Multispectral bathymetry using a simple physically based algorithm

Lyzenga, David R.; Malinas, N.P.; Tanis, F.J.

doi:10.1109/tgrs.2006.872909

Cited by 400 publications

(370 citation statements)

References 10 publications

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“…Therefore, remote sensing imagery from satellites, such as Landsat (Liceaga-Correa and Euan-Avila, 2002), QuickBird (Lyons et al, 2011), IKONOS (Stumpf et al, 2003), WorldView-2 (Hamylton et al, 2015;Lee et al, 2011), and aircrafts (Carbonneau et al, 2006;Marcus et al, 2003), has been used to monitor the bathymetry of inland water bodies. However, bathymetry can only be derived from optical imagery when water is very clear and shallow, the sediment is comparatively homogeneous, and atmospheric conditions are favorable (Legleiter et al, 2009;Lyzenga, 1981;Lyzenga et al, 2006;Overstreet and Legleiter, 2017).…”

Section: Introductionmentioning

confidence: 99%

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Bandini

Olesen

Jakobsen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. High-quality bathymetric maps of inland water bodies are a common requirement for hydraulic engineering and hydrological science applications. Remote sensing methods, such as space-borne and airborne multispectral imaging or lidar, have been developed to estimate water depth, but are ineffective for most inland water bodies, because of the attenuation of electromagnetic radiation in water, especially under turbid conditions. Surveys conducted with boats equipped with sonars can retrieve accurate water depths, but are expensive, time-consuming, and unsuitable for unnavigable water bodies.We develop and assess a novel approach to retrieve accurate and high-resolution bathymetry maps. We measured accurate water depths using a tethered floating sonar controlled by an unmanned aerial vehicle (UAV) in a lake and in two different rivers located in Denmark. The developed technique combines the advantages of remote sensing with the potential of bathymetric sonars. UAV surveys can be conducted also in unnavigable, inaccessible, or remote water bodies. The tethered sonar can measure bathymetry with an accuracy of ∼ 2.1 % of the actual depth for observations up to 35 m, without being significantly affected by water turbidity, bed form, or bed material.

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Section: Introductionmentioning

confidence: 99%

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Bandini

Olesen

Jakobsen

et al. 2018

Hydrol. Earth Syst. Sci.

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“…The presence and duration of flooding can be informative in lieu of more intensive field measurements (Elphick, 2008;Farmer and Parent, 1997;Reiter et al, 2015). For example, these attributes influence invertebrate prey densities, which are challenging to measure precisely in situ over large areas (Batzer, 2013;Lee et al, 1999;Lyzenga et al, 2006). Landsat's historical timespan permits analysis of multiple El Niño cycles, which strongly influence the frequency and intensity of precipitation for many regions, including the western U.S. (Feldl and Roe, 2011;Hurrell and Loon, 1997).…”

Section: Introductionmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

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“…Retrieval of bathymetry under restrictive environmental conditions is limited. The linear band algorithm modified from Lyzenga et al [61] can provide slightly better results than Stumpf model using WV-2 data. The additional 4 spectral bands provided by the WV-2 have been found to be improved the linear band model marginally.…”

Section: Summary and Discussionmentioning

confidence: 95%

“…(IKONOS, SHOALS LIDAR) [61] Lyzenga Model A single set of coefficients derived from a set of the IKONOS images gives good performance over a variety of conditions, with an aggregate RMS of 2.3 m over all of the data sets.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…However, the effect of deep water radiance was almost negligible in shallow water bodies. To account for water quality heterogeneity and depth-independent variability in reflectance values between bands this algorithm was updated by Lyzenga [36], and again Lyzenga et al [61]. This produced (N + 1) depth-independent variables that could be used as indices of bottom type, and finally modeled depth as:…”

Section: Lyzenga Model or Linear Band Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons

Jawak¹,

Vadlamani²,

Luis³

2015

ARS

107

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This paper discusses the bathymetric mapping technologies by means of satellite remote sensing (RS) with special emphasis on bathymetry derivation models, methods, accuracies, advantages, limitations, and comparisons. Traditionally, bathymetry can be mapped using echo sounding sounders. However, this method is constrained by its inefficiency in shallow waters and very high operating logistic costs. In comparison, RS technologies present efficient and cost-effective means of mapping bathymetry over remote and broad areas. RS of bathymetry can be categorised into two broad classes: active RS and passive RS. Active RS methods are based on active satellite sensors, which emit artificial radiation to study the earth surface or atmospheric features, e.g. light detection and ranging (LIDAR), polarimetric synthetic aperture radar (SAR), altimeters, etc. Passive RS methods are based on passive satellite sensors, which detect sunlight (natural source of light) radiation reflected from the earth and thermal radiation in the visible and infrared portion of the electromagnetic spectrum, e.g. multispectral or optical satellite sensors. Bathymetric methods can also be categorised as imaging methods and non-imaging methods. The non-imaging method is elucidated by laser scanners or LIDAR, which measures the distance between the sensor and the water surface or the ocean floor using a single wave pulse or double waves. On the other hand, imaging methods approximate the water depth based on the pixel values or digital numbers (DN) (representing reflectance or backscatter) of an image. Imaging methods make use of the visible and/or near infrared (NIR) and microwave radiation. Imaging methods are implemented with either analytical modelling or empirical modelling, or by a blend of both. This paper presents the development of bathymetric mapping technology by using RS, and discusses the state-of-the-art S. D. Jawak et al. 148bathymetry derivation methods/algorithms and their implications in practical applications.

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Multispectral bathymetry using a simple physically based algorithm

Cited by 400 publications

References 10 publications

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Technical note: Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an unmanned aerial vehicle

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

A Synoptic Review on Deriving Bathymetry Information Using Remote Sensing Technologies: Models, Methods and Comparisons

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