Using Synthetic Aperture Radar to Define Spring Breakup on the Kuparuk River, Northern Alaska

Spring runoff measurements of Arctic watersheds are challenging given the remote location and the often dangerous field conditions. This study combines remote sensing techniques and field measurements to evaluate the applicability of synthetic aperture radar (SAR) to defining spring breakup of the braided lower Kuparuk River, North Slope, Alaska. A statistical analysis was carried out on a time series (2001–10) of SAR images acquired from the European Remote-Sensing Satellite (ERS-2) and the Canadian RADARSAT satellite, as well as on measured runoff. On the basis of field information, the SAR images were separated into pre-breakup, breakup, and post-breakup periods. Three variables were analyzed for their suitability to bracket the river breakup period: image brightness, variance in brightness over the river length, and a sum of rank order change analysis. Variance in brightness was found to be the most reliable indicator. A combined use of that variance and sum of rank order change appeared promising when enough images were available. The temporal resolution of imagery served as the major limitation in constraining the timing of the hydrologic event. Challenges associated with spring runoff monitoring and the sensitive nature of SAR likely resulted in an earlier detection of surficial changes by the remote sensing technique compared to the field runoff observations. Given a sufficient temporal resolution, SAR imagery has the potential to improve the spatiotemporal monitoring of Arctic watersheds for river breakup investigations.La mesure de l’écoulement printanier des bassins hydrographiques de l’Arctique n’est pas facile à réaliser en raison de l’éloignement ainsi qu’en raison des conditions souvent dangereuses qui ont cours sur le terrain. Cette étude fait appel à des techniques de télédétection de même qu’aux mesures prises sur le terrain pour évaluer l’applicabilité du radar à synthèse d’ouverture SAR pour définir la débâcle printanière de la basse rivière Kuparuk anastomosée sur la North Slope de l’Alaska. L’analyse statistique d’une série temporelle (2001-2010) d’images SAR acquises à partir du satellite européen de télédétection (ERS-2) et du satellite canadien RADARSAT ainsi que des écoulements mesurés a été effectuée dans le cadre de cette étude. D’après les renseignements recueillis sur le terrain, les images SAR ont été divisées en fonction de la période précédant la débâcle, de la période de la débâcle même et de la période suivant la débâcle. Trois variables ont été analysées afin de déterminer si elles permettaient d’isoler la période de la débâcle de la rivière, soit la luminance de l’image, la variance de la luminance en fonction de la longueur de la rivière et la somme de l’analyse des changements de classement suivant le rang. La variance de la luminance s’est avérée l’indicateur le plus fiable. L’utilisation conjointe de cette variance et de la somme des changements de classement suivant le rang s’avéraient prometteuse lorsque le nombre d’images était suffisant. La résolution temporelle de l’imagerie a constitué la plus grande limitation pour contraindre la temporisation de l’événement hydrologique. Les défis liés à la surveillance de l’écoulement printanier et la nature sensible du SAR ont vraisemblablement donné lieu à la détection précoce des changements superficiels au moyen de la technique de télédétection comparativement aux observations mêmes de l’écoulement printanier. Moyennant une résolution temporelle suffisante, l’imagerie SAR pourrait permettre d’améliorer la surveillance spatiotemporelle des bassins hydrographiques de l’Arctique en vue de l’étude des débâcles printaniers

Detecting covariance symmetries for classification of polarimetric SAR images

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image

Формирование ледяной плотины в низовьях рек Мезень и Кулой с 1983 по 2020 г.

The features of ice cover formation within the macro-tidal estuaries of the Mezen River and the Kuloy River are considered. The investigated rivers flow into the Mezen Bay of the White Sea. The main sources of information for the ice monitoring were the high spatial resolution images from the satellites Landsat 5‑8 and Sentinel 1.2 with addition of medium spatial resolution images of MODIS/Terra and VIIRS/SuomiNPP. Every year, zones of continuous hummocks and ice dams are formed in the estuaries, which exert influence upon characteristics of the tidal waves. The fast ice and drifting ice areas are observed below the ice dam. Above the dam, amplitude the tidal fluctuations in the water level is reduced by 3–4 times. According to the data for the period 2017–2020, the area of relatively smooth ice cover is located at the distance of 48–49 km above the mouth of the Mezen River and 40–42 km above the same of the Kuloy River. Advancing of the ice edge to the mouth is accompanied by the formation of hummocky ice bridges, the position of which is confined to the narrowing and sharp turns of the channel. According to satellite images, it is established that the ice dam changes its position from year to year. In the period from 1983 to 2020, on the Mezen River, the ice dam was located at a distance of 21.0 to 30.5 km from the mouth, and on the Kuloy – from 13.7 to 27.5 km above the mouth. The position of the ice dam is weather dependent. In severe winters, the dam is located closer to the mouth gauge line. Snow, falling in November and December on areas of open water, delays advancing of the ice edge to the mouth. For Mezen, the relation between the position of the ice dam and three following predictors had been obtained: the sum of precipitation for November and December at the air temperature below –5 °C, the sum of air temperatures below –5 °C for January and March, and the sum of positive air temperatures for February. The proposed dependence made it possible to restore the positions of the ice dam for the years which were not provided with satellite data. These are 1994 and 1999.На основе данных космических снимков оптического и радиолокационного диапазонов рассмотрены особенности формирования ледяного покрова в пределах макроприливных устьевых участков рек Мезень и Кулой. Ежегодно в исследуемых эстуариях образуются зона сплошных торосов и ледяная плотина, влияющие на характеристики приливной волны. С 1983 по 2020 г. створ ледяной плотины на Мезени располагался на расстоянии от 21,0 до 30,5 км от устья, на Кулое – от 13,7 до 27,5 км от устья. Формирование ледяной плотины и её разрушение происходят с остановками в створах, приуроченных к сужениям и крутым поворотам русла. Показано, что в ноябре и декабре твёрдые осадки значимо задерживают продвижение кромки льда

