabilité spatiale et temporelle des paramètres influençant le régime thermique du sol tel que, les écosystèmes terrestres (i.e., écotype) et la présence de neige. L’étude se concentre sur une zone à l’intérieur du bassin versant du lac Greiner à proximité de la ville de Cambridge Bay au Nunavut. La normalisation de l’angle d’incidence a permis de diminuer le bruit dans les séries temporelles ainsi que de rendre possible l’utilisation d'images acquises à l'intérieur de plusieurs orbites d’observation. Cela a aussi permis d’uniformiser les données des deux capteurs pour les combiner en une seule série temporelle. Deux algorithmes de détections ont été utilisés, soit un algorithme de seuil saisonnier (STA) ainsi qu’un algorithme de détection de changement (CPD). La validation s’est faite à partir des données spatialement distribuées de température du sol et de l’air indépendamment sous forme de précision (%) et de délai (#jours) de détection. Les deux algorithmes ont permis d’obtenir une précision de détection de plus de 97% sur les sites de référence. Une spatialisation, pixel par pixel, de la méthode STA a permis la création de cartes de jour de gel/dégel pour le site d’étude. La combinaison des cartes de jour de transition avec la carte d’écotype a permis de modéliser l’impact des caractéristiques des écotypes sur le jour de transition. Les résultats obtenus dans ce projet démontrent clairement le potentiel de l’utilisation des données RSO en bande C pour la détection des cycles de gel/dégel, ce qui constitue un résultat important en raison de la quantité grandissante de données à cette fréquence (e.g., RCM, Sentinel-1A-C-D). La méthode présentée dans ce projet pourrait permettre de créer des cartes de transition pour tout le bassin versant du lac Greiner à partir de données RSO en bande C.Abstract : The observed average annual surface temperature increase of 2.3°C in the Canadian Arctic regions between 1948 and 2016 has significant effects on the Arctic snow cover and on the vegetation in place. Those two parameters influence the thermal regime of the ground and therefore the freeze and thaw (F/T) cycles of the soil active layer in the Arctic tundra ecosystem. The importance of monitoring these cycles lies in their influence on several parameters of the cryosphere such as the hydrological and carbon cycle, the vegetation growing season, the state of the underlying permafrost and the thickness of its active layer. The use of punctual data or low-resolution passive microwave sensors presents a challenge for the spatial and temporal monitoring of these cycles. The project aims to develop an algorithm for soil freeze/thaw cycles detection in arctic tundra from multisensor C-band imagery (i.e., Sentinel-1 and RADARSAT-2) to assess the impact of the spatial and temporal variability of the parameters influencing the thermal regime of the ground, such as the terrestrial ecosystems (i.e., ecotype) and the snow cover. The study focused on a region of the Greiner lake watershed on Victoria Island in Nunavut. An incidence angle normalization was applied to the backscatter time series to remove influence of the acquisition angle on backscatter and to allow for the use of images acquired within several orbits of observation. This also standardized the data from the two sensors to combine them into a single time series. Two detection algorithms were used on the normalized backscatter coefficient data, namely a seasonal threshold algorithm (STA) and a change point detection algorithm (CPD). A spatially distributed network of soil and air temperature were used for validation in the form of accuracy (%) and delay (#days) of detection. Both algorithms achieved a detection accuracy of more than 97% for the entire analysis period on the reference sites. A pixel-by-pixel spatialization of the STA method allowed to create F/T transition maps for the extended study site. The combination of the transition maps with the ecotype data made it possible to model the impact of ecotype characteristics on the day of transition. The results obtained in this project clearly demonstrate the potential of using C-band for the detection of F/T cycles, which is an important aspect due to the increasing number of data at this frequency (e.g., RCM, Sentinel -1A-C-D). The method presented in this project could then make it possible to create transition maps for the entire Greiner Lake watershed from C-band SAR data and thus improve the integration of this parameter in climate models

Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas

Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments

Synthetic aperture radar (SAR) applications often require normalization to a common incidence angle. Angular signatures of radar backscatter depend on surface roughness and vegetation cover, and thus differ, from location to location. Comprehensive reference datasets are therefore required in heterogeneous landscapes. Multiple acquisitions from overlapping orbits with sufficient incidence angle range are processed in order to obtain parameters of the location specific normalization function. We propose a simpler method for C-band data, using single scenes only. It requires stable dielectric properties (no variations of liquid water content). This method is therefore applicable for frozen conditions. Winter C-band data have been shown of high value for a number of applications in high latitudes before. In this paper we explore the relationship of incidence angle and Sentinel-1 backscatter across the tundra to boreal transition zone. A linear relationship (coefficient of determination R 2 = 0.64) can be found between backscatter and incidence angle dependence (slope of normalization function) as determined by multiple acquisitions on a pixel by pixel basis for typical land cover classes in these regions. This allows a simplified normalization and thus reduced processing effort for applications over larger areas

Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv

To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...K zajištění co nejvyššího možného časového rozlišení dat SAR je nutné použít všechny dostupné dráhy družic nad daným územím. To může představovat výzvu v hornatém terénu, kde boční snímání družic SAR v kombinaci s různými sklony a aspekty terénu může silně ovlivnit intenzitu zpětného radarového rozptylu. Tyto chyby způsobené terénem je třeba odstranit pro možné porovnání dat v čase. Ačkoli v literatuře jsou popsány metody, které se zabývají tímto problémem, žádná z těchto metod není připravena na operativní a snadno přístupnou analýzu časových řad v horských oblastech. Tato studie se zabývá metodou korekce lokálního dopadového úhlu (LIA) pro analýzu časových řad lesů v horských oblastech. Metodika je založena na použití lineární závislosti mezi radarovým zpětným rozptylem a LIA, který se počítá pro každý satelitní snímek zvlášť. Použitím kombinace databází CORINE a Hansen Global Forest můžeme pro každý jednotlivý snímek získat širokou škálu různých LIA pro konkrétní typ lesa. Algoritmus korekce byl připraven v cloudové platformě Google Earth Engine (GEE) s využitím volně dostupných dat Sentinel-1, digitálního modelu terénu SRTM a databází CORINE a Hansen Global...Katedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyFaculty of SciencePřírodovědecká fakult