The Ultimate Aerial Photography Interpretation Cheat Sheet

Introduction: Understanding Aerial Photography

Aerial photography is the process of capturing images of the Earth’s surface from an elevated position using cameras mounted on aircraft, drones, balloons, or satellites. Interpreting these images requires specific skills to extract meaningful information about terrain, land use, environmental conditions, and human-made structures. This cheat sheet provides a comprehensive guide to aerial photo interpretation techniques, essential for professionals in urban planning, environmental monitoring, archaeology, military intelligence, disaster management, and geographic information systems (GIS).

Core Concepts & Principles

Key Elements of Aerial Photographs

Types of Aerial Photography

• Vertical Photography: Camera axis perpendicular to the ground (±3°)

  • Provides consistent scale across image
  • Preferred for mapping and measurement
  • Minimal distortion at image center

• Oblique Photography: Camera axis intentionally tilted from vertical

  • Low Oblique: Horizon not visible in image
  • High Oblique: Horizon visible in image
  • More intuitive interpretation but variable scale

• Stereo Photography: Overlapping images enabling 3D viewing

  • Requires 60% forward overlap between consecutive photos
  • Enables accurate height measurement and terrain analysis

Interpretation Process: Step-by-Step Methodology

1. Pre-Analysis Preparation

   • Verify image metadata (date, time, location, camera parameters)
   • Check scale and orientation information
   • Gather reference data and ground truth information

2. Initial Assessment

   • Identify prominent features and general landscape characteristics
   • Note image quality, cloud cover, shadows, and potential distortions
   • Establish context (geographic region, season, weather conditions)

3. Systematic Examination

   • Work systematically across the image (grid pattern)
   • Apply the elements of interpretation (described below)
   • Mark and describe features of interest

4. Detailed Analysis

   • Measure dimensions, distances, areas using scale information
   • Compare with previous imagery to detect changes
   • Correlate features with maps and other data sources

5. Verification & Validation

   • Cross-reference with ground data when available
   • Verify interpretations with multiple sources
   • Document confidence levels for interpretations

The 9 Elements of Image Interpretation

1. Shape

• The outline or form of individual objects
• Examples: Rectangular buildings, circular water tanks, linear roads
• Tip: Shape is often the first recognition element for human-made features

2. Size

• Dimensions or extent of objects (length, width, height)
• Must be considered in context with image scale
• Tip: Compare relative sizes to known objects for estimation

3. Pattern

• Spatial arrangement of objects
• Examples: Grid pattern of urban streets, contour plowing on hillsides
• Tip: Patterns often indicate human activity or natural processes

4. Tone/Color

• Relative brightness (grayscale) or color of features
• Influenced by material properties, moisture content, and lighting
• Tip: Dark tones often indicate moisture, vegetation, or shadow

5. Texture

• Frequency of tone/color change within a feature
• Described as smooth, rough, mottled, etc.
• Tip: Forests typically appear rough, water appears smooth

6. Shadow

• Dark areas cast by elevated objects
• Reveals height and shape information
• Tip: Note sun angle and direction when interpreting shadows

7. Site/Association

• Relationship between features and their surroundings
• Context often aids identification of ambiguous features
• Tip: Industrial facilities are often near transportation networks

8. Resolution

• The detail discernible in an image
• Determines the smallest identifiable feature
• Tip: Consider resolution limitations before making conclusions

9. Phenology

• Time-dependent characteristics, especially vegetation
• Seasonal changes in appearance
• Tip: Compare imagery from different seasons for comprehensive analysis

Specialized Interpretation Techniques

Land Cover/Land Use Classification

Infrastructure Assessment

• Transportation Networks

  • Roads: Linear features with consistent width
  • Railways: Linear features with gentle curves
  • Airports: Distinctive runway patterns, hangar facilities
  • Ports: Docking structures along waterfronts

• Urban Features

  • Residential: Regular patterns, small structures, vegetation
  • Commercial: Larger buildings, parking areas, less vegetation
  • Industrial: Large structures, storage tanks, rail access

• Utilities

  • Power lines: Linear features with regular spacing of towers
  • Pipelines: Linear clearings through vegetation
  • Water treatment: Circular or rectangular settling ponds

Environmental Monitoring

• Vegetation Health

  • Healthy: Dark tone in visible bands, bright in near-infrared
  • Stressed: Lighter tone, irregular patterns
  • Deforested: Sharp boundaries, contrast with surrounding areas

