Chapter – 3

REDEFINING THE NEKA RIVER WATERSHED

BOUNDARY LINE OF IRAN COMPARING ASTER,

SRTM, DIGITAL TOPOGRAPHY DEM AND TOPO

SHEET USING GIS AND REMOTE SENSING

TECHNIQUES

40

CHAPTER - 3

REDEFINING THE NEKA RIVER WATERSHED

BOUNDARY LINE OF IRAN COMPARING ASTER, SRTM,

DIGITAL TOPOGRAPHY DEM AND TOPO SHEET USING

GIS AND REMOTE SENSING TECHNIQUES

3.1 Abstract

Accurate area calculation and for land use evaluation, land use change,

Geomorphologic classification, Hydrological analysis delineation of watershed

boundaries are the base. With the advent of advanced spatial tools redefining the

manual drawn topographical boundaries is necessary. An integrated approach of data

analysis and modeling can accomplish the task of delineation. The main objectives of

this paper is to redraw the already drawn manual boundary by comparing four

different data source, such as Aster DEM, SRTM DEM, Digital Topography DEM

and Topo sheet data. Secondly, it is also required to ascertain the most accurate

method and the appropriate remote sensing data and GIS for delineation purpose. A

true comparison of all the four methods the mean distance existing between the

ground GPS data and the Aster Dem was 43 mts, between Ground data and SRTM

was 307 mts, the mean distance between the Digital Topographic map and the ground

GPS points were 269. It is also tested between Topo sheet and the GPS ground points

was 304. The regression analyses comparing 230 x-y points along the complete

boundaries yielded an R2 of 0.082 between the ASTER and SRTM boundaries; the R2

for the comparison between the ASTER and the digital topography boundaries was

0.0157, between ASTER and Topo sheet Map was 0.171. After, One-way Analysis of

variance (ANOVA) in SPSS, the means obtained from the four methods of SRTM,

ASTER, Digital Topography and Top sheet were compared. The initial results showed

41

that there was a statistically significant difference among the four means. Apart from

this method the comparison of stream distance with the ground point proved that aster

stream path is closer than the other three stream path difference. Thus it is proved that

the Aster Dem is the most suitable application for the delineation of the rugged terrain

when compared with the other three methods.

3.2 Introduction

Land use evaluation and forest management in the mountain regions is gaining

importance at the government and at community level. The proper demarcation of

boundary is the real challenge ahead. Hydrological research in mountain watersheds of

developing countries is a relatively new field, therefore, hydrologic and erosion data are

necessary for the development of mathematical watershed models that can simulate and

evaluate existing and proposed management scenarios (de Jong et al., 2005).

Across the world only few research works have been carried at the small scale

to delineate the boundary. The demarcation boundary study done by the American

Society of Agriculture and Biological Research team (Julia K. Pryde 2007 et.al) has

published in its report the demarcation done for Illamanga Sub watershed in North

America.

3.3 Digital Elevation Data

Digital elevation data are of limited availability in many less developed areas or in

a rugged terrain with steep slopes. The space borne earth observation sensors and google

earth images have reduced the complexness of the authenticity of the elevation data. The

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

42

onboard and the NASA Terra satellite has the capability of taking along-track stereo

images allowing the generation of high-resolution DEMs (Kääb, 2002; Stevens

etoral., 2004; Kääb, 2005).

Digital elevation models (DEMs) are topographic models of the Earth's terrain

(bare ground) that have had the heights of vegetation, buildings, and other cultural

features digitally removed. DEMs are commonly referred in the remote sensing world

as digital terrain models (DTMs) typically offered as a continuous elevation surface

as a grid (Podobnikar, 2009). Different techniques for the generation of DTMs have

been developed since their inception more than fIfty years ago (Miller and Laflamme,

1958; Gesch et al., 2002; Hirano et al., 2003; Maune, 2007; Intermap, 2009).

