❄️Earth Surface Processes Unit 15 – Remote Sensing in Geomorphology

What's Remote Sensing?

• Remote sensing involves gathering information about the Earth's surface from a distance using sensors on satellites or aircraft
• Enables mapping and monitoring of large areas of the Earth's surface that would be difficult or impossible to survey from the ground
• Provides a synoptic view of the Earth's surface allows for the identification of large-scale patterns and processes
• Remote sensing data can be used to create digital elevation models (DEMs), land cover maps, and other geospatial products
• Offers a cost-effective and efficient means of collecting data over large areas compared to ground-based surveys
• Enables the study of inaccessible or hazardous areas (volcanic regions, glaciers) without putting researchers at risk
• Provides a means of monitoring changes in the Earth's surface over time (deforestation, urban sprawl, coastal erosion)

Key Remote Sensing Tools

• Sensors are the primary tools used in remote sensing to detect and record electromagnetic radiation reflected or emitted by the Earth's surface
  • Passive sensors detect naturally reflected or emitted energy (sunlight, thermal radiation)
  • Active sensors emit their own energy and record the reflection (radar, lidar)
• Satellites are the most common platforms for remote sensing sensors provide a stable and consistent vantage point for data collection
  • Polar-orbiting satellites (Landsat, Sentinel) orbit from north to south and cover the entire Earth's surface
  • Geostationary satellites (GOES, Meteosat) orbit above a fixed point on the equator and provide continuous coverage of a specific region
• Airborne platforms (planes, drones) offer higher spatial resolution and flexibility compared to satellites but cover smaller areas
• Spectroradiometers measure the intensity of electromagnetic radiation at different wavelengths used for calibration and validation of satellite data
• GPS receivers provide precise location information for ground control points and field data collection

Electromagnetic Spectrum Basics

• The electromagnetic spectrum is the range of all possible frequencies of electromagnetic radiation from low-frequency radio waves to high-frequency gamma rays
• Different parts of the spectrum interact with the Earth's surface in different ways depending on their wavelength and energy
• Visible light (400-700 nm) is the part of the spectrum that humans can see consists of the colors red, orange, yellow, green, blue, and violet
• Near-infrared (700-1400 nm) is sensitive to healthy vegetation often used for vegetation mapping and monitoring
• Shortwave infrared (1400-3000 nm) is sensitive to water content in plants and soils used for drought monitoring and mineral mapping
• Thermal infrared (3000-14000 nm) is emitted by all objects above absolute zero used for temperature mapping and heat loss studies
• Microwave (1 mm - 1 m) can penetrate clouds, smoke, and vegetation used for soil moisture mapping and sea ice monitoring

Remote Sensing Data Types

• Multispectral data consists of multiple bands of data collected at different wavelengths across the electromagnetic spectrum
  • Landsat and Sentinel satellites collect multispectral data with 4-12 bands
  • Enables the identification of different land cover types and surface features based on their spectral signatures
• Hyperspectral data consists of hundreds of narrow bands collected across the electromagnetic spectrum
  • Provides more detailed spectral information compared to multispectral data
  • Enables the identification of specific minerals, vegetation species, and other surface materials
• Radar data is collected using active sensors that emit microwave energy and record the backscattered signal
  • Enables the mapping of surface roughness, soil moisture, and vegetation structure
  • Can penetrate clouds, smoke, and vegetation to map the underlying surface
• Lidar data is collected using active sensors that emit laser pulses and record the reflected signal
  • Provides high-resolution 3D point clouds of the Earth's surface
  • Enables the mapping of vegetation height, building heights, and other surface features

