Executive Summary

In pursuance of the directives of Hon'able "National Green Tribunal, New Delhi, ordered dated

23 January 2017 in application no. 145/2015, it is submitted that a precise study has been carried

out by National institute of Hydrology, Roorkee along with the officials of Central Water

Commission, Agra; State Pollution Control Board, Agra; Jal Nigam, Agra; Agra Development

Authority, Agra and UP Irrigation Department, Agra. The study includes the demarcation of

flood line as on 27 September 2010 using inundation obtained from satellite images. From the

flood inundation map, the flood line has been delineated and the points are extracted at regular

interval of 100 m in general, and at few critical locations at closer interval also. Subsequently,

these points have been transferred onto the ground using a precise instrument (DGPS). The

demarcation of these points clearly displays the extent of encroachment as on today with respect

to flood line.

The report also includes the methodology, steps involved in flood mapping and demarcation of

points, photographs during field work. The extent of encroachment is also tabulated at various

buildings/ locations in tabular form. Out of 620 points marked on flood line, about 150 points

have been transferred on the ground till 17 February 2017 and balance work is under progress.

Pankaj Mani

Scientist E, NIH Patna

Vipin Kumar Singh

Executive Engineer, CWC, LYD Agra

Sharad Kumar Singh

Executive Engineer, Lower Division Agra

Canal, Agra

Rajiv Dixit

Executive Engineer, ADA, Agra

Khalid Ahmad

PM, YPCU, Jal Nigam, Agra

Dr. Ram Karan

RO, UPPCB, Agra

Flood Inundation Mapping in Yamuna River at Agra for 2010 flood.

1. Hydrological analysis of September 2010 flood at Poiyaghat

1.1. Rainfall Pattern

The rainfall observed at Poiyaghat during September 2010 is shown in Figure 1. The figure

shows that on the day of maximum flood, i.e. 27 Sept 2010, there was no rainfall occurred in

Agra. Instead, heavy spells of rainfall occurred on 19, 20 and 21 Sept 2010, continuously for

three days. Since then no rainfall occurred till date of maximum flooding in Yamuna river on 27

September 2010. Further, there is no rainfall observed after this major flood event. Thus a dry

period of almost 9 days are available during month of September 2010 around the occurrence of

peak flood event. The identification of dry period is helpful in identifying the cloud free satellite

data for flood inundation mapping. Further, it is also used in differentiating the inundation area

due to pluvial (flooding due to local site rainfall, drainage congestion etc) and fluvial (spilling of

river) flood. As locally rainfall occurred almost 6 days prior to occurrence of peak flood level in

Yamuna river, almost all the inundation may be considered due to fluvial flooding only. Hence,

the extent of inundation delineated from the satellite image during this maximum flood event

will provide the inundation map for this specific flood of 27 September 2010. The rainfall data

obtained from Central Water Comission (CWC), Agra for the month of September 2010 is

shown in Figure 1.

Figure 1: Daily rainfall at Poiyaghat during September 2010.

1.2. Flood level in Yamuna river at Poiyaghat

Daily flood level of Yamuna river observed by CWC at Poiyaghat GD site is shown in Figure 2.

The figure shows the flood level on 26 Sept 2010 was 152.4 m, and within 24 hour it raises by

0.12 m (12 cm) and became 152.52 m on 27 Sept 2010. The maximum flood level has occurred

during these days and the cloud free satellite images are explored to be available for these two

dates and have been used in inundation mapping.

Figure 2: Daily flood level at Poiyaghat during September 2010

The flood discharge corresponding to flood level of 152.52 m at Poiyaghat is computed as 6063

m3/sec (Source: CWC). Based on the flood frequency analysis (FFA) of long term annual peak

flood discharge observed at Poiyaghat, the return period of 2010 flood is estimated as 79 years

using L-moment approach. The detailed FFA is enclosed in annexure-I.

