Large-area, 1964 land cover classifications of Corona spy satellite imagery for the Caucasus Mountains

Abstract

Historical land use strongly influences current landscapes and ecosystems making maps of historical land cover an important reference point. However, the earliest satellite-based land cover maps typically date back to the 1980s only, after 30-m Landsat data became available. Our goal was to develop a methodology to automatically map land cover for large areas using high-resolution panchromatic Corona spy satellite imagery for 1964. Specifically, we a) conducted a comprehensive analysis on the feature selection and parameter setting for large-area classification processes for 2.5-m historical panchromatic Corona imagery for a full suite of land cover classes, b) compared the pixel-based and object-oriented methods of classifying the land cover, and c) examined the benefits of adding a digital elevation model for the pixel-based and object-oriented land cover classifications. We mapped land cover in parts of the Caucasus Mountains (158,000 km2), a study area with great variability in land cover types and illumination conditions. The overall accuracies of our pixel-based and object-oriented land cover maps were 63.0 ± 5.0% and 67.3 ± 4.0%, respectively, showing that object-oriented classifications performed better for Corona satellite data. Incorporating the digital elevation model improved the overall accuracy to 75.3 ± 3.0% and 78.7 ± 2.5%, respectively. The digital elevation model was especially useful for differentiating forest and snow-and-ice from lakes in mountainous areas affected by cast shadows. Our results highlight the feasibility of accurately and automatically classifying land cover for large areas based on Corona spy satellite imagery for the 1960s. Such land cover maps predate the earliest 30-m Landsat land cover classifications by two decades, and those from high-resolution satellite imagery by four decades. As such, we demonstrate here that Corona imagery can make important contributions to global change science.