Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

Snowmelt Detection on Alpine Glaciers using Synthetic Aperture Radar Time Series

Hindu Kush Himalayan (HKH) glaciers serve as some of the most sensitive indicators of changes in global climate. These glaciers shape the hydrologic dynamics of river systems supplying freshwater to over 2 billion people throughout Asia and regulate the geochemistry of sensitive aquatic alpine ecosystems. As snowmelt onsets sooner, lasts longer, and snowfields retreat due to increases in global temperature, the hydrologic dynamics of catchments draining HKH threaten to change the availability of surface freshwater resources for nearly one fifth of the global population, disturb sensitive aquatic habitat, and precipitate hazards associated with glacier wasting. Informed planning and decision-making around adaption to a changing climate requires operational monitoring of glacier melt dynamics to improve study of predicted disturbances to HKH hydrologic systems. This research presents a method for spatially resolved alpine glacier melt detection using synthetic aperture radar (SAR) time series. Building on research into melt detection from passive microwave scatterometers over large ice sheets, this study detects melt characteristics from Sentinel-1 SAR backscatter intensity time series over glacier surfaces using a classification threshold based on a decrease in backscatter intensity relative to average values across the frozen season. Statistical analysis of the radiometric response to dielectric loss on glaciated area within the study region (70,789 km2) shows that cross-polarized melt classification accounts for 24% more of glacier surface area than co-polarized observations. Illustrative comparison of melt classification results to optical imagery captured near the end of seasonal melt reveals that dual polarized melt measurements are concentrated within areas of apparent glacier accumulation yet cross-polarized melt detection occurs more homogeneously across glacier surfaces relative to co-polarized observations. The results of this study suggest that physical characteristics of the glacier surface may be radiometrically distinct across positive and negative zones of glacier mass balance. Improvements to radiometric terrain correction of SAR data in complex high mountain terrain would improve the accuracy of temporal thresholding algorithms for melt detection

Master of Science

thesisRecent accelerated mass loss offset by increased Arctic precipitation highlights the importance of a comprehensive understanding of the mechanisms controlling mass balance on the Greenland ice sheet. Knowledge of the spatiotemporal variability of snow accumulation is critical to accurately quantify mass balance, yet, considerable uncertainty remains in current snow accumulation estimates. Previous studies have shown the potential for large-scale retrievals of snow accumulation rates in regions that experience seasonal melt-refreeze metamorphosis using active microwave remote sensing. Theoretical backscatter models used in these studies to validate the hypothesis that observed decreasing freezing season backscatter signatures are linked to snow accumulation rates suggest the relationship is inverse and linear (dB). The net backscatter measurement is dominated by a Mie scattering response from the underlying ice-facie. Two-way attenuation resulting from a Raleigh scattering response within the overlying layer of snow accumulation forces a decrease in the backscatter measurement over time with increased snow accumulation rates. Backscatter measurements acquired from NASA's Ku-band SeaWinds scatterometer on the QuikSCAT satellite together with spatially calibrated snow accumulation rates acquired from the Polar MM5 mesoscale climate model are used to evaluate this relationship. Regions that experienced seasonal melt-refreeze metamorphosis and potentially formed dominant scattering layers are delineated, iv freeze-up and melt-onset dates identifying the freezing season are detected on a pixel-by-pixel basis, freezing season backscatter time series are linearly regressed, and a microwave snow accumulation metric is retrieved. A simple empirical relationship between the retrieved microwave snow accumulation metric (dB), , and spatially calibrated Polar MM5 snow accumulation rates (m w. e.), , is derived with a negative correlation coefficient of R=-.82 and a least squares linear fit equation of . Results indicate that an inverse relationship exists between decreasing freezing season backscatter decreases and snow accumulation rates; however, this technique fails to retrieve accurate snow accumulation estimates. An alternate geometric relationship is suggested between decreasing freezing season backscatter signatures, snow accumulation rates, and snowpack stratigraphy in the underlying ice-facie, which significantly influences the microwave scattering mechanism. To understand this complex relationship, additional research is required

Predicting Water Availability in the Antarctic Dry Valleys using Geographic Information Systems and Remote Sensing

