Modified Lyzenga's Method for Estimating Generalized Coefficients of Satellite-Based Predictor of Shallow Water Depth

Kanno, Ariyo; Tanaka, Yoshisato

doi:10.1109/lgrs.2011.2179517

Cited by 48 publications

(43 citation statements)

References 17 publications

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“…In the case of a multispectral image, there are simple and applicable SDB methods such as linear regression of reflectance logarithm (Lyzenga, Malinas, & Tanis, 2006;Lyzenga, 1978;Paredes & Spero, 1983), the linear ratio (Stumpf, Holderied, & Sinclair, 2003), and Depth of Penetration Zone (Jupp, 1988). At the current state, the linear regression is the most common and widely used SDB (Flener et al, 2012;Kanno & Tanaka, 2012;Liceaga-Correa & Euan-Avila, 2002;Yuzugullu & Aksoy, 2014).…”

Section: Introductionmentioning

confidence: 99%

Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery

Manessa

Kanno

Sekine

et al. 2016

Geoplanning: Journal of Geomatics and Planning

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Section: Introductionmentioning

confidence: 99%

Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery

Manessa

Kanno

Sekine

et al. 2016

Geoplanning: Journal of Geomatics and Planning

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“…A variety of empirical models have been proposed, from linear functions [20], band ratios to log transformed regression models [21], and non-linear inverse models [22] with varying degrees of corrections applied (atmospheric, sun glint, seafloor). The evolution of empirical models has been largely linked to the chronology of satellite platforms: coarse spatial resolution using Landsat TM [13,23,24]; medium spatial resolution SPOT images in shallow waters [15,25] or RapidEye in lakes [26]; and high spatial resolution with the use of commercial satellites such as WorldView [27,28], QuickBird [29,30] and IKONOS [31].…”

Section: Introductionmentioning

confidence: 99%

“…Although [32] have demonstrated an artificial neural network that is not influenced by bottom type or vegetation on the seabed. The Lyzenga method [20] is not restricted to imagery from a single satellite-imagery from Landsat TM [13,21,23,24], QuickBird [29,30], SPOT [15,25] and WorldView-2 [27,28] have all been employed. Nor is the source of the imagery limited to satellites-Flener [33] have performed a comparison between mobile laser scanners and UAV imagery for bathymetric surveys of rivers.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

et al. 2015

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Abstract:The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other OPEN ACCESSRemote Sens. 2015, 7 13783 applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management.

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“…The main functionalities of the module are (1) delineating water region; (2) atmospheric and water corrections; (3) GWR. An atmospheric and water corrections algorithm was adopted from previous authors and improvements have been made in terms of band selection [9,12]. Geometrically and radiometrically corrected spectral bands of any optical multispectral remote sensing data can be used to estimate SDB from the suitable coastal region.…”

Section: Implementation Of Sdb Model As Grass Gis Modulementioning

confidence: 99%

“…Many authors have proposed various algorithms for atmospheric and water corrections [6,8,10]. Among them, some authors [12] have suggested to perform atmospheric correction prior to the water corrections. Nonetheless, this study adopted a more refined way of retrieving bottom reflectance originally proposed by [9] by removing atmospheric, water surface, and water column components.…”

Section: Atmospheric and Water Correctionsmentioning

confidence: 99%

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

Vinayaraj

Raghavan

Metz

et al. 2017

IJGI

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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.

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Modified Lyzenga's Method for Estimating Generalized Coefficients of Satellite-Based Predictor of Shallow Water Depth

Cited by 48 publications

References 17 publications

Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery

Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

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

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