chapter 2 2 review of literature -...
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CHAPTER 2
2 REVIEW OF LITERATURE
Disaster management poses significant challenges for real-time data collection,
monitoring, processing, management, discovery, translation, integration, visualisation
and communication of information. Challenges to geo-information technologies are
rather extreme due to the heterogeneous information sources with numerous variations:
scale/resolution, dimension (2D or 3D), type of representation (vector or raster),
classification and at-tributes schemes, temporal aspects (timely delivery, history,
predictions of the future), spatial reference system used, etc.
Natural and anthropogenesis disasters cause widespread loss of life and property
and therefore it is critical to work on preventing hazards to become disasters. This can be
achieved by improved monitoring of hazards through development of observation
systems, integration of multi-source data and efficient dissemination of knowledge to
concerned people. Geo-information technologies have proven to offer a variety of
opportunities to aid management and recovery in the aftermath. Intelligent context-aware
technologies can provide access to needed information, facilitate the interoperability of
emergency services, and provide high-quality care to the public [237].
Effective utilization of satellite positioning and remote sensing in disaster
monitoring and management requires research and development in numerous areas: data
collection, access and delivery, information extraction and analysis, management and
their integration with other data sources (airborne and terrestrial imagery, GIS data, etc.)
and data standardization. Establishment of Spatial Data Infrastructure at national and
international level would greatly help in supplying these data when necessary. In this
respect legal and organization agreements could contribute greatly to the sharing and
harmonization of data.
Quality of data in case of disaster is still a tricky issue. Data with less quality but
supplied in the first hour might be of higher importance in saving lives and reducing
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damages compared to trusted, high quality data but after two days. Apparently a balance
should be found in searching and Charters and international organizations have already
launched various initiatives on the extended utilization of satellite positioning and remote
sensing technologies in disaster monitoring and management. For example, the
International Charter is often given as a good example of availability of data and
expertise after a disaster, but still the coordination between the different initiatives at
local and international level is considered insufficient. This observation is especially
strong for developing countries, al-though some authorities in developed countries (e.g.
USA in the case of Hurricane Katrina) also fail to react appropriately. Capacity building
needs to be further strengthened and the governments must be the major driving
providing data as the general intention should be increased use of accurate, trusted data.
2.1 Role of RS for Damage Assessment in Tsunami
(Emphasis to Coastal Ecosystems in India in Dec. 2004)
The tsunami ‘run up’ (a measure of the height of water observed onshore above mean
sea level) have significantly affected the coastal ecosystems on the Andaman and Nicobar
Islands. Its effect on the mainland coast was less pronounced. Tsunami struck the Indian
Coast including the Andaman and Nicobar Islands and the mainland coast on December
26, 2004.
Satellite data along with few ground surveys were used to assess damage to various
ecosystems. Pre- and post-tsunami satellite data, mainly RESOURCESAT-1 AWiFs were
used for the preliminary assessment. IRS LISS III and LISS IV data were also used in
few cases. The impact on major ecosystems, such as mangroves, coral reef, sandy
beaches, mudflats, tidal inlets, saline areas, forest, etc. was studied. The damage to
ecosystems was categorized in to two types, total loss and degradation of ecosystems
[236].
A tsunami is not a single wave but a series of traveling ocean waves generated by the
geological changes near or below the ocean floor. The recent tsunami was set off as a
result of a massive earthquake in the region registering a magnitude of 9.3, with its
epicenter under the sea (more than 8-9 km below the sea bed), off the northern tip of the
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Indonesian archipelago near Aceh. The tsunami waves traveled as far as 6400 km from
the epicenter. The tsunami moves rapidly across the ocean (900 km/h) and takes the form
of destructive high waves along shallow coastal waters (10 m high and a speed of 40
km/h). The tsunami ‘run-up’ travel fast and much farther inland than the normal waves
[234].
Data Used
Pre-tsunami and post-tsunami data of the Indian Remote Sensing Satellite P6
(RESOURCESAT) AWiFS was primarily used to assess the impact. LISS III data was
used wherever available.
