detection of oil pollution on sea ice with airborne

8/6/2019 Detection of Oil Pollution on Sea Ice With Airborne

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Detection of oil pollution on sea ice with airborne

and spaceborne spectrometer

Jaan Praks1, Miia Eskelinen1, Jouni Pulliainen1, Timo Pyhalahti2, Martti Hallikainen1

1Laboratory of Space Technology, Helsinki University of Technology

P.O. Box 3000, 02015 Finland2Finnish Environment Institute

P.O. Box 140,00251 Finland

ABSTRACT

In this work we demonstrate the feasibility of imaging

spectrometer for the detection of oil spills on sea ice. We

show that optical spectrometer images can be used as an

alternative for oil spill mapping in winter when SAR-based

detection algorithms fail due to ice. By comparing high-

resolution airborne spectrometer image to satellite images,

we evaluate the usability of MODIS and Landsat imagesfor oil pollution detection on ice and discuss the limitations,

set by image resolution and spectral band availability. We

evaluate here several spectral indices and discuss the results.

We propose simple algorithms for oil detection on ice. Our

study strongly suggests that an imaging spectrometer suits

very well to oil detection on sea ice. However usability of

satellite instruments like MODIS have serious limitations set

by the image resolution and band selection. Landsat ETM

has significantly better resolution and it is therefore more

suitable for most typical, small-scale pollution detection, but

its imaging frequency does not meet the monitoring demands.

I. INTRODUCTIONBaltic Sea is a unique ecosystem with original flora and

fauna. It is highly vulnerable to pollution, as its water change

with the Atlantic Ocean is limited and the sea itself is very

shallow. At the same time the Baltic Sea is an important

shipping route for the surrounding countries. This creates a

risk of pollution.

Small-scale oil pollution is one of most frequent problems

in the Baltic Sea. To avoid port waste taxes, some ships wash

their tanks on open sea and pump oily waste water directly to

the sea. This action is illegal, but due to insufficient monitoring

system, it happens frequently. Also the risk of large-scale oil

pollution catastrophe increases substantially during the coming

years as oil shipping volumes on the Baltic Sea increasewith new oil terminals. Rough sea, storms and difficult ice

conditions can damage tankers resulting in oil leakage to the

sea.

Remote sensing techniques are clearly in the position to

provide tools for monitor the sea better and combat with illegal

pollution. It has been shown in the early 1970’s [1] that radar

is an efficient tool for detection oil slicks on open water. SAR

can be used with good resolution from space regardless of

weather conditions as it sees through cloud cover. Today, fully

Fig. 1. MODIS image of Gulf of Finland 22. April 2003. Image size 100×100 km. The polluted area is marked by rectangle. R= band 1, G= band 2,B= (band 1 - band 2).

automated systems for oil spill detection from satellite imageshave been developed [2]. SAR is very sensitive to the surface

roughness and therefore can detect smooth oil film on the

surface of the water. However, on very rough surfaces like ice

and brash ice, oil detection with SAR is difficult. Here optical

instruments have an advantage over SAR. On optical images,

dark oil on white ice can be easily detected. Cold winter days

are often sunny allowing the use of optical sensors. However,

biggest risk for oil pollution occurs during bad weather and

rough sea, when optical instruments cannot be used. Therefore

the optical spectrometers are suitable mostly for pollution area

mapping after a disaster.

I I . MATERIAL

A relatively large oil pollution on sea ice was discovered

on 20 April 2003 in Gulf of Finland in the outskirts of

Helsinki. Pollution consisted of several smaller spills mainly

on ice and mixed with brash ice. A cleaning ship was sent to

clean the area on 21 April. The cleaning work was finished

on 23 April, when having cleaned approximately 2.5 km2

area. We acquired an airborne spectrometer (AISA) image

of the area on 22 April 2003 between 12:06 - 12:32 local

time. Imaging weather was cloudless and the sea was calm.



Fig. 2. Fragment of the airborne spectrometer image mosaic from contami-nated area.

400 450 500 550 600 650 700 750 800 850 900 9500

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Wavelength (nm)

R e f l e c t a n c e

Clean iceClean waterClean brash iceContaminated ice

Fig. 3. Measuresd sample reflectance spectra for ice, water, brash ice and oilcontaminated ice. Yellow bars indicate Landsat ETM bands and green barsindicate MODIS bands in visible-NIR region.