2. Flood Inundation Mapping using Satellite Data

The various steps carried out in the process of marking of inundation map corresponding to 27

September 2010 Flood are discussed as below:

2.1. Satellite data inventory

1. The availability of satellite image for the period September 26-28 September 2010 is

browsed over the National Remote Sensing Centre and USGS EarthExplorer web sites

for cloud free and fine resolution satellite image to capture the flood event of 27

September 2010. In addition the service provider of other foreign satellite data were also

contacted (email correspondence enclosed in Annexure-II).

2. The satellite data (Landsat-5 TM and Lansat-7 ETM) for 26 and 27 September 2010 are

available and downloaded from USGS EarthExplorer web sites. From NRSC Hyderabad,

high resolution satellite data for 11 June 2010 (this is the nearest available high resolution

image) was procured to map the ground condition/ status of constructions etc. along river

bank line close to the flood event. Additionally, one high resolution image is also

procured for 23 January 2017 to assess the construction activity within the flood line

since the occurrence of flood on 27 September 2010. The followings satellite images are

thus used in the study:

SN Date of image Satellite/ Sensor Spatial resolution (m)

1 27 Sept 2010 Landsat-7/ETM, multispectral (SLC OFF) 30 m, 15 m

2 26 Sept 2010 Landsat-5 /TM, multispectral (used for

rectifying SLC off image of 27 Sept 2010)

30 m

3 11 Jun 2010 IRS P5/ PAN (for assessing ground situation

just after the event, this is the earliest high

resolution available image)

2.5 m

(high resolution)

4 23 Jan 2017 IRS P5/ PAN (for assessing current ground

condition)

2.5 m

(high resolution)

5 18 Nov 2010 GoogleEarth, Service Provider: Digital Globe

(for assessing current ground condition using

very high resolution image)

< 1 m

(very high resolution)

6 21 Oct 2016 GoogleEarth, Service Provider: Digital Globe

(for assessing current ground condition using

very high resolution image)

< 1 m

(very high resolution)

2.2. Image processing for flood mapping

3. The ground control points (GCP) were selected from the Google Earth image and its

geographical locations (latitude and longitude) were collected from the field using DGPS

instruments. Image of 11 June 2010 is first geo-referenced using GCPs and subsequently

used for registration of other images.

The root mean square error for image registration is kept within 1 pixel, i.e. 2.5 m. The

location of GCP points and its ground coordinates are given in Annexure III.

4. Multispectral image is available for 27 September 2010 and is used to classify the water

pixels. Though, the multispectral image of Landsat is having spatial resolution of 15/30

m, water pixel can be easily extracted from it. To define the flood line more accurately,

the multispectal images are fused with high resolution image of P5/PAN data which has

spatial resolution of 2.5 m. The methodology of flood inundation mapping using satellite

images is discussed briefly in Annexure IV. Some of the remote sensing and GIS terms

used in this report are also defined in this section. The resulting image is then used for

improving the flood line. The figure below shows the flood map extracted from

multispectral image (Figure 3) and its improvement with the fused image (Figure 4).

Thus using the fused image the error in marking of flood line can be restricted to 1 pixel

of fused image, i.e. 2.5 m.

Figure 3: Flood map extracted from multi spectral image

Figure 4: Flood map delineation improved with fused image

5. Subsequently, the flood line is transformed into points spaced at regular interval of 100

m. However, at few locations where flood line changes sharply within short distance,

points at closer interval have also been extracted. Thus altogether, 620 points have been

marked on the flood line. Figure 5 below shows the points along the flood line in a

particular location (Mangalam Estate and Mangalam Extension). The geographical

coordinates of these points are read from GIS data base and transferred into DGPS

system. Subsequently, DGPS instruments are carried to the field to transfer these points

on ground. The field photographs showing the flood line points transferred on the ground

are shown in Annexure V. The geographical coordinates of flood line points are given in

Annexure VI.

Figure 5: Flood line is converted into points at regular interval of 100 m.

6. On earlier occasion, the computation of flood level at various locations were carried out

using the flood level of 2010 at Poiyaghat GD site and the water surface profile and the

pillars were marked over the ground by UP Irrigation Department. Through ground

survey, the geographical coordinates of these points were obtained using GPS instrument.

Using their coordinates, the pillar locations are also plotted in Geographical Information

System (GIS) environment.

