Field calibration and validation of remote-sensing surveys

The Optical Collection Suite (OCS) is a ground-truth sampling system designed to perform in situ measurements that help calibrate and validate optical remote-sensing and swath-sonar surveys for mapping and monitoring coastal ecosystems and ocean planning. The OCS system enables researchers to collect underwater imagery with real-time feedback, measure the spectral response, and quantify the water clarity with simple and relatively inexpensive instruments that can be hand-deployed from a small vessel. This article reviews the design and performance of the system, based on operational and logistical considerations, as well as the data requirements to support a number of coastal science and management projects. The OCS system has been operational since 2009 and has been used in several ground-truth missions that overlapped with airborne lidar bathymetry (ALB), hyperspectral imagery (HSI), and swath-sonar bathymetric surveys in the Gulf of Maine, southwest Alaska, and the US Virgin Islands (USVI). Research projects that have used the system include a comparison of backscatter intensity derived from acoustic (multibeam/interferometric sonars) versus active optical (ALB) sensors, ALB bottom detection, and seafloor characterization using HSI and ALB.     

Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: the mouth of the Guadiana Estuary

Periodic calculation of coastal bathymetries can show the evolution of geomorphological features in active areas such as mesotidal estuary mouths. Bathymetries in shallow coastal areas have been addressed mainly by two technologies, lidar and optical remote sensing. Lidar provides good accuracy, but is an expensive technique, requiring planned flights for each region and dates of interest. Optical remote sensing acquires images periodically but its results are limited by water turbidity. Here we use a lidar bathymetry to compare different bathymetry computation methods using a SPOT optical image from a nearby date. Three statistical models (green-band, PCA correlations, and GLM) were applied to obtain mathematical expressions to estimate bathymetry from that image: all gave errors lower than 1 m in an area with depths ranging from 0 to 6 m. These algorithms were then applied to images from three different dates, correcting the effects caused by different tidal and atmospheric conditions. We show how this allows the study of morphological changes. We discuss the accuracy obtained with respect to the reference bathymetry (0.9 m on average, but less than 0.5 m in low-turbidity areas), the effects of the turbidity on our estimations, and compare both with previously published results. The results show that this approach is effective and allows identification of known features of coastal dynamics, and thus it would be an important step towards short-term bathymetry monitoring based on optical satellite remote sensing.     

滩海水深遥感反演模型应用研究

研究船舶准确探测滩海水深的信息问题。由于海水深度不同,散射特征和输入均有信号衰减,影响准确探测定位。传统探测方法利用船载声纳测深方法测量难度大,更新速度慢。为了弥补不足,提出了遥感探测滩海水深的方法,利用遥感的水深探测技术可快速获取大面积的滩海水深信息,根据遥感反演水深的基本原理,采用我国渤海滩海光学数据和实测水深资料,结合半理论半经验模型和统计相关模型,进行水深探测模型仿真,得到平均标准误差为2.7m。实验结果表明了方法的快速有效,可为渤海湾滩浅海水深地形研究提供可靠的依据。     

Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging (lidar) for shallow water (<2 m) bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.     

A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data

In order to retrieve bathymetry, substratum type and the concentrations of the optically active constituents of the water column, an integrated physics based mapping approach was applied to airborne hyperspectral data of Moreton Bay, Australia. The remotely sensed data were sub-optimal due to high and mid-level cloud covers. Critical to the correct interpretation of the resultant coastal bathymetry map was the development of a quality control procedure based on additional outputs of the integrated physics based mapping approach and the characteristics of the instrument. These two outputs were: an optical closure term which defines differences between the image and model based remote sensing signal; and an estimate of the relative contribution of the substratum signal to the remote sensing signal. This quality control procedure was able to identify those pixels with a reliable retrieval of depth and to detect thin and thick clouds and their shadows, which were subsequently masked out from further analysis. The derived coastal bathymetry in depths ranging 4-13 m for the mapped area was within ± 15% of boat-based multi-beam acoustic mapping survey of the same area. The agreement between the imaging spectrometry and the acoustic datasets varies as a function of the contribution of the bottom visibility to the remote sensing signal. As expected, there was greater agreement in shallower clear water (± 0.67 m) than quasi-optically deep water (± 1.35 m). The quantitative identification and screening of the optically deep waters and the quasi-optically deep waters led to improved precision in the depth retrieval. These results suggest that the physics based mapping approach adopted in this study performs well for retrieving water column depths in coastal waters in water depths ranging 4-13 m for the area and conditions studied, even with sub-optimal imagery.     

