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Original Articles

Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model

, &
Pages 458-478 | Received 15 Jun 2016, Accepted 30 Sep 2016, Published online: 07 Oct 2016

References

  • Baban, S. M. J. 1993. The evaluation of different algorithms for bathymetric charting of lakes using landsat imagery. International Journal of Remote Sensing 14:2263–73.
  • Benny, A. H., and G. J. Dawson. 1983. Satellite imagery as an aid to bathymetric charting in the Red Sea. The Cartographic Journal 20(1):5–16.
  • Brunsdon, A. C., S. Fotheringham, and E. M. Charlton. 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis 28(2):283–297.
  • Ceyhun, O., and A. Yalçın. 2010. Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science 89:89–96.
  • Chen, G., K. Zhao, G. J. McDermid, and G. J. Hay. 2012. The influence of sampling density on geographically weighted regression: A case study using forest canopy height and optical data. International Journal of Remote Sensing 33:2909–2924.
  • Clark, R. K., T. H. Fay, and C. L.Walker. 1987. Bathymetry calculations with landsat-4-TM Imagery under a generalized ratio assumption. Applied Optics 26(19):4036–4038.
  • Clark, R. K., T. H. Fay, and C. L. Walker. 1988. Bathymetry using thematic mapper imagery. Proc. SPIE, Ocean Optics IX 925:229–231; doi:10.1117/12.945728
  • Costa, B. M., T. A. Battista, S. J. Pittman. 2009. Comparative evaluation of airborne lidar and ship-based multibeam sonar bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment 113:1082–1100.
  • Fonstad, M. A., and W. A. Marcus. 2005. Remote sensing of stream depths with hydraulically assisted bathymetry (HAB) models. Geomorphology 72:320–339.
  • Fotheringham, A. S., M. E. Charlton, and C. Brunsdon. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30(11):1905–1927.
  • Gao, J. 2009. Bathymetric mapping by means of remote sensing methods, accuracy and limitations. Progress in Physical Geography 33(1):103–116.
  • George, D. G. 1997. Bathymetric mapping using a compact airborne spectrographic imager (CASI). International Journal of Remote Sensing 18:2067–2071.
  • Gholamalifard, M., A. Esmaili Sari, A. Abkar, and B. Naimi. 2013. Bathymetric modeling from satellite imagery via single band algorithm (SBA) and principal components analysis (PCA) in southern caspian sea. International Journal of Environmental Research 7(4):877–886.
  • Gollini, I., B. Lu, M. Charlton, C. Brunsdon, and P. Harris. 2015. GWmodel: An R Package for exploring spatial heterogeneity using geographically weighted models. Journal of Statistical Software 63(17):1548–7660.
  • Guo, L., Z. Ma, and L. Zhang. 2008. Comparison of bandwidth selection in application of geographically weighted regression: a case study. Canadian Journal of Forest Research 38:2526–2534; doi:10.1139/X08-091
  • Hamilton, M. K., and C. O. Davis. 1993. Estimating chlorophyll content and bathymetry of lake Tahoe using AVIRIS data. Remote Sensing of Environment 44(2–3):217–230.
  • Harris, P., C. Brunsdon, and A. S. Fotheringham. 2011. Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment 25:123–138.
  • Harris, P., A. S. Fotheringham, R. Crespo, and M. Charlton. 2010. The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences 42:657–680.
  • Hernandez, W. J., and R. A. Armstrong. 2016. Deriving bathymetry from multispectral remote sensing data. Journal of Marine Science and Engineering 4(8):1–16; doi:10.3390/jmse4010008
  • Ibrahim, M., and A. P. Cracknell. 1990. Bathymetry using landsat MSS data of penang island in Malayasia. International Journal of Remote Sensing 11:557–559.
  • Jupp, D. L. B. 1988. Background and extensions to depth of penetration (DOP) mapping in shallow coastal Waters. Symposium on Remote Sensing of the Coastal Zone, Gold Coast, Queensland.