The spectrometer image covered a 6 km2 area with a spatial

resolution of 1m by 1m. The image area covered contaminated

ice, contaminated ice brash ice, clean ice and clean water. The

image had 25 narrow spectral bands between 440 nm - 890 nm.

We calibrated airborne spectrometer images radiometrically

and made a mosaic. Radiance and reflectance at the sensor

level were calculated (for reflectance calculation, an upward-

looking sensor was used). Part of the image mosaic is shown

in Fig. 2. A cloud-free MODIS image of the target area was

available for 19, 20, 22 and 23 April. The MODIS image and

the polluted area are shown in Fig. 1. Landsat-7 ETM image

was available for 23 April 2003. All the material was geo-

referenced to Finnish coordinate system.

III. OIL RECOGNITION FROM THE IMAGES

A. Airborne spectrometer

From the airborne spectrometer image we collected sample

spectra for clean water, clean ice, clean brash ice, partly

contaminated brash ice, partly contaminated ice and fully

contaminated ice. Some of the collected reflectance spectra are

presented in Fig. 3. Fig. 3 shows that oil contaminated ice has

spectra characterized by high reflectance in the near-infrared.

As a contrast, clean open water, clean ice and clean brash ice

have always lower near-infrared (NIR) reflectance than green

reflectance. As it can be noticed, higher reflectance in NIR

region than in green spectral region, is characteristic only to

oil contamination. By using this spectral feature, a very simple

but effective classification algorithm can be created to detect

oil contamination on ice. If reflectance in the green band is

lower than reflectance in the near infrared band, the image

pixel contains oil contaminated ice, else pixel contain either

clean ice or clean water.RNIR > Rgreen⇒ OilRNIR < Rgreen⇒ Not oil

(1)

We found that for oil detection from airborne spectrometer

image reflectance at 842 nm and 552 nm are suitable and pro-

duce rather robust oil contamination estimate. The presented

algorithm does not depend on the general reflectance level of

ice. By applying Eq 1 to the spectrometer image sample in

Fig. 4A, we get oil mask shown in Fig. 4B. White represents

oil contaminated ice and black is non-contaminated ice orwater. Comparison of images in Fig. 4A and Fig. 4B, that

the algorithm can find even small areas of contaminated ice

with high accuracy.

For satellite images with lower resolution, sub-pixel detec-

tion is important issue. We show here that at least for airborne

spectrometer images, spectral index (RNIR−Rgreen) can be

used to calculate oil contaminated ice area in sub-pixel level.

We studied sensitivity of the parameter to oil contamination

by using a simple spectral mixture model, where water, ice

and oil spectra are mixed according to area. Model simulation

shows that this simple difference spectral index can be used

to estimate the contaminated area well for both, clean ice -

contaminated ice and clean water - contaminated ice situation.To test the usability of this index, we have used a simulated

low-resolution dataset based on the airborne spectrometer

image. The resolution of the original image in Fig. 4A was

downgraded to 50 m by using cubic convolution. The resulting

low-resolution image is shown in Fig. 5A. By using the same

technique, also the resolution of the oil mask, shown in Fig.

4B, was lowered to 50 m. The resulting the low-resolution

oil mask is shown in Fig. 5B. A pixel value of low-resolution

oil mask represents directly oil contaminated area contribution

from pixel area. To show the usability of the presented spectral

index, the low-resolution oil mask was calculated also directly

from the low-resolution airborne spectrometer image by using

the following equation,

Areaoil = A ((R842 −R552) + B), (2)

where A = 1250 and B = 0.04. All negative values are

classified to clean ice or clean water and all positive values

give directly the fraction of the contaminated area inside

pixel (from 1% to 100%). Unfortunately, given constants

apply probably only for our samples and are sensitive to

reflectance calibration accuracy. The classification result in



A B C D

Fig. 4. A) Airborne spectrometer image of partly contaminated ice field. Bands used for RGB: Red = 842 nm, Green = 552 nm, Blue = 459 nm. In thespectrometer image oil contamination appears brown, ice appears white and water nearly black. B) Oil classification using airborne spectrometer bands 842nm and 552 nm . In the classification result oil contamination appears white and clean areas black. C) Oil classification using simulated MODIS bands 1 and2. D) Oil classification using simulated Landsat ETM bands 2 and 4.

