1

Reservoir capacity estimation of the Singoor Reservoir, India,

using per-pixel and sub-pixel classification approaches.

Jeyakanthan, V.S.* and Sanjeevi, S@

*Scientist-D, Deltaic Regional Centre, National Institute of Hydrology, Kakinada, A.P

@ Professor, Department of Geology, Anna University, Chennai

Abstract

Traditional approaches of image classification such as maximum likelihood and the band

thresholding method, involve the per-pixel approach to delineate the water spread area of a

reservoir. One of the limitations of these approaches is that the pixels representing the reservoir

border, containing a mixture of water, soil and vegetation, are classified entirely as water,

thereby resulting in, inaccurate estimate of the water spread area. To compute the water spread

area accurately, the sub-pixel approach has been used in this study. The water spread areas

extracted using per-pixel and sub-pixel approaches from IRS-1D and RESOURCESAT satellite

image data were in turn used to quantify the capacity of the Singoor Reservoir, Andhra Pradesh,

India. The estimated capacity of the reservoir using the per-pixel and sub-pixel approaches was

727.75 Mm3 and 716.11 Mm

3, respectively. The validation shows that the sub-pixel approach

produced much less error (1.08%) than the per-pixel based approach (3.14%).

Key Words: Reservoir, water spread area, capacity estimation, sub-pixel approach.

1. Introduction

Natural processes, such as erosion in the catchment area, movement of sediment and its

deposition in various parts of the reservoir, require careful consideration in the planning of major

reservoir projects. The silt that is deposited at different levels reduces the storage capacity of the

reservoir (Smith and Pavelsky 2009, Sreenivasulu and Udayabaskar 2010). Reduction in the

storage capacity beyond a limit prevents the reservoir from fulfilling the purpose for which it is

designed. Periodic capacity surveys of the reservoir help to assess the rate of sedimentation and

reduction in storage capacity. Conventional techniques for the estimation of the capacity of a

reservoir, such as hydrographic survey and inflow-outflow approaches, are cumbersome, time

consuming and expensive, and they involve significant manpower. As an alternative to

conventional methods, the remote sensing technique provides cost- and time-effective estimation

of the live capacity of a reservoir (Sabastian 1995, Jain 2002). Multi-date satellite remote sensing

data provide information on elevation contours, in the form of water spread area, at different

water levels of a reservoir. The water spread area thus interpreted from the satellite data is used

as an input into a simple volume estimation formula to calculate the capacity of a reservoir. Such

work has been reported by Manavalan (1990) for the Malaprabha Reservoir in India, Jeyaseelan

and Thiruvengadachari (1997) for the Kuttiyadi Hydel Reservoir in India, Chandrasekar and

Jeyaseelan (2000) for the Tawa Reservoir in India, Jeyakanthan (2002) for the Poondi Reservoir

in India, Goel (2002) for the Bargi Reservoir in India, Jain (2002) for the Bhakra Reservoir in

India and Peng (2006) for the Fegman Reservoir in China.

2

To quantify the capacity of a reservoir, the only thematic information that has to be

extracted from the satellite data is the water spread area at different water levels of the reservoir

(Morris and Fan 1998, Peng 2006). Different approaches to delineate various thematic

information from the remote sensing digital data, such as maximum likelihood classification,

minimum distance to mean classification and the band threshold method, adopt the per-pixel

based methodology and assign a pixel to a single land cover type (Jensen 1996, Bastin 1997);

whereas in reality, a single pixel may contain more than one type of land cover (known as a

mixed pixel). Mixed pixels are common especially near the boundaries of two or more discrete

classes (Foody and Cox 1994, Shalan et al. 2003, Ibrahim et al. 2005). The boundary pixels of

the water spread area that are mixed in nature, representing soil or vegetation with moisture, are

classified as water pixels when a per-pixel based approach is applied, thereby producing an

inaccurate estimate of the water spread area. To accurately compute the water spread area to the

maximum possible extent, thereby reducing the error in the estimation of the capacity of a

reservoir, a sub-pixel or linear mixture model (LMM) approach has been chosen for classifying

the boundary pixels of the water spread area from different water levels of the Singoor Reservoir

located in the Andhra Pradesh state of India.

