9

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

10

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

11

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.

12

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)).

13

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.

14

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.

15

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

16

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.

17

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).

18

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.

19

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

20

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.

21

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.

22

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.

23

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.

24

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

25

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

26

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

27

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.

28

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

29

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

30

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

31

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;

32

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.