Methodology
The satellite sub-images were extracted from all the data sets and subjected to
radiometric and geometric corrections prior to unsupervised classification. Unsupervised
classification was used to get the classification based on natural clustering. The Iterative
Self-Organizing Data Analysis (ISODATA) clustering algorithm available in ERDAS/
IMAGINE image processing software was used for the purpose. In the classified image
the classes were visually assessed for their correctness and suitably labeled. Supervised
classification was attempted for few islands based on the extensive ground survey done
during 2001-2003. In certain cases visual interpretation was also performed with on-
screen digitization. Digitized maps were edited, labeled and projected in the polyconic
projection. The classification system helps in classifying the coral reefs based on geo-
morphological zoning [245].
Coral Reefs
Coral reefs are home to more than a quarter of all known marine fish species.
They provide food, livelihood and other essential services for millions of coastal
dwellers. Coral formations act as buffers during storm surges and tidal waves. When
giant tsunami waves smashed onto shores, the coral in nearby shallow areas were
destroyed, crushed and shrouded in debris [238].
The damage to coral reef due to backwash is clearly seen on the Sentinel island
reef.
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Figure 2.1 (a): The pre-tsunami map shows coral
reef surrounding the Sentinel Island.
Figure 2.1 (b): During Post-tsunami the entire
reef is covered with sand and detritus
Coastal Landforms
The major coastal landforms/wetland features, which have been affected by
tsunami, include beach, spits, sand dune, mud flat, tidal inlet, estuary, cliff, etc. A rapid
assessment of damages to these systems has been made.
Tidal inlets play an important role in coastal ecosystems facilitating mixing of
water, sediments, nutrients and organisms between terrestrial and marine environments.
These are also the water routes across the coast for ships between harbours and the open
sea. Tidal inlets such as lagoons and estuaries are highly productive ecosystems. Many
species migrate into lagoon/estuarine system to feed, thereby taking advantage of the
considerable production of organic matter and the lack of competing species [243]. The
major damages to tidal inlets were noticed along the south-east & southwest coast at few
places. In many locations the tidal inlets, which were closed (usual characteristic of fair-
weather season) got opened, e.g. Adyar estuary (Fig.2.2 (a) & (b)).
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Figure 2.2 (a): The closed tidal inlet of Adyar
estuary during pre-tsunami period
Figure 2.2 (b): Tidal inlet opened up after
tsunami
In some estuaries (tidal river mouth), the mouths have closed after tsunami. No
permanent damages to livelihood activities are expected. The tidal inlets will regain its
positions naturally. The shift in the location of tidal inlets can induce to shoreline
changes. It requires further investigations to understand whether the breaches are
permanent and to assess the possibility of enhanced shoreline changes.
The lagoon systems in all the affected areas on south-west coast were
contaminated by highly saline water over wash or through new openings due to breaching
of barrier beaches. There is also a possibility of a decrease in the depth due to the wash
over of beach and near shore sediments.
2.2 Reservoir Monitoring
Introduction
Irrigation is the largest consumer of fresh water. Seckler et al. (1998) estimated
that around 70 percent of all water used each year produces 30 to 40 per cent of the
world's food crops on 17 percent of land. As water scarcity becomes more acute and
competition for fresh water intensifies, better irrigation management will be required to
achieve greater efficiency in the use of this valuable resource.
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Reservoir and command are two facets of an irrigation system. While reservoir is
the source of irrigation water, command is the user. An integrated management and
monitoring of both is essential for the total development of an irrigation system. While
sedimentation and water quality degradation are two major problems of a reservoir, the
problems associated with I irrigation command area are (Ravi et al, 1997): a) increasing
water table, water logging and salinity, b) large water losses in the conveyance and
delivery systems, c) sub-optimal water availability (inadequate water or non-availability
at critical stages) and/ or over-utilization (excessive irrigation), d) inequitable water
distribution (differences in water availability at head land tail of canals) [253].
Regular monitoring of reservoir and command can give warnings about the above
problems and help the irrigation/water resources engineers to take necessary preventive
measures. Since the monitoring has to be done for a large area and at regular intervals,
remote sensing is the best available tool as the conventional ground-based methods are
time-consuming, costly and provide only point estimates, which is unlikely to be
representative of the whole scenario (Blyth, 1985).
Water quality
The rapid and continuous growth of industries, coupled with unregulated
discharge of industrial waste and municipal sewage, has accelerated the degradation of
water quality of rivers, lakes, reservoirs, tanks and estuaries. Thus water becomes
unsuitable for human consumption or for irrigation of agricultural land. Rapid and
synoptic detection of pollutants is very much required to control the water pollution.
