Preliminary results of mapping industrial spill in Hungary - presented at the ISPRS-WHISPERS conference in Lisbon, 2011.

1. MULTI-SENSOR INTEGRATION AND MAPPING
STRATEGIES FOR THE DETECTION AND REMEDIATION
OF THE RED MUD SPILL IN KOLONTAR, HUNGARY:
ESTIMATING THE THICKNESS OF THE SPILL LAYER
USING HYPERSPECTRAL IMAGING AND LIDAR
Csaba Lenart - Peter Burai - Amer Smailbegovic - Tibor Biro -
Zsolt Katona - Roko Andricevic
Karoly Robert College Photon d.o.o. Split
Envirosense Hungary Ltd University of Split

3. Location area and approximate direction of red-mud spill
assembled from satellite imagery

4. Applied hyperspectral sensor and platform
Piper Aztec
GPS/INS: OxTS 3003
Ground speed: 60m/s
Ground resolution: 1.1m
Sensor: AISA Eagle II
Swat with: 1126m
Band numbers: 253
Height of flight (AGL): 1655m
Spectral range: 400-970nm
Overlap: 30%
Spectral sampling: 2.5nm

5. Tracklog of hyperspectral aquisition
Date of aquisition: 10/10/2010 11:00-14:30 (UTM+2)

9. Post processing of hyperspectral images
Preliminary post processing was focused on flooded area

10. Spectral property of red mud
Reflectance spectra of red mud.

11. Botanical Park in the center of Devecser city
RGB subset - Red (650nm), Green (550nm), Blue (450nm)

12. Botanical Park in the center of Devecser city
False color image - Red: NIR (800nm), Green: Red (650nm), Blue: Green (550nm)

13. Botanical Park in the center of Devecser city
Image classification with Spectral Angle Mapper (SAM)
green: vegetation, magenta-red: red mud

14. Botanical Park in the center of Devecser city
Feature selection – Minimal Noise Transformation
False color image - Red: MNF1, Green: MNF2, Blue: MNF1

16. Mapping of flooded area
vegetation
Al koncentráció
(Al2O3)

17. Quantitative analysis of red mud
Absorption features of red mud layer were detected in the blue and
green region (480-570nm) with the reflectance maximum peaking in the
red region (650-720nm.).

18. Quantitative analysis of red mud
Scatter plot of red mud area
Scatter plot of 549nm and 682nm wavelengths of affected study area (28,900pixel).
A ellipsoid represents dry and moderated wet (dry matter: 50-92%) red mud, B
ellipsoid represents red mud with high wet content (dry matter: less than 50%).

19. Quantitative analysis of red mud
Cross section of red mud in sampling area
Red Mud Layer Index (RMLI) = (B682nm - B549nm) / (B682nm + B549nm)

20. Scatterplot of MNF (Minimal Noise Transformation) image
vegetation
Al koncentráció
(Al2O3)
MNF1 and MNF3 axis

21. Lidar intesity data
vegetation
Al koncentráció
(Al2O3)
Lidar intensity data draped over Lidar DTM showing areas of
return absorption due to wet mud

22. Mapping of flooded area
Estimated depth of red mud as a function of RMLI

23. Mapping of flooded area
class type area (m2)
1 red mud > 3cm 3,873,355
2 liquid red mud 128,500
sum 4,001,855

26. Integration of LIDAR and hyperspectral image (RGB)

27. Integration of LIDAR and hyperspectral image (RGB)

28. Modelling of flood dynamics based on Lidar DTM
Budapest University of Technology and Economics (BME), Józsa, J. (2011)

29. Red mud reservoirs are in Europe….
Mostar -
2011.05.18

30. Concluding Remarks
- Red mud storage areas are around Eastern/Southern Europe
- High pH and toxicity make it a problem
- Combined remote sensing tools can be used to address the problem before it
occurs
- There are observable characteristics that can help in assessment and
detection
- Best is to prevent rather than mapping the spread
- Remote sensing can be used in emergency to offer actionable information
where needed
- Cross border coordination and working together is a key to success
THANK YOU FOR YOUR ATTENTION