2. Study reservoir

The Singoor Reservoir (Figure 1) is a major irrigation project built across the river

Manjira, which is one of the tributaries of the Godavari River, the second largest river in the

Indian subcontinent. The project is located near Singoor village, Medak District, which is at a

distance of 100 km from Hyderabad, the capital city of Andhra Pradesh state. The total length of

the dam is 7.52 km, which includes a 327 m long overflow masonry dam in the river gorge

portion and an 81 m non-overflow masonry dam flanked on both sides by earthen embankments.

The Singoor project is also used as the water supply source for the twin cities of Hyderabad and

Secunderabad. The climate of the sub-basin is characterised by a hot summer and a mild winter.

The monsoon season begins early in the month of June and continues to the end of October. The

principal type of soil present in the catchment area, apart from the red soil, is black cotton mixed

soil. Due to various activities in the catchment area, the black cotton soil is easily eroded and an

enormous amount of silt is carried into the stream that drains into the Manjira River. The eroded

soil eventually is deposited into the Singoor Reservoir and drastically reduces its capacity.

Hence, estimation of the capacity is a problem when studying this reservoir.

2.1 Satellite data used

The image data used in this study were acquired by the Indian Remote Sensing (IRS)

satellites IRS-1D & RESOURCESAT/IRS-P6 (LISS-III sensors) that provide a spatial resolution

of 23.50m in four different spectral bands (0.52-0.59, 0.62-0.68, 0.77-0.86, 1.55-1.70μm). IRS

satellite image data was selected to study the reservoir, because the images from the different

satellites viz. IRS 1C, 1D and P6 can be utilized from the year 1996 onwards with the higher

temporal resolution. In addition, these sensors have similar spectral resolutions which are

mandatory for reservoir sedimentation studies. The different dates of the satellite data used and

the respective water level during the pass of the satellite over the reservoir are given in Table 1.

Reservoir water level data and the hydrographic survey details have been collected from the

Singoor Reservoir authority responsible for the maintenance and operation of the reservoir.

3

Table 1. Selected water levels and the corresponding date of satellite

pass over the Singoor Reservoir.

Sl.No. Date of

Satellite Pass

Reservoir Water

Level above

mean sea level

(m)

Satellite &

Sensor

Path/

Row

1. 12.11.2005 523.49 IRS 1D-LISS III 99/60

2. 06.10.2005 522.10 IRS P6-LISS III 99/60

3. 29.08.2005 517.10 IRS 1D-LISS III 99/60

4. 01.04.2005 514.92 IRS 1D-LISS III 99/60

5. 15.05.2005 513.71 IRS P6-LISS III 99/60

6. 15.06.2005 512.51 IRS 1D-LISS III 99/60

3. Methodology

The changes in the water spread could be accurately estimated by analysing the areal

spread of the reservoir at different elevations over a period of time using the satellite image data

(Morris and Fan 1998, Smith and Pavelsky 2009). Per-pixel and sub-pixel approaches have been

used in this study to extract the water spread area of the reservoir. Estimated water spread areas

were used in a simple volume estimation formula to compute the storage capacity of the

reservoir. Estimation of the water spread area and the computation of the capacity of the

reservoir are discussed in the following sections.

Figure 1. Location map of the Singoor Reservoir.

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3.1 Geo-referencing of satellite data

In the IRS-1D and P6 (LISS-III) satellite data the reservoir water spread area was free

from clouds and noise for all of the six temporal images used. The image scene of 12th

November 2005 was geo-referenced with respect to a set of 1:50,000 scale survey of India

topographic maps. The geo-referencing was performed using polyconic projection and the

nearest neighbour re-sampling technique to create a geo-referenced image with a pixel size of 24

m x 24 m. Subsequently, the other satellite images corresponding to various water levels were

also registered with the geo-referenced image using the image to image registration technique. In

every image, 25 to 30 ground control points were used, which resulted in a root mean squared

error (RMSE) of 0.12 to 0.19 of a pixel.