It is observed that mostly, the plotted pillar locations are on the flood line. This ensures

the accuracy of marking of flood line using satellite images. However, at some of the

locations, the field survey based pillars are deviating from the flood line (and that may be

due to utilization of the land since occurrence of 2010 flood. It is to be mentioned here

that the present marking of flood line is based on satellite image of 2010 flood while the

pillar marking is based on water level computation of 2010 flood on the existing ground

(that might have filled up at several locations).

These flood line are further used for estimating the extent of encroachments along the

river in study reach.

Figure 6: The flood line estimated from satellite images coincide with field survey based

flood level marking.

7. Very high resolution satellite images (spatial resolution < 1 m) are extracted from Google

Earth and geo-reference with the earlier used high resolution image using GIS analysis.

The flood line is superimposed over these images and the encroachment in flood line is

estimated. The encroachment of flood line near Mangalam Estate Exgtension is plotted in

Figure 7.

Figure 7: Estimation of encroachments within flood line near Mangalam Estate Extension.

8. Similarly, the encroachments at all the important locations are computed. The figures

showing the encroachment at these locations are shown in Annexure VII. The extent of

encroachments at some important and critical locations/ buildings/ farm houses (in view

of present OA 145/2015) are also tabulated below. Further, during transferring of flood

line points, the ground condition was also evaluated and the observation about filing of

land mass if any is also mentioned in this table.

SN Location Extent of

encroachment (m)

Ground

raised or not

1 Aparna river Views Out of flood line

2 Mangalam Shila Out of flood line

3 Puspanjali Heights 23 m Yes

4 Manglam Estate Out of flood line

5 Manglam EstateExtension 20-26 m Yes

6 Ram Mohan Vihar Out of flood line

7 Kalyani Heights Out of flood line

8 Jawahar Bag II (renamed as Vaibhav Vatika II)

(point no. 596)

5 m Yes

9 Rajshree Garden

10 Radha Ballabh Public School Out of flood line

11 Tanishq Rajshree Estate 21 m Yes

12 Pushpanjali Seasons Out of flood line

13 Vibhav Vatika Out of flood line

14 Ganpat Wonder City 11 m Yes

15 Jagdamba PG College, Naraich Out of flood line

16 Astha City Centre Out of flood line

17 Indira Mill Compund Out of flood line

18 Hotel Tajway Inn Out of flood line

19 Taj View Appartment Out of flood line

20 DRP College, Naraich 7 m Yes

21 Godown at point no. 245 5 m Yes

22 Farm house (point no. 454) 9 m Yes

23 Farm house (point no. 455) 3 m Yes

24 Farm house (point no. 456) 8 m Yes

25 Farm house (point no. 457) 3 m Yes

26 Open plot (point no. 463) 71.5 m Yes

27 Open plot (point no. 464) 16 m Yes

28 Bharat Engine Farm House (point no. 556) 13.5 m Yes

Annexure-I

Floods of various Return Periods estimated by NIH and UP Irrigation

Department for Yamuna at Poiyaghat, Agra

S. No. Return

period

(Years)

Growth

factors

Flood (m3/s)

NIH (GLO)

Flood (m3/s)

Irrigation Dept, UP (LP3)

1 25 2.410 3944.531 4155.15

2 50 3.143 5144.258 5322.09

3 100 4.098 6707.340 6736.47

• Estimated return period of the flood of 6063 m3/s observed in 2010 is 79 years (Fig.

1).

• Flood observed/ estimated by CWC for the year 1978 flood is 9,300 m3/s.

• Estimated return period for the flood occurred in 1978 i.e. 9,300 m3/s (by CWC) is

computed as 233 years from the discharge-return period curve (Fig. 1).

• Based on the Stage-Discharge Curve, the estimated discharge for the year 1978

flood level is 8,900 m3/s (Fig. 2).

