InletTracker: An open-source Python toolkit for historic and near real-time monitoring of coastal inlets from Landsat and Sentinel-2

Despite their global abundance and high ecological and socio-economic significance, the dynamics of coastal inlets often remain poorly quantified at multi-decadal time scales. Here, we introduce InletTracker, a new tool that reconstructs the time-evolving state of dynamic coastal inlets over the las...

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Vydáno v:Earth and Space Science Open Archive ESSOAr
Hlavní autoři: Heimhuber, Valentin, Vos, Kilian, Fu, Wanru, Glamore, William
Médium: Paper
Jazyk:angličtina
Vydáno: Washington American Geophysical Union 17.03.2021
Témata:
Coastal dynamics
Coastal engineering
Coastal inlets
Coastal management
Coastal zone management
Environmental monitoring
Floodplains
Geomorphology
Inlets
Landforms
Landsat
Landsat satellites
Monitoring
Open source software
Real time
Remote sensing
River channels
Satellite imagery
Satellite observation
Satellites
Shallow water
Tidal flats
Toolkits
Water levels
Wetlands
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Abstract Despite their global abundance and high ecological and socio-economic significance, the dynamics of coastal inlets often remain poorly quantified at multi-decadal time scales. Here, we introduce InletTracker, a new tool that reconstructs the time-evolving state of dynamic coastal inlets over the last 30+ years from publicly available Landsat 5, 7 and 8 and Sentinel 2 satellite imagery. InletTracker is a Google Earth Engine enabled python toolkit that uses a novel least cost pathfinding approach to trace inlets along and across the berm (i.e., barrier, bar), and then analyses the resulting transects to infer whether an inlet is open or closed. To evaluate the performance of InletTracker, we applied the tool at 12 intermittent coastal inlets with different maximum inlet widths (≤30-200m), geomorphological setting and opening frequency located across Southeastern and Southwestern Australia. This exercise involved 6363 unique binary inlet state predictions (i.e., open vs. closed) that were validated against visually inferred inlet states (from the satellite imagery itself), on-ground observational records, and in situ water levels from inside the inlets. InletTracker reproduced the visually inferred inlet states with an average accuracy across all sites of 89% for the combined Landsat and Sentinel 2 record (15-30m resolution) and 94% for the Sentinel 2 record only (10m resolution). Overall, we found good agreement between the predictions of the tool and the three independent validation datasets for all but the smallest sites. Our results demonstrate that InletTracker will enable coastal engineers, managers, and researchers to gain new insights into the dynamics and drivers of coastal inlets or similar shallow water landforms such as river mouths, tidal flats, floodplains, wetlands or delta channel networks. Further, the high spatial (i.e., 10m) and temporal (i.e., 5 daily) resolution provided by Sentinel 2 makes InletTracker a viable option for near real-time monitoring of even relatively small inlets with a minimum channel width of around 10m and frequent, short duration, openings.
AbstractList Despite their global abundance and high ecological and socio-economic significance, the dynamics of coastal inlets often remain poorly quantified at multi-decadal time scales. Here, we introduce InletTracker, a new tool that reconstructs the time-evolving state of dynamic coastal inlets over the last 30+ years from publicly available Landsat 5, 7 and 8 and Sentinel 2 satellite imagery. InletTracker is a Google Earth Engine enabled python toolkit that uses a novel least cost pathfinding approach to trace inlets along and across the berm (i.e., barrier, bar), and then analyses the resulting transects to infer whether an inlet is open or closed. To evaluate the performance of InletTracker, we applied the tool at 12 intermittent coastal inlets with different maximum inlet widths (≤30-200m), geomorphological setting and opening frequency located across Southeastern and Southwestern Australia. This exercise involved 6363 unique binary inlet state predictions (i.e., open vs. closed) that were validated against visually inferred inlet states (from the satellite imagery itself), on-ground observational records, and in situ water levels from inside the inlets. InletTracker reproduced the visually inferred inlet states with an average accuracy across all sites of 89% for the combined Landsat and Sentinel 2 record (15-30m resolution) and 94% for the Sentinel 2 record only (10m resolution). Overall, we found good agreement between the predictions of the tool and the three independent validation datasets for all but the smallest sites. Our results demonstrate that InletTracker will enable coastal engineers, managers, and researchers to gain new insights into the dynamics and drivers of coastal inlets or similar shallow water landforms such as river mouths, tidal flats, floodplains, wetlands or delta channel networks. Further, the high spatial (i.e., 10m) and temporal (i.e., 5 daily) resolution provided by Sentinel 2 makes InletTracker a viable option for near real-time monitoring of even relatively small inlets with a minimum channel width of around 10m and frequent, short duration, openings.
Author Fu, Wanru
Heimhuber, Valentin
Vos, Kilian
Glamore, William
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Snippet Despite their global abundance and high ecological and socio-economic significance, the dynamics of coastal inlets often remain poorly quantified at...
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SubjectTerms Coastal dynamics
Coastal engineering
Coastal inlets
Coastal management
Coastal zone management
Environmental monitoring
Floodplains
Geomorphology
Inlets
Landforms
Landsat
Landsat satellites
Monitoring
Open source software
Real time
Remote sensing
River channels
Satellite imagery
Satellite observation
Satellites
Shallow water
Tidal flats
Toolkits
Water levels
Wetlands
Title InletTracker: An open-source Python toolkit for historic and near real-time monitoring of coastal inlets from Landsat and Sentinel-2
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