Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways
Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways
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 (R
2 = 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.
Deep learning, Environmental impact, Human pressure, Human waterway footprint, PlanetScope, Ship detection
Smigaj, Magdalena
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Hackney, Christopher
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Diem, Phan Kieu
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Van, Pham Dang Tri
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Ngoc, Nguyen Thi
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Bui, Duong
47393e2a-e8a5-4357-8b8c-72582ad57f26
Darby, Stephen
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Leyland, Julian
6b1bb9b9-f3d5-4f40-8dd3-232139510e15
22 November 2022
Smigaj, Magdalena
58f27c03-b2bf-400f-9ac1-72e1c5f48d9e
Hackney, Christopher
bc99c3e8-243c-4933-9346-46c707e36e0b
Diem, Phan Kieu
4ff7b070-9897-4321-a1bb-1a480e1d2742
Van, Pham Dang Tri
1b680ebb-f7ee-4376-a3d1-589942946a6b
Ngoc, Nguyen Thi
24012205-3c13-453d-861c-140fd86a6406
Bui, Duong
47393e2a-e8a5-4357-8b8c-72582ad57f26
Darby, Stephen
4c3e1c76-d404-4ff3-86f8-84e42fbb7970
Leyland, Julian
6b1bb9b9-f3d5-4f40-8dd3-232139510e15
Smigaj, Magdalena, Hackney, Christopher, Diem, Phan Kieu, Van, Pham Dang Tri, Ngoc, Nguyen Thi, Bui, Duong, Darby, Stephen and Leyland, Julian
(2022)
Monitoring riverine traffic from space: The untapped potential of remote sensing for measuring human footprint on inland waterways.
Science of the Total Environment, [160363].
(doi:10.1016/j.scitotenv.2022.160363).
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 (R
2 = 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.
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More information
Accepted/In Press date: 17 November 2022
e-pub ahead of print date: 22 November 2022
Published date: 22 November 2022
Additional Information:
Funding Information:
This work was supported by the UK Global Challenges Research Fund [Grant number: EP/V036394/1 ].
Publisher Copyright:
© 2022 The Authors
Keywords:
Deep learning, Environmental impact, Human pressure, Human waterway footprint, PlanetScope, Ship detection
Identifiers
Local EPrints ID: 472889
URI: http://eprints.soton.ac.uk/id/eprint/472889
ISSN: 0048-9697
PURE UUID: 29ba2eb6-90bd-4b1d-a615-f5e8fabab868
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Date deposited: 05 Jan 2023 17:40
Last modified: 17 Mar 2024 03:04
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Contributors
Author:
Magdalena Smigaj
Author:
Christopher Hackney
Author:
Phan Kieu Diem
Author:
Pham Dang Tri Van
Author:
Nguyen Thi Ngoc
Author:
Duong Bui
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