Floods of various return periods estimated using L-moments approach by NIH

S. No. Return period

(Years)

Growth factors

Mean( 1636.735 )

Flood

(m3/s)

1 2 0.800 1309.388

2 10 1.687 2761.172

3 20 2.212 3620.458

4 25 2.410 3944.531

5 50 3.143 5144.258

6 100 4.098 6707.34

7 200 5.347 8751.622

8 500 7.616 12465.37

9 1000 9.965 16310.064

10 10000 24.486 40077.09

Fig. 1 Discharge Return Priod relationship

Fig 2. Flood level (Stage) discharge relationship

Brief Methodology: Twelve frequency distributions namely Extreme value (EV1),

Generalized extreme value (GEV), Logistic (LOS), Generalized logistic (GLO), Normal

(NOR), Generalized normal (GNO), Uniform (UNF), Pearson Type-III (PE3), Exponential

(EXP), Generalized Pareto (GPA), Kappa (KAP) and five parameter Wakeby (WAK) are

used for estimation of floods of various return periods. Based on the L-moments ratio

diagram and |Zidist| -statistic criteria, GLO is identified as the best fit frequency distribution

for the site.

Fig. L-moment ratio diagram (Best fit distribution is GLO)

0.10 0.30 0.500.00 0.20 0.40L-Skewness

-0.10

0.10

0.30

-0.20

0.00

0.20

0.40

L-K

urt

os

is

GLO

GEV GNO

GPA

PE3

WAKEXP

EV1LOS

NOR

UNF

(GLO, GEV, GNO, GPA, PE3 are three-parameter

distributions, and are shown as lines.

WAK is a five-parameter distribution, and is shown as a line.

UNF, NOR, LOS, EV1 and EXP are two-parameter

distributions shown as points, and are mentioned in italics)

Average

Zi dist –statistic for various distributions

Regional parameters for various distributions

Distribution Parameters of the Distribution

GLO =0.800 =0.254 k = -0.394

GEV =0.667 =0.321 k = -0.322

GNO =0.779 =0.439 k = -0.839

PE3 =1.000 =0.696 k = 2.374

GPA =0.378 =0.540 k = -0.131

WAK =0.270 = 1.211 Β = 2.967 = 0.241 δ = 0.433

Values of growth factors (QT/ Q )

Distribution Return period (Years)

2 10 20 25 50 100 200 500 10000

Growth factors

GLO 0.800 1.687 2.212 2.410 3.143 4.098 5.347 7.616 24.486

GEV 0.792 1.727 2.264 2.462 3.172 4.055 5.155 7.042 19.019

GNO 0.779 1.790 2.336 2.529 3.188 3.941 4.799 6.112 12.114

PE3 0.758 1.881 2.399 2.569 3.099 3.636 4.178 4.899 7.283

GPA 0.770 1.830 2.360 2.541 3.139 3.794 4.512 5.566 10.050

WAK 0.820 1.629 2.157 2.364 3.149 4.209 5.641 8.332 30.198

S. No. Distribution Zi dist –statistic

1 Generalized logistic (GLO) 0.66

2 Generalized Extreme Value (GEV) 0.78

3 Generalized Normal (GNO) 1.02

4 Generalized Pareto (GPA) 1.22

5 Pearson Type III (PE3) 1.41

Annexure-II

email correspondence with foreign satellite data service provider

1. NRSC Division dealing with foreign satellite data

Rebecca <rebeccaswarna_a@nrsc.gov.in>

To

Pankaj Mani

1 Feb at 2:14 PM

Dear Sir,

We have checked with the data providers and we regret to inform that no

data is available covering the area of interest on the requested dates.

With Regards,

Rebecca Swarna

040 2388 4446

Hide original message

> On Tuesday, 24 January 2017 4:58 PM, Pankaj Mani

>> <mailofpmani@yahoo.com> wrote:

>> Sir,

>> This query is in relation to a work assigned to NIH as per directive

>> of National Green Tribunal (NGT) and therefore is very urgent.

>> I need high resolution data to mark the extent of flooding for a

>> specific date

>> in my area of interest. The image is required for dates 26 Sept 2010

>> or 27 Sept 2010.

>> The extent of study area is 77.94-78.08 E, 27.15-27.28 N.

>> I have browsed the web site of NRSC and could not find the coverage

>> for PAN or LISS III data for the dame and area.

>> I request you to intimate us the availability of high resolution

>> foreign satellite for the above extent and period. Kindly also send

>> us the cost estimate for the same.