Remote sensing techniques have shown potential for measurements of water quality
parameters such as suspended sediments, chlorophyll concentrations, salinity,
temperature, etc. [254].
Most suspended materials and some dissolved materials generally cause a change
in reflected light intensity from a water body or a change in its color due to their
presence. Using simple photographic recording techniques, differences in water color or
brightness can frequently be recognized either as variations in image density in case of
black-and-white film, or more usefully as changes in color, hue, saturation, and
brightness with a color film.
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The water quality evaluation from satellite images can be done either through visual
interpretation of imageries or through digital techniques. For visual interpretation Moore
(1980) has provided following basic principles which should guide the analysis.
Turbid water is more reflective than clear water at all visible and near infrared
wavelengths.
Spectral signatures from turbid water represent only near surface conditions,
The measured signal at any wavelength interval is dependent on particle size as
well as concentration.
Based on these principles Patel et al. (1988) have prepared legend for qualitative mapping
of turbidity levels using IRS images (following Table). The grey scale provided at the
bottom of images was used as reference to assign the grey tones in the spectral bands for
mapping the turbidity levels.
Methodology
Table 2.1 Legend for qualitative mapping of turbidity levels using satellite visual images
Turbidity levels Grey tones in IRS-1 spectral bands
Band 1 Band 2 Band 3 Band4
Very low GB B DB DB Low BG GB DB DB
Low to moderate G BG B DB
Moderate WG G B DB
Moderate to high WG WG GB DB
High WG WG GB DB
DB - Dark black BG - Blackish grey GW- Greyish white
B - Black G -Grey W-White
GB - Greyish black WG - Whitish grey
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There are various digital techniques available to analyze satellite data for mapping water
pollutants. Those are:
Level slicing: The total grey level interval available for water body in an image
can be subdivided into various small intervals and color coded. This color-coded image is
used for visual interpretation.
Principal Component Analysis: Principal Components Analysis (PCA), can be
applied to compact the redundant data into fewer layers. Principal component analysis
can be used to transform a set of image bands, as that the new layers (also called
components) are not correlated with one another. Because of this, each component carries
new information. The principal component is that component of the multidimensional
image, which has the maximum variation. The first two components can be correlated
with other water quality parameters. Qualitative turbidity level mapping can be done with
level slicing of first component.
Band ratio: The ratios of digital grey values of the spectral bands for the water
bodies in the satellite imagery correlate well with turbidity. The ratios between green and
near infrared or red and green have been found to be useful for water quality analysis. .
Chromaticity analysis: This technique is a quantification of the colors visually
perceived. The RGB (red, green, blue) images are transformed into intensity (value), hue
(color) and saturation (color purity) components. These new co-ordinates are then
correlated with water quality parameters.
Characteristic or Eigenvector analysis: Here, characteristic 'directions' in multi-
spectral space are determined with respect to the clear water as origin for different water
quality parameters of concentrations. These directions are determined by eigenvector
analysis of data obtained from clear water and water having one parameter alone. Once
the directions have been determined, the variations along each direction are then
correlated with the concentration of the corresponding water quality parameter.
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Spectral Vegetation Indices
In optical remote sensing (RS) the typical reflectance pattern for a healthy
vegetation shows high absorption due to chlorophyll at 650 nm (red region) and high
reflection due to leaf ii structure at 750 nm (near infrared region). This differential
vegetation response in spectral regions has been used to develop various relationships,
commonly known as vegetation indices (VIs). These VIs have been found to have very
good relationship with various crop growth indicators like leaf area index (LAI), biomass,
stress etc. VIs are also indirectly related to fractions of absorbed photo synthetically
active radiation (PAR), canopy photosynthesis, stomatal conductance, land surface
albedo and crop yield. Some of the important VIs are:
Band Ratio
Spectral reflectances are themselves ratios of the reflected over the incoming radiation in
each spectral band individually; hence they take on values between 0.0 and 1.0
Normalized Difference Vegetation Index (NDVI):
The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that
can be used to analyze remote sensing measurements, typically from a space platform,
and assess whether the target being observed contains live green vegetation or not [260].
The NDVI itself thus varies between -1.0 and +1.0
NDVI = (NIR - R) / (NIR + R)
The NDVI of an area containing a dense vegetation canopy will tend to positive
values (say 0.3 to 0.8) while clouds and snow fields will be characterized by
negative values of this index. Other targets on Earth visible from space include
Free standing water (e.g., oceans, seas, lakes and rivers) which have a rather low
reflectance in both spectral bands (at least away from shores) and thus result in
very low positive or even slightly negative NDVI values
Soils which generally exhibit a near-infrared spectral reflectance somewhat larger
than the red, and thus tend to also generate rather small positive NDVI values (say
0.1 to 0.2).