Significant advances in remote sensing technologies have led to a new era of higher

quality global topographic observations, where reliable topographic measurements are

becoming a possibility (Homer et al., 2007). At small scales, space borne systems

(coarse ground sampling distance (GSD)) such as shuttle radar topographic mission

(SRTM) collected 80% of the earth's landmass with 30 m or 90 m resolution (Rabus

et al., 2003). At medium scales radar interferometric techniques (medium to high

resolution) had been applied to generate global DTMs (Madsen et aI., 1993; Farr and

Kobrik, 2000; Maune, 2001: Walker et al., 2007; Intermap, 2009). For larger scales

and more local usage, airborne laser scanning (LiDAR) and aerial photogrammetric

techniques (high spatial resolutions) have been applied to create DTMs (e.g. Lefsky et

al., 2002; Nresset, 2002; Andersen et al., 2006). Remote sensing and GIS applications

of DTMs have become widespread. Forest and water resource management

applications, including watershed management, flood hazard mapping, timber harvest,

and fire management are dominant users of DTMs. Terrain attributes often provide

direct inputs for environmental, forestry, topographic and hydrological models and

43

thus accuracy of the elevation models is critical to environmental modeling

(Kellndorfer et al., 2004; Thirion et al., 2006; Balzter et al., 2007a; 200Th; Anderson

et al., 2008). Mapping standards have tended to accept the data. If it is within

mapping standards such as the National Standard for Spatial Data Accuracy (FGDC,

1998) and the National Digital Elevation Program (NDEP, 2004).

3.4 SRTM DTM Accuracy Assessment

The SRTM radar signal measurement result in a reflec tive surface elevation

which depends on terrain cover and is a complicated function of the electromagnetic

and structural properties of the scattering medium (Bhang et al., 2007). In snow, the

penetration depth of the radar signal depends on wetness, temperature, and porosity

(Braun et al., 2007). Vegetation presents an even more complex scattering

environment. It has been estimated that C-band only penetrates a quarter or a third of

the canopy height (Carabajal, 2005). Performance evaluations by NIMA, the USGS,

and the SRTM project team have shown the absolute vertical error to be much

smaller, with the most reliable estimates being approximately 5 m (Rosen et al., 2001;

Sun et al., 2003). Brown et al. (2005) used GPS and NED data to evaluate the

accuracy of the SRTM data for southeastern Michigan. They reported that the SRTM

mission specifications for absolute and relative height errors for the GPS ground

control point targets were exceeded. A more extensive analysis of the SRTM DGPS

data indicates that it meets the absolute and relative accuracy requirements even for

bare surface areas. Previous research efforts indicated that accuracy for an IFSAR

derived DTM could be terrain dependent. According to the mission objectives,

SRTM data were expected to have an absolute horizontal circular accuracy of less

than 20 m. Absolute and relative vertical accuracy was anticipated to be less than 16

and 10m, respectively (Kellendorfer et al., 2004).

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3.5 ASTER DTM Accuracy Assessment

As part of ASTER digital elevation model (DEM) accuracy evaluation efforts

by the US/Japan ASTER Science Team, stereo image data for four study sites around

the world have been employed to validate prelaunch estimates of height accuracy

(Hirano et aI., 2003). Automated stereo correlation procedures were implemented

using the Desktop Mapping System (DMS) software on a personal computer to derive

DEMs with 30 to 150 m postings. Results indicate that a root-mean-square error

(RMSE) in elevation between ±7 and ±15 m can be achieved with ASTER stereo

image data of good quality. An evaluation of an ASTER DEM data product produced

at the US Geological Survey (USGS) EROS Data Center (EDC) yielded an RMSE of

± 8.6 m. Overall; the ability to extract elevations from ASTER stereo pairs using

stereo correlation techniques meets expectations. Studies were conducted by a large

group of international investigators, working under the joint leadership of U.S and

Japan ASTER Project participants, to validate the estimated accuracy of the new

ASTER Global DEM product and to identify and describe artifacts and anomalies

found in the ASTER GDEM (ASTER, 2009). They reported an overall vertical

RMSE for the 934 lOX 1° GDEM tiles of 10.87 meters, as compared to NED data;

which would equate to a an accuracy at 95% confidence of 21.31 meters, or a little

more than the 20 m accuracy at 95% confidence estimated for the ASTER GDEM

prior to its production. Vertical accuracy of NED data is approximately 2-3 m RMSE.

