Applying Declassified Cold War Intelligence Imagery for Long-Term Forest Cover Change Analysis

Furthermore, the 30-meter resolution of these datasets makes it challenging to distinguish fine-scale land cover variability in heterogeneous landscapes. This study addressed these limitations by utilizing panchromatic imagery that was captured by the United States’ intelligence agencies during the Cold War.

The study applies declassified Hexagon (KH-9) imagery from 1979 to assess long-term forest extent and cover change in Tajikistan’s Pamir Mountains. A hybrid classification approach was developed, integrating image segmentation and masking based on per-segment mean pixel values to exclude some areas that are not forested and therefore create a more balanced dataset. Texture-based analysis was applied to the panchromatic imagery, testing different texture metric window sizes (3×3, 7×7, and 11×11) to optimize classification accuracy.