Supraglacial Environment Analysis

Some places on Earth are so cold that water is a solid—ice or snow [1]. These frozen places of our planet are called the “Cryosphere” by our Scientists.The term “Cryosphere” comes from the Greek word “kryos” which means cold, frost or ice and “sphaira” which means globe. Those regions of the Earth’s surface where water is in the solid form, including sea ice, lake ice, river ice, snow cover, glaciers, ice caps, ice sheets and frozen grounds (which includes permafrost) are referred to as cryosphere. The cryosphere is an integral part of the global climate system with important linkages and feedbacks generated through its influence on surface energy and moisture fluxes, clouds, precipitation, hydrology, atmospheric and oceanic circulation. Through these feedback processes, the cryosphere plays a significant role in theglobal climateand inclimate modelresponse to global changes [2].

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Cryosphere is believed to be at the top and bottom of our planet, in the Polar Regions by most of the people. The area around the North Pole is the Arctic region and the area around the South Pole is the Antarctic region. Snow and ice are also found at many other locations on Earth, other than these two Polar Regions [1].

Located at the Earth’s South Pole, Antarctica is an icy continent. A huge ice sheet covers the land mass of Antarctica and, in some places, shelves of floating ice extend into the ocean. The outer sections of ice break off or “calve” from these shelves and form icebergs. The icebergs float in the oceans, melting and falling apart as they drift into warmer waters [1]. The Antarctic Ice Sheet is the largest mass of ice on Earth.

1. Supraglacial Environment

Literally, supraglacial means of, relating to, or situated or occurring at the surface of a glacier. Supraglacial environment consists of Supraglacial lakes and ponds, supraglacial streams and, supraglacial dust and debris.

1. Supraglacial Lakes and Ponds

Any pond of liquid water on the top of aglacier is called a supraglacial lake. Although these pools aretransient, they may reach kilometers in diameter and could be several meters deep. They usually last for months or even decades at a time, but can vacant in the course of hours. The lakes usually emerge from the assembly of summer meltwater in catchment basins.

1. Supraglacial Streams

A stream that flows over the surface of a glacier is called supraglacial streams. Most supraglacial streams descend viaMoulin into the depths or base of a glacier or originate from melting snow, ice fields and glacial ice.

2. Supraglacial Dust and Debris

Debris that is carried on the surface of a glacier is called supraglacial debris. It is also known as supraglacial moraines. It is normally derived from weathering processes that occur during seasonal precipitation, melt/ refreeze cycles or supraglacial activity (i.e. transport) rates and tends to be blocky angular boulders and sediments in character.Heavy volcanic supraglacial debris is composed of fine grained volcanic ash, tephra or large ballistics that is ejected during eruptions.

The Polar Regions, are remote and often inaccessible, in terms of their location. Investigations based on field of the entire Polar Region are very strenuous, and rarely possible. Satellite remote sensing is a low cost solution to obtain excellent coverage of the Polar Regions from a view point in space. By using remote sensing data and techniques it is now possible to investigate the high latitude regions in a way that was unimaginable even a few decades ago.

Over the past two decades, the Polar Regions have homogenously shown the first distinctive evidence of human industrial activity on Earth’s atmosphere and climate (Luban and Massom, 2007) [5].

• The first was the 1985 discovery of the ozone “hole,” an annual disappearance of most of the ozone layer over Antarctica and the Southern Ocean, whose cause was quickly identified as an interaction between industrial chlorofluorocarbon pollutants and unique ice clouds that form in the extremely cold Antarctic stratosphere.
• More recently, both satellite data and submarine research cruises have documented the dramatic decrease in both geographic extent and thickness of Arctic sea ice, such that if no action is taken to curb industrial greenhouse gas emissions, Arctic sea ice could disappear altogether in the summer season by the end of this century.

In 1981, India undertook its National Antarctic Program within the aegis of Department of Ocean Development (DOD) of the Government of India. ACentre dedicated to Antarctic Expeditions, named as ‘Antarctic Study Centre (ASC)’ was established at Goa in 1988.The ASC was eventually upgraded into an autonomous institute ‘National Centre for Antarctic and Ocean Research’ (NCAOR) under DOD (now known as Ministry of Earth Sciences).

