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A water budget analysis (precipitation (P), surface runoff (Q), evapotranspiration (ET) and storage variations (ΔS)) was completed over a 3-year span for two Sphagnum bogs, three patterned fens and two shallow lakes all located in the La Grande River watershed in central Québec. The high variability of P from 2005 to 2007 during summer and fall (July to October) allowed us to produce water budgets over a large spectrum of wetness conditions at seasonal and event timescales. Bogs and fens (not lakes) have the intrinsic ability to keep the water table near the surface most of the time, which affects Q. Fens and lakes showed a similar hydrological behavior when compared to bogs, in spite of differences in Q and ΔS variability due to the typical vegetation structure of fens. This structure also tends to produce sharper rises of Q when compared to lakes that have overall smoother hydrograms. The dominant water budget term for bogs, fens and lakes was ΔS, Q and ET, respectively. Finally, an adaptation of the Penman-Monteith equation was successfully used to estimate potential ET. This revised method is based on peatland vegetation identification that provides a simple weighing factor for stomatal resistance. [ABSTRACT FROM AUTHOR]

The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological processes in these minerotrophic peatlands (fens) in order to provide effective monitoring tools to water managers. The objective of this study was to use VHR remote sensing data to understand the seasonal dynamics of the hydrology in fens. A series of 10 multispectral pan-sharpened GeoEye-1 images (with a spatial resolution of 40 cm) were acquired during the snow-free season (May to October) in 2009 and 2010, centered on two study sites in the Laforge sector (54°06'N; 72°30'W). These are two fens instrumented for continuous hydrometeorological monitoring (water level, discharge, precipitation, air temperature). An object-based classification procedure was set up and applied. It consisted of segmenting the imagery into objects using the multiresolution segmentation algorithm (MRIS) to delineate internal structures in the peatlands (aquatic, semi-aquatic, and terrestrial). Then, the objects were labeled using a fuzzy logic based algorithm. The overall classification accuracy of the 10 images was assessed to be 82%. The time series of the peatland mapping demonstrated the existence of important intra-seasonal spatial dynamics in the aquatic and semi-aquatic compartments. It was revealed that the dynamics amplitude depended on the morphological features of the fens. The observed spatial dynamics was also closely related to the evolution of the measured water levels. [ABSTRACT FROM AUTHOR]

We focus on the Saint-François River (Quebec), which is known for recurrent ice jam-induced floods. This study addresses monitoring deficiencies and proposes solutions by presenting a comprehensive large-scale ice cover monitoring approach using diverse remote sensing tools for managing ice-jam risks effectively on this watercourse. We achieved three sub-objectives: (1) gathering spatial characteristics of the ice jam by acquiring images during the ice jam with an RGB camera-equipped drone; (2) mapping river ice using radar and optical images; and (3) river segmentation based on the dominant ice process. The methodological approach integrates data remotely sensed before and during the ice jam event, employing various tools. By comparing remote sensing methods with traditional monitoring, we underscore the importance of spatial data acquisition in ice-jam risk management. Orthomosaic and summary maps illustrate ice evolution processes, highlighting remote sensing efficacy in discerning hydro-meteorological events and emphasizing the need to target specific areas for risk mitigation. River segmentation based on the dominant ice process provides insights into freeze and thaw sequences, thereby illustrating ice evolution processes. [ABSTRACT FROM AUTHOR]

Improving the estimation of snow density is a key task in current snow research. Characterization of the variability of density in time and space is essential for the estimation of water equivalent, hydroelectric power production, assessment of natural hazards (avalanches, floods, etc.). Hyperspectral imaging is proving to be a promising and reliable tool for monitoring and estimating this physical property. Indeed, the spectral reflectance of snow is partly controlled by changes in its physical properties, particularly in the near-infrared (NIR) part of the spectrum. For this purpose, several models have been designed to estimate snow density from spectral information. However, none has yet achieved significant performance. One of the major difficulties is that the relationship between snow density and spectral reflectance is non-bijective (surjective). Indeed, several reflectance amplitudes can be associated with the same density and vice versa, so the correlation between density and spectral reflectance can be very poor. To resolve this issue, a hybrid snow density estimation model based on spectral data is proposed in this work. The principle behind this model is to classify the snow density prior to its estimation by means of a specific estimator corresponding to a predetermined snow density class. These additional steps eliminate the surjective relation by converting it into three bijective relations between density and spectral reflectance. The calibration step showed that the densities included within the three classes are sensitive to different spectral regions, with R2 > 0.80. The results of the cross-validation for the specific estimators were also satisfactory with R2 > 0.78 and RMSE < 36.36 kg m−3. The overall performance of the hybrid model (HM), when tested with independent data, demonstrated the effectiveness of using proximal NIR hyperspectral imagery to estimate snow density (R2 = NASH = 0.93). [ABSTRACT FROM AUTHOR]

Presents a study which discusses the C-band synthetic apertuer radar (SAR) data. Why extensive ground measurements were done; Revelation of the analysis; Information on results.

This paper proposes an innovative method for classifying the physical properties of the seasonal snowpack using near-infrared (NIR) hyperspectral imagery to discriminate the optical classes of snow at different degrees of metamorphosis. This imaging system leads to fast and non-invasive assessment of snow properties. Indeed, the spectral similarity of two samples indicates the similarity of their chemical composition and physical characteristics. This can be used to distinguish, without a priori recognition, between different classes of snow solely based on spectral information. A multivariate data analysis approach was used to validate this hypothesis. A principal component analysis (PCA) was first applied to the NIR spectral data to analyze field data distribution and to select the spectral range to be exploited in the classification. Next, an unsupervised classification was performed on the NIR spectral data to select the number of classes. Finally, a confusion matrix was calculated to evaluate the accuracy of the classification. The results allowed us to distinguish three snow classes of typical shape and size (weakly, moderately, and strongly metamorphosed snow). The evaluation of the proposed approach showed that it is possible to classify snow with a success rate of 85% and a kappa index of 0.75. This illustrates the potential of NIR hyperspectral imagery to distinguish between three snow classes with satisfactory success rates. This work will open new perspectives for the modelling of physical parameters of snow using spectral data. [ABSTRACT FROM AUTHOR]

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients. [ABSTRACT FROM AUTHOR]