EMECS 9(Baltimore, 27 – 31 August 2011)

Field Survey and Satellite Validation of

Water Quality Parameters of Rivers in

the Surroundings of Santo Domingo

Metropolitan Area,

Dominican Republic

Hiroshima UniversityE. R. Miño A., Y. Sakuno, T. Okuda, H. Mutsuda, S. Nakai, W. Nishijima

Santo Domingo Autonomous University (UASD)M. Rodriguez and F. Reyes

Municipality of Santo Domingo East, DRR. Castro

USA

Cuba

DominicanRepublic

Haiti

DominicanRepublic

Caribbean Sea

Haina RiverOzama River

Haina River Ozama River

Isabela River

Ozama River

H1

H2

H3

H4H5

H6H7

H8

H9

H10

O1

I1

I2O2

O3

O4O5

O6

O7

O8

O9

O10 O11

The Caribbean Sea, known as one of the world’s leading tourist destinations, is surroundedby many islands and facing pollution by rivers. Little concern has been paid to pollutantloadings by the rivers due to lack of resources. In this study, we focused on satelliteremote sensing technique as an affordable monitoring method to estimate the loadings.The studied rivers flow in the surroundings of Santo Domingo, the capital of the DominicanRepublic.

Background

• About 1/3 of the population of Dominican Republic is

concentrated in the Santo Domingo Metropolitan Area

(SDMA).

• Ozama-Isabela and Haina Rivers are the three main rivers

in the surroundings of SDMA.

• One of the main problems in SDMA seems to be the

pollution of these rivers caused by the discharge to the

rivers and ravines of untreated sewage and industrial

wastewater.

• Another problem is also the discharge of garbage to the

rivers front as well to the ravines and rivers.

• Ozama-Isabela and Haina Rivers discharge to the Caribean

Sea, therefore the loadings may have a great influence to

the environment and cause the coastal area production.

Problems of satellite water color remote sensing technique

Ocean color sensor Chl-a map (4km resolution)

Satellite Chl-a vs in-situ Chl-a

Satellite remote sensing is now

accepted as an important

monitoring technique

in the world’s oceans.

(SeaWiFS, MODIS, etc.)

(1)Utilization of the land sensor

(e.g. LANDSAT , ALOS)

(2)Local robust algorithm development

(Need: spectral reflectance data)

(1)The spatial resolution

of these satellites is

greater than 1 km.

Problems of satellite use

in coastal environments

(2)It is difficult to estimate

chlorophyll-a because of

co-existing SS

http://seawifs.gsfc.nasa.gov/SEAWIFS.html

Coastal area

D.R.

• Perform field surveys of the water

quality in Ozama-Isabela and Haina

Rivers, and the nearby Coastal area.

• Validate the data using satellite

imagery.

Objectives

Osama River

Isabela River

Caribbean Sea

Satellite image : ALOS/AVNIR-2 , Observation Date:2010/11/19

Haina River

Overview of Surveyed Area from Satellite

Santo Domingo City

Sampling Points

O-9

O-8

O-11

O-10

O-12

O-14

O-13

O-15

O-7

O-6

O-5

O-2

O-1O-3

O-4

H-1

H-2H-3

H-4H-5

H-6

H-7H-8

H-9

H-6

Caribbean sea

Sampling Points

O-9

O-8

O-11

O-10

O-12

O-14

O-13

O-15

O-7

O-6

O-5

O-2

O-1O-3

O-4

H-1

H-2H-3

H-4H-5

H-6

H-7H-8

H-9

H-6

Floating bridge

0

5

10

15

20

25

30

35

0 5 10 15

D istance from the m outh

Salinity (PSU)

J an

M arch

Salinity

Before Floating Bridge

After Floating BridgeRavine discharge

Ozama-Isabela Rivers

0

5

10

15

20

25

30

35

0 2 4 6

D istance from the m outh

Salinity (PSU)

J an

M arch

Haina River

Floating bridge before Ozama river mouth

0

5

10

15

20

25

30

35

0 2 4 6

D istance from the m outh

Turbidity (NTU)

J an

M arch

0

5

10

15

20

25

30

0 5 10 15

D istance from the m outh

Turbidity (NTU)

J an

M arch

Turbidity

Garbage dumping sites

Sewage discharge

Ravine discharge

Ozama-Isabela Rivers

Haina River

Ravine into Isabela River

DO 0.29 mg/l

Slum

Dumping of solid waste

Sewage pipe from an avocado processing plant

Garbage dumping

Chlorophyll-a

Ozama and Isabela Rivers

Haina River

0

10

20

30

40

50

60

70

80

0 2 4 6

D istance from the m outh

CHL-a (ug/

l)