Maitri station was built in 1989 on the Schirmacher Oasis in Queen Maud Land. India previously operated the station Dakshin Gangotri from 1983 – 1989 which was abandoned after being buried in ice. Around 3,000 kilometers from Maitri station, India extended its Antarctic presence by building a new station named Bharati in the Larsemann Hills region.The Bharati station has been operational since 18th March 2012.

Ordinarily, resolution is thought to be as the ability to separate and differentiate adjacent objects or items in a scene, be it in a photo, an image or real life. Frequently resolution is specified in terms of the linear size of the smallest features we can discriminate (often expressed in meters). But contrast impacts our ability to resolve between objects: if two items are the same color, they may be tough to separate, but if they are sharply different in color, tone, or brightness we can recognize them more clearly. Remote sensors estimate differences and variations of objects that are often reported in terms of four main resolutions, each of which affect the accuracy and functionality of remote sensors to habitat mapping.

The details noticeable in an image are dependent on the spatial resolution of the sensor and refer to the size of the smallest possible feature that can be identified. Spatial resolution of passive sensors depends essentially on their Instantaneous Field of View (IFOV). The IFOV is the angular cone of visibility of the sensor (A) and determines the area on the Earth’s surface which is “seen” from a given altitude at one particular moment in time (B). The size of the area viewed is determined by multiplying the IFOV by the distance from the ground to the sensor (C). This area on the ground is called the resolution cell and decides a sensor’s greatest spatial resolution. For an analogous feature to be detected, its size generally has to be equal to or larger than the resolution cell. If the feature is smaller than this, it may not be detectable as the average brightness of all features in that resolution cell will be recorded. However, smaller features may sometimes be detectable if their reflectance influences within a particular resolution cell allowing sub-pixel or resolution cell recognition.

Images where only large features are visible are said to have coarse or low resolution. In fine or high resolution images, small objects can be identified. Military sensors for example, are designed to for detailed view, and so have very fine resolution. Commercial satellites provide imagery with resolutions differing from a few meters to several kilometers. Usually finer the resolution, the less whole ground area would be visible.

The ratio of distance on an image or map, to actual ground distance is called scale. If a map is with scale of 1:100,000, an object of 1cm length on the map would really be an object 100,000cm (1km) long on the ground. Maps or images with small “map-to-ground ratios” are referred to as small scale (e.g. 1:100,000), and those with larger ratios (e.g. 1:5,000) are called large scale.

Spectral response and spectral emissivity curves specifies the reflectance and/or emittance of a feature or target across a range of wavelengths. Dissimilar classes of features and details in an image can frequently be distinguished by contrasting their responses over distinct ranges of wavelength. Extensive classes such as water and vegetation can generally be separated using very broad ranges of wavelength (the visible and near infrared). More specific classes like rock types may not be easily distinguishable using only these broad ranges of wavelength and require comparison at much finer ranges of wavelengths to separate them. Hence we require a sensor with higher spectral resolution for such specific classification. Spectral resolution describes the ability of a sensor to define fine intervals of wavelength. Finer is the spectral resolution, narrower will be the ranges of wavelength for a particular channel or band.

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As the spatial structure of an image is described by the arrangement of pixels, the actual information content in an image is described by the radiometric characteristics. The sensitivity of the image to the magnitude of the electromagnetic energy is determined by the radiometric resolution, whenever an image is captured on film or by a sensor. The radiometric resolution of an imaging system describes its ability to differentiate very slight contrast in energy. Finer the radiometric resolution of a sensor, more sensitive it is to detecting small differences in reflected or emitted energy. When a 2-bit image is compared with an 8-bit image, there is a large difference in the level of details observable depending on their radiometric resolutions.