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There are other index derivatives that can be use for various analysis.
NIR/R
Where NIR and R are near infrared and red reflectance, respectively
The simple ratio (unlike NDVI) is always positive, which may have practical advantages,
but it also has a mathematically infinite range (0 to infinity), which can be a practical
disadvantage as compared to NDVI.
[http://en.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index]
Soil Adjusted Vegetation Index (SAVI) [261]
[(NIR-R)/(NIR+R+L)]*(1+L)
Where L is a soil adjustment factor, which varies with the vegetation density.
Many research workers have reported a positive relationship between the spectral
signature, obtained from satellite data and water quality parameters. Tamilarasan (1988)
has reviewed the works related to monitoring of water quality using remote sensing. In
one study, Patel et al. 1988) used the techniques of characteristic vector analysis to
quantify the relationship between ground truth data such as turbidity and the remotely
sensed data for Matatila reservoir using data and also used visual techniques for
qualitative mapping of turbidity levels.
A number of derivatives and alternatives to NDVI have been proposed in the
scientific literature to address these limitations, including the Perpendicular Vegetation
Index, the Soil-Adjusted Vegetation Index, the Atmospherically Resistant Vegetation
Index and the Global Environment Monitoring Index. Each of these attempted to include
intrinsic correction(s) for one or more perturbing factors.
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2.3 Crop Coefficient:
The crop coefficient (Kc) accounts for the effect of the crop characteristics on
crop water requirements. Its value depends upon the crop characteristics, time of planting
or sowing, stages of crop development and general climatic conditions. Generally, for
computation of crop coefficient values from published literature (e.g. Doorenbos and
Kassin, 1977) are used. Though these tabulated coefficients provide a practical guide for
scheduling irrigation, but considerable error in estimating crop water requirements due to
their empirical nature.
Methodology
Neale et al. (1989) showed the usefulness of remotely sensed data to represent a
reflectance based crop coefficient (Kcr). The feasibility of estimating Kc from spectral
measurements occurs, because, both Kc and vegetation indices (derived from reflectance)
are affected by leaf area and fractional ground cover. Bausch (1993) used the soil
adjusted vegetation index (SAVI) (Huete, 1988) to represent the Kcr. Consequently, soil
background effects were minimized which eliminates additional calibration for different
soils. The empirical equation between SAVI and Kcr is as follows:
Kcr =1.46* SAVI+ 0.017
Where SAVI = [(NIR-R)/(NIR+R+L)]*(1+L)
L is an adjustment factor.
Ray and Dadhwal (2000) developed an approach to estimate seasonal crop water
requireirement of Mahi command area, Gujarat, using multi-date IRS-1C WiFS (Wide
Field Sensor) data and GIS tools.
Soil Moisture: Remote sensing of soil moisture depends upon the measurement of
radiation that has been reflected or emitted from the surface. Variation in the intensity of
this radiation depends on either its dielectric properties (e.g. index of refraction), or its
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temperature, or a combination of both. The property that is important depends upon the
wavelength region that is being considered as shown in below Table.
Table 2.2: Electromagnetic properties for soil moisture sensing (Myers, 1980) [299]
Wavelength Region Property observed
Reflected Visible & Infrared (0.3-3 m) Reflectance/ index of refraction
Thermal Infrared (10-12 m) Temperature
Active Microwave (1-50 cm) Back scatter coefficient/ dielectric
properties
Passive microwave (1-50 cm) Microwave emission/ dielectric
properties, temperature
The above discussion showed that remote sensing plays a great role in estimating
parameters needed for monitoring and management of reservoir and command areas. The
extent application can be appreciated from the fact that in many of the studies various
user organizations like, Central Water Commission, Command Area Development
Authority are directly involved. The capabilities of remote sensing technology are very
wide. Bastiaansse: (1998) has listed the current available sensors and satellites providing
images suitable for irrigation management. However, this is not the end of the road.
Many new satellites are being launched provide better capabilities, spatially, temporally,
radiometrically and spectrally. Hence, the satellite remote sensing in combination with
GIS techniques, simulation models and Decision Support System (DSS) can play a
vital role in enhancing the sustainability of the existing irrigation schemes of the
country.