When compared with more than 13,000 GCPs the RMSE dropped to 9.35 meters.

These values convert, respectively, to vertical errors of just over and just under the

estimated ASTER GDEM vertical error of 20 meters at 95% confidence. The ASTER

(2009) found the ASTER DTM to contain significant anomalies and artifacts, due to

clouds and the algorithm used to generate the final GDEM, which will affect its

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usefulness for certain user applications. Another shortcoming of the current ASTER

GDEM Version 1 is the fact that no inland water mask has been applied.

Consequently, the elevations of the vast majority of inland lakes are not accurate, and

the existence of most water bodies is not indicated in the ASTER GDEM. The

vertical accuracy of this ASTER DEM was checked against 40 DGPS survey points

and 12 points digitized from USGS 1 :24,000-scale topographic quadrangles, yielding

an RMSEz of ±8.6 m. This generally corresponds with other validation results

reported by EDC (EDC DAAC, 2001).

Since the development of Geographic Information Systems (GISs), digital

elevation models (DEMs) have been generated throughout the world. DEMs provide

good terrain representations and are applied routinely in watershed modeling. DEMs

can be used to derive flow networks and then automatically generate watershed

boundaries for given outlet points using GIS technology. Therefore, an essential

component to watershed delineation is a hydrologically sound DEM of the area of

interest.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer

(ASTER) is an advanced multispectral imager that was launched on board NASA’s

Terra spacecraft in December, 1999. ASTER covers a wide spectral region with 14

bands from the visible to the thermal infrared with high spatial, spectral, and

radiometric resolution. The spatial resolution varies with wavelength: 15 m in the

visible and near- infrared (VNIR), 30 m in the short wave infrared (SWIR), and 90 m in

the thermal infrared (TIR).

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The ASTER Digital Elevation Model (DEM) product is generated using bands

3N (nadir-viewing) and 3B (backward-viewing) of an ASTER Level-1A image

acquired by the Visible Near Infrared (VNIR) sensor. The VNIR subsystem includes

two independent telescope assemblies that facilitate the generation of stereoscopic data.

The Band-3 stereo pair is acquired in the spectral range of 0.78 and 0.86 microns with a

base-to-height ratio of 0.6 and an intersection angle of about 27.7°. There is a time lag

of approximately one minute between the acquisition of the nadir and backward images

(M. Lorraine Tighe and Drew Chamberlain 2009).

3.6 Accuracy Assessment of Google Image

Google Earth data has been used by millions of people and its potential is not

well harnessed. The google earth has potential application that extends beyond

visualization. The contribution of google for the study of land-cover and land use change

science, (Himiyama 2009), (Arun das et al. 2010) are significant. The recent high

resolution images with less than 2.5 meters resolution covers nearly 20 percent of the

earth’s surface. The high resolution images allow to extract Natural features and also

human built environment. To characterize the horizontal positional accuracy of the

high-resolution Google Earth archive, the locations of 436 control points in the GE

imagery to their equivalent positions in the Landsat GeoCover data set was used,

which has positional accuracy of 50 meters root-mean-squared error (RMSE). In

an ideal assessment of spatial accuracy, it would determine the position of these

Sensors 2008, 8 7976 control points through a global ground-based campaign using

global positioning satellites (GPS). Done for below cities. Sao Paolo, Brazil, San

Salvador, El Salvador, Chonan, South Korea, and Anqing, China (David Potere

2008).

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3.7 Data Base

Delineation of watershed boundary and comparing between the different data

sources has demanded various sources of data. All useful forms of scientific data

sources have been explored to derive the result Table3.1 Show the all data. t. The

topographical map of 1:50000 scale published in 1965 by the Iranian geographical

organization was scanned and geo referenced at WGS 1984 datum and projected with

UTM zone 39 having RMS error of less than 0.2 pixels. To cover the study area 11

topographical maps were mosaiced in ERDAS and digitized in Arc GIS to determine

the manual boundary.