Multispectral derivation of bathymetry in Singapore's shallow,turbid waters

In very shallow waters, active sensing determinations of bathymetry are often expensive and unwieldy. Sea depth estimation using passive remote-sensing methods is an attractive alternative, especially using cheap multispectral imagery with high spatial resolution. Three models for the determination of bathymetry from multispectral imagery were utilized with new eight-band images from DigitalGlobe's Worldview-2 satellite platform. All three were trained with electronic navigational chart data and evaluated for accuracy in Singapore's turbid shallow coastal waters. These waters are characterized by high turbidity, suspended sediment, and vehicle traffic. Of the three models, a linear band algorithm performed best, with a root-mean-square error (RMSE) of 0.48 m. A look-up table classification provided a precision of 0.64 m, but was limited by a training set that did not fully represent variance in water column and benthic properties. Possibly owing to the domination of particle backscatter over pigment absorption in these turbid waters, a linear ratio algorithm did not perform as well as the linear band algorithm, achieving an RMSE of only 0.56 m. Analysis found that the usual relationship between ratios of low-absorption to high-absorption bands and depth does not hold as well for these waters, likely due to backscatter dominating leaving-water signals, masking relative absorption effects. High turbidity, with a Secchi disk depth of 1.9 m, limited analysis to shallow reefs and coastline and likely impacted the sensitivity of the bathymetric algorithms. A larger validation data set containing water quality and benthic data is required for further investigation to determine specific sources of error.     

Lidar-based mapping of flood control levees in South Louisiana

Flood protection in south Louisiana is largely dependent on earthen levees, and in the aftermath of Hurricane Katrina the state’s levee system has received intense scrutiny. Accurate elevation data along the levees are critical to local levee district managers responsible for monitoring and maintaining the extensive system of non-federal levees in coastal Louisiana. In 2012, high resolution airborne lidar data were acquired over levees in Lafourche Parish, Louisiana, and a mobile terrestrial lidar survey was conducted for selected levee segments using a terrestrial lidar scanner mounted on a truck. The mobile terrestrial lidar data were collected to test the feasibility of using this relatively new technology to map flood control levees and to compare the accuracy of the terrestrial and airborne lidar. Metrics assessing levee geometry derived from the two lidar surveys are also presented as an efficient, comprehensive method to quantify levee height and stability. The vertical root mean square error values of the terrestrial lidar and airborne lidar digital-derived digital terrain models were 0.038 m and 0.055 m, respectively. The comparison of levee metrics derived from the airborne and terrestrial lidar-based digital terrain models showed that both types of lidar yielded similar results, indicating that either or both surveying techniques could be used to monitor geomorphic change over time. Because airborne lidar is costly, many parts of the USA and other countries have never been mapped with airborne lidar, and repeat surveys are often not available for change detection studies. Terrestrial lidar provides a practical option for conducting repeat surveys of levees and other terrain features that cover a relatively small area, such as eroding cliffs or stream banks, and dunes.     

