use of remote sensing to identify spatial and

7/27/2019 Use of Remote Sensing to Identify Spatial And

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National Central University

Master Program forEnvironmental Sustainable Development

Presented by: Guillermo EsquivelAdvisor:


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Overview Objectives

Introduction Study Area What to measure? Why to measure? How to measure? Literature review


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Data used Methodology

Results and Discussions Conclusions Recommendations


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The Gulf of Fonseca is one of the mostdynamic spots on Central America, shared by

three countries;El Salvador, Honduras andNicaragua is scenario of constant land usechanges, increase of urban developments,establishment of aquaculture industries at bigscale, industrial fishery, logistics and marinetransport.


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This study has two major aims: First one is to investigate spatial patterns in

selected water quality indicators (Turbidityand Total Suspended Solids), by usingLandsat 7 ETM+ and in situ measurements

The second aim is to determine temporal

variations patterns in order to identifywhether or not there is a factor thatprogressively affects to the environment or apermanent impact.


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To achieve those aims the approach used isRemote Sensing application.

Linear regression model to correlatephysical measures and remote sensed data.

In situ measurements were used taken in thesame day as the satellite image of theLandsat 7 ETM+.

Normalization of images to apply the modelto other years


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Coastal habitats alone account for approximately1/3 of all marine biological productivity, and

estuarine ecosystems the planet.

The Gulf of Fonseca is a large estuarine embayment

on the Pacific coast of Central America, bordered by

the countries ofEl Salvador, Honduras, andNicaragua.


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In El Salvador area the construction of one of the

biggest ports of the region was developed from

2005 to 2008. Dredging activities to build andaccess channel and to deeper the turning basin.

The accelerated growth rate of the urban area,

and population.

Untreated discharge of municipal water to the

sea.


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Many industries waste water discharges tothe bays.

The illegal cut of the mangrove forest. The grand scale shrimp aquaculture activities

developed in Honduras and Nicaragua.


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Port of La Union


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Shrimp PondsMangrove

Urban area


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Turbidity is defined as a measure of the levelof particles such as sediment, plankton, or

organic by-products, in a body of water(NOAA, 2010). As the turbidity of water increases, it

becomes denser and less clear due to a higherconcentration of these light-blockingparticles.


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The greater the amount oftotal suspendedsolids in the water, the murkier it appears the

higher the measured turbidity. Suspended matter such as clay, silt, and

organic matter, as well as plankton and othermicroscopic organisms, which interfere withthe passage of light through the water, cancause Turbidity.


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Increased turbidity affects a stream and theorganisms that live in it in many ways and if

the water becomes too turbid, it loses theability to support life. Reduced plant matter means less food and

habitat for herbivorous organisms such assnails, insects and juvenile fish.


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Suspended solids also provide adsorptionsurfaces and a route oftransmission for

many organic contaminants, heavy metals,and some nutrients.


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(Source T. Jensen, 1996)

(Source T. Jensen, 1996)


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Traditionally water samples are taken forlaboratory analysis. (more precise but

expensive) Nowadays we have better understanding of

the relationships between the light responseand the physical properties of the waterbodies, Remote Sensing is a very strong toolwhen it comes to address big spatialextensions and time patterns.


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Location Satellite Sensor Parameter Correlation R2 Reference

Lake Taihu,

(China)Landsat-5/TM TSS 0.63-74

Zhou et al.

(2005)

Lake Reelfoot,Tennessee and

Kentucky, (USA)

Landsat-5/TMTurbidity 0.537 Wang et

al.,2006TSS 0.522

Gulf of FinlandEOS Terra-

MODIS

Turbidity 0.76Koponen t al,

2009

Lake Beysehir,

TurkeyLandsat-5/TM

Turbidity 0.60 Bilgehan Nas

et al, 2010TSS 0.67

South Bay of

Biscay

MODIS-Aqua

1000-m

Turbidity 0.996 Petus et al,

2010TSS 0.974


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The Ministry of Environment of El Salvador(MARN) supplied a report made by the

Autonomous Port Executive Commission(CEPA) with date ofAugust 2005, containingwater quality analysis such as concentrationofsuspended solids (mg/l) and turbidity(NTU) of a campaign realized duringdredging activities for the port beingdeveloped.


