Water Depth Map Research Articles

Satellite-based remote sensing of running water habitats at large riverscape scales: Tools to analyze habitat heterogeneity for river ecosystem management

We illustrate an approach to quantify patterns in hydraulic habitat composition and local heterogeneity applicable at low cost over very large river extents, with selectable reach window scales. Ongoing developments in remote sensing and geographical information science massively improve efficiencies in analyzing earth surface features. With the development of new satellite sensors and drone platforms and with the lowered cost of high resolution multispectral imagery, fluvial geomorphology is experiencing a revolution in mapping streams at high resolution. Exploiting the power of aerial or satellite imagery is particularly useful in a riverscape research framework (Fausch et al., 2002), where high resolution sampling of fluvial features and very large coverage extents are needed. This study presents a satellite remote sensing method that requires very limited field calibration data to estimate over various scales ranging from 1m to many tens or river kilometers (i) spatial composition metrics for key hydraulic mesohabitat types and (ii) reach-scale wetted habitat heterogeneity indices such as the hydromorphological index of diversity (HMID). When the purpose is hydraulic habitat characterization applied over long river networks, the proposed method (although less accurate) is much less computationally expensive and less data demanding than two dimensional computational fluid dynamics (CFD). Here, we illustrate the tools based on a Worldview 2 satellite image of the Kiamika River, near Mont Laurier, Quebec, Canada, specifically over a 17-km river reach below the Kiamika dam. In the first step, a high resolution water depth (D) map is produced from a spectral band ratio (calculated from the multispectral image), calibrated with limited field measurements. Next, based only on known river discharge and estimated cross section depths at time of image capture, empirical-based pseudo-2D hydraulic rules are used to rapidly generate a two-dimensional map of flow velocity (V) over the 17-km Kiamika reach. The joint distribution of D and V variables over wetted zones then is used to reveal structural patterns in hydraulic habitat availability at patch, reach, and segment scales. Here we analyze 156 bivariate (D, V) density function plots estimated over moving reach windows along the satellite scene extent to extract 14 physical habitat metrics (such as river width, mean and modal depths and velocity, variances and covariance in D and V over 1-m pixels, HMID, entropy). A principal component analysis on the set of metrics is then used to cluster river reaches in regard to similarity in their hydraulic habitat composition and heterogeneity. Applications of this approach can include (i) specific fish habitat detection at riverscape scales (e.g., large areas of riffle spawning beds, deeper pools) for regional management, (ii) studying how river habitat heterogeneity is correlated to fish distribution and (iii) guidance for site location for restoration of key habitats or for post regulation monitoring of representative reaches of various types.

Uncertainty related to high resolution topographic data use for flood event modeling over urban areas: toward a sensitivity analysis approach

D Free-surface hydraulic modeling tools are commonly used to assess ood hazard for production of maximal water depth (hmax) maps, as support for ood risk assessment. High Resolu- tion (HR) topographic data are big data getting commonly available and used by hydraulic modeling community. Topographical information and its strategy of inclusion in models, are inputs of great importance for overland ow hmax calculation. To strengthen the assessment of condence level in these deterministic hydraulic models outputs, uncertainty analysis (UA) and global sensitivity analysis (SA) can provide useful information that is required by practitioners and decision makers. UA and SA approaches allow to identify eective strategies to reduce the uncertainty of a model output. In

Mapping wetland water depths over the central Congo Basin using PALSAR ScanSAR, Envisat altimetry, and MODIS VCF data