The concept of temporal resolution is also important to consider in a remote sensing system, in addition to spatial, spectral, and radiometric resolution. It is the concept of revisit period, which refers to the length of time it requires for a satellite to finish one complete orbit cycle. The revisit period is usually several days for a satellite sensor. Thus the absolute temporal resolution of a remote sensing system to image the exact same area at the same viewing angle a second time is equal to this period. Still some areas of the Earth tend to be re-imaged more frequently because of some degree of overlap in the imaging swaths of adjacent orbits for most satellites and the increase in this overlap with increasing latitude. Also, some satellite systems are able to point their sensors to image the same area between different satellite passes parted by periods from one to five days. So, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the swath overlap, and latitude.

WorldView-2 is the first high-resolution 8-band multispectral commercial satellite launched in October 2009. It operates at an altitude of 770 km and provides 50 cm panchromatic resolution and 2 m multispectral resolution. The average revisit time of World View-2 is of 1.1 days and it is also capable of collecting up to 1 million km² of 8-band imagery per day. Every sensor is narrowly concentrated on a specific range of the electromagnetic spectrum that is sensitive to a particular feature on the ground, or a property of the atmosphere. They are designed together to improve the segmentation and classification of land and aquatic features beyond any other space-based remote sensing platform.

The agricultural growth, increased urbanization and natural processes all contribute towards the reshaping nature of land use and land cover around the globe. Remote sensing is recognized as an essential tool for understanding the changes over a large and small scale. Presently various satellites are being engaged to observe and study the globe. WV-2 brings out a high degree of detail to classification processes, with 8 strictly focused spectral sensors ranging from visible to near infrared, combined with 2 meter spatial resolution, enabling a finer level of intolerance and improving decision-making in both the public and private sector. The table given below explains the various characteristics of the important high resolution satellites. The World Viiew-2 is the satellite, which is having 0.5 m spatial resolution which has been used for this particular study.

Features of World View-2 satellite are:

• Very high resolution
• The most spectral diversity commercially available
  • 4 standard colors: Blue, Green, Red, NIR-1
  • 4 new colors: Coastal, Yellow, Red Edge, NIR-2
• Bi- directional scanning
• Orbit altitude: 770 km, sun-synchronous
• Dynamic range: 11-bits per pixel
• Swath width: 16.4 km at nadir

Benefits of World View-2 satellite are:

• Provides highly detailed imagery for precise map creation, change detection, and in-depth image analysis.
• Geo-locate features to less than 5 m to create maps in remote areas, maximizing the utility of available resources.
• Collects, stores, and downlinks a greater supply of frequent update global imagery products than competitive systems.
• Stereoscopic collection on a single pass ensures image continuity and consistency of quality.
• Provides the ability to perform precise change detection, mapping and analysis at unprecedented resolutions in 8-band multispectral imagery.

In addition to dedicated satellite instruments and programs that have monitored critical manifestations of climate and atmospheric change, such as the retreat of Arctic Sea Ice, the motion of the Antarctica Ice Sheets and the evolution of the ozone ‘hole’ in both polar regions, many serendipitous applications of satellite remote sensing have come forward for polar research. A survey of polar remote sensing accomplishments is particularly useful at this point in time, as the earth science community is experiencing a transition to a new generation of satellite remote sensing instruments with an order of magnitude greater capability than their predecessors.

The Antarctica ice sheet alone covers an area of ~12.4×10⁶ km², and averages ~2.4 km in thickness, with a maximum of ~4.7 km (in the Wilkes sub-glacial basin between Casey and Vostok) and a volume of ~25.7×10⁶ km³. It stores ~90% of the world’s ice, equivalent to ~70% of its freshwater or an approx. 65 m rise in global sea level was it to melt. This Antarctica Ice sheet play a central role in the global climate system, interacting in a complex fashion with the atmosphere and ocean, acting as major hemispheric heat sinks as a result of the radiatively induced Equator to pole temperature difference, and dominating the high-latitude radiation balance by virtue of their high albedo. Ice sheets have profound direct and indirect impacts on patterns of oceanic and atmospheric temperature and circulation and also biogeochemical cycles [11].

