Retrospective image analysis for long‐term demography using Google Earth imagery

Abstract Ecosystems worldwide are undergoing rapid degradation, yet effective monitoring remains costly and time‐consuming. The proliferation of open‐access imagery from satellites, Google Earth, and citizen‐science platforms offers unprecedented opportunities to improve ecological monitoring, yet, their potential for species detection and demographic tracking remains underexplored. Here, we demonstrate a cost‐effective approach to retrospective image analysis by combining current ground truth data with historical Google Earth RGB imagery to extract long‐term demographic information. We apply this approach to two invasive plant taxa with contrasting growth forms in Mediterranean ecosystems. First, we use deep learning to detect individuals of prickly pear ( Opuntia spp.) across diverse habitats and image resolutions, and reconstruct 10 years of spatially explicit recruitment rates along a climatic gradient. Second, we quantify nearly 20 years of growth dynamics for the clonal invader Carpobrotus spp. in two contrasting environments. Our object detection model achieves 60%–80% accuracy in identifying Opuntia individuals, with performance enhanced by colour consistency and contrast. While detection is limited for individuals <4 m 2 , the approach captures ~80% of the population. For Carpobrotus spp., area‐based analysis mapped >7,900 m 2 across two sites and revealed a mean genet expansion of 12.95 ± 5.32 m 2 yr. −1 . Beyond detection, time‐series analysis of Google Earth imagery allows the estimation of recruitment, growth rates, climatic sensitivity, population structure, size–age relationships, and recruitment hotspots. With image series spanning a decade for Spain, Greece, and the UK, and two decades for Portugal, we provide spatially explicit demographic reconstructions at unprecedented scales. By harnessing publicly available imagery, our pipeline expands the capacity for long‐term, large‐scale demographic monitoring. Although demonstrated here with invasive plants, the approach is broadly applicable across taxa and ecosystems. Retrospective image analysis has the potential to accelerate conservation, guide restoration, and support robust ecological forecasting in the Anthropocene.