>> Thanking you.

>> Pankaj Mani

>> Scientist E

>> National Institute of Hydrology

>> WALMI Complex, Phulwarisharif,

>> Patna.

>> phone - 0612-2452219, 2452227.

>> 9471006278, 9334257300 (m),

>> mailofpmani@yahoo.com, cfmspatna@gmail.com

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2 European space Agency

RSS_TEAM@esa.int

To

Eohelp esatellus@service-now.com

CC

RSS_TEAM@esa.int eopi@esa.int Pankaj Mani

25 Jan at 4:24 PM

Dear EO-Help,

As anticipated, could you please take care of the attached inquiry submitted by the user Pankaj Mani

in copy of this email ?

He is interested in high resolution (under 20m) optical data of September 2010 over his area of

interest ( 77.94-78.08 E, 27.15-27.28 N ).

Thank you

Best Regards

Roberto

***********************************************************************************

ESA EO Research and Service Support Team

Email: rss_team@esa.int

Web: http://rssportal.esa.int | http://wiki.services.eoportal.org

Telephone: 0039-06-94180566 / 80784

Fax: 0039-06-94180296

**********************************************************************************

----- Forwarded by Roberto Cuccu/esrin/ESA on 25/01/2017 11:40 -----

From: Pankaj Mani <mailofpmani@yahoo.com> To: "RSS_TEAM@esa.int" <RSS_TEAM@esa.int> Date: 25/01/2017 02:56 Subject: Re: inquiry about high resolution satellite image for purchase

sir,

I am looking for optical images with spatial resolution (preferably) below 20 m.

Thanks

Pankaj Mani

Scientist E

National Institute of Hydrology

WALMI Complex, Phulwarisharif,

Patna.

phone - 0612-2452219, 2452227.

9471006278, 9334257300 (m),

mailofpmani@yahoo.com, cfmspatna@gmail.com

On Tuesday, 24 January 2017 11:53 PM, "RSS_TEAM@esa.int" <RSS_TEAM@esa.int> wrote:

Dear Pankaj Mani,

In order to better evaluate your request, please reply to the following questions.

• Could you please specify if you are interested in optical or radar imagery ?

• Could you please also specify the expected product ground resolution (e.g of the order of few

meters or if it would be ok also 10-30 meters, etc)?

Thank you

Best Regards

Roberto on behalf of RSS Team

***********************************************************************************

ESA EO Research and Service Support Team

Email: rss_team@esa.int

Web: http://rssportal.esa.int | http://wiki.services.eoportal.org

Telephone: 0039-06-94180566 / 80784

Fax: 0039-06-94180296

***********************************************************************************

Subject: inquiry about high resolution satellite image for purchase

Sir,

I need high resolution data to mark the extent of flooding for a specific date

in my area of interest. The image required is for 26 Sept 2010 or 27 Sept 2010.

Area of Interest is 77.94-78.08 E, 27.15-27.28 N

Kindly intimate availability and cost.

Thanks

Pankaj Mani

Scientist E

National Institute of Hydrology

WALMI Complex, Phulwarisharif,

Patna.

phone - 0612-2452219, 2452227.

9471006278, 9334257300 (m),

mailofpmani@yahoo.com, cfmspatna@gmail.com

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From: "Pankaj Mani" <mailofpmani@yahoo.com>

To: "rss_team@esa.int" <rss_team@esa.int>

Date: 24/01/2017 14:09

This message and any attachments are intended for the use of

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3 Imaging Corporation (Dealing with very high resolution images)

William Pora <william.pora@satimagingcorp.com>

To

mailofpmani@yahoo.com

CC

Leo J. Romeyn

24 Jan at 8:16 PM

Pankaj,

Unfortunately due to sales restrictions we cannot sell imagery to clients in India.

We suggest you try this government agency in your country.

http://www.nrsc.gov.in/

Best Regards,

William Pora

Satellite Imaging Corporation

P.O. Box 690502

Houston, TX 77269-0502

USA

Tel: (832) 237-2900 x202

Fax: (832) 442-5045

Email: william.pora@satimagingcorp.com

Web: www.satimagingcorp.com

The information contained in this communication may be legally privileged and is intended solely for the

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Additional restrictions may apply if you or your company is under a Non-Disclosure Agreement with

Satellite Imaging Corporation.

