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Special Issue "Remote Sensing in Coastal Ecosystem"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 January 2011)

Special Issue Editor

Guest Editor
Dr. Thomas R. Allen

Department of Geography, East Carolina University, Greenville, NC 27858, USA
Website | E-Mail
Phone: 1-252-328-6624
Fax: 757-328-6054
Interests: geographic information science and technology; emphasizing GIS, remote sensing and environmental modeling of coastal and estuarine environments; landscape ecology and spatial epidemiology of vector-borne diseases

Special Issue Information

Dear Colleagues,

Many coastal regions face continuing pressure from surging population growth, natural resource extraction, natural and technological hazards, climate change and attendant sea-level rise. These issues beckon advances and applications of remote sensing in the coastal environment for resources inventorying and monitoring, planning for sustainable development, and coastal hazards assessment. Dynamic coastal environments pose challenges and opportunities to remote sensing science, particularly the capture and characterization of highly dynamic coastal processes, complex estuarine and subaqueous habitats, and spectral characteristics of water and its suspended and solute contents. This special issue invites scholarly papers that advance the science of remote sensing for coastal environments through innovative algorithm development and novel applications.

Coastal-related topics especially welcome include:

Technical and algorithmic advances

  • coastal habitat mapping (wetlands, benthic, and estuarine habitats)
  • thermal and bio-optical characteristics of coastal waters
  • coastal modeling and RS/GIS integration
  • Airborne and terrestrial LiDAR
  • change detection
  • multi-sensor integration and fusion for coastal applications

Applied topics

  • coastal hazards
  • sea-level rise
  • shoreline erosion
  • population growth
  • coastal storms, hurricanes, and storm surges
  • wetlands
  • coastal habitats and resource mapping

Dr. Thomas R. Allen
Guest Editor

Published Papers (13 papers)