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2.4 Mapping Dangerous slope Using Satellite Imagery
The use of satellite sensor data can be used to detect discrete slope processes and
landforms with a high degree of accuracy.
Conventional attempts to classify slope features using per pixel spectral response patterns
have provided classification accuracies that are less than 60%, it is demonstrated that a
combination of high resolution optical imagery, image segmentation and ancillary data
derived from a digital elevation model can discriminate some types of mass wasting
processes with higher accuracies. The spatial resolution of the imagery is critical to the
successful classification of such features both in terms of information derived from
textural analysis and in the ability to successfully segment landslide features.
Furthermore, the data generated in this manner can be used for geo-morphic research in
terms of characterizing the occurrence of mass wasting within the bounds of the image
scene [264].
Maps generated from satellite sensor data using traditional methods of image
classification have produced less than satisfactory results (e.g. Epp & Beaven, 1988).
Indeed, Brardinoni et al., (2003) state that despite the huge advances that have been made
in remote sensing, no reliable method had been developed to identify mass movements
using digital image interpretation. Manual inspection of optical satellite imagery reveals a
great many geomorphic forms and features. However, an automated approach to
extracting these data over large areas has proven problematic.
Early work indicates per pixel spectral response patterns, used in conjunction with
maximum likelihood classification methods are unreliable in discriminating landslide
scars from other barren areas on the landscape (e.g. Sauchyn & Trench, 1978; Connors &
Gardner, 1987; Epp & Beaven, 1988). This inability to directly classify landslide features
using per pixel spectral response patterns resulted in attempts to identify landslide prone
sites using a combination of digital imagery and ancillary data such as DEM derivatives,
soil maps, image textural analysis and digital slope profiles.
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Rationale
While each data source has proven inadequate in classifying geomorphic features
on its own, the combination of data sources provides a great deal of synoptic information
regarding the surface characteristics of a given area and, consequently the geomorphic
processes that are in operation.
A different approach to the automated identification of slope processes using
digital data is to make use of associations between slope stability and land surface cover
that occur in some slope systems (Warner et al., 1994). McKean et al. (1991) noted
relationships between vegetation, soil moisture, and bedrock morphology that were
conducive to slope failure. They hypothesized colluvial deposits that provide the primary
source areas for debris flows were located primarily in bedrock hollows and possessed
different soil moisture characteristics and, consequently, differing vegetative properties.
The detection and classification of individual process types (Cruden & Varnes, 1996)
using an automated approach has been less successful using ETM+ data [265].
The textural analysis of landslide scars may be capable of discriminating between
rock slides and debris slides although the spatial resolution of image data was a limiting
factor. Barlow et al. (2006) used a similar approach using high-resolution data and
obtained classification accuracies of 80% or higher for debris slides and rock slides. A
similar approach has also proven successful for the mapping of snow avalanche tracks in
the Canadian Rockies (Barlow & Franklin, 2007). The innovation provided in the use of
image segmentation is the ability to asssign specific geo-morphometric properties to
differing image objects in order to place them within their geomorphic context. Both the
landslide and avalanche studies will be discussed in more detail below though the use of
two case studies: (1) the identification of mass movements using high resolution SPOT 5
data and (2) mapping snow avalanche tracks using Landsat TM data. The value of such
data will then be demonstrated by applying the results to an assessment of slope stability
and activity using the debris slide database.
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Methodology
The data layers used by Barlow et al. (2006) and the classification hierarchy are
illustrated in figure 1. The multi-spectral channels were fused with the panchromatic
channel using IHS (Intensity-Hue-Saturation) in order to create a 2.5 m multi-spectral
database (Pohl & Van Genderen, 1998). The process made use of an equal weighting of
the spectral bands as well as the plan curvature layer.
The classification worked by progressively eliminating image objects through
four Boolean decision criteria [267].
Study Area:
Study area is taken from United Kingdom (UK) having latitude 57.0699 north and
longitude -3.6789 west. White colour in right hand side image shows snow.
Figure 2.3: Hilly terrain with snow avalanche
Snow Avalanche:
It is a rapid flow of snow down a slope. Snow avalanche are found during winter
(December – February) in northern part of U.K. Some snow avalanches are found with
steep slope. These slopes become dangerous. Most avalanches occur spontaneously
during storms under increased load due to snowfall.