3.2 Per-pixel based approach

Water reflects most of the visible wavelengths, but the energy at the near-infrared (NIR)

wavelength is almost absorbed by the water, thus providing a significant contrast between land

and water in the NIR images (Lillesand and Kiefer 2004). This contrast helps in extracting the

water spread area of the reservoir. Different procedures have been adopted by many researchers

(Chopra et al. 2001, Sheng et al. 2001, Toyra et al. 2001, Dechka et al. 2002, Jain et al. 2002, Xu

2005, Rathore 2006, Alsdorf 2007) for water body identification in wetland areas and reservoirs,

each adopting the per-pixel based approach. Among these procedures, the band threshold

approach is a relatively easy and valid method for identifying the water body. It has also been

suggested that this per-pixel based approach can give acceptable estimates of the area of the

water body if the NIR band is used (Goel 1996, Jain 2002). Therefore, in the per-pixel based

approach, the band threshold technique was adopted to extract the water pixels that correspond to

various water levels of the reservoir. The following model equation has been used in the image

processing software to delineate the water spread area of the reservoir. The adopted algorithm

states that:

if

PV-NIR > TL-NIR and (1)

PV-NIR < TH-NIR then

the pixel is in the water spread area, where PV-NIR is the pixel value in NIR band and TL-NIR and

TH-NIR are the lower and higher thresholds for the NIR band.

Because the absorption of electromagnetic radiation by water is at a maximum in the NIR

spectral region, the digital number (DN) of water pixels is considerably lower than that

corresponding to other land cover types. Even if the water depth is shallow, the increased

absorption in the NIR band will restrict the DN value to less than that of the green and red bands.

If the soil is exposed (possibly saturated) at the surface, the reflectance will be as per the

signature of the soil, which increases with wavelength in this spectral range. Thus, by following

this algorithm (Equation 1) water pixels that belong to a particular water level of the reservoir

were extracted. The extracted water spread areas are shown in Figure 2.

5

Figure 2. Satellite data pertaining to six different water levels and the corresponding

extracted water spread area of the Singoor Reservoir based on the per-pixel approach.

Reservoir No. A B C D E F

Water level (m) 523.49 522.10 517.10 514.92 513.71 512.51

Extracted Area (Mm2) 138.37 110.96 52.01 22.36 17.28 10.32

A B C

D F E

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3.3 Sub-pixel based approach

The sub-pixel classifier uses the linear unmixing technique that allows for the

identification of the “material of interest” and the determination of its “material part fraction” or

cover percentage within a pixel.

Linear spectral unmixing is a perfect approximation for calculating the abundance or

fraction of an end-member in an image pixel. The LMM classification technique attempts to

estimate the proportions of specific classes that occur within each pixel using the linear mixing

approach (Foody 1996, Aplin and Atkinson 2001, Min Xu 2005). In this study, the reservoir

water spread area was estimated using the linear spectral unmixing approach.

The basic assumption of the LMM is that the measured reflectance of a pixel is the linear

sum of the reflectance of the components that make up the pixel. The basic hypothesis is that the

image spectra are the result of mixtures of surface materials, shade and clouds and that each of

these components is linearly independent from the others (Bosdogianni et al. 1997, Atkinson

1997, Robert et al. 1998). Linear unmixing also assumes that all of the materials within the

image have sufficient spectral contrast to allow their separation. In a soft classification, the

estimated variables (the fractions or proportions of each land cover class) are continuous, ranging

from 0 to 100 percent coverage within a pixel. Settle and Drake (1993) and Foody and Cox