• Water Quality

  • Clear water: Dark tone
  • Sediment-laden: Lighter tone, plume patterns
  • Algal blooms: Distinctive color patterns on water surface

• Erosion Features

  • Gullies: Dendritic patterns in sloped areas
  • Coastal erosion: Changing shoreline patterns
  • Landslides: Distinctive scars on slopes

Common Image Types and Their Application

Panchromatic (Black & White)

• Single channel grayscale image
• High resolution but limited feature discrimination
• Best for: Detecting edges, shapes, and fine details

True Color (RGB)

• Approximates human vision (red, green, blue channels)
• Intuitive interpretation but limited for certain analyses
• Best for: General reconnaissance, communication with non-specialists

False Color Infrared

• Near-infrared, red, and green bands displayed as RGB
• Vegetation appears red, water appears black
• Best for: Vegetation analysis, wetland mapping

Multispectral

• Multiple discrete spectral bands (typically 4-20)
• Enables specialized analysis and feature extraction
• Best for: Land cover classification, advanced environmental monitoring

Hyperspectral

• Hundreds of contiguous narrow spectral bands
• Enables detailed material identification
• Best for: Mineral mapping, pollution detection, detailed vegetation analysis

Common Challenges and Solutions

Challenge: Relief Displacement

• Cause: Tall objects appear to “lean” away from the center of vertical images
• Solution:
  • Use orthorectified images for accurate measurements
  • Consider perspective when interpreting feature positions

Challenge: Shadow Obscuration

• Cause: Features hidden in shadow areas, especially in low sun angles
• Solution:
  • Use imagery from different times of day
  • Enhance shadow areas with brightness/contrast adjustment

Challenge: Seasonal Variations

• Cause: Landscape appearance changes significantly with seasons
• Solution:
  • Compare images from same season for change detection
  • Use multi-seasonal imagery for comprehensive analysis

Challenge: Image Resolution Limitations

• Cause: Small features not discernible in lower resolution imagery
• Solution:
  • Match imagery resolution to analysis objectives
  • Use contextual clues to infer presence of small features

Challenge: Cloud Cover

• Cause: Clouds obscuring ground features
• Solution:
  • Use radar imagery which penetrates clouds
  • Mosaic multiple partial images from different dates

Best Practices and Practical Tips

Image Acquisition Planning

• Schedule flights for optimal sun angle (typically 30-50° elevation)
• Avoid seasons with extensive cloud cover or vegetation obscuration
• Plan for appropriate resolution based on smallest features of interest

Interpretation Workflow

• Begin with visual interpretation before automated analysis
• Work from known to unknown features
• Document metadata and interpretation methodology

Technical Considerations

• Use stereo viewing for accurate height determination
• Apply appropriate enhancements (contrast, edge detection) for specific features
• Maintain awareness of image limitations and potential misinterpretations

Quality Control

• Validate interpretations with ground truthing when possible
• Apply consistent interpretation keys across multiple images
• Review interpretations with subject matter experts

Tools for Aerial Image Interpretation

Software Tools

• GIS Software: ArcGIS, QGIS, Global Mapper
• Remote Sensing Platforms: ENVI, ERDAS Imagine, eCognition
• Stereo Viewing: StereoPhoto Maker, PurVIEW
• Image Processing: Adobe Photoshop, GIMP, ImageJ

Hardware Tools

• Stereoscopes: For viewing overlapping images in 3D
• High-resolution monitors: For detailed visual analysis
• Graphics tablets: For precise digitizing of features

Field Verification Tools

• GPS devices: For ground truth position recording
• Digital cameras: For documenting field conditions
• Field spectroradiometers: For calibrating image spectral responses

Resources for Further Learning

Publications

• “Manual of Photographic Interpretation” (American Society for Photogrammetry and Remote Sensing)
• “Remote Sensing and Image Interpretation” by Lillesand, Kiefer, and Chipman
• “Introduction to Air Photo Interpretation” by Avery and Berlin

Organizations

• American Society for Photogrammetry and Remote Sensing (ASPRS)
• International Society for Photogrammetry and Remote Sensing (ISPRS)
• National Geospatial-Intelligence Agency (NGA)

Online Resources

• NASA’s Applied Remote Sensing Training (ARSET) program
• USGS Earth Explorer (free satellite and aerial imagery)
• EarthData (NASA’s repository of Earth observation data)

Training Programs

• University certificate programs in remote sensing
• Online courses through Coursera, edX, and ESRI Training
• Professional workshops through ASPRS and similar organizations

Quick Reference: Visual Signatures Cheat Sheet