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Figure 2.4: Hilly terrain with dangerous snow avalanche
Contour Lines:
It is a curve along which the function has a constant value. Contour lines are curved or
straight lines on a map describing the intersection of a real or hypothetical surface with
one or more horizontal planes.
Figure 2.5: Hilly terrain with contour
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2.4.1 Mapping dangerous Slope using SPOT 5 Satellite Data:
SPOT 5 Satellite Data:
SPOT stands for Système Pour l'Observation de la Terre or "System for Earth
Observation". Technical details are shown as below.
Table 2.3: SPOT 5 satellite characteristics
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The first step divided objects into vegetated/ un-vegetated classes based on an
NDVI (Normalized Difference Vegetation Index) threshold of 0.15. NDVI values have
been demonstrated to have a high correlation with green leaf area and biomass (Kidwell,
1990). This accounts for over half of the image objects in the study area [272].
The next level assigns each of the un-vegetated image objects to either flat-land or
steep-land based on the slope layer. Here the threshold between the two was set at 0.27
(15 degrees), as no rapid mass movements were observed below this gradient. All of the
objects that were classified as steep-land were then evaluated based on a length to width
shape criterion.
Rapid mass movements are generally identified as long thin features. Empirical
inspection of the aerial photographic inventory demonstrated that mass movements had a
length to width ratio of 2.5 or higher. Therefore, this threshold was required to be
classified as a thin feature, whilst the remaining objects were classified and labeled as
'square features' [268].
Figure 2.6: layer stacking by multi-spectral data for SPOT 5
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Figure 2.7: Classification Hierarchy of rapid mass movement slope
One of the most obvious characteristics of rapid mass movements is their
dependence on gravity. Failure tracks tend to follow the path of steepest descent (fall
line) down a given slope. The geomorphic context of an image object is therefore a useful
tool in the classification. The orientation of the long axis of an object on the slope was
used to separate those that ran roughly parallel to the fall line to those that extend across
the slope.
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2.4.2 Mapping Snow Avalanche using Landsat TM Satellite Data:
Landsat TM (Thematic Mapper) Satellite Data:
Technical specifications for Landsat TM are given as below.
Snow avalanches are a common occurrence within the Canadian Rockies
(Luckman, 1978). During winter months, this process represents an increasingly serious
hazard as larger human populations use the region for recreational activities. Avalanche
activity results in a distinctive bio-geographic response that can be associated to
characteristic land cover patterns (Walsh et a!., 1990). Avalanche tracks typically
manifest themselves within a forest matrix as a non-forested strip of meadow, rocky
ground, willow shrubs or similar vegetation running vertically through the forest of a
mountain valley side (Suffling, 1993). The recurrence of avalanches in the same location
perpetuates the disruption of the forest canopy leaving more avalanche resistant shrubs
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and herbs to colonize these areas. Such areas are known as constrained avalanche tracks
(McClung, 1993)
As with other slope processes, the use of spectral data alone has proven
ineffective at mapping snow avalanche tracks as the vegetative communi¬ties that are
common in these areas are also found in other portions of the landscape (Connery, 1992).
However, the use of landcover with specific geomorphology has proven more favorable.
Barlow & Franklin (2007) used a combination of Landsat TM data, image segmentation
and DEM derivatives to map such features. Many of the peaks exceed 3000 m and snow
avalanches are common. The data layers and classification hierarchy used in the analysis
are shown in the below figure [276].
The classification strategy differs somewhat from the identification of mass
movements discussed above in that the spectral data layers and the DEM are used first to
create a land cover map as shown in below figure. As the tracks are typically associated
with shrub or herb type vegetation, these classes are subjected to a series of decisions
based on their shape and specific geo-morphometry in a similar manner to that described
above in the Chilliwack study.
Figure 2.8: Layer stacking by multi-spectral data for Landsat TM
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Figure 2.9: classification hierarchy for the Landsat TM
Image segmentation, when used in conjunction with optical satellite imagery and
ancillary data provides the necessary digital information to extract slope features from the
image scene. Both of the mapping techniques discussed here are dependent on land cover
changes that result from failure. The type of feature objects that can be detected and the
accuracy of the classification are dependent upon the spatial resolution of the imagery
used in the analysis. This research has investigated the applicability of two differing
satellite sensor platforms in the automated detection of mass movements.
