The Applicability of a Complete Archive of Keyhole Imagery for Land-Use Change Detection in China (1960–1984) - Hao Li; Tao Wang; Jinyu Sun (Sensors)
Declassified Keyhole imagery partially provides multi-temporal coverage that can support land-use change analysis. However, the volume of commercial (paid) Keyhole data is much larger than that of free imagery, and the extent to which commercial data can enhance the application of Keyhole imagery for land-use change analysis remains unknown. In this work, the full archive of Keyhole images for China was obtained from the USGS to identify regions with repeated coverage automatically by using the ArcPy library in Python. The years from 1960 to 1984 were divided into five 5-year periods (T1, 1960~1964; T2, 1965~1969; T3, 1970~1974; T4, 1975~1979; and T5, 1980~1984). The Keyhole images’ metadata, including resolution, acquisition time, and image extent, were utilized to classify the images into meter level (C1), five-meter level (C2), and ten-meter level (C3). The spatial distributions of combinations of imagery at different resolutions for each period and the repeated coverage of imagery at each resolution across the five periods were investigated to extract repeated-coverage regions. The coverage proportions were nearly 100% for C1 imagery for the T3, T4, and T5 periods; C2 for T1 and T2; and C3 for T1 and T3. The T3 period featured extensive coverage at all three resolutions (66%). The T1 period was mainly covered by C2/C3 (93%), and T4 had C1/C3 coverage (68%). In contrast, T2 relied primarily on C2 imagery (100%), and T5 was only covered by C1 (96%). For C1 imagery, land-use changes in almost all areas in China in the T3/T4/T5 time span could be detected, and for C2 and C3 images, the corresponding time spans were T1/T2 and T1/T3. Although this study focused on repeated-coverage area detection within China, the methodology and Python codes provided allow for the implementation of an automated process for land-use change detection from the 1960s to the 1980s in other regions worldwide.