Lake Ice Monitoring With Webcams and Crowd-Sourced Images

Abstract

Abstract. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model’s ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of ≈71% across different cameras and ≈69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.

Formulas for calculating ice cover thickness on selected spring lakes on the upper Radunia (Kashubian Lakeland, northern Poland)

“…That is why the monitoring of lakes not under hydrometric supervision is a particularly interesting issue. Such studies usually involve analysing ice cover with the use of satellite images or remote video monitoring data (Murfitt and Duguay 2020;Prabha et al 2020) and then ice models are created using such indirectly collected data sets (Tom et al 2020). There are only a few publications on monitoring lakes that are under hydrological control with the use of benchmark lakes (Barańczuk and Barańczuk 2018;Barańczuk 2019).…”

Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images

“…Previous attempts at automated analysis of ice scenes (closerange images) are either limited to a few freshwater ice features to understand/monitor river ice processes [30]- [33] or use traditional image processing techniques that are limited to broken ice with inclusions of brash, young gray, and frazil/nilas ice and specific lighting conditions [34]- [36]. A few recent studies have applied deep learning-based methods for the analysis of generalized ice scenes, but their focus is on the classification of the ice objects present in the optical image [37], [38] or segmentation of first-year ice types rather than on differentiation between ice objects (deformed ice, level ice, icebergs) [39].…”

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

“…Both these works dealt with small-and mid-sized Swiss mountain lakes. Xiao et al (2018) and Prabha et al (2020) explored the potential of convolutional neural networks (CNNs) for lake ice detection in terrestrial webcam images (RGB). They performed a supervised classification of the lake pixels using the Tiramisu (Jégou et al, 2016), respectively Deeplab v3+ (Chen et al, 2018) networks, into the four classes: water, ice, snow and clutter.…”