A combination of SPOT 5 and DEM data can be used to develop an automated
system to detect and classify rapid mass movements that were fresh and over 1 ha in area
in a high mountain region in British Columbia. The method yields an overall accuracy of
more than 70% for all rapid mass movements. These features were further divided
according to the classification system of Cruden & Varnes (1996) into debris slides,
debris flows, and rockslides. The use of Land-sat TM data with a similar set of DEM
derivatives proved capable of mapping snow avalanche tracks with an overall accuracy of
more than 75%. This can be due to the distinctive land cover associated with these
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features as well as their shape characteristics and orientation on the landscape. These
results strongly support the viability of using satellite remote sensing data and digital
elevation models to map slope processes.
The ability to generate accurate maps of slope processes allows for great
efficiency in data throughput to geomorphic research. Such research is critical in the
identification of unstable slopes and the estimation of landslide hazard to human
populations.
2.5 Harnessing AWiFS SWIR Band for Crop Classification
Crop classification is the leading step in many agricultural applications such as crop
acreage, yield and production estimation, cropping system analysis, crop stress
physiology and precision agriculture. However, crop classification using remote sensing
data has been a constant challenge, especially in the eastern Indian regions because of the
small land holdings and the highly diversified cropping pattern. Czapleski (1992) has
observed that estimates achieved by the remote sensing classification procedure are
sensitive to various crops as well as sensor-related parameters. While reviewing the
approaches of crop discrimination, Dadhwal et al. (2002) have high-lighted the need for a
high spatial resolution multi-spectral sensor with large area coverage, keeping in view the
limitations of WiFS (Wide Field Sensor) onboard IRS 1C/1D satellites. With 56 m spatial
resolution, 10 bit radiometric resolution and 5 days revisit period, the AWiFS (Advanced
Wide Field Sensor) onboard Resources at 1 (IRS-P6) provides a huge potential for
agricultural applications. AWiFS operates in four multi-spectral bands such as green
(0.52-0.59 m), red (0.62-0.68 m), near infrared (0.77-0.86 m) and short wave infrared
(1.55-1.70 m). The SWIR (short wave infrared) band is particularly significant because
of its strong relation with the water content in the vegetation canopy cover. The SWIR
band is known to provide information on water content of plants and has been used to
assess water stress of plants (Tucker, 1980; Alrichs and Bayer, 1983; Hunt et al., 1987;
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Baret et al, 1983; Chuvieco et al., 2002). Apart from studying the water stress, the utility
of SWIR band has been demonstrated for better crop separability (Sharma et al., 1995;
Dadhwal et al, 1996) and more accurate crop classification (Panigrahy and Parihar, 1992;
Manjunath et al., 1998). In this context, the present study was conducted to find out the
usefulness of SWIR band in AWiFS data, for the discrimination of different Rabi season
crops and other vegetation using various multivariate statistics and classification
approaches [209].
Study area
The study area is the undivided Cuttack district of Orissa State, which includes four
districts namely, Jajpur, Kendrapada, Jagatsinghpur and Cuttack. It extends from 84°50'
E to 87°03' E in longitude and 19°57' N to 21°15' N in latitude. Figure 1 shows the false
colour composite of the AWiFS scene for the area with the district boundary overlaid on
it. These districts are major rabi-season (period from the month of November to April)
crop growing areas of Orissa, where rabi season crops occupy more than 30 per cent of
the geographical area [210]. The study area is spread in the delta of three rivers,
Mahanadi, Brahmani and Baitarani and on the eastern side, it is surrounded by the Bay of
Bengal. The croplands are mainly spread through the low-lying delta plains. The study
area contains some winter waterlogged sites that provide adequate moisture to grow rice
crops in the summer season. The major crops during the rabi season include pulses, rabi
rice, groundnut and vegetables. Apart from these, the other vegetation classes include
forest and mangroves. The sowing and harvesting times of rabi season crops are
presented in following table.
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Table 2.4 Sowing and harvesting times of major rabi season crops in the study area
Data used
The multispectral AWiFS data of IRS-P6 path-105 and row-58 and quadrant-B, during
the period from 10 December 2003 to 2 May 2004 were used for the study. The ground-
truth information was collected during December 2003 and March 2004.
