Search

Floodplain wetland ecosystems experience significant seasonal water fluctuation over the year, resulting in a dynamic hydroperiod, with a range of vegetation community responses. This paper assesses trends and changes in landcover and hydro-climatological variables, including air temperature, river discharge, and water level in the Upper Columbia River Wetlands (UCRW), British Columbia, Canada. A time series landcover classification from the Landsat image archive was generated using a Random Forest algorithm from 1984 to 2022. Peak river flow timing, duration, and anomalies were examined to evaluate temporal coincidence with observed landcover trends. The land cover classifier used to segment changes in wetland area and open water performed well (Kappa = 0.82). Over the last four decades, observed river discharge and air temperature have increased, precipitation has decreased, the timing of peak flow is earlier, and flow duration has been reduced. The frequency of both high discharge events and dry years have increased, indicating a shift towards more extreme floodplain flow behavior. These hydrometeorological changes are associated with a shift in the timing of snow melt from April to mid-May and are associated with seasonal changes in the vegetative communities over the 39-year period. The area of woody shrub landcover has increased in the spring (April to mid-May), peak flow period (late-May to July) and early fall (August to mid-September) by +6% to +12% since 1984. In the spring and early fall, the area of open water has decreased -3% to -6% since 1984, while it has increased 3% during the peak flow period. The area of marsh land cover (mostly bulrush and cattails) has declined in every season by -29% in spring, -19% in the peak flow period and -5% in early fall. These findings suggest that increasing temperatures have already impacted regional hydrology, wetland hydroperiod and floodplain landcover in the Upper Columbia Valley in Canada. Overall, there is substantial variation in seasonal and annual land cover reflecting the dynamic nature of floodplain wetlands, but the results show that the wetlands are drying out with increasing the areas of woody/shrubby habitat and loss of aquatic habitat. The results suggest that floodplain wetlands, particularly marsh and open water habitats are vulnerable to climatic and hydrological changes that could further reduce their areal extent in the future. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods can provide solutions to address PolSAR feature extraction challenges. The convolutional neural networks (CNNs) and graph convolutional networks (GCNs) can drive PolSAR image characteristics by deploying kernel abilities in considering neighborhood (local) information and graphs in considering long-range similarities. A novel dual-branch fusion of CNN and mini-GCN is proposed in this study for PolSAR image classification. To fully utilize the PolSAR image capacity, different spatial-based and polarimetric-based features are incorporated into CNN and mini-GCN branches of the proposed model. The performance of the proposed method is verified by comparing the classification results to multiple state-of-the-art approaches on the airborne synthetic aperture radar (AIRSAR) dataset of Flevoland and San Francisco. The proposed approach showed 1.3% and 2.7% improvements in overall accuracy compared to conventional methods with these AIRSAR datasets. Meanwhile, it enhanced its one-branch version by 0.73% and 1.82%. Analyses over Flevoland data further indicated the effectiveness of the dual-branch model using varied training sampling ratios, leading to a promising overall accuracy of 99.9% with a 10% sampling ratio. [ABSTRACT FROM AUTHOR]

In cold regions deltas influenced by semi-arid climates, such as the Peace-Athabasca Delta (PAD) in western Canada, surface water connectivity influences aquatic conditions by offsetting evaporative losses that are generally greater than precipitation inputs (Kääb et al., [6]; Lesack & Marsh, [7]; Peters, Prowse, Marsh, et al., [11]). This information is necessary to arrive at key hydrological information for an inland basin in question: (i) total basin area delineation, (ii) runoff contribution areas to the water body, (iii) maximum water surface extent, and (iv) frequency of surface water connectivity. TAKE AWAY MESSAGES We employed a visual and numerical aid to detect ice-jam and open-water condition surface water connectivity points in a sector of the Peace-Athabasca Delta. [Extracted from the article]