DETERMINATION OF THE BEST METHODOLOGY FOR BATHYMETRY MAPPING USING SPOT 6 IMAGERY: A STUDY OF 12 EMPIRICAL ALGORITHMS
Abstract
For the past four decades, many researchers have published a novel empirical methodology for bathymetry extraction using remote sensing data. However, a comparative analysis of each method has not yet been done. Which is important to determine the best method that gives a good accuracy prediction. This study focuses on empirical bathymetry extraction methodology for multispectral data with three visible band, specifically SPOT 6 Image. Twelve algorithms have been chosen intentionally, namely, 1) Ratio transform (RT); 2) Multiple linear regression (MLR); 3) Multiple nonlinear regression (RF); 4) Second-order polynomial of ratio transform (SPR); 5) Principle component (PC); 6) Multiple linear regression using relaxing uniformity assumption on water and atmosphere (KNW); 7) Semiparametric regression using depth-independent variables (SMP); 8) Semiparametric regression using spatial coordinates (STR); 9) Semiparametric regression using depth-independent variables and spatial coordinates (TNP), 10) bagging fitting ensemble (BAG); 11) least squares boosting fitting ensemble (LSB); and 12) support vector regression (SVR). This study assesses the performance of 12 empirical models for bathymetry calculations in two different areas: Gili Mantra Islands, West Nusa Tenggara and Menjangan Island, Bali. The estimated depth from each method was compared with echosounder data; RF, STR, and TNP results demonstrate higher accuracy ranges from 0.02 to 0.63 m more than other nine methods. The TNP algorithm, producing the most accurate results (Gili Mantra Island RMSE = 1.01 m and R2=0.82, Menjangan Island RMSE = 1.09 m and R2=0.45), proved to be the preferred algorithm for bathymetry mapping.
Keywords
Full Text:
PDFReferences
Arya A., Winarso G., Santoso AI, (2016), Ekstraksi Kedalaman Laut Menggunakan Data SPOT 7 di Teluk Belangbelang Mamuju (Accuracy Assesment of Satellite Derived Bathymetry using Lyzenga Method and it’s Modification using SPOT 7 Data at the Belangbelang Bay Waters Mamuju). J Ilm Geomatika 22:9–19.
Bierwirth PN, Lee TJ, Burne RV, (1993), Shallow Sea-Floor Reflectance and Water Depth Derived by Unmixing Multispectral Imagery. Photogramm Eng Remote Sensing 59:331–338.
Bramante JF, Raju DK, Sin TM, (2013), Multispectral Derivation of Bathymetry in Singapore’s Shallow, Turbid Waters. Int J Remote Sens 34:2070–2088. doi: 10.1080/ 01431161.2012.734934.
Clark RK, Fay TH, Walker CL, (1987), Bathymetry Calculations with Landsat 4 TM Imagery Under a Generalized Ratio Assumption. Appl Opt 26:4036. doi: 10.1364/AO.26.4036_1.
Conger CL, Hochberg EJ, Fletcher CH, Atkinson MJ, (2006), Decorrelating Remote Sensing Color Bands from Bathymetry in Optically Shallow Waters. IEEE Trans Geosci Remote Sens 44:1655–1660. doi: 10.1109/TGRS. 2006.870405.
Daniell JJ, (2008), Development of a Bathymetric Grid for the Gulf of Papua and Adjacent Areas: A Note Describing its Development. J Geophys Res Earth Surf 113:1–8. doi: 10.1029/2006JF000673.
Deidda M., Sanna G., (2012), Bathymetric Extraction using Worldview-2 High Resolution Images. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B8:153–157. doi: 10.5194/ isprsarchives-XXXIX-B8-153-2012.
Doxani G., Papadopoulou M., Lafazani P., et al., (2012), Shallow-Water Bathymetry Over Variable Bottom Types Using Multispectral Worldview-2 Image. In: ESA 2nd Space for Hydrology Workshop. 159–164.
Eugenio F., Marcello J., Martin J., (2015), High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery. IEEE Trans Geosci Remote Sens 53:3539–3549. doi: 10.1109/ TGRS.2014.2377300.
Guzinski R., Spondylis E., Michalis M., et al., (2016), Exploring the Utility of Bathymetry Maps Derived With Multispectral Satellite Observations in the Field of Underwater Archaeology. Open Archaeol 2:243–263. doi: 10.1515/opar-2016-0018.
Hernandez W., Armstrong R., (2016), Deriving Bathymetry from Multispectral Remote Sensing Data. J Mar Sci Eng 4:8. doi: 10.3390/jmse4010008.
Hogrefe KR, Wright DJ, Hochberg EJ, (2008), Derivation and Integration of Shallow-Water Bathymetry: Implications for Coastal Terrain Modeling and Subsequent Analyses. Mar Geod 31:299–317. doi: 10.1080/01490410802466710.
Kabiri K., (2017), Accuracy Assessment of Near-Shore Bathymetry Information Retrieved from Landsat-8 Imagery. Earth Sci Informatics 10:235–245. doi: 10.1007/ s12145-017-0293-7.
Kanno A., Koibuchi Y., Isobe M., (2011), Shallow Water Bathymetry from Multispectral Satellite Images: Extensions of Lyzenga’S Method for Improving Accuracy. Coast Eng J 53:431–450. doi: 10.1142/S057856341 1002410.