Determination of shallow water depth using optical satellite images

The Penghu archipelago comprises 64 basaltic volcanic isles lying on the Taiwan Strait between mainland China and Taiwan. The water around and within these isles is shallow and poses considerable difficulty in echo sounding detection for bathymetry. Most existing bathymetry data around such areas are in water depths of greater than 5 m. Therefore, when the water depth is less than 5 m the data tend to be over-extrapolated. In this study, a remote sensing method provides a more effective approach to recording shallow water depths compared to traditional soundings using multitemporal images collected by optical/near-infrared sensors from SPOT satellites. This method employs optical energy reflections to obtain the water depth. In this study, we made several improvements wherein a relative atmosphere correction technique was used to calibrate two images within a similar atmospheric condition. We then compared the satellite images acquired from different dates to obtain the local water attenuation coefficient of sunlight. Finally, we developed a means to estimate the water attenuation coefficient and bottom reflectance which will satisfy the two parameters across the study area. Our results show a high-resolution map of shallow bathymetry for the Penghu archipelago and revealed a maximum depth of about 20 m. This study provides an efficient approach for shallow bathymetry retrieval. Many detailed features revealed by this approach may contribute to further geological research and developments in harbour and coastal engineering.     

Remote sensing techniques in estuaries and coastal zones an update

This paper reviews briefly the current state of the use of remote sensing in estuaries and coastal waters. This covers the established uses, the recent developments in relation to hyperspectral imaging systems (imaging spectrometers), interferometric SAR and lidar bathymetry. The opportunities that are expected to occur soon, as 1-m resolution space-flown systems become available, are also discussed.     

Mapping of complex bedrock structure using the high-resolution reflection seismic technique

In May of 1996, four reflection seismic profiles and one VSP (vertical seismic profile) were acquired near Colonsay, Saskatchewan. This survey was conducted as part of a site-specific study of the high-resolution reflection seismic profiling, and shallow open-borehole VSP methods. The reflection seismic/VSP survey was designed with four specific objectives in mind. The sponsor wanted to determine: (1) whether high-resolution reflection seismic data could provide an essentially continuous structural image of the shallow subsurface (Quaternary alluvium and Cretaceous shale to a depth of ca 200 m); (2) if the Cretaceous bedrock and overlying Quaternary strata in the study area are extensively faulted; (3) whether useful VSP data could be acquired from shallow open-boreholes in the study area; and (4) optimum high-resolution reflection seismic acquisition parameters (given the target zone and site conditions) for future production-oriented work in the area. From a technical perspective the survey was successful. The acquired high-resolution reflection seismic data effectively image the shallow subsurface, and support the thesis that bedrock and overlying Quaternary strata are extensively faulted in the study area. The VSP data establish that sonic log velocities recorded above the water table in a fluid-filled borehole are anomalously high (relative to the seismic velocity of an equivalent thickness of unsaturated sediment). On the basis of the VSP control, the sonic log data were modified such that the corresponding synthetic seismograms correlate well with the reflection seismic profiles. Additionally, on the basis of field testing, optimum site/target specific field acquisition parameters were determined. These will be of use in designing any subsequent seismic reflection and VSP surveys in the study area.     

基于稳健最小二乘支持向量机的测深激光信号处理

针对浅海探测中激光回波噪声源多、信噪比低,传统非加权最小二乘支持向量机和加权最小二乘支持向量机对低信噪比信号滤波不足的问题,提出将稳健最小二乘法与加权最小二乘支持向量机相结合的滤波方法(HW-LS-SVM)。首先采用强淘汰权函数计算先验权值、残差和均方误差,然后采用权函数模型计算最小二乘支持向量机的权值,最后通过迭代计算实现回波信号滤波。通过仿真实验结果表明, HW-LS-SVM方法较最小二乘支持向量机、贝叶斯最小二乘支持向量机和传统加权最小二乘支持向量机滤波效果更加稳健,在噪声率为45%的情况下,滤波效果较为理想,水面和水底回波提取正确率为100%;对实测4组深水区和4组浅水区数据滤波后提取的海水深度均与背景资料的深度吻合。由此表明, HW-LS-SVM方法具有更好的抗噪性,更适合于对信噪比低的测深激光信号的滤波处理。     