J an

M arch

0

10

20

30

40

50

60

70

80

0 5 10 15

D istance from the m outh

Chl-a (ug/

l)

J an

M arch

Garbage dumping sites

Sewage discharge

Ravine discharge

Port

Sampling Points

O-9

O-8

O-11

O-10

O-12

O-14

O-13

O-15

O-7

O-6

O-5

O-2

O-1O-3

O-4

H-1

H-2H-3

H-4H-5

H-6

H-7H-8

H-9

H-6

PORT

Dissolved Oxygen

Ozama and Isabela Rivers

Haina River

0

2

4

6

8

10

12

0 2 4 6

D istance from the m outh

DO (mg/

l)

J an

M arch

0

2

4

6

8

10

12

14

0 5 10 15

D istance from the m outh

DO (mg/

l)

J an

M arch

Garbage Dumping sites

Sewage discharge

Ravine discharge

SS and Chl-a DistributionsOzama-Isabela Rivers (March)

SS Color AnalysisHaina Ozama - Isabela

O-3 O-4

O-6 O-8

Correlation between turbidity and ALOS bands

Example of correlation analysis between satellite

data and in-situ data (Osama-Isabela)

St B1 B2 B3 B4 Chl-a SS Turb

1 93.444 71.222 41.333 7.111 7.6 22.76 2.2

2 92.556 70.556 41.778 8.222 2.8 23.36 1.8

3 93.778 66.333 40 8.444 4.2 22.28 1.7

4 91.222 58.556 35.444 8.444 3.4 26.21 0.6

5 91.444 54.889 29.222 8.111 16.98 0.5

6 89.556 69 41.889 8.222 19.56 1.7

7 88.444 65.444 41.333 8.667 16.1 24.17 3.3

8 84.111 55.778 37.778 22.222 16.7 11.93 9.3

10 90.556 73.222 51.889 11.333 6.67 2.9

11 89.556 70.667 48.444 10 32.6 19 19.9

12 85.111 58.778 41.111 13.333 26.9 11.08 7.3

13 91.889 69 50.778 16.111 31.8 12.62 4.1

Satellite Reflectance data In-situ data

2010/11/19 2011/1/30

√The satellite and the simultaneous water quality data acquisition are necessary to validate the water quality estimation algorithm true.

Correlation between satellite and in-situ data

B1 B2 B3 B4 B1/B2 B1/B3 B1/B4 B2/B3 B2/B4 B3/B4 Ｎ

SS 0.47 0.05 -0.41 -0.65 -0.27 0.20 0.87 0.58 0.86 0.85 13

Chl-a -0.46 0.04 0.74 0.47 -0.31 -0.89 -0.65 -0.92 -0.56 -0.41 11

B: Band, Exception of “Chl-a=0” condition

Correlation coefficient (r)

5

10

15

20

25

30

2 4 6 8 10 12 14

y = 2.8893 + 1.6958x R= 0.87476

SS (mg/l)

ALOS DN[Band1/Band4]

SS Chl-a

0

5

10

15

20

25

30

35

40

1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75

y = 149.68 - 85.895x R= 0.92129

Chl-a (

g/l)

ALOS DN[Band2/Band3]

Mapping SS and Chl-a distributions

0 25 0 30

SS (mg/l) Chl-a (μg/l)

→If many new satellite/in-situ data sets are obtained, a robust water quality estimationalgorithm by satellite in this region will be constructed. It is necessary to strenghtenthe observation environment to obtaining such data set. (e.g. Buoy[tower] or in-situ

SS or Chl-a constantly monitoring system)

Osama River

Isabela River

Haina River

Osama River

Isabela River

Haina River

Mask Mask

River Influence

Detail WorldView-2 image of Jun 7th, 2011

Caribbean Sea

High turbidity

Santo Domingo

Isabela River

Ozama River

Santo Domingo

Ozama River

Satellite Image (June 2011)

SS map Chl-a map

0 200 mg/l 0 20 ug/l

SS: World View-2 Band4/Band1(608nm/427nm) Chl-a: World View-2 Band6/Band5(724nm/608nm)

Conclusions

• A significant correlation was observed between the color spectra of water and ALOS bands, which shows the possibility of the use of remote sensing to determine the macro situation of pollution in the rivers and coastal areas.

• SS, turbidity and chlorophyll-a values are very high while DO levels are very low in places near ravine discharge, sewage pipe ends and garbage dump sites.

• Both rivers present hyper-eutrophic characteristics, especially in areas near ravine and sewage discharges.

• The hyper-eutrophicated rivers may be inducing a high load of nutrients to the coastal area. Remote sensing could be a tool to estimate loadings from rivers.

THANK YOU

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