Image Processing Techniques

• Geometric correction involves correcting distortions in the image caused by the sensor, platform, or Earth's curvature
  • Includes orthorectification, which corrects for topographic distortions using a DEM
  • Ensures that the image is properly aligned with a map projection and coordinate system
• Radiometric correction involves correcting for variations in the sensor's response and atmospheric effects
  • Includes sensor calibration, atmospheric correction, and topographic correction
  • Ensures that the pixel values accurately represent the reflectance or emittance of the surface
• Image enhancement involves improving the visual quality and interpretability of the image
  • Includes contrast stretching, color composites, and spatial filtering
  • Enhances specific features or patterns in the image for better visualization and analysis
• Image classification involves assigning each pixel to a specific land cover or surface type based on its spectral signature
  • Includes supervised classification (using training data) and unsupervised classification (using clustering algorithms)
  • Produces a thematic map of the surface showing the distribution of different land cover types
• Change detection involves comparing two or more images of the same area at different times to identify changes in the surface
  • Includes image differencing, principal component analysis, and post-classification comparison
  • Enables the monitoring of land cover changes, natural disasters, and human activities over time

Geomorphological Applications

• Terrain analysis involves using DEMs to extract geomorphological parameters (slope, aspect, curvature) and identify landforms (valleys, ridges, peaks)
• Landslide mapping involves using remote sensing data to identify and map the distribution of landslides and assess their hazard potential
  • Multispectral and radar data can be used to identify vegetation and soil changes associated with landslides
  • Lidar data can be used to create high-resolution DEMs for landslide susceptibility modeling
• Coastal change monitoring involves using remote sensing data to map and monitor changes in coastlines, beaches, and dunes over time
  • Multispectral data can be used to map coastal land cover and sediment types
  • Lidar data can be used to create high-resolution DEMs for coastal erosion and accretion studies
• Glacial mapping involves using remote sensing data to map and monitor changes in glaciers and ice sheets over time
  • Multispectral data can be used to map glacier extent, snow cover, and melt patterns
  • Radar data can be used to measure glacier velocity and ice thickness
• Volcanic monitoring involves using remote sensing data to monitor volcanic activity and assess hazard potential
  • Thermal infrared data can be used to map lava flows and detect heat anomalies
  • Radar data can be used to measure ground deformation associated with volcanic activity

Limitations and Challenges

• Cloud cover can obscure the Earth's surface and limit the availability of optical remote sensing data in some regions and seasons
• Shadows can affect the spectral response of the surface and create challenges for image classification and change detection
• Atmospheric effects (haze, dust, smoke) can reduce the quality and accuracy of remote sensing data if not properly corrected
• Spatial resolution limitations can affect the ability to detect and map small-scale features and changes on the Earth's surface
  • Higher spatial resolution data (< 1 m) is often more expensive and has smaller coverage areas compared to lower resolution data
• Temporal resolution limitations can affect the ability to monitor rapid or short-term changes on the Earth's surface
  • Satellites have fixed revisit times (days to weeks) that may not capture all relevant changes
  • Airborne and drone data can provide more frequent coverage but are limited by weather conditions and flight regulations
• Data volume and processing requirements can be challenging for large-scale or high-resolution remote sensing studies
  • Hyperspectral and lidar data can generate terabytes of data that require specialized software and computing resources to process and analyze

Future Trends in Remote Sensing

• Increased use of small satellites (CubeSats) and satellite constellations will provide more frequent and higher resolution data for Earth observation
• Advances in sensor technology (hyperspectral, thermal, lidar) will enable new applications and more detailed analysis of the Earth's surface
• Integration of remote sensing data with other geospatial data (GPS, GIS, crowdsourcing) will enable more comprehensive and multi-scale analysis of Earth surface processes
• Increased use of machine learning and artificial intelligence techniques will enable more automated and efficient processing and analysis of remote sensing data
  • Deep learning algorithms can be used for image classification, object detection, and change detection
  • Cloud computing platforms (Google Earth Engine, Amazon Web Services) will enable more accessible and scalable processing of remote sensing data
• Expansion of remote sensing applications beyond geomorphology to other fields (agriculture, forestry, urban planning, disaster response) will drive innovation and interdisciplinary collaboration
• Increased emphasis on open data and open science will make remote sensing data and tools more accessible and reproducible for researchers and decision-makers worldwide