Remote sensing of coastal waters by airborne lidar and satellite radiometer Part 1: A model study

Abstract

Radiative transfer calculations for remote sensing of coastal waters by airborne lidar and satellite radiometer have been compared in order to answer the question, whether an airborne lidar may be used instead of in situ measurements from ships to calibrate a satellite radiometer. The radiative transfer of laserlight measuring the Raman-scattering of water molecules, the fluorescence of chlorophyll-a and the fluorescence of yellow substance or Gelbstoff is simulated by the lidar equations while the radiance to a satellite radiometer is calculated with an ocean-atmosphere model based on the matrix-operator method. Including multiple scattering in the lidar equations, an eigenvalue analysis shows that three oceanic constituents (chlorophyll-a, nonchlorophyllous particles and Gelbstoff) can be separated measuring the backscattered laserlight at three wavelengths from a height of 100 to 200m. Changes in the concentration of all three substances are detected with higher accuracy with an airborne lidar than with a radiometer even at the same height. A comparison of different algorithms indicates that the common blue-green algorithms fail in coastal waters due to the variability of several oceanic constituents, which influence the colour of sea water. In this case, algorithms using the sun-induced chlorophyll-a fluorescence at 685 nm, are superior to blue-green algorithms. Airborne lidar measurements of the chlorophyll-a fluorescence at 685 nm, normalized by the Raman-signal at 650 nm, are as good as in situ data and can be used to calibrate satellite measurements of chlorophyll-a.     

Small‐footprint,waveform‐resolving lidar estimation of submerged and sub‐canopy topography in coastal environments

The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near‐shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare‐earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last‐surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0–2.5 m water depth) and sub‐canopy environments. Root mean square elevation errors (RMSEs) ranged from 10–14 cm for submerged topography to 16–20 cm for sub‐canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross‐environment capability of a green‐wavelength, waveform‐resolving lidar system, making it an ideal tool for mapping coastal environments.     

A graph-based approach for assessing storm-induced coastal changes

Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs.     

Mapping bottom depth and albedo in coastal waters of the South China Sea islands and reefs using Landsat TM and ETM+ data

Optical models for the retrieval of shallow water bottom depth and albedo using multispectral data usually require in situ water depth data to tune the model parameters. In the South China Sea (SCS), however, such in situ data are often lacking or obsolete (perhaps from half a century ago) for most coastal waters around its islands and reefs. Here, we combine multispectral data collected by MODIS and Landsat to estimate bottom depth and albedo for four coral reef regions in the SCS, with results partially validated by some scarce in situ data. The waters in these remote regions are oligotrophic whose optical properties can be well derived from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements when the waters are optically deep. The MODIS-derived optical properties are used to estimate the water column attenuation to the Landsat measurements over shallow waters, thus eliminating the requirement of model tuning using field measured water depths. The model is applied to four Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images covering Pratas Atoll, Woody Island, Scarborough Shoal, and North Danger Reefs. The retrieved bathymetry around Pratas Atoll and North Danger Reefs are validated with some in situ data between 1 and 25 m. The relative difference and root mean square difference between the two measurements were 17% and 1.6 m, for Pratas Atoll and 11% and 1.1 m for North Danger Reefs, respectively. These results suggest that the approach developed here may be extended to other shallow, clear waters in the SCS.     

Comparison of airborne lidar,aerial photography,and field surveys to model the habitat suitability of a cryptic forest species – the hazel grouse

