Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas and Data
2.2. Automated Shoreline Change Detection Workflow
2.2.1. Noise Removal
2.2.2. Water Body Detection
2.2.3. Removal of Misclassifications and Infrastructural Elements
2.2.4. Calculation of Movement Rates
2.3. Quality Assessment
- 0.1–0.36 ha
- 0.36–1.44 ha
- 1.44–5.76 ha
- 5.76–23.04 ha
- >23.04 ha
3. Results
3.1. Evaluation of the Workflow
3.1.1. Code and Performance
3.1.2. Quality of Noise Removal
3.1.3. Accuracy of Water Body Detection and Filtering
3.1.4. Deadhorse: Accuracy of Shoreline Movement Rates and Directions
3.1.5. MP 396: Water Body Detection
3.1.6. Identification of Error Sources
- i
- Distortion in the shore area due to sediment entry or shallow water depths results in an underestimation of the water surface area, and therefore the erroneous calculation of the lake shoreline movement rate, when the water surface area is fully identified in the next time step and vice versa (category: shore area);
- ii
- Speckle from sunglint leads to an underestimation of the water surface area. (category: sunglint);
- iii
- Undetected connections between adjacent lakes through water-filled patches, troughs of polygonal tundra or erosion and drainage gullies lead to the classification of several smaller lakes instead of one large lake (category: connectivity);
- iv
- Seasonal variations in water surface area. Depending on the amount of precipitation preceding the image acquisition, areas might appear as water bodies in one time step but as land in the next or vice versa. This can also lead to an overestimation of water surface area and therefore the incorrect derivation of shoreline geometry, when some moist patches are classified as (part of the) water bodies (category: water level).
4. Discussion
5. Conclusions and Outlook
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
VHR | Very high resolution |
MP | Mile post |
pan | Panchromatic |
LiDAR | Light detection and ranging |
RGB | Red–green–blue |
NIR | Near infrared |
SWIR | Shortwave infrared |
OSM | OpenStreetMap |
yr | year |
Appendix A
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Acquisition Date [mm/dd/yyyy] | Spatial Resolution [m] | Sensor | |
---|---|---|---|
Deadhorse | |||
08/15/2006 | 2.8 m | 0.7 m | Quickbird-2 |
07/09/2010 | 2.0 m | 0.5 m | WorldView-2 |
07/16/2013 | 2.0 m | 0.5 m | WorldView-2 |
07/10/2016 | 2.0 m | 0.3 m | WorldView-3 |
Dalton Highway MP 396 | |||
08/27/2016 | 2.0 m | 0.5 m | WorldView-2 |
Deadhorse | MP 396 | |||
---|---|---|---|---|
2006–2010 | 2010–2013 | 2013–2016 | 2016 | |
Detection | 205 | 233 | 227 | 66 |
Empty | 31 | 73 | 91 | – |
0.10–0.36 ha | 87 | 62 | 48 | 39 |
0.36–1.44 ha | 58 | 52 | 58 | 15 |
1.44–5.76 ha | 16 | 25 | 12 | 6 |
5.76–23.04 ha | 5 | 6 | 5 | 6 |
>23.04 ha | 8 | 15 | 13 | 0 |
Total | 174 | 160 | 136 | 66 |
In every time step | 109 | – |
Category Error Sources | Water Body Detection Incorrect | Shoreline Movement Rate Incorrect |
---|---|---|
Deadhorse | 23 lakes | 52 lakes |
Shore area | ||
Sunglint | ||
Connectivity | ||
Water level | − | |
MP 396 | 13 lakes | − |
Shore area | − | |
Sunglint | − | − |
Connectivity | − | |
Water level | − |
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Kaiser, S.; Grosse, G.; Boike, J.; Langer, M. Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery. Remote Sens. 2021, 13, 2802. https://doi.org/10.3390/rs13142802
Kaiser S, Grosse G, Boike J, Langer M. Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery. Remote Sensing. 2021; 13(14):2802. https://doi.org/10.3390/rs13142802
Chicago/Turabian StyleKaiser, Soraya, Guido Grosse, Julia Boike, and Moritz Langer. 2021. "Monitoring the Transformation of Arctic Landscapes: Automated Shoreline Change Detection of Lakes Using Very High Resolution Imagery" Remote Sensing 13, no. 14: 2802. https://doi.org/10.3390/rs13142802