Remote Sensing Applications in
Pollution Monitoring
Erik Norderud
GPHY 426
11/25/2014
Introduction
Pollution is becoming an increasing problem in the world’s
ecosystems creating a need for monitoring. Remote sensing
technologies are progressively becoming a useful tool to
accomplish this by detecting types, concentration, and trends
of pollutants in water bodies, the atmosphere, and in terrestrial
environments.
Case Study 1: Monitoring Water Body Pollution through
Remote Sensing in Heavy Areas of Coal Extraction
(Oparin et al.)
Location: Kuzbass Coal Basin, Russia
Objective: Identify areas polluted by coal mining
products based on spectral reflectance properties
Methods: Article based upon the hypothesis that runoff from these
mines are seen in the form of suspended particles that change
reflectance of water
-Examined spectral and textural properties of objects in images
obtained from Landsat, MODIS, and Rapideye satellites
-First analyzed texture features to determine boundaries between
natural features (water, vegetation) vs anthropogenic features (mines,
roads)
-Interest zones defined and spectral properties analyzed based on
spectral curves for water vs turbidity of water to determine pollution
areas
Example of ‘Inte
basin (Image So
937)
Case Study 2: Using Nighttime Imagery to Monitor
Trends in Light Pollution in China
(Han et al.)
Location: China
Objective: Detect trends in light pollution over a 20 year
period due to increasing populations of urban areas in
China
Methods: - Used nighttime images from the Defense
Meteorological Satellite Program’s Operational Linescan
System (referred to as DMSP/OLS) for the study, which is
responsive to radiation at the 0.4 um to 1.1um
wavelengths.
-Assigned each pixel a DN value between 0 and 63,
where 0=darkness and 63=brightness
Range of brightness across China. Notice heavy light pollution on the coast. (Image Source: Han et al. pg
5544)
Results: Ability to analyze increasing or
decreasing trends of light pollution across China
from 1992-2012
Increasing and decreasing trends of light pollution across China
from 1992-2012 (Image Source: Han et al. pg 5549)
Case Study 3: Using Remote Sensing to Monitor
Illegal Pollution Dumping Activities
(Lega et al.)
Location: Southwest Italy
Objective: Monitor Illegal dumping activities through use
of IR thermal imagery onboard aerial platforms
Types of potential platforms and
sensors for detecting illegal pollutio
dumping. (Image source: Lega et al.
pg 8292)
Method
s
Pattern Recognition
Thermal Tracking
Compares previously classified data with multiple thermal images to develop detectable trait patterns of pollution
Uses ‘Edge Detection’ and pattern recognition to link changes in pixel intensity to identify pollution
sources
Example of thermal patterns of a river
and thermal tracking of a water
surface near a factory (Image Source:
Lega et al. pg 8296 and 8297)
Results: Able to use thermal imagery through
pattern recognition and thermal tracking to monitor
illegal pollution activities
Case Study 4: Oil Spill Monitoring Based on
Its Spectral Characteristics
(Ma et al.)
(Image Source: http://lms.seosproject.eu/learning_modules/marinepollution/marinepoll
ution-c01-p05.html)
Location: Yellow Sea
Objective: Study spectrum of different oil types to
provide a basis for oil spill remote sensing
Sensor Used: MODIS (250m resolution)
Effective Bands: 0.30-0.95 um
Spectral Characteristics of Oil affected by:
Thickness/Type of Oil
Oceanic/Weather Conditions
Study focused on spectral features of common types of
oil with different thicknesses:
Thin Oil:
Thick Oil:
Kerosene/Lubricating Oil
(Spectral responses between differing
types of oil. Source: Ma et al. pg 318
and 319)
Thin Oil: Spectral response influenced by seawater more than
thick oil (Ma et al. pg 320)
Thick Oil: Reflectance decreases as thickness increases (Ma et al.
pg 320)
Spectral Response of Thick Oil vs Seawater
Spectral Response of Thin oil vs Seawater
Spectral response of floating black oil vs seawater for reference.
Note the low brightness of the thick oil across all wavelengths.
(Source: Lu et al. pg 337)
Sources
Lega, M., G. Persechino, and P. Bishop. "Remote Sensing in
Environmental Police Investigations: Aerial Platforms and an Innovative
Application of Thermography to Detect Several Illegal Activities."
Environmental Monitoring and Assessment (2014): 8291-301. Web of
Science. Web. 25 Nov. 2014.
Ma, Long, Ying Li, and Yu Liu. "Oil Spill Monitoring Based on Its Spectral
Characteristics." Environmental Forensics 10.4 (2009): 317-23.
Academic Search Complete. Web. 25 Nov. 2014.
Oparin, V. N., V. P. Potapov, O. L. Giniyatullina, and N. V. Andreeva.
"Water Body Pollution Monitoring in Vigorous Coal Extraction Areas
Using Remote Sensing Data." Journal of Mining Science 48.5 (2012):
934-40. Academic Search Complete. Web. 25 Nov. 2014.
Pengpeng, Han, et al. "Monitoring Trends In Light Pollution In China
Based On Nighttime Satellite Imagery." Remote Sensing 6.6 (2014):
5541-5558. Academic Search Complete. Web. 25 Nov. 2014.
Image Citation (Spectral response of thick oil vs seawater)
Lu, Yingcheng, Xiang Li, Qingjiu Tian, Guang Zheng, Shaojie Sun,
Yongxue Liu, and Qiang Yang. "Progress in Marine Oil Spill Optical
Remote Sensing: Detected Targets, Spectral Response Characteristics,
and Theories." Marine Geodesy 36.3 (2013): 337. Academic Search
Complete. Web. 25 Nov. 2014.