Light detection and ranging (lidar) is a useful tool for measuring three-dimensional habitat structure; hence, its use in habitat suitability models has been explored, both as a single resource and in combination with other remote-sensing techniques. Here, we evaluated the suitability of airborne lidar data in comparison with aerial photographs and field surveys for modelling the distribution of an endangered and cryptic forest species, the hazel grouse (Bonasa bonasia). The study was conducted in the Bavarian Forest National Park of southeast Germany. Subsequently, a prediction map for conservation planning was generated for a large area, which encompassed the National Park. We examined the utility of lidar data for generating a hazel grouse distribution model by using machine learning (boosted regression trees), and then compared the results to variables derived from field surveys and aerial photographs, both separately and in combination. The cross-validated discrimination ability of the model was slightly higher when using lidar data (area under the receiver operator characteristic curve (AUC), 0.79) compared to models using aerial photographs (AUC, 0.75) or field survey data (AUC, 0.78). The predictive performance consistently increased when combining the predictors from different sources, with an AUC of 0.86 being produced in the model combining all three data sources. The three data sources complemented one another, with each data source probably having an advantage at deriving one of three key aspects of the hazel grouse habitat, namely, vertically well-structured forest stands, horizontally mixed successional vegetation stages, and certain deciduous trees as food resources such as mountain ash (Sorbus aucuparia). In addition, the diverse lidar metrics might be applied to simultaneously characterize vertically and horizontally well-structured forest stands. We conclude that public available airborne lidar data are a viable source for creating habitat suitability maps for large areas and may have increased utility for detecting forest characteristics and valuable wildlife habitats.     

Evaluation of the influence of yellow substance absorption on the remote sensing of water quality in the Gulf of Naples: a case study

Abstract

The yellow substance absorption in the coastal waters of the Gulf of Naples was measured by a method combining beam transmission and induced fluorescence measurements. A water optical model specific to that test area, derived from experimentation carried out in situ has been used to evaluate the impact of the measured yellow substance absorption on the remote sensing of water quality parameters, such as chlorophyll-like pigment and total suspended sediment concentrations.     

Metrological evaluation of vessel-based mobile lidar for survey of coastal structures

Surveying of coastal structures is necessary to ensure that the structures are in good operating condition. Mobile lidar systems could be installed on vessels to monitor the coastal structures, even if there is no previous metrological information about their reliability. The aim of this work is to perform the metrological verification of the mentioned survey instruments. The verification methodology is based on the comparison of mobile lidar data from a breakwater with ground truth data provided by a Riegl LMS Z390i terrestrial lidar. Mobile lidar data are obtained from an Optech Lynx system installed on a vessel. The results show errors lower than 0.09 m based on distance measurements. Universal Transverse Mercator (UTM) coordinates show absolute errors lower than 0.12 m in the horizontal plane and 0.18 m in height. Precision analysis from the mobile lidar shows error values up to 0.055 m, while the terrestrial lidar gives 0.018 m for the same region of interest. The spatial resolution from the mobile lidar gives values of 321 points m^?2, in comparison with the 22,013 points m^?2 from the terrestrial lidar. Two point clouds from the same region of the breakwaters, obtained using the mobile and the terrestrial lidar, were triangulated and rasterized. The standard deviation of the vertical distances between the nodes of the raster data shows a value of 0.064 m. The results obtained show the potential of mobile lidar systems in combination with vessels for the monitoring of certain coastal structures, such as breakwaters, sea walls, bridges, wharves, and jetties.     

Barriers to adopting satellite remote sensing for water quality management

Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.     

Integrating service quality evaluation model to improve employees' satisfaction for high‐tech iindustry

This study addresses the deficiency research of service quality by integrating the importance–satisfaction model (I‐S model) and the performance control matrix to provide a more comprehensive assessment model for improving specific quality attributes. The study applies this integrated measuring instrument in a Taiwanese high‐tech industry by using a questionnaire survey of production‐line employees to assess importance and satisfaction in their capacity as internal customers of the company. The study thus identifies and prioritizes quality attributes that require improvement. The findings are significant for service providers because they take into account: (i) the relative importance of quality attributes; (ii) the relative satisfaction level of these attributes; and (iii) the resources available for improvement. The study demonstrates that the I‐S model and the performance control matrix, taken together, provide an excellent measuring instrument for assessing priorities for quality improvement. © 2011 Wiley Periodicals, Inc.