Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Sensing Mountains

Sensing mountains by close-range and remote techniques is a challenging task. The 4th edition of the international Innsbruck Summer School of Alpine Research 2022 – Close-range Sensing Techniques in Alpine Terrain brings together early career and experienced scientists from technical-, geo- and environmental-related research fields. The interdisciplinary setting of the summer school creates a creative space for exchanging and learning new concepts and solutions for mapping, monitoring and quantifying mountain environments under ongoing conditions of change

Towards Daily High-resolution Inundation Observations using Deep Learning and EO

Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used. While satellites do offer timely inundation information when they happen to cover an ongoing flood event, they are limited by their spatiotemporal resolution in terms of their ability to dynamically monitor flood evolution at various scales. Constantly improving access to new satellite data sources as well as big data processing capabilities has unlocked an unprecedented number of possibilities in terms of data-driven solutions to this problem. Specifically, the fusion of data from satellites, such as the Copernicus Sentinels, which have high spatial and low temporal resolution, with data from NASA SMAP and GPM missions, which have low spatial but high temporal resolutions could yield high-resolution flood inundation at a daily scale. Here a Convolutional-Neural-Network is trained using flood inundation maps derived from Sentinel-1 Synthetic Aperture Radar and various hydrological, topographical, and land-use based predictors for the first time, to predict high-resolution probabilistic maps of flood inundation. The performance of UNet and SegNet model architectures for this task is evaluated, using flood masks derived from Sentinel-1 and Sentinel-2, separately with 95 percent-confidence intervals. The Area under the Curve (AUC) of the Precision Recall Curve (PR-AUC) is used as the main evaluation metric, due to the inherently imbalanced nature of classes in a binary flood mapping problem, with the best model delivering a PR-AUC of 0.85

Soil-Water Conservation, Erosion, and Landslide

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems

Glacial Lake Outburst Flood (GLOF) Hazard Mitigation at Himalayan Region, Nepal

Glacier retreat is a strong indicator of climate change and global warming. The anthropogenic changes in the Earth's atmosphere are mostly to blame for the climate extremes and their consequences in the last few decades. The Himalayan region is no exclusion to the trend. As glaciers begin to retreat, the glacial lake starts to fill or form behind the natural moraine or ice dam in the glaciers. The sudden release of the water, known as the Glacial Lake Outburst Flood (GLOF), can release a large amount of water and sediment. There have been various destructive GLOFs recorded in Nepal since the 1960s. It is vital to understand the GLOF dynamics, geomorphology and historical events to mitigate the GLOF hazards in the region. An advanced approach based on remote sensing data and empirical evidence is more suitable to tackle these issues. This research investigated 11 among 30 past events recorded in the HKH region (Nepal) to establish the causes and triggering factors that led to the catastrophic failure, which helped establish the vulnerability assessment of these glacial lakes. This eventually led to creating a GLOF vulnerability assessment framework that is unique and useful to the communities. This research concluded that 40% of the GLOF events was due to the moraine dam failure. In the retrospective approach, 5 out of 11 glacial lakes scored a very high total vulnerability score (TVR), which suffered catastrophic events in the past. The TVR of the currently existing 21 potential dangerous glacial lakes (PDGL) in Nepal was also conducted using the proposed assessment framework that concluded the 7 very high, 4 high, 5 medium, and the rest are low. Hence, this assessment tool's reliability is very high. This research also concluded that there should integrated approach to climate change adaptation and hazard mitigations in the region

Development and evaluation of a hydrological and hydraulic coupled flood prediction system enabled by remote sensing, numerical weather prediction, and deep learning technologies

Floods triggered by extreme precipitation are the most frequently occurring and disastrous natural hazards in the world. However, it is still challenging to provide accurate and flood mapping, flood damage estimation, and flood forecast. The purpose of this dissertation is to develop a hydrological and hydraulic coupled flood prediction system, inundation MApping and Prediction (iMAP), which can provide comprehensive flood simulation and prediction including channel flow rate, flood return period, flood extent, surface flow speed, and direction, as well as inundation depth and soil moisture. Up until now, the Coupled Routing and Excess STorage (CREST) model family has been well documented and established both in research and in real-world operation. As a new member of the CREST family, the work in this dissertation carries on the features of CREST model, as being robust, efficient, automated, and globally applicable. Moreover, the study also evaluates multiple remote sensing and precipitation prediction technologies during the historical event Hurricane Harvey. The results of the studies demonstrate that the CREST-iMAP system has the ability to provide comparable Harvey flood simulation as multiple real-time and operational flood monitoring systems in the world, and the best result comes from using Multi-Radar Multi Sensor (MRMS) Quantitative Precipitation Estimates (QPE), which the combination considers as the best practice in the Contiguous United States (USA). The results also indicate that the uncalibrated precipitation estimates perform better during extreme events like Hurricane Harvey, and precipitation forecasts still need more improvement to provide more information on flood prediction. However, the Numerical Weather Prediction (NWP) product can provide a preliminary forecast of the maximum flood extent, while the deep learning method could potentially improve the displacement issues from NWP forecasts