First woody cover vegetation map of Kruger National Park in 1939–1944. Evidence from historical black and white aerial photography

Long-term spatial studies are crucial for understanding how the Earth's surface has changed. Before satellite imagery, landscapes were monitored using black and white (B&W) aerial photographs. However, surveys were infrequent and image analysis was a manual process that was both time-consuming and costly. In this study, we created a composite of high spatial resolution (0.5-0.75 m) B&W aerial images from 1939-1944, covering about 91% of Kruger National Park (KNP)'s nearly 2 million ha. We used this to produce the first historical woody cover (tall trees and shrubs) map of KNP, which until now was only partially understood through fragmented descriptions in period literature and small-area case studies. We established a supervised learning workflow using Google Earth Engine (GEE) which included performing an Object-based Image Analysis (OBIA) with a Random Forest classifier. This approach, enhanced by integrating texture, shape, neighboring features, and spectral variables into the training/validation dataset, enabled the identification of woody vegetation from B&W landscape objects. To enhance accuracy, we guided our sampling method using vegetation types with comparable woody cover and species composition. Initially, we tested our method on a smaller set of images (25 km2), and after confirming its effectiveness, we then expanded the approach to cover all available historical aerial imagery. Our results show that in 1939-1944, 26% of KNP was covered in woody vegetation (overall accuracy of 89%, producer's accuracy (non-woody = 88%, woody = 90%), and user's accuracy (non-woody = 90%, woody = 87%)). The importance of geological substrate in driving vegetation pattern is reflected in a higher woody cover percentage on granite (28%) than on basalt (21%) soils, with the lowest woody cover on northern basalts (11%) and the highest on north-central granites (32%). This study highlights the potential of GEE and OBIA for analyzing large-area, high spatial resolution B&W aerial photographs in a systematic and efficient manner and the importance of creating large-scale historical land cover baselines to support environmental planning and landscape management.