Studying and analyzing Antarctica using satellite remote sensing is not an old practice. It started way back in 1972 with the launch of Landsat-1(formerly ERTS-1). It kept on growing since then with the use of microwave and thermal remote sensing datasets. Moderate Resolution Imaging Spectrometer (MODIS) datasets have played a vital role in understanding this remote continent.

In this research we have applied a combination of existing image processing methods and a novel feature extraction workflow to DigitalGlobe’s WorldView-2 (WV-2) satellite imagery, in order to study Antarctica at maximum achievable spatial resolution. Our protocol also compares widely used image fusion algorithms all over the researcher’s community.

During the past 30-40 years, satellite and other remote sensing methods have provided a massive wealth of new data to transform understanding of the Antarctic. While important, field measurements are logistically demanding, expensive, and scanty. Satellites can measure and monitor remote and vast areas in a sustained, consistent, systematic, repetitive, and cost-effective fashion and on a variety of scales alone. Using high-spatial resolution data is the only practical approach for generating detailed and accurate information on the landscape and land cover in the Antarctic, where field measurements are laborious. The capabilities of these state-of-the-art satellites have not been thoroughly explored for mapping land cover in cryospheric regions.

Widely, sea-ice spectral reflectance (ratio of radiant energy reflected by a body to that incident upon it) depends upon its age and thickness, and the presence/absence of a snow cover. Snow reflectance depends on the refractive index of ice, grain-size distribution, density, depth, and liquid-water content. Maps of surface albedo (the ratio of upwelling to down welling radiative flux at the surface) can be retrieved from satellite radiance data after accurately masking cloud, correcting atmospheric effects, and converting angular measurements to the ‘‘full hemisphere’’ angular distribution of the surface (the bidirectional reflectance distribution function [BRDF]). While the broadband albedo of ice-free ocean is ~0.05-0.1 that of sea ice ranges from ~0.1 to ~0.9 enabling ice-ocean discrimination and ice type classification. The strong sensitivity of NIR radiation to snow grain-size growth with melting further enables the detection/monitoring of seasonal melt/refreeze. Ice-sheet surface grain size is itself retrievable from 1.6 mm data (e.g., from the Global Imager [GLI] aboard ADEOS-II [operational from 2002–2003]).

High and very high resolution sensors acquire data over a narrow swath (<200 km and <20 km respectively) with a nominal repeat cycle of 16-26 days, and a spatial resolution of ~5 to ~100 m and <1 m to <5 m respectively (limiting polar applications to very detailed localized studies). Current commercial examples include the Enhanced Thematic Mapper Plus (ETM+) on Landsat-7 (1999-present), High-Resolution Geometric (HRG) sensors aboard SPOT-5 (2002-present), Ikonos- 2 (launch 1999), Quickbird-2 (2001), and EROSA1 (2001). While SPOT sensors offer higher resolution than Landsat and to a higher latitude (~86° versus ~82.5°), Landsat scene coverage is greater (i.e., 185X185 km versus 60X60 km). Moreover, SPOT sensors (1986- present) lack the night time viewing capability of modern Landsat TIR channels. SPOT-5 has a 2.5 m resolution mode with wider coverage (60 km). Non-commercial examples include the EOS Terra Advanced Spaceborn Thermal Emission and Reflection radiometer (ASTER) and the Advanced Visible and Near-Infrared Radiometer-2 (AVNIR-2) on the Japanese Advanced Land Observing Satellite (ALOS, launched 2005). This sensor class is best suited to studies requiring localized detailed coverage. While sea-ice applications are largely limited to the validation of coarser-resolution data, Landsat in particular has made a major contribution to ice-sheet research (since 1972) (e.g., in the production of high-density maps of ice velocity by measuring temporal displacement of conspicuous features [e.g., crevasses], and mapping/ monitoring of ice-sheet mporphology morphology/margins [many subtle morphological features which cannot be identified at ground level are readily apparent from space]). The limited numbers of cloud-free scenes available provides tantalizing glimpses of past conditions, and have been combined with modern imagery to monitor change (e.g., in glacier and ice-sheet margins).