Coastal change detection

Satellite(s) Landsat TM, ETM; Sentinel-2 and Sentinel-1.	Monitoring element Land/water surface reflectance, SAR backscatter.

Satellite(s)

Landsat TM, ETM;
Sentinel-2 and Sentinel-1.

Monitoring element

Land/water surface reflectance, SAR backscatter.

Description technique

Konko et al. (2018) pre-processed both optical and radar data. Normalized Difference Water Index (NDWI) calculations were then used to improve the discrimination of aquatic and terrestrial areas.
The set of land use units discriminated on optical and radar images allowed the definition of an interpretation key for the classification of optical images. Supervised classification with the Support Vector Machine (SVM) was applied.

Accuracy / Resolution

Overall accuracy: 87%.
Kappa index: 84%.

Case study

Mono Delta, in the south of Togo and Benin (Konko et al. 2018)
Whatipu Beach, Auckland NZ (as part of the EnviroSatTools project). Example of Google Earth Engine script:

https://gitlab.com/envirosattools/documentation/-/wikis/coastal

Also fits domain

Freshwater.

Benefits

The approach contributed to spatial-temporal monitoring of a beach, its resources and of the littoral coastline.
Combination of optical and radar satellite imagery data allowed for a better cartographic performance.
These calculations helped to inform predictions to coastal immersion hazard.

Limitations

Imagery spatial resolution limits the clear distinction between classes.
Cloud coverage for optical data.

Applicability for Northland

Yes. Approaches combining optical and radar satellite imagery data would allow for a better cartographic performance and would very likely be useful for coastal hazard mapping and coastal erosion monitoring in Northland. Applications are already developed for Whatipu Beach, Auckland NZ (EnviroSatTools 2021).

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature, and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication references

Konko, Y. , Bagaram, B. , Julien, F. , Akpamou, K. and Kokou, K. (2018) Multitemporal Analysis of Coastal Erosion Based on Multisource Satellite Images in the South of the Mono Transboundary Biosphere Reserve in Togo (West Africa). Open Access Library Journal, 5, 1-21. doi: 10.4236/oalib.1104526.

https://www.mdpi.com/2072-4292/8/4/354?utm_source=trendmdwidget&utm_medium=cpc&utm_campaign=trendmd&trendmd_shared=0

Other comments or information

Du et al. (2015) suggested to take full advantage of the 10-m information provided by Sentinel-2 images to produce a 10-m spatial resolution MDNWI maps, by downscaling the 20-m resolution SWIR band to 10-m based on pan-sharpening.

Other references

Aryastana P, Ardantha IM, Candrayana KW. 2018. Coastline change analysis and erosion prediction using satellite images. MATEC Web Conf. 197:13003.
https://www.matec-conferences.org/articles/matecconf/pdf/2018/56/matecconf_aasec2018_13003.pdf

Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X. 2016. Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sensing. 8(4):354.
https://www.mdpi.com/2072-4292/8/4/354

Hasan M, Karmaker S, Rahman M. 2020. Geomorphological change assessment of south western coastal region: a case study of Mongla Upazila, Bagerhat, Bangladesh. Journal of Applied Water Engineering and Research. 9. doi:10.1080/23249676.2020.1831977.

https://www.tandfonline.com/doi/full/10.1080/23249676.2020.1831977