(1994) proposed a mathematical expression for linear spectral unmixing. The theory behind this

is that a series of end-members present within a pixel contribute to the overall spectral signature

of that pixel. Hence, the spectral signature of a pixel would be derived from the sum of the

products of the single spectrum of each end-member it contains, each weighted by a fraction,

plus a residue as explained by the following mathematical model:

Ri = ∑fk Rik + Ei (2)

where ∑ fk = 1 (3)

and 0 ≤ fk ≤ 1 (4)

i = 1, . . ., m (number of spectral bands)

k = 1,. . ., n (number of end-members)

Ri = Spectral reflectance of band i of a pixel which contains one or more end-members

f k = Proportion of end-member k within the pixel

Rik = Known spectral reflectance of end-member k within the pixel in band i

Ei = Error for band i (Difference between the observed pixel reflectance Ri and the

reflectance of that pixel computed from the model).

Equations 2 and 3 introduce the constraints that the sums of the fractions are equal to one and

they are non-negative. To solve for fk, the following conditions must be satisfied: (i) selected

end-members should be independent of each other, (ii) the number of end-members should be

less than or equal to the spectral bands used, and (iii) selected spectral bands should not be

highly correlated.

In this study, linear spectral unmixing is adopted based on the equations described below to

segregate the actual information within a pixel of an image:

7

R1 = Fwater * R1water + FVeg* R1Veg + FSoil*R1Soil + ε1

R2 = Fwater * R2water + FVeg* R2Veg + FSoil*R2Soil + ε2 (5)

R3 = Fwater * R3water + FVeg* R3Veg + FSoil*R3Soil + ε3

where,

R1, R2 and R3 represent the signal recorded by the satellite (DN values) in the green, red and

NIR bands of the LISS-III sensor.

Fwater , FVeg and FSoil are the fraction of the pixel covered by water, vegetation and soil.

R1water, R2water and R3water represent the DN of water in each of the three spectral bands (water

end-members).

R1Veg, R2Veg and R3Veg represent the DN of vegetation in each of the three spectral bands

(vegetation end-members).

R1Soil, R2Soil and R3Soil represent the DN of soil in each of the three spectral Bands (soil end-

members).

ε1, ε2 and ε3 are the error components of band 1, 2 and 3.

The system of linear equations shown above can be solved by a least squares solution that

minimises the sum of squares of errors. To maintain the consistency between the per-pixel and

sub-pixel processes DN values were used as input parameter in the equation 1 & 5.

The sub-pixel based approach was applied to determine the proportion or fraction of the

water class that exits in the peripheral pixels of the reservoir. The first step executed in the sub-

pixel approach was the selection of the pure pixels (known as end-members) belonging to a

specific class. In general, the border pixels may contain any combination and proportion of

water, vegetation and soil classes. Hence, these three classes were chosen to represent the end-

members. The scatter plot method was used to identify the end-members. The locations of the

end-members in the image data were identified from the extremes of the scatter plot. The

identified end-member spectra were supplied as input to the LMM approach. The output of the

model run contains three images labelled as water-, soil- and vegetation- fraction images. A

description of the fraction images is given in section 4.2.

3.4 Computation of volume between successive water levels

Traditionally the reservoir volume between two consecutive reservoir water levels was computed

using the prismoidal formula, the Simpson formula or the trapezoidal formula (Patra 2001). Of

these, the trapezoidal formula has been most widely used for the computation of volume (Rao et

al. 1985, Goel and Jain 1996, Morris and Fan 1998, Rathore 2006). The water spread area

estimated using the per-pixel and sub-pixel approaches were separately used as an input to the

volume estimation formula to determine the volume at different water levels of the reservoir. In

8

this study the volume between two consecutive reservoir water levels was computed using the

following trapezoidal formula:

V = H *(A1 + A2 + √A1*A2)/3 (6)

where V is the volume between two consecutive water levels, A1 and A2 are the water spread

areas at the reservoir water levels 1 and 2 respectively and H is the difference between these two

water levels.

