Monitoring water pollution in the River Ganga with innovations in airborne remote sensing and drone technology. Rajiv Sinha (Indian Institute of Technology Kanpur)

1. Monitoring water pollution in the river Ganga with innovations
in airborne remote sensing and drone technology
RAJIV SINHA, DIPRO SARKAR
DEPARTMENT OF EARTH SCIENCES, INDIAN INSTITUTE OF TECHNOLOGY KANPUR, INDIA
PATRICE CARBONNEAU
DEPARTMENT OF GEOGRAPHY, UNIVERSIY OF DURHAM, UK

2. AIRBORNE REMOTE SENSING
(Lavender, 2005).
Airships Aeroplanes UAVs Helicopters
 FixedWing UAV
 Advantage
 Efficient aerodynamics
 Greater coverage
 High speed, Natural
gliding – w/o power
 Mildly wind /rain resistant
 Greater payload
 Disadvantage
 Needs a runway/launcher
 Cannot stay stationary
 Rotary Wing UAV
 Advantage
 Vertical take-offs
 Agile manoeuvrings
 Payload configurable
 Can stay stationary
 Disadvantage
 Smaller coverage
 More maintenance due to more
moving parts
 Susceptible to damage during wind
/rain

3. WATER QUALITY ASSESSMENT
 Water quality refers to the chemical, physical, biological, and
radiological characteristics of water.
• Limitations of prevailing Water Quality measurements methods:
• The techniques are labour intensive, time consuming, and costly.
• Involves long term commitment on local infrastructure.
• Low awareness of the social importance of water quality leads to
lower priority in funding leading to operational budget cuts.
• Point data source seldom reveals the big picture when it comes to
assessing large waterbodies.
• Site inaccessibility can hinder the acquisition of data.
• Field error during sample acquisition and laboratory error during
processing affects the result.
(El-Din et al, 2013; Ritchie, 2003; Chipman,2009.)

4. REMOTE SENSING METHOD OF WATER
QUALITY ASSESSMENT
Advantages of Remote
Sensing method:
 Provides a synoptic
view of large
waterbodies which is
more effective in
monitoring the
temporal and spatial
changes
 Takes lesser time to
get a comprehensive
idea.
 Variation in
concentration are
more prominent due
to continuous data.
 Less costly. (Seyhan et al,1974, Kallio 2000)
Optically active parameters -
Chlorophyll a, Temperature, Coloured
Dissolved Organic Matters, Total
Suspended Matters, Dissolved
Organic Carbon, Turbidity, Sea
Surface Salinity.
Reflectance vs
wavelength

5. GENERAL
METHODOLOGY
 Empirical Approach:
Y =A+BX or Y = ABX
Where,
Y = Reflectance value
A & B = empirically derived factors
determined statistically from the
spectral value and in-situ
measurements.
(Seyhan et al,1986, Conrad et al, 1971)

6. MULTISPECTRALVS HYPERSPECTRAL
Multispectral Hyperspectral
• In a single observation a multispectral
sensor generate 3-10 spectral bands
• Covers large spectral bands
• Images are captured in snapshot
• Until a mechanical shutter is used,
electronic shutter creates geometric
distortion.
• In a single observation a hyperspectral
sensor can observe more than 100
spectral bands
• Covers narrow spectral bands
• Images are captured in single digital array
ensuring larger instantaneous view.
• Lowest possible geometrical distortion
Observations
• Water is optically active only in a small part of the spectrum.
• Due to very low concentration of the heavy metals, the bulk of the reflective spectra constitutes of those
reflected by suspended sediments and chlorophyll.
• Spaceborne sensors provides multispectral images with large band gaps and large swath.
(Siegmund, Menz, 2005. Jensen,2007. Ferrato, 2012)

7. RESEARCH OBJECTIVES ANDTOOLS
 Water quality mapping of
large rivers for airborne
platforms – Cessna Aircraft
and UAVs
 Identifications of major
hotspots of water
pollution
 Characterisation of
pollutants based on
spectral characteristics
 Tools
 Cessna Aircraft
 UAVs (UX5, Phantom 4)
 Cameras and filters
Cessna Aircraft
UAV – UX5
Hyperspectral CameraDJI Phanton 4
Multispectral Camera
Single band cameras

8. DESIGN OF MOUNT FORTHE CESSNA AIRCRAFT
Four cameras – nearly co-axial
Filters: 520, 650, 800, 852 nm
Video capture

9. FLIGHT PLAN AND IMAGES
520 nm 650 nm
800 nm 852 nm
Flight path: Bithur to Jajmau (25km)
Height: 1100 ft
Speed: Variable
Image resolution: 1 mp
GSD: ~ 1m
Filters: 520,650,800,852 (nm)

