Let’s break down how we can implement satellite SAR data to improve the accuracy of susceptibility maps.
Firstly, we must acquire data and go through the pre-processing phase. Synthetic Aperture Radar (SAR) sensors on satellites send microwave signals down to the Earth's surface.
These radar signals interact with the ground and bounce back to the sensor. This process captures information about the surface's properties, roughness, and changes.
Analysts can process the raw SAR data in several ways. First, it may undergo radiometric calibration. Next, we apply speckle reduction to minimise noise. Finally, researchers perform co-registration to align multiple satellite images of the same area.
After this pre-processing, we can create interferograms. Analysts can achieve this by comparing the phase difference between two or more SAR images taken at different times.
Phase information helps measure ground movement along the radar’s line of sight. This line is perpendicular to the satellite’s flight path. By analysing the interferograms, researchers can identify areas undergoing surface deformation that may indicate potential landslides.
At this stage, researchers extract terrain and environmental data from other sources. Geospatial data includes topography, slope, aspect, lithology, and land cover patterns.
Such data provide essential contextual information for the identifying factors contributing to the occurrence of landslides. and therefore integrating them into the process of generating landslide susceptibility maps enhances the accuracy and reliability of results.
Analysts must pre-process, georeference, and integrate these ancillary information into the GIS environment alongside SAR-derived data. Building landslide susceptibility maps in this way offers numerous benefits.
One significant advantage is the ability to regularly update these maps. Since researchers can collect SAR imagery at regular intervals, they can monitor terrain changes in near real-time.
This means the maps can adjust to changing environmental conditions. They provide current information to decision-makers. This feature is extremely valuable as climate change impacts grow.
Next, researchers apply the calibrated model to the study area to generate a landslide susceptibility map. The map shows different areas based on how likely they are to have landslides. These categories include low, moderate, high, and extreme susceptibility. You can load all this data into a Geographic Information System (GIS) for ease of use.
Finally, we provide the landslide susceptibility map to policymakers and stakeholders. This helps them understand and use the information. It supports them in making informed decisions.
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