The ASTER DEM data of 2009 was downloaded from G-ASTER and

resample to 29mtr, to cross verify SRTM data (DEM) of 90mtr resolution (2002) was

downloaded from USGS. The digital topographical map published in 2004 by Iranian

geographical organization, at the scale of 1:25,000, was employed to obtained in the

watershed boundary shape file of Neka River. Two types of field GPS points were

used, one for demarcating boundary and another type for calculating the distance

difference between the stream and the GPS point. The Google image downloaded

through Google map catcher was stitched using python programmer, more than 50

ground control points were used to geo-reference and the RMS error was less than 0.4

pixels. The resample was done using 1.60 meters SPOT of 2010. WGS 1984 datum

was assigned and was projected on UTM 39 zone.

The IRS panchromatic data of 1st June 2004, was georeferenced with 50 GPS

Points and the RMS error was less than 0.5 pixels, image Geo referenced to the UTM

zone 39, projection based on the WGS84 datum. The resampled was done at 5.5 mtr,

Similarly, Four resample scenes were mosaiced.

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Table 3.1 : Data Base

Data Date Spatial

Resolution Source

SRTM (DEM) 2002 90 M USGS

ASTER (DEM) 2009 29 M ASTER G DEM

Digital Topographical Map 2004 1:25000 Iranian Geographical Organization

Topographical Map 1965 1:50000 Iranian Geographical Organization

PAN IRS 2004 5.5 M Indian Remote Sensing

Google Image 2010 1.5 M Google Earth (spot)

3.8 Methodology

Four different sources of data were used to delineate watershed boundaries for

the analysis. The Iranian Geographical Organization topo sheet was used to digitize the

Neka river watershed boundary through ArcGIS 9.3. The total area was 188.62 sq kms

(figure 3.1). Digital topography DEM, Aster DEM and SRTM DEM was analyzed

using spatial tools of ArcGIS hydrology and obtained the watershed boundary. To

enable spatial analysis tools ArcGIS Extension extension was installed.

Figure 3.1 Digitized watershed boundary line based on the toposheet mosaic

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The GIS technique for watershed delineation consists of the following steps.

First, the “Fill” tool was used to fill sinks in the elevation grid; this removed small

imperfections in the data and enabled the “Flow Direction” tool (the second step) to run

properly and create a grid of flow direction from each cell in the elevation grid to its

steepest down slope neighbor. Then, the “Flow Accumulation” tool was used to create a

grid of accumulated flow to each cell from all other cells in the flow direction gr id. The

next step was to identify the watershed outlet grid, ensuring that was located directly

over a grid cell from the drainage network. Finally, the “Watershed” tool was used to

delineate the watershed for the specified outlet. Boundaries (in grid format) were

defined. Using Spatial Analyst, the watershed boundary and the stream grids were then

vectorized to produce polygon and polyline themes, respectively, for further analysis

and comparison (stream).

3.9 Analysis

The four watershed boundaries were compared visually. Regression analyses

were employed to compare each of the DEM-based watershed boundaries with the 230

GPS points. For the regression analyses, a Cartesian coordinate system was used to

compare the values of x at the same y location on the three boundaries to determine

how similar they were. A total of 230 points, at constant intervals of 120 m, were

utilized in each regression analysis for the complete watershed boundary. Then, one

way Anova was conducted using ssps to determine the difference between the GPS

point and the water shed boundary line.

Visually it is evident from the figure 3.2, there are many differences compared

between the ground GPS points and the delineated ASTER, SRTM, Topo sheet

(manual) and Digital topographical map boundaries. The area of the watershed

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delineated from topo sheet (manually) is 1887.62 sq km, while the digital topography

DEM is 1906.72the SRTM-based watershed area is 1934.31 sq km and the ASTER-

based watershed area is 1901sq km. Through this method the area difference is lesser

than between one another. For land evaluation research instead of checking the error in

the total area in the watershed much importantly the exact water divide point is required.