10. Results: Spectral characteristics of different parts of channel
Id Class
1 Dense crops
2 Sparse crops
3 Inland Water bodies
4 Dry Sand Bar
1
6
5
4
3
2
7
Colour Band
Red 520
Green 650
Blue 800
Id Class
5 Damp Sand Bar
6 Wet Sand Bar
7 Water Body
Brightness (DN)
12830

11. Along the channel variation in water quality
due to pollutants (394,520,650 nm)
DownstreamUpstream Flight path
Frame 1 Frame 2 Frame 3
Frame 1 Frame 2 Frame 3
Red 520 nm
Green 650 nm
Blue 800 nm
Major drain
Blue band shift, sensitive
to metallic ions
Infrared signal- two peaks
due to turbidity
Three bands have distinct
signals, respond to different
parameters
(Blue – metals, green-
chlorophyll, infrared-
turbidity)
Similar to Frame 1, a little
darker due to difference in
illumination
Effect of the
pollutants added
by the drain

12. Identifying a source of
pollution through
multispectral camera at
Kanpur
• Flight height: 1100 feet (335m)
• Image resolution: 20cm (Gopro),
40cm(FCC)
• Bit Depth: 32bit (GoPro 4), 8-bit
(Multispectral Camera System)
• Bands Used: RGB (Go Pro 4),
520,652,800nm and 852 nm (MCS).
• The 852 nm band did not captured
any reproducible image.
• Base image : Bing World Map Service
RGB GoPro 4 image
FCC Multispectral Camera System (MCS)
City Outlet
City Outlet

13. City
Outlet
1
2
3
Identifying source of pollution using a multispectral camera
• Blue band shifting to left, darker, not much
metallic ions
• Green band most affected, chlorophyll
concentration may be higher due higher
flux of N and P
• Red band very sharp at the outlet – higher
particulate matter, and therefore very high
turbidity
1. Fresh Water
3. D/s of outlet
2. At outlet
Legend:
Red ~ 800nm Band
Green ~ 650nm Band
Blue ~ 520nm Band
* All bands are have
±5nm tolerance
Brightness (DN number)
No.ofpixels
Histogram comparison of RGB and
multispectral image (520, 650 & 800 nm band)
30 128

14. Spectral variation in a stretch of stagnant water
Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
No.ofpixels
Input from
drain
Brightness (DN number)

15. 1.No Pollution
5. Highly
Polluted Water
3 & 4. Medium
Mixing
2.Light mixing
Legend: Arrows
1.Yellow – Primary drain;
2.Green – Small drains;
3.Red – Medium sized drain
Drone flights: Imaging and Histogram
analysis over an outlet in Ganga
6A. Discharge from
first drain
6B. Discharge from
multiple small drain
6C. Discharge from
multiple small drain
Images from left to right:
Original image with inputs from
drains; After removing sandbar and
then applying histogram
equalization on PC1 image;
Zonation of the area.
6D. Discharge from
medium size drain
Total stretch: 4 km
Flight height: 273 m
Image capturing: 20 m
Camera: RGB
PCA analysis of three
band data (PC1 image
shown)
30 12870
No.ofpixels

16. 1.No Pollution (DJI)
6. Discharge from
outlet (DJI)
3 & 4. Medium
Mixing (DJI)
Histograms of RGB and multispectral image (520, 650 & 800 nm band)
Comparisons: Cessana and Drone flights
1.No Pollution
(Cessna)
3 & 4. Medium
Mixing (Cessna)
6. Discharge from
outlet (Cessna)
* All filters in multispectral camera is having ±5nm tolerance
Cessna images:
Red ~ 800nm Band
Green ~ 650nm Band
Blue ~ 520nm Band
DJI images: Standard RGB
Shivrajpur Kanpur Jajmau Sultanpur

17. NEXT STEPS
 Design and customization of the UAV for the
Multispectral and hyperspectral imaging
payload
 Multispectral imaging
 Trace pollution plume back to the source
 Relate spectal characteristics to specific pollutants
 Hyperspectral imaging
 Needs designing of a gimbal to carry the payload
 High resolution data in narrow spectral bands
 Should allow better differentiation of specral
response of clean and polluted water
 Ground measurements and community
participation
 Ground sampling and laboratory analysis for
validating the UAV based measurements
 Training the community for water quality
measurements using portable kits
LALE UAS developed by IIT Kanpur