Methodology
One good cloud-free AWiFS data was selected as the master database, which was
georeferenced with 1:50,000 Survey of India toposheets and other dates' AWiFS scenes
were registered to the master database using image-image registration with GCPs
collected with <0.5 RMSE using a second order polynomial. The image of the undivided
Cuttack district was extracted. Training sites were selected for different land cover
classes such as crops, forests, water and fallow lands, using the ground-truth information.
Geomatica V8.2.3 image processing software was used to carry out the analysis. Looking
at the false colour composite (FCC) of the AWiFS scene (below figure), one can observe
that apart from crops, there are three categories of vegetation: the forest on the western
side, mangroves on the eastern side along the coastal areas and scrublands distributed all
over the study area. The crops found in December were mostly groundnut, whereas, the
red tone was dominated by pulses in February and by rice in the March and May dataset.
Hence multi-date RS dataset spread throughout the season was a prerequisite for studying
the cropping pattern of different crops grown during a particular season.
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Figure 2.10: False color composite (FCC) of AWiFS image of 10 DEC 2003 showing location of study
area
The supervised maximum likelihood (MXL) classification for single date data
(Feb. 20, 2004) was carried out with and without the SWIR band. This dataset was
selected on the basis that most of the crops grown during the rabi season could be found
during February. The results of the supervised MXL classification showed that inclusion
of SWIR band increased the overall accuracy and kappa coefficient, which were due to
the increase in classification accuracy of specific crop classes especially two different
categories of rice. There arc two distinct rice classes found growing in the locality. One is
grown little early in the low lying lands that remain fallow in the kharif season due to
water logging and when the water level comes down, rice is transplanted into it and the
normal irrigated summer rice. The separation of the low land rice from other classes
increased significantly (Fig. 2.11a), because the water absorption property of the SWIR
band mainly influenced the accuracy. In addition, inclusion of SWIR band also improved
the separability of mangrove forest from other forest and crop classes by decreasing the
confusion among them (Fig. 2.11b). The unclassified area can be reduced by increasing
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the training sites catering to the variations among the existing classes raised due to
inclusion of the SWIR band [217].
Figure 2.11: Parts (a & b) of the classified image with different input bands using February 20, 2004
data set.
Apart from the widely used NDVI (Normalized difference Vegetation Index)
which is given as (NIR-Red)/(NIR+Red), indices where used typically suited to the use of
SWIR band. For detecting stress due to water, the water absorption band (SWIR) can be
compared with a reference wavelength (NIR), which is not absorbed significantly by
water. Such an index, known as the Moisture Stress Index (MSI) is the ratio between the
reflectance of SWIR band and the NIR band. NDVISWIR which is given by (SWIR-
Red)/(SWIR+ Red) is mathematically equivalent to NDVI except that NIR is replaced by
SWIR band as it is preferable in situations of dense green biomass (Tucker, 1979).
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Authors attempted to develop a 'Three Band Ratio' (TBI) index (NIR/ (Red + SWIR)),
which considers three of the bands (viz., Red, NIR and SWIR) from the dataset. Since
both Red and SWIR band have high absorption and NIR has high reflectance for healthy
crops, NIR was kept in numerator, while Red and SWIR were kept in denominator.
Table 2.5: Supervised MXL classification statistics for single date dataset
Conclusion and Findings:
This study indicates that inclusion of SWIR Band improved the classification accuracy of
crops and other classes. The three band ratio index based on NIR, SWIR and red bands
showed improved classification. The SWIR band could be used to explain different
conditions of vegetation classes. This present study shows that, so far only a few works
have been reported on using SWIR band for crop classification. Indices based on Red,
NIR and SWIR bands, should be further studied for better utilization in crop studies.
Summary
All the models discussed above have various shortcomings. Most common
shortfall is that none of the model is comprehensive, so that it can be applied in case of
any kind of disaster.
The selection of most reliable survey for the area under consideration is really
very difficult, as the surveys do not depict the sand type, construction type of building,
i.e. factors that contribute to the vulnerability of the buildings. For each region under
consideration comprehensive survey including social, economic and all other factors
needs to be undertaken, which is very exhaustive task and revision in such database
becomes really difficult to capture. This makes collection of such data in under-
developing or developing countries irrelevant due to the update cost of the database. The
models do not provide any idea about the tentative effect of the disaster on the economy
and population. Role of remote sensing, GIS and GPS usage has not been taken into
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consideration. One model stresses the use of GIS, the other talks about remote sensing.
Thus none of the models have taken all the three into consideration along with other
supports like internet, etc.