Eminently, finding error in area was discarded and instead the distance error between the

GPS point and other boundary line has been calculated.

Figure 3.2 : Comparison of Neka (Iran) river watershed boundaries with ASTER,

SRTM, Digital Topographical DEM and TopoSheet.

The ArcGIS - tool that measures the straight-line distance from each GPS point

cell to the closest boundary line source were used to obtain the statistical descriptions of

the differences in distance and compare between four DEM-based boundary to find out

which boundary is closer to the exact ground data as shown in Table3. 2 Calculation of

error were made between ground GPS points and the boundary line derived from

ASTER DEM, Top sheet hand boundary, Digital Topography DEM and SRTM DEM

using Analysis tools, Proximity and Generate Near Table in ArcGIS. As per the

analysis the ASTER DEM boundary line is having mean variation of 43 mts distance

from the GPS point which is lesser than the other three boundary line while SRTM is

304, Topo Sheet is 307 mts and Digital topography DEM is 269 mts.

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Table 3.2 : Descriptive statistics of the difference in distance between limits

SRTM Aster

digital topography

DEM

topo

sheet

Mean 304.29 43.41 269.87 307.33

Standard Error 42.08 13.22 32.47 32.12

Median 111.67 23.44 129.33 171.75

Mode 68.93 8.60 129.36 73.23

Standard Deviation 639.56 200.89 493.52 488.13

Sample Variance 409037.6

0 40355.9

0 243562.81 238267.1

3

Kurtosis 10.37 217.29 14.98 15.73

Skewness 3.33 14.54 3.82 3.78

Range 3155.17 3037.78 3190.11 3403.47

Minimum 1.54 0.01 1.77 1.35

Maximum 3156.71 3037.79 3191.88 3404.82

Sum 70291.21 10027.0

5 62339.95 70992.57

Count 231.00 231.00 231.00 231.00

Confidence Level (95.0%) 82.91 26.04 63.98 63.28

The regression analyses comparing 230 x-y points along the complete

boundaries yielded an R2 of 0.082 between the aster and srtm boundaries; the R2 for

the comparison between the ASTER and the digital topography boundaries was 0.0157,

between ASTER and Topo sheet Map was 0.171 as shown in figure 3.3, 3.4 and 3.5

respectively. Therefore the regression analysis performed to test further any relation

between the other three boundaries and the Aster boundary indicates Aster is having

less relationship between the three boundary.

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Figure 3.3, 3.4 and 3.5 : Regression analysis compared between ASTER – with

SRTM, Digital Topographical Map and Topo sheet

y = 0.9135x + 264.64 R² = 0.0823

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

3500.00

0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 3500.00

(3.3) Aster and srtm

53

To compare the four means, the researcher subjected them to the One-way

Analysis of variance (ANOVA) in SPSS. The means obtained from the four methods of

SRTM DEM, ASTER DEM, Digital Topography DEM and Topo sheet manual

boundary were compared. The initial results showed that there was a statistically

significant difference among the four mean as shown in Table 3.3 for SPSS output. As

it is observed in the last column the P-value is less than 0.001. (p< 0.001).

Table 3.3 : One way Analysis of variance (ANOVA)

To see where the differences are exactly lied, the post hoc multiple comparisons

(Scheffe) was used. The level of significance was set at 0.001 (P=0.001). The results of

the post hoc multiple comparisons revealed a statistically significant difference between

the distances as measured through ASTER and all the other methods of distance

measurement. The distances measured by the other three groups were equally inexact

and much less exact than that of ASTER as shown in the table 3.4 below for the

summary of ad hoc multiple comparisons.

Sum of Squares df Mean Square F Sig.

Between Groups 11064704.063 3 3688234.688 15.843 .000

Within Groups 214181392.113 920 232805.861

Total 225246096.176 923

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Table 3.4 : Post HOC Multiple Comparisons (SCHEFFE )

Data

Mean

Difference

(I-J)

Std.

Error Sig.