We have, for the first time, developed a simple linear regression model based on PALSAR ScanSAR backscattering coefficients (σ0), water levels from Envisat altimetry, and MODIS Vegetation Continuous Field (VCF) product to generate water depth maps over flooded forest in the central Congo Basin. The water depth maps we generated are relative to the lowest water level from Envisat altimetry, which is assumed to be a base level with essentially zero depth. The predicted and observed water depths along the Envisat pass showed excellent agreements with RMS differences of 12.8cm to 17.8cm. The water depth maps were also independently validated with ∂h/∂t obtained from PALSAR interferometry, and storage anomalies estimated by multiplying inundation extents from PALSAR ScanSAR with spatially averaged water level anomalies from Envisat altimetry. The water storage volumes calculated from our water depth maps were calculated to be 11.3±2.0km3, 10.3±2.3km3, and 9.3±1.8km3 for 12/05/2006, 12/08/2007, and 12/10/2008, respectively. It is expected that our method can be applied to other river basins that have flooded forest with satellite radar altimeter over-passes. The water depth maps can be directly used to calibrate and validate the spatial and temporal variation of inundation extent and water depth in the wetland derived from a 2-D hydrodynamic model. Furthermore, our water depth maps can be used as a “true” dataset to perform a pre-launch “virtual mission” study of the Surface Water Ocean Topography (SWOT) mission to be launched in 2020.

Multi-scale spatial sensitivity analysis of a model for economic appraisal of flood risk management policies

We demonstrate the use of sensitivity analysis to rank sources of uncertainty in models for economic appraisal of flood risk management policies, taking into account spatial scale issues. A methodology of multi-scale variance-based global sensitivity analysis is developed, and illustrated on the NOE model on the Orb River, France. The variability of the amount of expected annual flood avoided damages, and the associated sensitivity indices, are estimated over different spatial supports, ranging from small cells to the entire floodplain. Both uncertainty maps and sensitivity maps are produced to identify the key input variables in the NOE model at different spatial scales. Our results show that on small spatial supports, variance of the output indicator is mainly due to the water depth maps and the assets map (spatially distributed model inputs), while on large spatial supports, it is mainly due to the flood frequencies and depth–damage curves (non spatial inputs).

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

AbstractSatellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).

Developing a Great Lakes remote sensing community

Observational data collection of the Laurentian Great Lakes has advanced during the past decade to such a level as to allow real-time analysis from moorings and near real-time from satellite data. Ocean color satellite-based remote sensing provides a rich data set that when properly analyzed allows for the generation of geospatial maps of chlorophyll, dissolved organic carbon, suspended minerals, harmful algae blooms (HABs), surface plumes, benthic vegetation communities, primary productivity (pp) and optical water properties (extinction coefficient (kd), photosynthetically active radiation (PAR) and photic zone depth) on a 2 m to 1 km grid dating back in some cases to the early 1970s. Microwave satellite sensors such as synthetic aperture radar (SAR) and scatterometers provide near real-time information on lake ice cover, winds and waves. Multi-temporal Landsat and ALOS PALSAR satellite data are also being used in the Great Lakes to map wetlands and invasive plantswithin these coastal areas. Airborne LiDARs alsoprovide useful nearshore water depth and bottom type mapping in Great Lakes waters. Making the most of such improvements in the historical Great Lakes datasetwill require diligence and a comprehensive strategy, with recognition of the importance of open collaboration in developing a regional working strategy for remote sensing technologies, sensor data applications, and the datamanagementmethods thatwill integrate the technologies within regional and global observation systems. The IOOS/Great Lakes Observing System (GLOS), in collaboration with NOAA/GLERL, and in conjunction with the ongoing NOAA CoastWatch Great Lakes programwith its stakeholders that include federal, state, local government alongwith theprivate sector and academia, can implement into operational scenarios the suite of remote sensing algorithms developed to generate meaningful Great Lakes products.

