A Fixed-Threshold Approach to Generate High-Resolution Vegetation Maps for IKONOS Imagery

Area of Science:

• Environmental Science
• Remote Sensing
• Geospatial Analysis

Background:

• Vegetation distribution maps are crucial for urban planning and environmental policy.
• Normalized Difference Vegetation Index (NDVI) is commonly used but generates low-resolution maps requiring manual thresholding.
• Existing methods struggle with accurate vegetation extraction from high-resolution imagery.

Purpose of the Study:

• To develop a high-resolution (1-meter) vegetation mapping method for IKONOS imagery.
• To address the challenge of manual threshold selection in NDVI-based vegetation mapping.
• To improve the accuracy and utility of vegetation maps for practical applications.

Main Methods:

• Integration of an extended Tasseled Cap transformation with a novel image fusion technique.
• Development of a fixed-threshold approach to automate vegetation extraction.
• Application to IKONOS imagery for generating high-resolution vegetation distribution maps.

Main Results:

• The proposed method successfully generates 1-meter resolution vegetation maps.
• Experimental results demonstrate improved accuracy compared to traditional NDVI methods.
• The approach effectively overcomes the manual threshold selection problem.

Conclusions:

• The developed fixed-threshold approach offers a more accurate and efficient way to create high-resolution vegetation maps.
• This method enhances the utility of IKONOS imagery for detailed environmental assessment and urban planning.
• The study provides a promising solution for vegetation mapping in remote sensing applications.