Multispectral Imagery for Land Classification

Sep 23, 2024

Webinar on Land Classification with Multispectral Imagery

Introduction

  • Presented by CanDrone: Leading consultancy in aerial and ground-based remote sensing in Canada.
  • Services: Drone-based solutions for forestry, agriculture, and urban development.
  • Presenters: Cody Wildgust (Strategic Sales Expert) and Ian Perry (Solutions Specialist).

Agenda

  1. Introduction to technology
  2. Explanation of multispectral imagery
  3. Components of land cover classification
  4. Use cases of the technology

Multispectral Imagery

  • Definition: Captures data from light in various bands of the electromagnetic spectrum.
  • Visible Spectrum: 400-700 nanometers (red, green, blue).
  • Multispectral Cameras: Can capture additional bands (e.g., infrared, red edge).
  • Example: Micasense Altum camera with 5 lenses for various bands.

Data Processing

  • Equipment Setup: Camera mounted on drones like DJI M300.
  • Software: Photogrammetry software (e.g., Pix4D) for processing data into orthomosaics.

Multispectral Analysis

  • Spectral Signature: Identifies objects by their interaction with light.
  • NDVI (Normalized Difference Vegetation Index): Measures plant health by comparing near-infrared and red light reflectance.

Practical Applications

  • Efficient Vegetation Monitoring: Identifying stressed or healthy crops from the air.
  • Precision Agriculture: Reducing resource usage and improving field efficiency.

Land Cover Classification

  • Process:
    1. Select training and testing data samples.
    2. Use image segmentation to group pixels.
    3. Apply machine learning for classification (e.g., Random Forest algorithm).
  • Tools: QGIS for segmentation and analysis.
  • Python Scripting: For automating large scale analysis and ensuring consistency.

Variations of NDVI

  • Soil Adjusted Vegetation Index (SAVI): For sparse vegetation areas.
  • Normalized Difference Red Edge: For complex canopies.

Fire Assessment

  • Normalized Burn Ratio: Assesses fire impact using multispectral imagery.

Software and Resources

  • QGIS: Open-source GIS platform for spatial data analysis.
  • Python: For scripting workflows and processing large datasets.

Questions and Answers

  • RGB cameras can isolate bands but lack near-infrared and red edge capabilities.
  • Multispectral satellite images are available, e.g., Landsat.
  • Discussion on using drones vs. satellites depending on resolution needs.

Conclusion

  • Contact CanDrone: For multispectral services and consultation.
  • Feedback: Suggestions for future webinar topics are welcome.

Overall, this webinar provided a comprehensive overview of using multispectral imagery for land classification, emphasizing the technology's efficiency and applications in various industries.