Processing of Multitemporal Landsat TM Imagery to Optimize Extraction of Forest Cover Change Features

Keyphrases

• Multi-temporal Landsat 100%
• Forest Cover Change 100%
• Landsat TM Imagery 100%
• Change Features 100%
• Bitemporal 100%
• Vegetation Index 100%
• Information Content 50%
• Water Price 50%
• Landscape Features 50%
• Forest Canopy 50%
• Time-based 50%
• Well-defined 50%
• Digital Data 50%
• Principal Coordinate Analysis (PCoA) 50%
• Forest Cover 50%
• Divergence Measure 50%
• Forest Change Detection 50%
• Sensor Calibration 50%
• Correction Mechanism 50%
• Statistical Regression 50%
• Linear Correlation Coefficient 50%
• TM Data 50%
• Optimal Feature Selection 50%
• Spectrally Stable 50%
• Reflectance Value 50%
• Water Reflectance 50%
• Index Pair 50%
• Landsat TM Data 50%
• Change Detection Algorithms 50%
• Statistical Divergence 50%
• Operational Use 50%
• Subtraction Technique 50%
• Canopy Change 50%
• Dark Object Subtraction 50%
• Geometric Rectification 50%
• Digital Techniques 50%

Earth and Planetary Sciences

• Forest Cover 100%
• Landsat Thematic Mapper 100%
• Reflectance 100%
• Change Detection 50%
• Vegetation Index 50%
• Data Acquisition 25%
• Atmospherics 25%
• Correlation Coefficient 25%
• Sensor Calibration 25%
• Rectification 25%
• Forest Canopy 25%
• Digital Data 25%
• Principal Component Analysis 25%