3.5 Computation of storage capacity of the reservoir

The volumes computed (using equation 6) between different water levels (i.e., from minimum

draw down level (MDDL) to full reservoir level (FRL)) were added together to calculate the

cumulative or storage capacity of the reservoir.

4. Results and discussion

4.1 Computation of reservoir capacity by the per-pixel approach

Six different water levels varying from 512.51 m to 523.49 m of the reservoir were selected

based on the availability of cloud free satellite data to estimate the water spread of the reservoir

(the impoundment MDDL and FRL of the reservoir are 510.6 m and 523.6 m respectively). To

extract the water pixels from the images using the per-pixel approach, the algorithm presented in

Equation 1 was used. This algorithm requires separate minimum and maximum threshold DNs

from the NIR band for the six satellite images used in the study. With the help of the algorithm,

the pixels which contain a DN between the given minimum and maximum threshold values were

labelled as water pixels. Basic statistical parameters of an image that could be obtained using the

image processing software were utilised to determine the minimum pixel values of the water

spread area. The locations of these pixels in the water spread area were also verified, and showed

that the pixels with low DN were located in the deeper and central portion of the water spread

area of the reservoir. The lower (14) and higher (41) DN values can be attributed to the

irradiance of the water-body during the winter and summer seasons of the study area. The

extracted minimum DN were 28, 41, 25, 21, 21 and 14 for the images pertaining to the months of

April, June, July, August, October and November, respectively. The pixel values of 14 and 41

pertain to the winter (November) and summer (June) seasons of the study area. The analysis of

DN values of the water body show that the pixel value increases towards the periphery of the

water body and the border pixels have the maximum DN. In selecting the maximum value of the

water spread area, one must examine the pixels along the periphery of the reservoir. However, it

is not an easy task to select a maximum threshold value along the border of the reservoir area.

For example, at one location of the border area, one may be satisfied with a pixel value of 35, but

in another location it may change to a DN value of 43. Thus, the differences in pixel values over

a large range consume enormous time and effort to obtain a conclusive maximum threshold for

the DN to extract the water spread area. The extracted maximum pixel values were 36, 49, 44,

43, 49 and 37 for the images pertaining to the months of April, June, July, August, October and

November, respectively.

9

Table 2. Capacity estimation of the Singoor Reservoir using per-pixel and sub-pixel based

approaches.

Date of

Satellite

Pass

Reservoir

Elevation

above

m.s.l (m)

Water spread

area – Per-

pixel

approach

(Mm2)

Water spread

area – Sub-

pixel approach

(Mm2)

Cumulative

Volume –

Per-pixel

approach

(Mm3)

Cumulative

Volume –Sub-

pixel approach

(Mm3)

12.11.05 523.49 138.37 136.37 727.75 716.11

06.10.05 522.10 110.96 110.36 554.82 544.95

29.08.05 517.10 52.01 50.51 156.59 152.40

01.04.05 514.92 22.36 21.59 77.67 76.01

15.05.05 513.71 17.28 16.46 53.85 53.06

15.06.05 512.51 10.32 9.82 37.47 37.47

The total number of water pixels that were extracted was multiplied by the area (24 m x

24 m) of a single pixel to compute the water spread area. The same technique was adopted to

convert the extracted water pixels into the water spread area in all six images used in this study.

The water spread area thus estimated in each image using the per-pixel approach has been

used as an input to the trapezoidal formula (Equation 6) to calculate the consecutive volumes of

the reservoir. The storage capacity between the bed level (500.17 m) of the reservoir and the

lowest observed water level (512.51 m) could not be estimated using remote sensing

methodology due to the unavailability of cloud free satellite data. Therefore, the capacity (37.47

Mm3) between these two levels was adopted from the 1997 elevation-capacity table, available

from the Dam Authority. Above the lowest observed level (512.51 m), the estimated capacities

between the consecutive water levels were added up to arrive at the cumulative capacity of the

reservoir at the maximum observed level (523.49 m). The estimated cumulative capacity of the

reservoir at a water level of 523.49 m (near FRL) using the per-pixel classification approach was

727.75 Mm3. Thus, the per-pixel approach was adopted for all six images and the capacity was

estimated (Table 2). To compare the efficiency of this method with that of the sub-pixel

approach, another set of experiments was carried out, as described in the next section.