-----Original Message-----

From: info@satimagingcorp.com [mailto:info@satimagingcorp.com]

Sent: Monday, January 23, 2017 8:29 PM

To: info@satimagingcorp.com

Subject: Satellite Imaging Corp - Contact Form Submission

Name: Pankaj Mani

Company or Organization: National Institute of Hydrology

Country: India

E-Mail Address: mailofpmani@yahoo.com

Phone: +91 9471006278

Annexure-III

Ground Control Points Details

Annexure-IV

Flood Inundation Mapping (Method)

Generally, two approaches are adopted for delineation of flood extent; (i) flow model based

inundation mapping (also known as flood inundation modelling) and (ii) satellite image based

flood mapping.

The advantage of flood modelling is the inclusion of catchment and floodplain dynamics in

flow model and thus able to simulate the flood inundation for futuristic development. This

method is also used for probabilistic flood inundation mapping of ungauged rivers (Sarhadi et

al., 2012). The flood inundation modelling in low relief flood plain (small variation in ground

value) requires high resolution elevation data for the river and floodplain which is a measure

constrain. The process is data and computational intensive. Moreover, the modelled

inundation requires the ground verification (calibration and validation) for which either very

accurate digital elevation model (DEM) or the satellite image derived flood inundation for the

specific historical flooding event are used (Patro et al., 2009; Kuldeep and Garg, 2015) in

geographical information system (GIS) based environment.

Mapping of the flooded areas using satellite images (optical) is based on the fact that water

absorbs or transmits most of the electromagnetic energy in the near infrared (NIR) and mid

infra red (MIR) wavelengths, which results in less reflection and in turn a dark color on (false

colour composite) FCCs. Sensors having very fine spatial resolution working in optical

wavelength region are capable of differentiating the water body from other land features.

Satellite images acquired in different spectral bands (wavelength region) during a flood event

can provide valuable information about flood inundation. Through the selection of

appropriate sensors and platforms, remote sensing can provide accurate and timely estimation

of flood inundation and flood-prone areas. A list of sensors used for flood analyses are given

in Table 1.

Table 1: List of satellite sensors with their use for flood monitoring

(Bhanumurthy et al., 2010)

Sl

No:

Satellite

Sensor/

Mode

Spatial

Resolution

(m)

Spectral

Resolution

(µm)

Swath

(km)

Used For

1.

IRS-P6

AWiFS

56

B2 : 0.52-0.59

B3 : 0.62-0.68

B4 : 0.77-0.86

B5 : 1.55-1.70

740

Regional level

flood mapping

2.

IRS-P6

LISS-III

23.5

B2 : 0.52-0.59

B3 : 0.62-0.68

B4 : 0.77-0.86

B5 : 1.55-1.70

141

District-level

flood mapping

3.

IRS-P6

LISS-IV

5.8 at nadir

B2 : 0.52-0.59

B3 : 0.62-0.68

B4:0.77-0.86

23.9

Detailed

Mapping

4.

IRS-1D

WiFS

188

B3: 0.62-0.68

B4 : 0.77-0.86

810

Regional level

flood mapping

5.

IRS-1D

LISS-III

23.5

B2: 0.52-0.59

B3 : 0.62-0.68

B4: 0.77-0.86

B5:1.55-1.70

141

Detailed

Mapping

6.

Aqua /

Terra

MODIS

250

36 in visible

NIR & thermal

2330

Regional level

Mapping

7.

IRS-P4

OCM

360

Eight narrow

bands in

visible & NIR

1420

Regional level

Mapping

8. Cartosat-1 PAN 2.5 0.5-0.85 30 Detailed

Mapping

9. Cartosat-2 PAN 1 0.45-0.85 9.6 Detailed

Mapping

10.

