23.2 Remote Sensing Technologies and Data Analysis

Remote sensing technologies revolutionize how we observe and analyze our planet. From satellites to drones, these tools capture detailed images and data about Earth's surface, atmosphere, and oceans. They're crucial for understanding global changes and making informed decisions.

Combining remote sensing with GIS creates a powerful toolkit for various fields. Scientists and planners use this tech to map land use, manage resources, monitor disasters, and plan cities. It's changing how we see and interact with our world, from tracking deforestation to optimizing crop yields.

Remote sensing principles and techniques

Electromagnetic radiation and remote sensing

• Remote sensing detects and monitors physical characteristics of an area by measuring reflected and emitted radiation at a distance (typically from satellite or aircraft)
• Electromagnetic radiation is energy propagated through free space or a material medium in the form of electromagnetic waves
  • Forms the basis for remote sensing
• Passive remote sensing systems detect natural energy reflected or emitted from the Earth's surface
  • Most passive systems operate in visible, infrared, thermal infrared, and microwave portions of the electromagnetic spectrum
• Active remote sensing systems (radar, LiDAR) provide their own energy source to illuminate observed objects
  • Emit radiation towards the target, then detect and measure radiation reflected or backscattered from the target

Sensor characteristics and resolutions

• Spectral resolution describes a sensor's ability to define fine wavelength intervals
  • Finer spectral resolution means narrower wavelength range for a particular channel or band
• Radiometric resolution is the energy required to increase a pixel value by one
  • Higher radiometric resolution means more sensitivity to detecting small differences in reflected or emitted energy
• Temporal resolution is the frequency with which a sensor obtains imagery of a particular area
  • Depends on factors like satellite/sensor capability, swath overlap, and latitude

Platforms for remote sensing

Satellites and orbits

• Satellites are objects that orbit Earth and serve as platforms for remote sensors
• Two basic types of satellite orbit: geostationary and sun synchronous
• Satellites can carry passive or active sensors and offer a wide range of spatial, spectral, radiometric, and temporal resolutions

Aerial photography and UAVs

• Aerial photography involves taking photographs from an aircraft (airplane, helicopter)
  • Offers very high spatial resolution but limited spectral resolution
  • Used for mapping, surveying, and monitoring landscape changes over time
• Unmanned Aerial Vehicles (UAVs) or drones are increasingly popular for remote sensing
  • Offer high spatial resolution and flexibility in data acquisition
  • Have limited payload capacity and flight time compared to traditional aircraft

LiDAR systems

• LiDAR (Light Detection and Ranging) is an active remote sensing system using a laser to measure sensor-to-surface distance
• Can create high-resolution digital elevation models (DEMs) and 3D point clouds of the Earth's surface

Interpretation of remote sensing data

Image interpretation techniques

• Image interpretation examines aerial photographs and satellite imagery to identify objects and assess their significance
• Involves studying basic image characteristics like tone, texture, pattern, shape, size, shadow, site, and association

Digital image processing

• Digital image processing manipulates and interprets digital images with the aid of computers
• Includes preprocessing (radiometric and geometric corrections), image enhancement, image transformation, and image classification
• Supervised classification requires the analyst to select representative samples for each land cover class ("training sites")
• Unsupervised classification automatically groups pixels with similar spectral characteristics into unique clusters based on statistically determined criteria

Change detection and temporal analysis

• Change detection identifies differences in an object or phenomenon's state by observing it at different times
  • Involves quantifying temporal effects using multi-temporal datasets

Remote sensing applications with GIS

Integration of remote sensing and GIS

• Remote sensing and GIS are complementary technologies that enhance spatial data analysis and visualization when integrated
  • Remote sensing provides data for GIS analysis
  • GIS provides tools for managing, analyzing, and visualizing remotely sensed data

Applications in various fields

• Land use/land cover mapping, natural resource management, disaster management, urban planning, environmental monitoring
• Precision agriculture: monitoring crop health, estimating yields, optimizing farm management practices
  • Uses multispectral and hyperspectral imagery to detect variations in plant health and soil conditions
• Forestry: mapping forest cover, monitoring deforestation, assessing forest health
  • LiDAR data creates high-resolution DEMs of forest canopy and estimates biomass
• Disaster management: assessing extent and severity of damage caused by natural disasters (hurricanes, floods, wildfires)
  • Satellite imagery maps affected areas and identifies areas requiring immediate attention
• Urban planning: mapping and monitoring urban growth, assessing urban infrastructure, supporting land use planning and transportation decisions