Unlike many other remote sensing techniques, satellite-derived bathymetry is a passive remote sensing technique. So, instead of actively sending out a signal and measuring the intensity of the returned signal to derived distances, SDB uses the light of the sun. Throughout this blog we have explored the use of SDB in tropical areas with clear water and the sun shining overhead. Having a high sun angle will greatly improve how much light will penetrate through the water and how accurate the derived depth measurements will be. This is one of the many challenges faced by those conducting SDB in locations of high latitude, where the angle of the sun is considerably low. Using this technique to map remote and difficult to survey locations may become one of the most powerful applications of SDB. The Arctic, for example, has a limited amount of time during the year when ship surveys can be done due to ice coverage. But with increased exploration of this area for research on climate change, resource development and travel passages, it is important to have hydrographic surveys conducted and for ships to be provided with information of hazardous areas and shoreline depths. SDB can prove to be an important tool for researchers looking to explore the great North.
Here are just a few of the challenges faced when conducting SDB in Northern areas:
- Low sun angle
- Ice and clouds in satellite images
- Sediment in the water
Researchers Chenier, Faucher and Ahola (2018) from the Canadian Hydrographic Service took three study sites in Northern Canada to test the implementation of SDB in these challenging climates.
The three study sites and associated problem to tackle were:
- Cambridge Bay, Victoria Island, Nunavut – assessing SDB in an Arctic environment
- Heath Point, Anticosti Island, Quebec – assessing SDB for updating hydrographic charts
- Havre-aux-Maisons, Magdelen Islands, Quebec – assessing SDB in heavy sediment waters
If possible, Chenier et al. (2018) stress that to have the accurate SDB information, it is key to have good site data collected by surveys for calibration and accuracy assessment. The best method for surveying is multi-beam echosounder done close to the data on the image to be analyzed. Each of these sites had available multi-beam survey information and the Cambridge Bay site data was supported by LiDAR surveys (a remote sensing survey technique, most likely conducted by airplanes). All images underwent geometric and radiometric correction to evaluate the positional accuracy and account for the reflection from clouds or ice in the images. Two general models were used to determine depths from images, a band ratio model and a multi-band model. I have spoken about band ratio models in previous posts. It is the ratio of the light intensity reflected by two bands of visible light, most often the blue and green bands are used. Here three different ratios were used – Blue:Green, Coastal Band:Green, Blue:Yellow. The multi-band model works on the same principles, but considers each band to have a relationship with water depth rather than a ratio of two, and so uses a more complicated equation determine SDB depths. Below are the results of this study.
Table 1. RMSE statistics for band ratio and multiple-band SDB approaches.
From this table we can see that no method produces the same result. What else do we see:
- RMSE is the root mean square error. Simply, this is how far the resulting value is from the line of best fit. This is called regression – the smaller the number, generally the more accurate the result.
- The smallest regression values for each method seems to occur at 4-6 meter depths for the Cambridge Bay and Heath Point Sites.
- The regression values for the Havre-aux-Maisons site is similar for 0-2 meter, 2-4 meter depths and 4-6 meter depths.
- The site Havre-aux-Maisons likely can not achieve deeper than 6 meter depth measurements because it is an area with high levels of sediment in the water.
- Cambridge bay probably had the the most accurate data to compare results with – three multibeam surveys from different years and LiDAR data. This site was able to determine depths up to 10-15 meters.
- The multi-band model appears to generally give the best depth calculations.
Chenier et al. (2018) found that the results of their study showed that applications of SDB is promising, and results can be within a 0.5-1 meter error. This technique only has this level of accuracy for depths up to 10 meters, but this can have great impacts for navigation and safety in Arctic areas, and shows the potential for research in this field.
Satellite-derived bathymetry has come a long way since research on the topic first made its way into research in 1978, but there is still more to discover. Presenters at the recent Shallow Survey Conference in 2018 spoke about a Project Trident and the use of three different methods for SDB processing:
- Optically Derived Bathymetry
- Stereo-Photogrammetric Bathymetry
- Wave Kinetic Bathymetry
The optically derived bathymetry is the processing method I have focused on in this blog and includes methods such as the band ratio models used in Chenier et al. (2018) research above. At best it can create a bathymetric surface with a 2 meter resolution. More research is pouring into the stereo-photogrammetric and wave kinematic processing methods as technology and research advances. Stereo-photogrammetric processing uses multiple overlapping satellite images taken at slightly different angles to derive depths. Wave kinematic bathymetry is a physics based processing method. Based on wave theory, this process method derives depths by analyzing two satellite images taken within a very short time of each other
Figure 2. shows that with a wave kinematic bathymetry processing method and validation from stereo-photogrammetric processing, a bathymetric surface with a resolution of 100 meters. By validating the results of each method with the results of the other two methods, a combined shallow water bathymetric surface can be produced. Using multiple processing methods will reduce the need for traditional ship surveys to validate the SDB derived depths. This will provide necessary knowledge of a shallow water environment before sending crews travelling to remote or dangerous areas. More work needs to be done to improve the resolution of these surfaces, but these techniques works well with sediment filled, turbid waters. Project Trident is currently using these combination bathymetric surfaces to map 5000 km of Brazil’s coast line, work that will valuable to ironing out the wrinkles of these newer processing techniques. Future research and applications of satellite-derived bathymetry will continue to propel our knowledge of our Earth and and it’s big blue ocean.
Abileah, R. (October, 2018). Recent advances in exploiting the wavecelerity inversion methods for shallow water bathymetry. Presentation at the Shallow Survey 2018, St. John’s, N.L.
Chenier, R., Ahola, R., Sagram, M., Faucher, M. & Shelat, Y. (2019). Consideration of Level of Confidence within Multi-approach Satellite-Derived Bathymetry. ISPRS Int. J. Geo-Inf, 8(1), 48.