Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways
Magdalena Smigaj, Christopher R. Hackney, Phan Kieu Diem, Van Pham Dang Tri, Nguyen Thi Ngoc, Duong Du Bui, Stephen E. Darby, Julian Leyland
Published in ‘Science of The Total Environment’
Abstract
Mass urbanisation and intensive agricultural development across river deltas have driven ecosystem degradation, impacting deltaic socio-ecological systems and reducing their resilience to climate change. Assessments of the drivers of these changes have so far been focused on human activity on the subaerial delta plains. However, the fragile nature of deltaic ecosystems and the need for biodiversity conservation on a global scale require more accurate quantification of the footprint of anthropogenic activity across delta waterways. To address this need, we investigated the potential of deep learning and high spatiotemporal resolution satellite imagery to identify river vessels, using the Vietnamese Mekong Delta (VMD) as a focus area. We trained the Faster R-CNN Resnet101 model to detect two classes of objects: (i) vessels and (ii) clusters of vessels, and achieved high detection accuracies for both classes (f-score = 0.84–0.85). The model was subsequently applied to available PlanetScope imagery across 2018–2021; the resultant detections were used to generate monthly, seasonal and annual products mapping the riverine activity, termed here the Human Waterway Footprint (HWF), with which we showed how waterborne activity has increased in the VMD (from approx. 1650 active vessels in 2018 to 2070 in 2021 - a 25 % increase). Whilst HWF values correlated well with population density estimates (R2 = 0.59–0.61, p < 0.001), many riverine activity hotspots were located away from population centres and varied spatially across the investigated period, highlighting that more detailed information is needed to fully evaluate the extent, and type, of human footprint on waterways. High spatiotemporal resolution satellite imagery in combination with deep learning methods offers great promise for such monitoring, which can subsequently enable local and regional assessment of environmental impacts of anthropogenic activities on delta ecosystems around the globe.