4.2 Computation of reservoir capacity by the sub-pixel approach

The fraction images (Figure 3 and 3a) generated using the sub-pixel approach described in the

Methodology section contain a wealth of information about the reservoir. Each fraction image

corresponds to a single type of land cover only. For example, the pixels in the water fraction

image provide information only on the proportion or amount of water contained in the pixel.

Likewise, the vegetation and soil fraction images provide information on the proportions of their

respective classes only. However, in this study the interest is only to determine the amount of

water present in the border pixels of the reservoir. The value of the pixels in the fraction image

10

Figure 3. (a) Feature space plot (NIR Vs RED),(b) End-member spectra of Soil, Water and Vegetation (c) Fraction

images obtained by spectral unmixing of image data pertaining to the highest water level (523.49m)

Figure 3a. (a) Feature space plot (NIR Vs RED, (b) End-member spectra of Soil, Water and Vegetation

(c) Fraction images obtained by spectral unmixing of image data pertaining to the

lowest water level (512.51m)

(Due to the recurring nature of the fraction images only figures pertaining to the highest and lowest water levels are

presented)

Dig

ital

Nu

mb

er

R

ED

NIR

Band

(b) (a)

NIR

R

ED

(a) (b)

Dig

ita

l N

um

ber

Band

11

ranges from 0 to 1. A pixel from the water fraction image having a value of 0 indicates that there

is no water at all in that pixel, whereas a pixel having a value of 0.3 indicates that 30% of the

area of the pixel is occupied by water while a pixel value of 1 indicates that 100% of the area of

the pixel is occupied by water (i.e., the pixel is fully occupied by water). Therefore, for a pixel

having a value of 0.7, the area of water occupied by that pixel is 403.2 m2 (0.7 x 24 m x 24 m).

The pixels representing the peripheral portion of the reservoir, which have a minimum

value of 0.1 in the water fraction image (i.e., a pixel with a minimum of 10% of its area

containing water) were isolated from the water-fraction image and the area covered by water in

these peripheral/border pixels was estimated. After examining the peripheral pixels it was

ascertained that none of the border pixels contain water spread area less than 10%. Hence, a

threshold value of 10% was selected and used for analysis. The number of pixels that contain

100% water was also determined. By summing the area occupied by these two types of pixels,

the total water spread area corresponding to a particular water level of the reservoir was

computed. This exercise was carried out for each of the six images used in the study. The water

spread area thus estimated was again used as an input to the trapezoidal formula to compute the

storage capacity or cumulative capacity of the Singoor Reservoir using the sub-pixel

classification approach.

It is worth mentioning that a pixel containing 65% water may be labelled as containing

100% water by the per-pixel approach. Thus, the water spread area is overestimated. Conversely,

if the pixel contains 40% water then the entire pixel is not considered for the water spread area

estimation. Hence, the water spread area is underestimated. Such errors due to overestimation or

underestimation do not occur in the sub-pixel approach. Thus, the sub-pixel approach reduces the

error imposed by the per-pixel approach. The estimated cumulative capacity of the reservoir at a

water level of 523.49 m (near FRL) using the sub-pixel approach was 716.11 Mm3. The capacity

estimated using the sub-pixel approach is given in Table 2. Elevation capacity curve of Singoor

Reservoir using per-pixel and sub-pixel approaches is given in Figure 4.

Figure 4. Elevation-Capacity curve of Singoor Reservoir using per-pixel and sub-pixel

approaches.

12

4.3 Validation of the per-pixel and sub-pixel approaches

Several investigators (Quaramby et al. 1992, Oleson et al. 1994, Haertel et al. 2004,

Foody 2007) have shown that the recovery of sub-pixel information from medium resolution

data is feasible, and this information can be directly compared to that obtained at higher scales.