Temporal variability of the selected flood parameters in the Biebrza River valley

Temporal variability of the selected flood parameters in the Biebrza River valley The paper presents the application of the hydrodynamic one-dimensional model of river flow based on St Venant equations for calculation of annual floods characteristics in the Lower Biebrza River Basin situated in the Northeast of Poland. This model was combined with the Digital Elevation Model (DEM) for determining of area of flooding, its mean depth and flood volume. The model was calibrated using measurements of flood extent and verified comparing calculated flood extent to satellite images. The water level values calculated with the numerical model of flood-flow for cross-sections were next used to determine the digital model of the floodwater table in the valley. Then, inundation extent maps and water depth maps were calculated for whole area of the valley by overlaying the DEM and water table layers. This procedure, flood simulations with the hydrodynamic model and GIS analysis for determinations of inundation extent, was repeated for each day of the analyzed period of 1966-2000. A set of each year maximum calculated areas of flooding with related mean water depths and flood volumes was created and it was submitted to the trend analysis in order to determine possible change tendencies. Statistical analysis of three above described time series of annual flood characteristics was conducted with the use of moving average technique and linear regression method. Moving averages were calculate in two variants: with time step of 5 years and of 10 years. Next, linear trends were calculated by linear regression method. The results indicate that the developed hydrodynamic model of river flow combined with the DEM was useful for the calculation of annual maximum floods in the Lower Biebrza Valley and their parameters. Analysis of calculated flood parameters variability within the analyzed time period of 1961-2000 show decreasing trends for investigated area. This means that results of climate changes are perceptible in investigated area and they form serious danger to riparian wetlands.

Statistical Combination of Spatial Interpolation and Multispectral Remote Sensing for Shallow Water Bathymetry

There is often a need for making a high-resolution or a complete bathymetric map based on sparse point measurements of water depth. Well-known feasible methods for this problem include spatial interpolation and passive remote sensing using readily available multispectral imagery, whose accuracies depend strongly on geometric and optical conditions, respectively. For a more accurate and robust water-depth mapping, in this letter, the two methods are combined into a new method in a statistically reasonable and beneficial manner. The new method is based on a semiparametric regression model that consists of a parametric imagery-based term and a nonparametric spatial interpolation term that complement one another. An accuracy comparison in a test site shows that the new method is more accurate than either of the existing methods when sufficient training data are available and far more accurate than the spatial interpolation method when the training data are scarce.

A Generalized Satellite-based Method of Water Depth Mapping with a Semiparametric Optical Model

A Generalized Satellite-based Method of Water Depth Mapping with a Semiparametric Optical Model

Representing soil pollution by heavy metals using continuous limitation scores

The paper suggests a methodology to represent overall soil pollution in a sampled area using continuous limitation scores. The interpolated heavy metal concentrations are first transformed to limitation scores using the exponential transfer function determined by using two threshold values: permissible concentration (0 limitation points) and seriously polluted soil (4 limitation points). The limitation scores can then be summed to produce the map of cumulative limitation scores and visualize the most critically polluted areas. The methodology was illustrated using the 784 soil samples analyzed for Cd, Cr, Cu, Ni, Pb and Zn in the central region of Croatia. The samples were taken at 1×1 and 2×2km grids and at fixed depths of 20cm. Heavy metal concentrations in soil were determined by ICP-OES after microwave assisted aqua regia digestion. The sampled concentrations were interpolated using block regression-kriging with geology and land cover maps, terrain parameters and industrialization parameters as auxiliary predictors. The results showed that the best auxiliary predictors are geological map, ground water depth, NDVI and slope map and distance to urban areas. The spatial prediction was satisfactory for Cd, Ni, Pb and Zn, and somewhat less satisfactory for Cu and Cr. The final map of cumulative limitation scores showed that 33.5% of the total area is suitable for organic agriculture and 7.2% of the total area is seriously polluted by one or more heavy metals. This procedure can be used to assess suitability of soils for agricultural production and as a basis for possible legal commitments to maintain the soil quality.

Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management

Since several space-borne synthetic aperture radar (SAR) instruments providing high spatial resolutions and multi-polarisation capabilities will be mounted on satellites to be launched from 2006 onwards, radar imagery promises to become an indispensable asset for many environmental monitoring applications. Due to its all weather, day and night capabilities, SAR imagery presents obvious advantages over optical instruments, especially in flood management applications. To date, however, the coarse spatial resolution of available SAR datasets restricts the information that can be reliably extracted and processing techniques tend to be limited to binary floodplain segmentation into ‘flooded’ and ‘non flooded’ areas. It is the purpose of this paper to further improve the exploitation of SAR images in hydraulic modelling and near real-time crisis management by means of developing image processing methodologies that allow for the extraction of water levels at any point of the floodplain. As high-precision digital elevation models (DEM) produced, for instance, from airborne laser scanning become more readily available, methods can be exploited that combine SAR-derived flood extent maps and precise topographic data for retrieving water depth maps. In a case study of a well-documented flood event in January 2003 on the River Alzette, Grand Duchy of Luxembourg, a root mean squared error (R.M.S.E.) of 41cm was obtained by comparing the SAR-derived water heights with surveyed high water marks that were collected during image acquisition. Water levels that were computed by a previously calibrated hydraulic model also suggest that the water surface profiles provided by the combined use of topographic data and SAR accurately reflect the true water line. The extraction of flooded areas within vegetated areas further demonstrates the usefulness of the proposed methodology.

Hierarchical Markovian segmentation of multispectral images for the reconstruction of water depth maps

This paper presents an unsupervised method to segment multispectral images, involving a correlated non-Gaussian noise. The efficiency of the Markovian quadtree-based method we propose will be illustrated on a satellite image segmentation task with multispectral observations, in order to update nautical charts. The proposed method relies on a hierarchical Markovian modeling and includes the estimation of all involved parameters. The parameters of the prior model are automatically calibrated while the estimation of the noise parameters is solved by identifying generalized distribution mixtures [P. Rostaing, J.-N. Provost, C. Collet, Proc. International Workshop EMMCVPR’99: Energy Minimisation Methods in Computer Vision and Pattern Recognition, Springer Verlag, New York, 1999, p. 141], by means of an iterative conditional estimation (ICE) procedure. Generalized Gaussian (GG) distributions are considered to model various intensity distributions of the multispectral images. They are indeed well suited to a large variety of correlated multispectral data. Our segmentation method is applied to Satellite Pour l’Observation de la Terre (SPOT) remote multispectral images. Within each segmented region, a bathymetric inversion model is then estimated to recover the water depth map. Experiments on different real images have demonstrated the efficiency of the whole process and the accuracy of the obtained results has been assessed using ground truth data. The designed segmentation method can be extended to images for which it is required to segment a region of interest using an unsupervised approach.

Remote Sensing Investigation and Survey of Qinghai Lake in the Past 25 Years

青海湖是我国最大的内陆水体,它及其流域的生态环境近来一直倍受广泛关注. 其水位下降、湖水面积缩小、湖体分离等更是研究的热点问题. 本文针对这些问题展开遥感调查与研究,收集了多时相、多种信息源的影像数据;分析了1975年至2000年25年以来湖泊的变迁及成因,湖岸变化及湖体分离状况;用遥感方法反推25年以来湖水位的变化;计算了1975、2000年两个年份的湖水面积,并遥感分析了湖水面积萎缩的原因. 此外,对青海湖进行了实地调查与水深测量,建立了该湖泊水深反演模型.;Qinghai Lake is the largest one of inland lakes in China. Some issues about water level drop, lake area shrinking and lake separateies have brought to broad attentions in recent years. Remote sensing researches for these issues were discussed in this paper. Landsat MSS, TM, ETM⁺ images and Chinese FY-1C, FY-1D, HY-1A satellitic data from 1975 to 2002 were applied to this study. Furthermore, we collected some useful data including relief map, water depth map and hydrological data. Those satellitic data, combined with useful data, were processed and analyzed, some results revealed that the lakeshore physiognomy have greatly changed from 1975 to 2000. The region changed mostly located in the west shore near the Bird Island and Buha River, in the north shore-Shaliu River and the east shore-Sha Island and Haiyan Bay. Due to rich land-carriage materials from Buha River and lake currents action, the west shore of lake is progressing towards lake. The famous Bird Island and Haisepi Island become the part of land; Also, the east shore was accumulated eroded by the removal of sand dune. Shadao Lake and Haiyanwan Lake were gradually separated from Qinghai Lake. On basis of lakeshore line change, we estimated that lake water level dropped about 2 m from 1975 to 2000, by the approach to matching two images and contrasting water depth data. This numeric value estimated was close to hydrological observation value 1.71 m. Also, this study estimated that the area of Qinghai Lake was 4405.64 km² in 1975, and was 4256.04 km² in 2000. FY-1D image on June, 2002 and field investigating results revealed that Haiyanwan Lake with an area or 100 km² has been separated from Qinghai Lake. Water level drop and lake variation were the main cause of lake shrinking. In addition, we carried through field investigating and lake water depth measuring in the south-easters lakeshore of Qinghai Lake in Sep., 2002. The study indicated that the relativity of the grey scale of TM1 band image and water depth was very high in this lake. As a result, the water depth retrieval model was established.