Radarsat-1

SAR/

ScanSAR

Wide

100

C-band

(5.3 cm; HH

Polarization)

500

Regional level

mapping

Sl

No:

Satellite

Sensor/

Mode

Spatial

Resolution

(m)

Spectral

Resolution

(µm)

Swath

(km)

Used For

11.

Radarsat-1

SAR

/ScanSAR

Narrow

50

C-band

(5.3 cm)

300

District-level

mapping

12

Radarsat-1

Standard

25

C-band

100

District-level

mapping

13

Radarsat-1

Fine

beam

8

C-band

(5.3 cm)

50

Detailed

mapping

14

Radarsat-2

SAR

3m

ultra- fine mode

and 10m multi-

llik fine mode

C –band

20 in

ultra

fine

mode

Detailed

mapping

14

ERS

SAR

25

C-band ; VV

Polarization

100

District-level

mapping

Additional sensors details for which data have been used in the study

15 Landsat-5 TM B1:B5, B7-30 m,

B6-120 m,

B1 0.45-0.52

B2 0.52-0.60

B3 0.63- 0.69

B4 0.76-0.90

B5 1.55-1.75

B610.40-12.50

B7 2.08 - 2.35

170

Sl

No:

Satellite

Sensor/

Mode

Spatial

Resolution

(m)

Spectral

Resolution

(µm)

Swath

(km)

Used For

16 Landsat-7

(SLC OFF)

ETM+ B1:B5, B7-30 m,

B6-60 m,

B8-15 m

B1 0.45-0.515

B2 0.525-0.605

B3 0.63-0.69

B4 0.775-0.9

B5 1.55-1.75

B6 10.4-12.5

B7 2.08-2.35

B8 0.52-0.9

183 Detailed

mapping

References:

Bhanumurthy, V., Manjusree, P., Srinivasa Rao, G. (2010). Chapter on “Flood Disaster

Management”. In book Remote Sensing Applications, (P. S. Roy, R. S. Dwivedi and

D. Vijayan, eds.), National Remote Sensing Center, Hyderabad, India.

Kuldeepa and Garg P. K., 2015, The role of satellite derived data for flood inundation

mapping using GIS, The International Archives of the Photogrammetry, Remote

Sensing and Spatial Information Sciences, Volume XL-3/W3.

NRSC, 2016, https://nrsc.gov.in/IRS_Data_Products

Patro, S., Chatterjee, C., Mohanty, S., Singh R. and Raghuwanshi, N. S., 2009. Flood

inundation modeling using MIKE FLOOD and remote sensing data, Journal of the

Indian Society of Remote Sensing 37(1):107-118 · March 2009.

Sarhadi, A., Soltani, S. and Modarres, R., 2012. Probabilistic flood inundation mapping of

ungauged rivers: Linking GIS techniques and frequency analysis. Journal of

Hydrology, 458, pp. 68-86.

Some Remote Sensing Terms Referred in Text

Pixel: The smallest unit in a digital image. A satellite image is made up of a matrix of many

pixels, each having its own digital value.

Spatial resolution: The amount of ground covered in one pixel of the image. For example an

image with one meter resolution means that each pixel in the image represents one square

meter on the ground. The ranges for high (5.8m and smaller), medium (5.8 m to 56 m) and

low (360 m and coarser) spatial resolution for Indian remote sensing satellites are specified

by National Remote Sensing Centre web site (NRSC, 2016).

Temporal Resolution: The temporal resolution specifies the revisiting frequency of a

satellite sensor for a specific location. Temporal images are used for change detection and

monitoring of progress of an event.

Spectral resolution: A sensor's spectral resolution specifies the number of spectral bands in

which the sensor can collect reflected radiance (energy reflected from an object on land

surface). Higher the spectral resolution, better would be image interpretability. Multispectral

sensors are capable of taking two or more images simultaneously but each in a different part

of the electromagnetic spectrum (wavelength region).

Radiometric resolution: The radiometric resolution of an imaging system describes its

ability to discriminate very slight differences in the energy level. The finer the radiometric

resolution of a sensor, the more sensitive it is to detecting small differences in reflected or

emitted energy and thus more useful in differentiating one land feature from other.