In line with the above findings, it was decided to validate the result of the sub-pixel classification

approach (which was carried out using the 24 m resolution image data) using high resolution

image data with a spatial resolution of 5 m (resampled IRS 1C-PAN). However, concurrent high

resolution data was not available for any of the six images used in the study. Therefore, three

new sets of image data were procured in such a way that for a particular water level of the

reservoir, both the 24 m and 5 m resolution image data were available. The band threshold and

sub-pixel classification techniques were again carried out on the new 24 m resolution image data

sets. The high resolution PAN data were classified using the band threshold method. The results

of these two experiments are given in Table 3. By analysing the results for all three sets of

images, it was ascertained that the band threshold method applied to the 24 m resolution image

data

Table 3. Validation of the per-pixel and sub-pixel approaches.

Satellite/Sensor Reservoir

water level

above m.s.l

(m)

IRS-P6 / LISS-III (24 m) IRS-1C/PAN

(5m)

Date of Satellite Pass Water spread area

Per-Pixel (Mm2)

Water spread area

Sub-Pixel (Mm2)

Water spread area

Per-pixel (Mm2)

03 Feb 2006 (Validation-1) 522.68 123.73 118.69 119.82

23 Mar 2006 (Validation-2) 521.71 104.89 106.73 107.49

15 Jan 2005 (Validation-3) 516.55 42.04 39.68 40.32

Table 4. Percentage (%) of error between the per-pixel and sub-pixel based approaches.

No. of

Validation

Processing Approach

Per-pixel Sub-pixel

Validation-1 3.26% 0.94%

Validation-2 2.42% 0.71%

Validation-3 3.75% 1.58%

Average Error 3.14% 1.08%

overestimates whereas the sub-pixel method underestimates the water spread area when

compared to the high resolution (5 m) data. To select the best approach the percentage of error

between both these approaches were calculated. For example the percentage of error for

validation 2 was calculated as follow: ((104.89 – 107.49)/107.49) x 100 = 2.42% and ((106.73 –

107.49)/107.49) x 100 = 0.71%. Results of percentage of error between per-pixel and sub-pixel

approaches are given in Table 4. Analysis of Table 4 reveals that the sub-pixel approach

13

produced much less error (1.08%) than the band threshold/per-pixel approach (3.14%). This

shows that the sub-pixel based approach can be applied to estimate the capacity of the Singoor

Reservoir with higher accuracy than the per-pixel approach.

5. Conclusion

High spatial-resolution image data enables accurate mapping of terrain features. The use

of high spatial resolution satellite image data, however, is constrained by factors such as cost and

the smaller area covered by the sensor. Hence, in hydrological applications estimating the water

spread area may be difficult because a reservoir may not be imaged in a single pass of the

satellite and atmospheric conditions would be different from path to path (Hung and Wu 2005).

An alternative method to overcome such constraints is the use of the sub-pixel based approach.

The simplest methodology for such an approach is the linear mixture model, which has been

demonstrated in this study to accurately estimate the capacity of the Singoor Reservoir in India.

In this study both the per-pixel and sub-pixel approaches have been performed to extract

the water spread area of the reservoir using medium resolution multi-spectral image data and the

results were validated using high resolution panchromatic image data. The validation shows that

the application of the sub-pixel approach produced much less error (1.08%) than the per-pixel

(3.14%) based approach. The relatively lower error shown by the sub-pixel approach presents a

potential use of the technology for the estimation of other reservoirs with acceptable accuracy.

Although the sub-pixel approach was found to be a better alternative than the per-pixel

approach, there are certain limitations to the method, such as the fact that the spatial locations of

the various fractions within a pixel are unknown. In addition, the sub-pixel classifier produces

more accurate results only with hyperspectral images. Hence, the use of hyperspectral image data

with higher spatial resolution would have yielded better results.

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