99.9% Confidence

Interval

Lower

Bound

Upper

Bound

SRTM ASTER 260.88 (*) 44.89 .000 79.05 442.70

Digital TOPO 34.42 44.89 .899 -147.40 216.24

Toposheet -3.03 44.89 1.000 -184.86 178.78

ASTER SRTM -260.88(*) 44.89 .000 -442.70 -79.05

Digital TOPO -226.46(*) 44.89 .000 -408.28 -44.63

Toposheet -263.91

(*) 44.89 .000 -445.74 -82.09

Digital

TOPO

SRTM -34.42 44.89 .899 -216.24 147.40

ASTER 226.46 (*) 44.89 .000 44.63 408.28

Toposheet -37.455 44.89 .874 -219.28 144.36

Toposheet SRTM 3.03 44.89 1.000 -178.78 184.86

ASTER 263.91 (*) 44.89 .000 82.095 445.74

Digital TOPO 37.45 44.89 .874 -144.36 219.28

3.10 Visual Cross Examination of four boundary line with Google Image and

PAN IRS.

Visually the four boundary line overlaid on Google image, are over lapping on

each other on steep slopes and on gentle slopes the boundary line are deviating. Among

the four boundary lines, the Aster DEM boundary line is exactly cutting across the

water divide as shown in figure 3.6 Similarly when the boundary lines were overlaid on

PAN IRS it is confirmed that the boundary lines merge on steep slopes but on gentle

slopes they deviate each other as one can see from the figures 3.7 In this case also Aster

Dem boundary line is cutting exactly on the water divide point, either on steep or gentle

slope.

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Figure 3.6 : Visual Cross Examination of four boundary line with Google Image

Figure 3.7 : Visual Cross Examination of four boundary line with PAN IRS

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3.11 Stream Network Analysis

Based on the GPS points picked up on the steam path were compared between

each stream line. The mean distance between GPS point and the Aster DEM is 58.70

mts, Digital Topo graphical stream is 129.34, topo sheet is 118.79 mts and SRTM is

98.76 mts. Only ASTER DEM is closer to the ground data compared between the other

three as shown in the figure 3.8 and 3.9

Figure 3.8 : Stream Network comparison between ASTER, SRTM, Digital

Topography DEM and Toposheet of Neka River Watershed

Figure 3.9 : Stream Network and watershed boundary comparison between

ASTER, SRTM, Digital Topography DEM and Toposheet of Neka River

(Iran)

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3.12 Conclusions

The methodology described in this paper allows evaluate watershed delineation

on DEMs of different source. The accuracy of the watershed delineation it is highly

dependant on the accuracy and good quality of the Digital Elevation Model available

(DEM).

Secondly it is also proved that, the ASTER is good for demarcation on rugged

and steep slopes.

The Stream network analysis also proved that the ASTER DEM possess less

error compared between the SRTM, Digital Topography and Topo sheet.

Lastly, the Google visual comparison also has proved that, the ASTER data has

less error compared with the other three boundary lines.

Similarly, the comparison made using IRS PAN data also proved that, the

ASTER data is the best for delineation and to good to extract the watershed boundary.

ASTER data have several advantages, including low cost, high spatial resolution, good

correlation over vegetated areas. Its disadvantages include mainly the potential masking

by clouds. On the other hand, elevation models produced from SRTM data will be the

highest resolution topographic dataset ever produced for the Earth’s land surface.

Therefore, an obvious advantage of SRTM is the significant increase in spatial

resolution and vertical accuracy over existing global elevation data. Although, the

accuracy is clearly dependent upon the terrain vegetation as a radar cannot penetrate it.

Finally, ASTER DEMs appear to be highly complementary to other types of satellite-

derived data, such as Shuttle Radar Topography Mission (SRTM). It had been shown

that a fusion of DEM from different sources (optics and radar) leads to improved results

in comparison to the reference DEM.

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The overall methodology adopted in this paper has evaluated the delineation of

the watershed boundary comparing with each other and proved that, ASTER is the best

source of data for the delineation.

Based on the above testing and comparisons made, it is also strongly felt that,

the future researcher can straight away use the ASTER data for any Hydrological and

Land use and land evaluation studies.