Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model

Vinayaraj, Poliyapram; Raghavan, Venkatesh; Masumoto, Shinji

doi:10.1080/01490419.2016.1245227

Cited by 51 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If deep water regions are not supplied, the correction band will be used for correction without the use of deep water regression coefficients. In addition, our previous studies [20] have suggested the use of the SWIR band for correction instead of the NIR band. NIR band is suggested to be used for correction only when multispectral imagery does not include the SWIR band, such as imageries like ASTER.…”

Section: Resultsmentioning

confidence: 99%

“…The estimation has been carried out using 1260 depth points as the dependent variables, and another 2000 depth points were used to validate the results. Both calibration and validation depth points ranged from 0 to 20 m. First of all, a global model [20] has been used to estimate SDB, and the results show reliable accuracy in terms of correlation coefficient (R) of 0.86, coefficient of determination (R 2 ) of 0.74 and Root Mean Square Error (RMSE) of (2.53 m) as shown in Table 2. Nonetheless, a detailed investigation using a residual depth map (Figure 3a) indicates that spatial heterogeneity was not effectively addressed by a global model.…”

Section: Puerto Rico Northeastern Caribbean Seamentioning

confidence: 99%

“…where V(λ) is the in-water volume scattering, B(λ) is the bottom reflectance, S(λ) is the water surface reflectance, and A(λ) is the atmospheric scattering [20]. In deep water, even a short wavelength band does not include a bottom reflectance component and can be considered to correspond to pixels of infinite depth (L(λ ∞ ) i ).…”

Section: Atmospheric and Water Correctionsmentioning

confidence: 99%

“…Prior knowledge is required in order to choose the appropriate kernel type. [20] has discussed in detail the selection of the appropriate kernel in terms of the surface to be estimated and the distribution of calibration points.…”

Section: Geographical Weighted Regressionmentioning

confidence: 99%

“…The radiance observed by a satellite sensor on shallow water basically consists of four components, namely, atmospheric scattering, surface reflection, in-water volume scattering, and bottom reflection components [20]. Many authors have proposed various algorithms for atmospheric and water corrections [6,8,10].…”

Section: Atmospheric and Water Correctionsmentioning

confidence: 99%

See 4 more Smart Citations

Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation

Vinayaraj

Raghavan

Metz

et al. 2017

IJGI

Self Cite

View full text Add to dashboard Cite

Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB algorithms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module (i.image.bathymetry). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R 2 ) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R 2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R 2 of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Puerto Rico Northeastern Caribbean Seamentioning

confidence: 99%

Section: Atmospheric and Water Correctionsmentioning

confidence: 99%

Section: Geographical Weighted Regressionmentioning

confidence: 99%

Section: Atmospheric and Water Correctionsmentioning

confidence: 99%

See 3 more Smart Citations