Kanno A., Tanaka Y., Kurosawa A., Sekine M., (2013), Generalized Lyzenga’s Predictor of Shallow Water Depth for Multispectral Satellite Imagery. Mar Geod 36:365–376. doi: 10.1080/01490419.2013.839974.
Kibele J., Shears NT, (2016), Nonparametric Empirical Depth Regression for Bathymetric Mapping in Coastal Waters. IEEE J Sel Top Appl Earth Obs Remote Sens 9:5130–5138. doi: 10.1109/JSTARS.2016. 2598152.
Lafon V., Froidefond J-MM, Lahet F., et al., (2002), SPOT shallow water bathymetry of a moderate turbid tidal inlet based on field measurements. Remote Sens Environ 81:136–148. doi: 10.1016/S0034-4257(01) 00340-6.
Lee K., Kim A., (2011), Determination of bottom-type and bathymetry using WorldView-2. In: Proc. SPIE Ocean Sens. Monitoring III. p 80300D–1.
Liu S., Zhang J., Ma Y., (2010), Bathymetric Ability of SPOT 5 Multi-Spectral Image in Shallow Coastal Water. In: Proc. 18th International Conference on Geoinformatics. 2–6
Lyons M., Phinn S., Roelfsema C., (2011), Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007. Remote Sens 3:42–64. doi: 10.3390/rs3010042.
Lyzenga DR, (1978), Passive Remote Sensing Techniques for Mapping Water Depth and Bottom Features. Appl Opt 17:379. doi: 10.1364/AO.17.000379.
Lyzenga DR, (1985), Shallow-Water Bathymetry Using Combined Lidar and Passive Multispectral Scanner Data. Int J Remote Sens 6:115–125. doi: 10.1080/014311685 08948428.
Lyzenga DR, Malinas NP, Tanis FJ, (2006), Multispectral Bathymetry Using a Simple Physically Based Algorithm. IEEE Trans Geosci Remote Sens 44:2251–2259. doi: 10.1109/TGRS.2006.872909.
Manessa MDM, Kanno A., Sekine M., et al., (2016a), Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery. Geoplanning J Geomatics Plan 3:117. doi: 10.14710/geoplanning.3.2.117-126.
Manessa MDM, Kanno A., Sekine M., et al., (2016b), Lyzenga Multispectral Bathymetry Formula for Indonesian Shallow Coral Reef: Evaluation and Proposed Generalized Coefficient. In: Bostater CH, Neyt X, Nichol C, Aldred O (eds) Proc. Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016. SPIE, Edinburgh, UK, 99990O.
Melsheimer C., Chin LS, (2001), Extracting Bathymetry from Multi-Temporal SPOT Images. In: Proc. The 22nd Asian Conference on Remote Sensing.
Mishra D., Narumalani S., Rundquist D., Lawson M., (2006), Benthic Habitat Mapping in Tropical Marine Environments Using QuickBird Multispectral Data. Photogramm Eng Remote Sensing, 72:1037–1048. doi: 10.14358/PERS.72.9.1037.
Mohamed H., Abdelazim Negm, Salah M., et al., (2017), Assessment of Proposed Approaches for Bathymetry Calculations Using Multispectral Satellite Images in Shallow Coastal/Lake Areas: a Comparison of Five Models. Arab J Geosci 10:1–17. doi: 10. 1007/s12517-016-2803-1.
Pacheco A., Horta J., Loureiro C., Ferreira, (2015), Retrieval of Nearshore Bathymetry From Landsat 8 Images: A Tool for Coastal Monitoring in Shallow Waters. Remote Sens Environ 159:102–116. doi: 10.1016/j.rse. 2014.12.004.
Pushparaj J., Hegde AV, (2017), Estimation of Bathymetry Along the Coast of Mangaluru using Landsat-8 Imagery. Int J Ocean Clim Syst 8:71–83. doi: 10.1177/17593 131166 79672.
Sánchez-Carnero N., Ojeda-Zujar J., RodrÃguez-Pérez D., Marquez-Perez J., (2014), Assessment of Different Models for Bathymetry Calculation using SPOT Multispectral Images in a High-Turbidity Area: The Mouth of the Guadiana Estuary. Int J Remote Sens 35:493–514. doi: 10.1080/01431161.2013.871402
Stumpf RP, Holderied K., Sinclair M., (2003), Determination of Water Depth with High-Resolution Satellite Imagery Over Variable Bottom Types. Limnology Oceanogr 48:547–556. doi: 10.4319/lo.2003.48.1_part_2. 0547.
Su H., Liu H., Wang L., et al., (2014), Geographically Adaptive Inversion Model for Improving Bathymetric Retrieval from Satellite Multispectral imagery. IEEE Trans Geosci Remote Sens 52:465–476. doi: 10.1109/TGRS.2013.2241772.
Van Hengel W., Spltzer D., (1991), Multi-Temporal Water Depth Mapping by Means of Landsat TM. Int J Remote Sens 12:703–712. doi: 10.1080/01431169108929687.
Vinayaraj P., Raghavan V., Masumoto S., (2016), Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model. Mar Geod 39:458–478. doi: 10.1080/01490419.2016.1245227.
Walpole RE., (1968), Introduction to Statistics. Macmillan, Madison.
Yuzugullu O., Aksoy A., (2014), Generation of the Bathymetry of a Eutrophic Shallow Lake Using WorldView-2 Imagery. J. Hydroinformatics 16:50. doi: 10.2166/ hydro.2013.133.
Refbacks
- There are currently no refbacks.
