Questions: Disaster Monitoring with Remote Sensing

Question 1

A major flood occurs in a tropical region with persistent cloud cover. Which satellite sensor would provide the most reliable flood extent map?

• A. Landsat optical imagery in a true-color composite
• B. Sentinel-1 SAR, because microwave signals penetrate clouds and flood water appears as dark, smooth surfaces with low backscatter compared to the surrounding rough terrain and vegetation
• C. MODIS thermal imagery to detect cooler flood water
• D. Sentinel-2 optical imagery with atmospheric correction

Answer: (B)

Cloud cover makes optical and thermal sensors unreliable during active flooding events, particularly in tropical regions. SAR operates through clouds, and flood water produces a distinctive low-backscatter signature because smooth water surfaces reflect radar away from the sensor (specular reflection). Pre-flood vs during-flood SAR comparison reliably delineates inundation extent regardless of weather conditions.

Question 2

Satellite-based disaster monitoring has largely replaced ground-based disaster assessment.

• T. True
• F. False

Answer: False

Satellite remote sensing complements but does not replace ground assessment. Satellites provide broad-area coverage and access to hazardous zones, but ground teams provide building-level damage grades, casualty counts, infrastructure functionality, and needs assessment that satellites cannot determine. Satellite maps guide ground teams to the worst-affected areas, optimizing response resource allocation. The most effective disaster response integrates both.

Question 3

Explain how InSAR is used to assess earthquake damage and why this technique works.

Model answer: InSAR compares the phase of SAR images acquired before and after an earthquake to map ground surface displacement caused by fault rupture. Areas of coherent phase change reveal the spatial pattern and magnitude of co-seismic deformation (often centimeters to meters), constraining fault geometry and slip distribution. Additionally, coherence loss between pre- and post-earthquake images indicates areas where the surface was severely disrupted (building collapse, landslides, liquefaction), serving as a proxy for damage intensity. This works because damaged areas scramble the radar scattering pattern, destroying the phase coherence that intact surfaces maintain.

InSAR provides two complementary damage indicators: phase change maps ground displacement (tectonic deformation), while coherence loss maps surface disruption (structural damage).