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Landsat 7 Enhanced ThematicMapper +:

Launch Date: April 15, 1999

Status: operational despite Scan Line

Corrector (SLC) failure May 31, 2003

Sensors: ETM+Altitude: 705 km

Inclination: 98.2

Orbit: polar, sun-synchronous

Equatorial Crossing Time: nominally

10 AM ( 15 min.) local time

(descending node)Period of Revolution : 99 minutes;

~14.5 orbits/day

Repeat Coverage : 16 days

No cost images

Band

Number m

Spectral

response Resolution

1 0.45-0.515 Blue-Green 30 m

2 0.525-0.605 Green 30 m

3 0.63-0.69 Red 30 m

4 0.75-0.90 Near IR 30 m

5 1.55-1.75 Mid IR 30 m

6 10.4-12.5 Thermal 60 m

7 2.09-2.35 Mid IR 30 m

8 0.52-0.9 Panchromatic 15 m


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Month season 2004 2005 2006 2007 2008 2009 2010

Jan

Dry

Feb

Mar

Apr

May

Rainy

Jun

Jul

Aug

Sep

Oct

Nov

DryDec

Dredging works


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Band 1

Band 2

Band 3

Band 4

Layer

Stack

Read

radiance

value 5x5

Georeference of

monitoring

stations

Linear

regression

analysis

Values of

samples in situ

Total

Suspended

Solids Model

Turbidity

Model

TSS

Model

NTU

Model

Radiance correction EQ 5.2

Radiance

multispectral

image

Map of Suspended

Solids August 2005

Map of Turbidity

August 2005

Gap filled

multispectral images

Landsat 7 ETM+

Digital numbers

STEP 1

STEP 2 STEP 3

STEP 4


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Uni- variate: band1, band2, band3, band4,

(band4/band3), (band2/ band1), (band1/band3),

(band3/band1), (band4/band1), (band3/band2),

(band4/band2), (band4/band3)

Bi-variate (band1 and band2), (band1 and band3), (band1 and

band4), (band2 and band3), (band2 and band4),

(band3 and band4)

Multi-variate (band1, band2, and band3), (band1, band2, and

band4), (band2, band3, and band4), (band1, band2,

band3, and band4).


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Satelite Image

August 2nd

2005

In situ measurements

TSS and Turbidity

August 2nd

2005

Linear

RegressionAnalysis

Choose the

higher R

2

value

No

Satelite Image

August 2nd

2005

In situ measurements

TSS and Turbidity

August 2nd

2005

Linear

RegressionAnalysis

Overall is

the model

significant?

Choose the

higher R

2

value

Discard model

Are the

variables

significant?

No

Final Linear Modelyes

ANOVA test

Significance

value


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Turbidity linear model(R2=0.73)

NTU=-2.223+0.77*band3-1.072*band4

Total Suspended Solids linear model(R2=0.74)TSS =-7.669+1.715*band3-2.408*band4


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Ideal situation is having in situ measurementsand satellite images same day for everyseason from 2004 to 2010.

Since performing an atmospheric correctionwas not possible because there is a lack ofatmospheric information, we used radiance

image. Radiance corrected images were normalized

in order to simulate same atmosphericconditions.


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Where:

Band X : is the radiance values of the band X before normalized; in our

particular case, bands 3 and 4.

Mean Band X : is the mean value of the pixels identified as water in the band X.

Sdev Band X: stands for the standard deviation of the band X

Sdev REF Band : is the standard deviation of the reference image, in our case

the image ofaugust 2nd 2005 .

Mean REF Band X : is the mean values of the pixel identified as water in the

band X of the reference image; august 2nd ,2005

.

Band X =

(Band X Mean Band X)

Sdev Band X

* Sdev REF Band X+ Mean REF Band X

Normalized


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Band 3

Band 4

CROP

Radiance correction EQ 5.2

Gap filled

multispectral images

Landsat 7 ETM+

Digital numbers

STEP 1

Classification

land and water

Only water

radiance

Bands 3 and 4

Radiance

Normalization

TSS

Model

NTU

ModelNomalized

radiance

Bands 3 and 4

Maps of Suspended

Solids from

2004-2010

Maps of Turbidity

from 2004-2010

STEP 2

STEP 3

Using August 2005

As reference value EQ 5.6

STEP 4


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Dry season image

From Nov. to Apr.