Spatial and temporal patterns in Arctic river ice breakup revealed by automated ice detection from MODIS imagery

The annual spring breakup of river ice has important consequences for northern ecosystems and significant economic implications for Arctic industry and transportation. River ice breakup research is restricted by the sparse distribution of hydrological stations in the Arctic, where limited available data suggests a trend towards earlier ice breakup. The specific climatic mechanisms driving this trend, however, are complex and can vary both regionally and within river systems. Consequently, understanding the response of river ice processes to a warming Arctic requires simultaneous examination of spatial and temporal patterns in breakup timing. In this paper, we describe an automated algorithm for river ice breakup detection using MODIS satellite imagery that enables identification of spatial and temporal breakup patterns at large scales. We examine breakup timing on the Mackenzie, Lena, Ob' and Yenisey rivers for the period 2000-2014. By dividing the rivers into 10 km segments and classifying each river pixel in each segment as snow/ice, mixed ice/water or open water based on MODIS reflectance, we determine breakup dates with a mean uncertainty of ±. 1.3 days. All statistically significant temporal trends are negative, indicating an overall shift towards earlier breakup. Considerable variability in the statistical significance and magnitude of trends along each river suggests that different climatic and physiographic drivers are impacting spatial patterns in breakup. Trends detected on the lower Mackenzie corroborate recent studies indicating weakening ice resistance and earlier breakup timing near the Mackenzie Delta. In Siberia, the increased magnitude of trends upstream and strong correlation between breakup initiation and whole-river breakup patterns suggest that earlier onset of upstream discharge may play the dominant role in determining breakup timing. Exploratory analysis demonstrates that MODIS imagery may also be used to differentiate thermal and mechanical breakup events

PolSAR covariance structure detection and classification based on the EM algorithm

This paper proposes a new method for clustering polarimetric synthetic aperture radar images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM). Specifically, the feature used for classification is the PCM structure. To this end, the problem of detecting and classifying spatial variations in PCM structure is formulated as a multiple hypothesis test, where one null hypothesis and multiple alternative hypotheses are present. The estimation problems are solved by resorting to hidden random variables representative of covariance structure classes in conjunction with the expectation-maximization algorithm. These estimates are then used to form a penalized likelihood ratio test. The effectiveness of the proposed detection strategies is demonstrated on real polarimetric SAR data

Innovative solutions based on the EM-algorithm for covariance structure detection and classification in polarimetric SAR images

This paper addresses the challenge of identifying the polarimetric covariance matrix (PCM) structures associated with a polarimetric SAR image. Interestingly, such information can be used, for instance, to improve the scene interpretation or to enhance the performance of (possibly PCM-based) segmentation algorithms as well as other kinds of methods. To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only one unknown structure is assumed for data belonging to a 2-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different PCM structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation maximization (EM) algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms

Monitoring Ice Break-Up on the Mackenzie River Using Remote Sensing

The Mackenzie Basin is composed of eight sub-basins (North Mountains, Liard, Peace, Athabasca, Great Bear Low Plains, Great Slave and Arctic Red) and includes three large lakes (Great Bear Lake, Great Slave Lake, Lake Athabasca) and three deltas (Peace-Athabasca Delta, Slave Delta, Mackenzie Delta), one of which is the world’s largest inland delta (Peace-Athabasca Delta). Annually, the Mackenzie River experiences freeze-up during the fall season and ice break-up in the spring, having an important influence on the basin hydrology Furthermore, the type of ice break-up event is dependent on the magnitude of hydrological and meteorological conditions present. In light of the decreasing network of ground-based stations operated by the Water Survey of Canada on the Mackenzie River, this study explored the use of satellite remote sensing data to improve monitoring capabilities during the ice break-up period. MODIS Level 3 snow products (MOD/MYD10A1) and MODIS Level 1B radiance products (MOD/MYD02QKM) are used to monitor ice cover during the break-up period on the Mackenzie River, Canada, for 13 ice seasons (2001-2013). The initiation of the break-up period was observed to occur between days of year (DOY) 115-125 and end DOY 145-155, resulting in average melt durations of 30-40 days. Floating ice running northbound could therefore generate multiple periods of ice-on and ice-off observations at the same geographic location. At the headwaters of the Mackenzie River, ice break-up was thermodynamically driven as opposed to dynamically, as observed downstream near the Mackenzie Delta. MODIS observations also revealed that ice runs were largely influenced by channel morphology (islands and bars, confluences and channel constriction). MODIS was found to be a powerful tool for monitoring ice break-up processes at multiple geographical locations simultaneously along the Mackenzie River. Finally, MODIS was found to be a viable tool for estimating river ice velocity where channel morphology least affected river flow. Ice run velocities north of 66° N ranged from 1.21-1.84 ms-1

Retrieval of river ice thickness from C-band PolSAR data.

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