  • Kanno, A., Y. T. Koibuchi, and M. Isobe. 2011. Statistical combination of spatial interpolation and multispectral remote sensing for shallow water bathymetry. IEEE Geoscience and Remote Sensing Letters 8(1):64–68.
  • Kanno, A., and Y. Tanaka. 2012. Modified lyzenga's method for estimating generalized coefficients of satellite-based predictor of shallow water depth. IEEE Geoscience and Remote Sensing Letters 9:715–719.
  • Lu, B., P. Harris, M. Charlton, and C. Brunsdon. 2014. The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science 17(2):85–101; doi:http://dx.doi.org/10.1080/10095020.2014.917453
  • Lyzenga, D. R. 1978. Passive remote sensing techniques for mapping water Depth and bottom features. Applied Optics 17(3):379–383.
  • Lyzenga, D. R. 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and landsat data. International Journal of Remote Sensing 2:72–82.
  • Lyzenga, D. R., N. R. Malinas, and F. J. Tanis. 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing 44(8):2251–2259.
  • Manessa, M. D. M., A. Kanno, M. Sekine, E. E. Ampou, N. Widagti, and A. R. As-syakur. 2014. Shallow-water benthic identification using multispectral satellite imagery: Investigation on the effects of improving noise correction method and spectral cover. Remote Sensing 6:4454–4472.
  • Monteys, X., P. Harris, S. Caloca, and C. Cahalane. 2015. Spatial prediction of coastal bathymetry based on multispectral satellite imagery and multibeam data. Remote Sensing 7:13782–13806; doi:10.3390/rs71013782
  • Muslim, A. M., and G. M. Foody. 2008. DEM and bathymetry estimation for mapping a tide-coordinated shoreline from fine spatial resolution satellite sensor imagery. International Journal of Remote Sensing 29:4515–4536.
  • Pacheco, A., J. Horta, C. Loureiro, and O. Ferreira. 2015. Retrieval of nearshore bathymetry from landsat-8 Images: a tool for coastal monitoring in shallow waters. Remote Sensing of Environment 159:102–116.
  • Philpot, W. D. 1989. Bathymetric mapping with passive multispectral imagery. Applied Optics 28(8):1569–1579.
  • Stoffle, R. W., and D.B. Halmo. 1991. Satellite monitoring of coastal marine ecosystems: a case from the dominican republic. Consortium for Integrated Earth Science Information Network (CIESIN). Saginaw, Michigan.
  • Stumpf, R. P., K. Holderied, and M. Sinclair. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography 48(2):547–556.
  • Su, H., H. Liu, W. Lei, M. Philipi, W. Heyman, and A. Beck. 2013. Geographically adaptive inversion model for improving bathymetric retrieval from multispectral satellite imagery. IEEE Transaction on Geosciences and Remote Sensing 52(1):465–476. doi: 10.1109/TGRS.2013.2241772
  • Tripathi, N. K., and A.M. Rao. 2002. Bathymetric mapping in kakinada bay, India, using IRS-1D LISS-III Data. International Journal of Remote Sensing 23(6):1013–1025.
  • Van Hengel, W., and D. Spitzer. 1991. Multi-temporal water depth mapping by means of landsat TM. International Journal of Remote Sensing 12(4):703–712.
  • Vinayaraj, P., V. Raghavan, S. Masumoto, and J. Glejin. 2015. Comparative evaluation and refinement of algorithm for water depth estimation using medium resolution remote sensing data. International Journal of Geoinformatics 11(3):17–29.
  • Yi, G., and T. Li. 1988. The ocean information contents of remotely sensed image and the acquisition of water depth message. Proceedings of the Ninth Asian Conference on Remote Sensing, Bangkok, Thailand (Asian Association of Remote Sensing, University of Tokyo, Japan) F-3-1–F-3-8.
  • Yrigoyen, C. C., I. G. Rodrigouz, and J. V. Otera. 2008. Modeling spatial variations in household disposable income with geographically weighted regression. Estadística española 50(168):321–360.
  • Warne, D. K. 1972. Landsat as an aid in the preparation of hydrographic charts. Photogrammetric Engineering and Remote Sensing 44:1011–16.

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