SAME YEAR MAPS Rainy season image

From May. to Oct.

Dry season-Rainy season

= Seasonal Variation

Increase

D

ecrease

F

romr

ainytodryseaso

n


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Dry season image

Year 2006

CONSECUTIVE YEARS Dry season image

Year 2007

Dry season 2007-Dry season 2006

= Yearly variation of the dry season

Increase

D

ecrease

From2

006to2007


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Our results on turbidity model as previousstudies is a multivariate combination(Bilgehan Nas et al, 2010) by the use of the

bands 3 and 4 of the Landsat 7ETM+ with acorrelation coefficient of 0.736, that explainsat least the 74% of the behavior of the

turbidity . Statistically proven the analysis on thevariance (ANOVA) indicates that both modelsare significant with a confidence level of 90%.


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The resultant maps of turbidity and TotalSuspended Solids provides a powerful tool toassist the interpretation on the spatial

patterns of these two important indicators ofpollution.

Generally speaking our maps suggest that a

major concentration on total suspendedsolids and turbidity on the Bay of SanBernardo (Honduras and Nicaragua),followed by the Bay of La Union.


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These high concentrations on suspendedsolids and turbidity are most likely associatedwith anthropogenic activities, such as

municipal waste water discharge to the oceanwithout previous treatment, and industrialactivities in the city of la Union, as well as

dredging works for the construction of theport observed in August, 2005, and the wastewater coming from the shrimp aquacultureactivities.


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Throughout seasonal variations (from rainy todry) it can be observed that in the central area ofthe Bay of San Bernardo and La Union, the

turbidity as well as TSS concentrations arehigher during the dry seasons.

During the rainy season the predominant factoris the inflow of fresh water coming from every

river that ends in the Gulf of Fonseca; patternssuggest that the rivers during this season carrysolids in suspension from upstream of their path


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In addition in the rainy season the gravitationalcirculation plays a very relevant role, causecarries out the flux of fresh water, less dense

upon the oceanic water which has more salinityand thus a higher specific weight, carryingwithin organic matter from the anthropogenicactivity, waste water from the shrimp farms and

some other suspended pollutants (PROGOLFO,2000) and that fact is congruent with the resultsobserved in the present study.


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According with our results on change analysis forconsecutive years of each season, there is no relationbetween year to year variations; no factor can be

identified to be generating a constant change onTurbidity and TSS on our study area. There is no evidence of a permanent impact on the

natural turbidity to the Bay of La Union and for theentire Gulf of Fonseca, stronger predominant factors

are seasonal variations and extreme meteorologicalevents, such as an increase on the precipitationaverage values or tropical storms.


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Quantity and distribution of in situ samples should behigh enough to have a good correlation and also to letsome amount of data for validation process. Most of

the previous reviewed studies use more than 40 in situmeasurements. We recommend having at least one model for every

season, to address seasonal variations. The ideal scenario will be to have always monitoring

campaigns at the moment of acquisition of theimages, since that is rather impossible and costly, atleast having a model for each season in order to use itin long terms observations.


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If there is any interest on monitoring the impactof Shrimp related activity on the Bays of the Gulfof Fonseca, this should integrate a monitoring

at source of discharge, in the flux of the river,and within the complete Bay, until reaching thecentral part of the Gulf of Fonseca.

To measure the impact from dredging works

activities is recommended a shorter period oftime observations, to study day to dayvariations and fluctuations during high and lowtidal levels.


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Finally the researcher encourage to do furtherresearch in order to have more precise data andresults that reveals how anthropogenic activities

affect the quality of the water in the Gulf ofFonseca.

Every monitoring program corresponding towater quality should be properly geo-referenced

and integrated into a geo-database for furtheruses and help to the promotion of thisrevolutionary technology the Remote Sensing


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use of remote sensing to identify spatial and

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