Water is one of the most important ingredients for life on Earth. The presence or absence of biologically available water determines whether or not life will exist. Antarctica is an environment where abiotic constraints, particularly water, strongly influence the distribution and diversity of biota. As Antarctic biology is relatively simple when compared to more temperate climates, it is a prime location for researching constraints on biodiversity, and what may be the impacts of changes to these constraints resulting from climate change and human disturbance. This research uses Geographic Information Systems (GIS) and remote sensing to develop a relative water availability index of three Dry Valleys in Southern Victoria Land, Antarctica. This study area is being used for the IPY Terrestrial Biocomplexity project, an international collaboration researching the distribution, diversity and complexity of biology in the Dry Valleys. The development of a predictive water availability model will contribute greatly to their research goals. This thesis describes the sources of biologically available water in the Dry Valleys and its interaction with biota. Remotely sensed data of these sources is gathered and various methods of analysing the data are explored. This includes creating a mean snow cover distribution model from MODIS data over 4 summer seasons, and Landsat7 ETM+ surface temperature data. These data sets, combined with a high resolution LIDAR Digital Elevation Model and glacier and lake locations, are then analysed with GIS to produce a Compound Topographic Index (CTI), a model showing the likely accumulation and dispersal of liquid water given the spatial distribution of water sources and the flow of water over the terrain according to the influence of gravity. Visualisation techniques are used to validate the resulting model, including the use of 3D visualisation and comparison of drainage patterns using overlays of a high resolution ALOS image. This research concludes that GIS and remote sensing are valuable tools for predicting water distribution in Antarctica. Although cloud cover, varied illumination and differing spatial resolutions can create limitations, remote sensing's cost effective and environmentally sound method of data capture and the computational and spatial modelling capabilities of GIS make their use well suited to the Antarctic environment

A new high-resolution elevation model of Greenland derived from TanDEM-X

In this paper we present for the first time the new digital elevation model (DEM) for Greenland produced by the TanDEM-X (TerraSAR add-on for digital elevation measurement) mission. The new, full coverage DEM of Greenland has a resolution of 0.4 arc seconds corresponding to 12 m. It is composed of more than 7.000 interferometric synthetic aperture radar (InSAR) DEM scenes. X- Band SAR penetrates the snow and ice pack by several meters depending on the structures within the snow, the acquisition parameters, and the dielectricity constant of the medium. Hence, the resulting SAR measurements do not represent the surface but the elevation of the mean phase center of the backscattered signal. Special adaptations on the nominal TanDEM-X DEM generation are conducted to maintain these characteristics and not to raise or even deform the DEM to surface reference data. For the block adjustment, only on the outer coastal regions ICESat (Ice, Cloud, and land Elevation Satellite) elevations as ground control points (GCPs) are used where mostly rock and surface scattering predominates. Comparisons with ICESat data and snow facies are performed. In the inner ice and snow pack, the final X-Band InSAR DEM of Greenland lies up to 10 m below the ICESat measurements. At the outer coastal regions it corresponds well with the GCPs. The resulting DEM is outstanding due to its resolution, accuracy and full coverage. It provides a high resolution dataset as basis for research on climate change in the arctic

Cryosphere Applications

Synthetic aperture radar (SAR) provides large coverage and high resolution, and it has been proven to be sensitive to both surface and near-surface features related to accumulation, ablation, and metamorphism of snow and firn. Exploiting this sensitivity, SAR polarimetry and polarimetric interferometry found application to land ice for instance for the estimation of wave extinction (which relates to sub surface ice volume structure) and for the estimation of snow water equivalent (which relates to snow density and depth). After presenting these applications, the Chapter proceeds by reviewing applications of SAR polarimetry to sea ice for the classification of different ice types, the estimation of thickness, and the characterisation of its surface. Finally, an application to the characterisation of permafrost regions is considered. For each application, the used (model-based) decomposition and polarimetric parameters are critically described, and real data results from relevant airborne campaigns and space borne acquisitions are reported

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

Earth resources: A continuing bibliography with indexes (issue 47)

This bibliography lists 524 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis