Glaciers and glacial lakes are generally located in remote areas, where access must through tough and difficult terrain. The study of glaciers and glacial lakes, as well as carrying out glacial lake outburst flood (GLOF) inventories and field investigations using conventional methods, requires extensive time and resources together with undergoing hardship in the field. Creating inventories and monitoring of the glaciers, glacial lakes and extent of GLOF impacting on downstream can be done quickly and correctly using satellite images and aerial photographs. Use of these images and photographs for the evaluation of physical conditions of the area provides greater accuracy. The multi-stage approach using remotely sensed data and field investigation increases the ability and accuracy of the work. Visual and digital image analysis techniques integrated with GIS techniques are very useful for the study of glaciers, glacial lakes, and GLOFs.

Remote sensing is the science and art of acquiring information (spectral, spatial, and temporal) about material objects, areas, or phenomena through the analysis of data acquired by a device from measurements made at a distance, without coming into physical contact with the objects, area, or phenomena under investigation.

Remote-sensing technology makes use of the wide range of the electro-magnetic spectrum (EMS). Most of the commercially available remote-sensing data are acquired in the visible, infrared, and microwave wavelength portion of the EMS. For the present study, the data acquired within the visible and infrared wavelength ranges were used.

There are different types of commercial satellite data available. Digital data sets of the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER), the Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) were used mostly for the present study. Some data sets of the China-Brazil Earth Resources Satellite (CBERS) and Indian Remote Sensing Satellite Series 1D (IRS 1D), Linear Imaging and Self Scanning Sensor (LISS) 3 were also used. This study adopts the TM and ETM+ images, the list of the images relevant to the present study are given in Chapter 4.

Thematic Mapper (TM)

Thematic Mapper (TM) is a multi-spectral scanning radiometer that was carried on board Landsat 5. The TM sensors have provided nearly continuous coverage from July 1984 to present, with a 16-day repeat cycle. TM image data consists of seven spectral bands with a spatial resolution of 30 meters for most bands (1-5 and 7) (see table 6.1). Spatial resolution for the thermal infrared (band 6) during image acquisition is 120 meters, but the delivered TM band 6 will be resampled to 30 meter pixel size. The approximate scene size will be 185 x 185 kilometers.

Table 6.1 Channel Characteristics of Thematic Mapper

Thematic Mapper (TM)

Landsat-5

Wavelength
(mm)

Resolution
(m)

Band 1

0.45-0.52

30

Band 2

0.52-0.60

30

Band 3

0.63-0.69

30

Band 4

0.76-0.90

30

Band 5

1.55-1.75

30

Band 6

10.40-12.50

120

Band 7

2.08-2.35

30

This is a photograph of the Thematic Mapper on the ground before it was mated to the spacecraft. Note the gold leaf that is used to shield the inner workings.

Figure 6.1 The TM Sensor

This cutaway diagram shows the major components of the TM system:

Figure 6.2 The TM System and configuration

The sketch below shows some of the components in the optical train and detector layout of the TM

Figure 6.3 The design of TM

Table 6.2 Orbit Characteristics

Type

Sun Synchronous, polar

Altitude

705 km

Inclination

98.2 degree

Period

98.9 minute

Recurrent period

16 days

equatorial crossing time

09:30 am

Swath Width

185 km

Enhanced Thematic Mapper plus (ETM+)

The Enhanced Thematic Mapper Plus (ETM+) is a multi-spectral scanning radiometer that is carried on board the Landsat 7 satellite. The sensor has provided nearly continuous acquisitions since July 1999, with a 16-day repeat cycle. An instrument malfunction occurred on May 31, 2003, with the result that all Landsat-7 scenes acquired since July 14, 2003 have been collected in "SLC-off" mode.

The ETM+ instrument provides image data from eight spectral bands. The spatial resolution is 30 meters for the visible and near-infrared (bands 1-5 and 7). Resolution for the panchromatic (band 8) is 15 meters, and the thermal infrared (band 6) is 60 meters. The approximate scene size is 185 x 185 kilometers.

Table 6.3 The Channel Characteristic of Enhance Thematic Mapper plus

Enhance Thematic Mapper plus (ETM+)

Landsat-7

Wavelength
(mm)

Resolution
(m)

Band 1

0.45-0.52

30

Band 2

0.52-0.60

30

Band 3

0.63-0.69

30

Band 4

0.76-0.90

30

Band 5

1.55-1.75

30

Band 6

10.40-12.50

120

Band 7

2.08-2.35

30

Pan

0.52-0.90

15

 

Table 6.4 Orbit Characteristics

Altitude

705 km

Inclination

98.2 degree

Period

98.9 minute

Recurrent period

16 days

equatorial crossing time

10:00 am

Swath Width

185 km

APPLICATION OF REMOTE SENSING

When electro-magnetic energy is incident on any given earth surface feature, three fundamental energy interactions with the feature are possible. Various fractions of energy incident on the element are reflected, absorbed, and/or transmitted. All components of incident, reflected, absorbed, and/or transmitted energy are a function of the wavelength. The proportions of energy reflected, absorbed, and transmitted vary for different earth features, depending on their material types and conditions. These differences permit us to distinguish different features on an image. Thus, two features may be distinguishable in one spectral range and may be very different on another wavelength band. Within the visible portion of the spectrum, these spectral variations result in the visual effect called color. For example, blue objects reflect highly in the blue portion of the spectrum, likewise green reflects highly in the ‘green’ spectral region, and so on. Thus, the eye uses spectral variations in the magnitude of reflected energy to discriminate between various objects.

Satellite data are digital records of the spectral reflectance of the Earth’s surface features. These digital values of spectral reflectance are used for image processing and image interpretations. A graph of the spectral reflectance of an object as a function of wavelength is called a spectral reflectance curve. The configuration of spectral reflectance curves provides insight into the characteristics of an object and has a strong influence on the choice of wavelength region(s) in which remote-sensing data are acquired for a particular application. Figure 6.4 shows the typical spectral reflectance curves for three basic types of earth feature: green vegetation, soil, and water. The lines in this figure represent average reflectance curves compiled by measuring large sample features. It should be noted how distinctive the curves are for each feature. In general, the configuration of these curves is an indicator of the type and condition of the features to which they apply. Although the reflectance of individual features may vary considerably above and below the average, these curves demonstrate some fundamental points concerning spectral reflectance.

Figure 6.4: Typical spectral reflectance curves for vegetation, soil, and water (after Swain and Davis 1979)

Spectral reflectance curves for vegetation almost always manifest the ‘peak-and-valley’ configuration (Figure 6.4). Valleys in the different parts of the spectral reflectance curve are the result of the absorption of energy due to plants, leaves, pigments, and chlorophyll content at 0.45 and 0.67 µm wavelength bands and water content at 1.4, 1.9, and 2.7 µm wavelength bands. In near infrared spectrum wavelength bands ranging from about 0.7-1.3µm, plants reflect 40-50% of energy incident upon them. The reflectance is due to plant leaf structure and is highly variable among plant species, which permits discrimination between species. Different plant species reflect differently in different portions of wavelength.

The soil curve in Figure 6.4 shows considerably less peak-and-valley variation in reflectance. This is because the factors that influence soil reflectance act over less specific spectral bands. Some of the factors affecting soil reflectance are moisture content, soil texture (proportion of sand, silt, and clay), surface roughness, presence of iron oxide, and organic matter content. These factors are complex, variable, and inter-related. For example, the presence of moisture in soil will decrease its reflectance. As with vegetation, this effect is greatest in the water absorption bands at about 1.4, 1.9, and 2.7 µm (clay soils also have hydroxyl absorption bands at about 1.4 and 2.2 µm). Soil moisture content is strongly related to soil texture; coarse and sandy soils are usually well drained, resulting in low moisture content and relatively high reflectance; poorly drained and fine-textured soils will generally have lower reflectance. In the absence of water, however, the soil may exhibit the reverse tendency, that is, coarse-textured soils may appear darker than fine-textured soils. Thus, the reflectance properties of soil are consistent only within a particular range of conditions. Two other factors that reduce soil reflectance are surface roughness and organic matter content. Soil reflectance normally decreases when surface roughness and organic matter content increases. The presence of iron oxide in soil also significantly decreases reflectance, at least in the visible wavelengths. In any case, it is essential that the analyst be familiar with the existing conditions.

When considering the spectral reflectance of water, probably the most distinctive characteristic is the energy absorption at near infrared wavelengths. Water absorbs energy in these wavelengths, whether considering water features per se (such as lakes and streams) or water contained in vegetation or soil. Locating and delineating water bodies with remote-sensing data are carried out easily in near infrared wavelengths because of this absorption property. However, various conditions of water bodies manifest themselves primarily in visible wavelengths. The energy/matter interactions at these wavelengths are very complex and depend on a number of inter-related factors. For example, the reflectance from a water body can stem from an interaction with the water surface (specula reflection), with material suspended in the water, or with the bottom of the water body. Even in deep water where bottom effects are negligible, the reflectance properties of a water body are not only a function of the water per se but also of the material in the water.

Clear water absorbs relatively little energy with wavelengths of less than about 0.6 µm. High transmittance typifies these wavelengths with a maximum in the blue-green portion of the spectrum. However, as the turbidity of water changes (because of the presence of organic or inorganic materials), transmittance, and therefore reflectance, changes dramatically. This is true in the case of water bodies in the same geographic area. Spectral reflectance increases as the turbidity of water increases. Likewise, the reflectance of water depends on the concentration of chlorophyll. Increases in chlorophyll concentration tend to decrease water reflectance in blue wavelengths and increase it in green wavelengths. Many important water characteristics, such as dissolved oxygen concentration, pH, and salt concentration, cannot be observed directly through changes in water reflectance. However, such parameters sometimes correlate with observed reflectance. In short, there are many complex inter-relationships between the spectral reflectance of water and its particular characteristics. One must use appropriate reference data to correctly interpret reflectance measurements made over water.

Snow and ice are the frozen state of water. Early work with satellite data indicated that snow and ice could not be reliably mapped because of the similarity in spectral response between snow and clouds due to limitations in the then available data set. Today satellite remote sensing systems’ data are available in more spectral bands (eg, Landsat TM in seven bands). It is now possible to differentiate snow and cloud easily in the middle infrared portion of the spectrum, particularly in the 1.55-1.75µm and 2.10-2.35µm wavelength bands (bands 5 and 7 of Landsat TM). In these wavelengths, the clouds have a very high reflectance and appear white on the image, while the snow has a very low reflectance and appears black on the image. In the visible, near infrared, and thermal infrared bands, spectral discrimination between snow and clouds is not possible, while in the middle infrared it is. The reflectance of snow is generally very high in the visible portions and decreases throughout the reflective infrared portions of the spectrum. The reflectance of old snow and ice is always lower than that of fresh snow and clean/fresh glacier in all the visible and reflective infrared portions of the spectrum. Compared to clean glacier and snow (fresh as well as old), debris covered glacier and very old/dirty snow have much lower reflectance in the visible portions of the spectrum and higher in the middle infrared portions of spectrum.

  Figure 6.5: Spectral reflectance characteristics of snow/ice, clean glaciers, debris covered glaciers, clouds,
      and water bodies. Reflectance in terms of pixel value based on a September 22, 1992 Landsat TM seven-band
data set of the Tama Koshi and Dudh Koshi areas of Nepal. Red lines-clean glaciers and fresh snow (A);   
black lines-clouds (B); green lines-recent debris from GLOFs (C); maroon lines-debris covered glacier (D);
blue lines-clean/melted (E); and silty and/or partly frozen water (lake) (F)                                                

To identify the individual glaciers and glacial lakes, different image enhancement techniques are useful. However, complemented by the visual interpretation method (visual pattern recognition), with the knowledge and experience of the terrain conditions, glacier and glacial lake inventories and monitoring can be done. With different spectral band combinations in false color composite (FCC) and in individual spectral bands, glaciers and glacial lakes can be identified and studied using the knowledge of image interpretation keys: color, tone, texture, pattern, association, shape, shadow, etc. Combinations of different bands can be used to prepare FCC. Different color composite images highlight different land-cover features.