False Colour Composite (FCC): Multispectral images contain information inside and

outside the visible electromagnetic spectrum (EM). The images are an array of gray levels

and thus have limited interpretability. To enhance the image interpretability, the wavelengths

outside the visible spectrum need to be reassigned to the visible spectrum so that it is visible

to the human eye. FCC is a colour image where images acquired in various of parts of EM

spectrum (wavelength region) are filtered through one or more of the red, green, and blue

components to produce a colour composite for better interpretability. The most commonly

seen FCC display the very-near infrared as red, red as green, and green as blue. In FCC water

pixel appears in various tone of blue depending upon its depth and turbidity.

spectral band: an interval in the electromagnetic spectrum (aka wavelength) recorded by a

remote sensing instrument.

Visible wavelengths: the radiation range in which the human eye is sensitive, approximately

0.4 to 0.7 µm

Multispectral: refers to remote sensing in two or more bands or wavelengths

Panchromatic (PAN): one band covering all the visible bands displayed as a black and white

photo or image.

Image fusion: process combining two or more images to produce a single image with high

visual interpretability. On a high spatial resolution panchromatic image (PAN), detailed

geometric features can easily be recognized, while the multispectral images contain affluent

spectral information.The capabilities of the images can be enhanced if the advantages of both

high spatial and spectral resolution can be integrated into one single image that can be done

using a process known as image fusion. The outcome of image fusion is a new image which

is more worthy for human and machine perception or further image-processing tasks such as

segmentation, feature extraction and object recognition.

Image classification: Digital image classification techniques group pixels to represent land

cover features. Land cover could be forested, urban, agricultural, water body and other types

of features. There are three main image classification techniques; unsupervised classification,

supervised classification and object based image classification.

• In unsupervised classification pixels are grouped together based on the reflectance

properties of pixels. In this techniques, iso-data are first grouped together

subsequently classes are identified based on ground information.

• In supervised classification techniques, the representative samples for each land

cover class are selected, known as training set. Based on the reflectance

characteristics the training signature for all identified features are defined and used for

classifying the entire image. The common supervised classification algorithms are

maximum likelihood and minimum-distance classification.

• Object-based image analysis supports the use of multiple bands for multiresolution

segmentation and classification. After multi-resolution segmentation, the user

identifies sample sites for each land cover class. The statistics to classify image

objects are defined and used for classifying the various land features from entire

image.

Image registration: Exact pixel-to-pixel matching of two different images or matching of one

image to a map. Satellite image registration is a process to match and align different images

which is captured at at different times (multi temporal), different viewpoints (multi view),

different sensors (multi modal). The image registrations process consist of four steps; feature

extraction, feature matching, transform model estimation and image resampling and

transformation.

Landsat 7 ETM+ SLC-off data refers to all Landsat 7 images collected after May 31, 2003,

when the Scan Line Corrector (SLC) failed. These products have data gaps, but are still

useful and maintain the same radiometric and geometric corrections as data collected prior to

the SLC failure. (https://www2.usgs.gov/faq/node/3849)

NRSC National Remote Sensing Centre is a full-fledged centres of ISRO. The Centre is

responsible for remote sensing satellite data acquisition and processing, data dissemination,

aerial remote sensing and decision support for disaster management.

USGS EarthExplorer: The EarthExplorer user interface is an online search, discovery, and

ordering tool developed by the United States Geological Survey (USGS). EarthExplorer

supports the searching of satellite, aircraft, and other remote sensing inventories through

interactive and textual-based query capabilities. Through the interface, users can identify

search areas, datasets, and display metadata, browse and integrated visual services within the

interface.

GPS: The Global Positioning System (GPS) is a satellite-based navigation system made up of

at least 24 satellites. GPS satellites circle the Earth twice a day in a precise orbit. Each

satellite transmits a unique signal and orbital parameters that allow GPS devices to decode

and compute the precise location of the satellite. GPS receivers use this information and

trilateration to calculate a user's exact location. Essentially, the GPS receiver measures the

distance to each satellite by the amount of time it takes to receive a transmitted signal.

Annexure V.

Field Photographs of Pillar along Flood Lines

Annexure VII

Encroachments within Flood Line at Various Locations