Estimation of water depth by using correlation between the sea-depth and remotely sensed data

Satellite imagery of LANDSAT, SPOT, JERS-1 is used to estimate a water depth. Analgorithm for water depth mapping is used of Lyzenga algorithm. As bathymetry method for satellite imagery, we used a linear-regression method to obtain correlation R2 between water depth and estimated depth. The main purpose is to obtain the maximum R2 for each sensor and to obtain a contribution band for correlation. In this study, we analyze the relation between absolute water depth and remotely sensed data by linearregression, specially, STEPWISE method of SAS cluster. JERS-1/OPS is new sensors by NASDA, and which is used for the first analysis for bathymetry.

Multi-temporal water depth mapping by means of Landsat TM

Abstract The water depth in coastal areas was assumed to be linear with the first principal component of the logarithms of the detected signals within the spectral bands of the satellite sensor. Hence, relative water depths (digital counts) were computed. Charts of absolute water depths were then derived by the application of several calibration points with known water depth. The charts produced in this way were compared with available sounding charts. Multi-temporal images were used to assess the quality of the method and to detect morphological changes.

Geology and Resource Potential of the Manteo Prospect

The Manteo exploration unit, located approximately 32 mi northeast of Cape Hatteras, North Carolina, is a potential East Coast elephant-size field. The unit consists of 21 leases that cost industry $296 million in bonuses since 1981. Mobil has estimated that the unit may contain as much as 5 tcf of natural gas, which would be the largest domestic find since Prudhoe Bay. The Manteo prospect, on the seaward margin of the Carolina Platform, lies between two major basins: the Baltimore Canyon Trough to the north and the Carolina Trough to the south. The prospect is within the western Atlantic carbonate trend, a Jurassic-Early Cretaceous reef and platform limestone complex that stretches from the Gulf of Mexico to Newfoundland. Seismic interpretation is complicated by distortions caused by abrupt changes of water depth map near the top of the Jurassic shows a large eat-northeast-trending structure about 30 mi long and 5 mi wide with structural closure of over 1,000 ft. Source rock analyses suggest that Upper Jurassic carbonate reservoirs would more likely contain gas than oil. The Minerals Management Service (MMS) estimates reservoir parameters from wells drilled on trend (Baltimore Canyon Trough) and recovery parameters from worldwide analogs. The 21-block unit wasmore » approved by the MMS in May of 1990. Since 1988, the MMS has worked with North Carolina, Federal and local agencies, and Mobil in preparation for wildcat exploration approval. The MMS has prepared an extensive environmental report for this exploration proposal.« less

Water depth mapping from passive remote sensing data under a generalized ratio assumption

Water depth mapping from passive remote sensing data under a generalized ratio assumption