Figure 6.6 The FCC of ETM

Figures 6.6 show color composite images assigning red, green, and blue colors to different bands of ETM image of 22 November 2000. Colors in the colour composite images and tones in the individual band images are the outcome of the reflectance values. Glaciers appear white (in individual bands and colour composite) to light blue (in colour composite) colour of variable sizes, with linear and regular shape having fine to medium texture, whereas, in the thermal band, they appear gray to black. The distinct linear and dendritic pattern associated with slopes and valley floors of the high mountains covered with seasonal snow can be distinguished in the glaciers in the mountains.

The lake water in color composite images ranges in appearance from light blue to blue to black. In the case of frozen lakes, it appears white. Sizes are generally small, having circular, semi-circular, or elongated shapes with very fine texture and are generally associated with glaciers in the case of high lying areas, or rivers in the case of low lying areas. In general, erosion lakes and some cirque lakes are not necessarily associated with glaciers or rivers at present. The debris flow path along the drainage channel gives a white to light gray and bright tone.

For glacier and glacial lake identification from satellite images, the images should be with least snow cover and cloud free. Least snow cover in the Himalayas occurs generally in the summer season (May-September). But during this season, monsoon clouds will block the views. If snow precipitation is late in the year, winter images are also suitable except for the problem of long relief shadows in the high mountain regions. Knowledge of the physical characteristics of the glaciers, lakes, and their associated features is always necessary for the interpretation of the images. For example, the end moraine damming the lake may range from a regular curved shape to a semi-circular crescent shape. The frozen lake and glacier ice field may have the same reflectance, but the frozen lake always has a level surface and is generally situated in the ablation areas of glaciers or at the toe of the glacier tongue, and there is greater possibility of association with drainage features downstream.

The technique of digital image analysis facilitates image enhancement and spectral classification of the ground features and, hence, greatly helps in the study of glaciers and lakes. Monitoring of the lakes and glaciers can be done visually as well as digitally. In both the visual interpretation and digital feature extraction techniques, the analyst’s experience and adequate field knowledge are necessary. The satellite images have to be geometrically rectified based on the appropriate geo-reference system and cell sizes. The same geo-reference system is required for the integration and analysis of the remote sensing satellite data in the GIS database for better results.

The lakes that have already burst in the past can be identified from the disturbed damming materials and the drainage characteristics associated with the debris along the valley.

An ice-cored moraine dam usually has a hummock dissected end moraine with smaller ponds in some cases, which show a coarse texture in satellite images. The lateral moraine ridges are generally of a smooth, narrow, linear appearance and are easily identifiable on the images. The channel path along which glacial lake outburst flooding has occurred shows distinct light tone widths along the drainage channel and banks due to bank erosion and deposition in different places along the river. The loose materials transported and deposited along the streams have higher spectral reflectance compared to their surroundings and old stable river channels, which appear relatively lighter and brighter in the satellite image.

The technique of integrating remote-sensing data with GIS does help a lot with identification and monitoring of lakes and glaciers. The generated DEM combined with stereo satellite images, aerial photographs, and digitization of topographic map data can play a big role in deciding the rules for discrimination of features and land-cover types in GIS techniques and for better perspective viewing and presentations. DEM itself can be used to create various data sets of the glaciers (e.g., slope, aspect). DEM should be compatible with and of reliable quality when compared with other data sets. The satellite images or orthophotos can be draped over the DEM for interpretation or presentation.