A B C

Fig. 5. A) Airborne spectrometer image of partly contaminated ice field with downgraded 50 m resolution. B) Oil contaminated area estimate calculatedfrom high-resolution oil mask presented in Fig. 4B. C) Oil contaminated area estimate calculated directly from the low-resolution airborne spectrometer image(Fig. 5B) using Eq. 2.



Fig. 5C shows that the oil areas, estimated from the high-

resolution images, and low-resolution image are very similar.

Also (RNIR/Rgreen) could be used.

B. MODIS

The satellite based MODIS spectrometer is one of the

most interesting instruments for operative spectral monitoring

applications because its frequent coverage, good availability

and cheap image price. Unfortunately, the MODIS has ground

resolution only 250 m or more and its spectral band selection is

limited. Most interesting for our study are the high resolution

bands 1 (620-670 nm) and 2 (841-876 nm). Also bands 3-7 in

visible and infrared (IR) region with 500 m are interesting. To

study the MODIS usability for oil spill monitoring, we first

simulated the MODIS spectral configuration on the airborne

spectrometer images of oil contaminated ice. First, we applied

Eq 1 to the simulated MODIS spectral bands 1 and 2. Instead

of the green and NIR bands, we had to use here red and NIR

bands 1 and 2. As a result, we got oil mask shown in Fig.

4C. As it can be seen, bands 1 and 2 don’t provide same

sensitivity and robustness as green-NIR combination, but the

oil contamination can still be mapped. By using MODIS bands

2 and 4 we could have more sensitivity, but different ground

resolution of the bands cause difficulties. Next we applied the

algorithm to MODIS scenes taken on 19, 20, 22 and 23 April.

Although algorithm detects well islands and land, we could not

find any oil on the images in the known contaminated region.

This is probably due to poor ground resolution compared to oil

spill size. Contaminated areas were rather small, only tens of

meters wide and also spread to larger area. Oil spill detectable

with MODIS instrument should be rather hundreds of meters

in diameter. Using the band ratio based indices (RNIR/Rred)gave us very similar results.

C. Landsat ETM

The Landsat ETM instrument has much better ground

resolution than MODIS, 30 m. In the visible-NIR region it

has four spectral bands. For our study, ETM bands 2 (520-

600 nm) and 4 (760-900 nm) suite best.

First we studied airborne spectrometer images of oil spill

with simulated Landsat ETM spectral configuration. We ap-

plied Eq 1 to the simulated ETM spectral bands 2 and 4. As

a result, we got oil mask shown in Fig. 4D. By comparing

oil masks calculated from narrow airborne bands and from

wider Landsat bands, we can see that both perform equally

well. The Landsat ETM spectral configuration suits well for

oil mapping on ice. Next we applied the same algorithm to

Landsat ETM scene acquired 23 April. As for the MODIS

image, algorithm detects well islands and land, but we could

not find oil traces on contaminated region. Most obvious

reason for that is that most of the oil has been already cleaned

by 23. April. Remaining contamination was marginal and

apparently substantially smaller than Landsat ETM ground

resolution.

IV. CONCLUSIONS

Our study strongly suggests that an imaging spectrometer

suits very well to oil detection on sea ice, where SAR based oil

detection algorithms fail. Usability of satellite instruments like

MODIS have serious limitations set by the image resolution

and spectral band placement. For the larger scale disaster

mapping MODIS should suit well. The Landsat ETM has good

enough resolution for most typical, small-scale pollution detec-

tion. However, the Landsat imaging frequency does not meet

the needs of monitoring applications. Several algorithms for oil

detection on sea ice was proposed and evaluated. Algorithms

can produce oil contaminated sea ice area estimates for low-

resolution satellite images. However, contaminated area should

be at least 50% of the image resolution cell to be detected.

Proposed methods suit for operative oil contamination moni-

toring from airborne and spaceborne spectrometer images in

winter.

REFERENCES

[1] K. Krishen. Detection of oil spills using a 13.3-GHz radar scatterometer. J. Geophys. Res., 78(2):1952–1953, 1973.

[2] H. S. Storvik G. Solberg R. Volden E. Solberg, A. Automatic detectionof oil spills in ERS SAR images. IEEE Transactions on Geoscience and

Remote Sensing, 37(4):1916–1924, July 1999.

detection of oil pollution on sea ice with airborne

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