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Open AccessArticle Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA
Remote Sens. 2011, 3(12), 2663-2681; doi:10.3390/rs3122663
Received: 26 October 2011 / Revised: 9 December 2011 / Accepted: 9 December 2011 / Published: 12 December 2011
Cited by 9 | PDF Full-text (814 KB) | HTML Full-text | XML Full-text
Abstract
The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes’ ability to carry disease-causing pathogens, particularly in low-lying, poorly drained coastal plains vulnerable to tropical cyclones. In areas with reservoirs of disease, mosquito abundance information
[...] Read more.
The increase in mosquito populations following extreme weather events poses a major threat to humans because of mosquitoes’ ability to carry disease-causing pathogens, particularly in low-lying, poorly drained coastal plains vulnerable to tropical cyclones. In areas with reservoirs of disease, mosquito abundance information can help to identify the areas at higher risk of disease transmission. Using a Geographic Information System (GIS), mosquito abundance is predicted across the City of Chesapeake, Virginia. The mosquito abundance model uses mosquito light trap counts, a habitat suitability model, and dynamic environmental variables (temperature and precipitation) to predict the abundance of the species Culiseta melanura, as well as the combined abundance of the ephemeral species, Aedes vexans and Psorophora columbiae, for the year 2003. Remote sensing techniques were used to quantify environmental variables for a potential habitat suitability index for the mosquito species. The goal of this study was to produce an abundance model that could guide risk assessment, surveillance, and potential disease transmission. Results highlight the utility of integrating field surveillance, remote sensing for synoptic landscape habitat distributions, and dynamic environmental data for predicting mosquito vector abundance across low-lying coastal plains. Limitations of mosquito trapping and multi-source geospatial environmental data are highlighted for future spatial modeling of disease transmission risk. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR)
Remote Sens. 2011, 3(12), 2630-2662; doi:10.3390/rs3122630
Received: 19 October 2011 / Revised: 30 November 2011 / Accepted: 5 December 2011 / Published: 7 December 2011
Cited by 26 | PDF Full-text (9953 KB) | HTML Full-text | XML Full-text
Abstract
The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor
[...] Read more.
The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico. UAVSAR is a fully polarimetric L-band Synthetic Aperture Radar (SAR) sensor for obtaining data at high spatial resolutions. Starting a month prior to the UAVSAR collections, visual observations confirmed oil impacts along shorelines within northeastern Barataria Bay waters in eastern coastal Louisiana. UAVSAR data along several flight lines over Barataria Bay were collected on 23 June 2010, including the repeat flight line for which data were collected in June 2009. Our analysis of calibrated single-look complex data for these flight lines shows that structural damage of shoreline marsh accompanied by oil occurrence manifested as anomalous features not evident in pre-spill data. Freeman-Durden (FD) and Cloude-Pottier (CP) decompositions of the polarimetric data and Wishart classifications seeded with the FD and CP classes also highlighted these nearshore features as a change in dominant scattering mechanism. All decompositions and classifications also identify a class of interior marshes that reproduce the spatially extensive changes in backscatter indicated by the pre- and post-spill comparison of multi-polarization radar backscatter data. FD and CP decompositions reveal that those changes indicate a transform of dominant scatter from primarily surface or volumetric to double or even bounce. Given supportive evidence that oil-polluted waters penetrated into the interior marshes, it is reasonable that these backscatter changes correspond with oil exposure; however, multiple factors prevent unambiguous determination of whether UAVSAR detected oil in interior marshes. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Impacts of Coastal Inundation Due to Climate Change in a CLUSTER of Urban Coastal Communities in Ghana, West Africa
Remote Sens. 2011, 3(9), 2029-2050; doi:10.3390/rs3092029
Received: 19 July 2011 / Revised: 30 August 2011 / Accepted: 2 September 2011 / Published: 7 September 2011
Cited by 10 | PDF Full-text (2070 KB) | HTML Full-text | XML Full-text
Abstract
The increasing rates of sea level rise caused by global warming within the 21st century are expected to exacerbate inundation and episodic flooding tide in low-lying coastal environments. This development threatens both human development and natural habitats within such coastal communities. The impact
[...] Read more.
The increasing rates of sea level rise caused by global warming within the 21st century are expected to exacerbate inundation and episodic flooding tide in low-lying coastal environments. This development threatens both human development and natural habitats within such coastal communities. The impact of sea level rise will be more pronounced in developing countries where there is limited adaptation capacity. This paper presents a comprehensive assessment of the expected impacts of sea level rise in three communities in the Dansoman coastal area of Accra, Ghana. Future sea level rises were projected based on global scenarios and the Commonwealth Scientific and Industrial Research Organization General Circulation Models—CSIRO_MK2_GS GCM. These were used in the SimCLIM model based on the modified Bruun rule and the simulated results overlaid on near vertical aerial photographs taken in 2005. It emerged that the Dansoman coastline could recede by about 202 m by the year 2100 with baseline from 1970 to 1990. The potential impacts on the socioeconomic and natural systems of the Dansoman coastal area were characterized at the Panbros, Grefi and Gbegbeyise communities. The study revealed that about 84% of the local dwellers is aware of the rising sea level in the coastal area but have poor measures of adapting to the effects of flood disasters. Analysis of the likely impacts of coastal inundation revealed that about 650,000 people, 926 buildings and a total area of about 0.80 km2 of land are vulnerable to permanent inundation by the year 2100. The study has shown that there will be significant losses to both life and property by the year 2100 in the Dansoman coastal community in the event of sea level rise. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery
Remote Sens. 2011, 3(8), 1568-1583; doi:10.3390/rs3081568
Received: 27 April 2011 / Revised: 5 July 2011 / Accepted: 8 July 2011 / Published: 25 July 2011
Cited by 12 | PDF Full-text (626 KB) | HTML Full-text | XML Full-text
Abstract
In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate
[...] Read more.
In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non‑mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Shoreline Change along Sheltered Coastlines: Insights from the Neuse River Estuary, NC, USA
Remote Sens. 2011, 3(7), 1516-1534; doi:10.3390/rs3071516
Received: 1 June 2011 / Revised: 29 June 2011 / Accepted: 29 June 2011 / Published: 12 July 2011
Cited by 11 | PDF Full-text (1655 KB) | HTML Full-text | XML Full-text
Abstract
Coastlines are constantly changing due to both natural and anthropogenic forces, and climate change and associated sea level rise will continue to reshape coasts in the future. Erosion is not only apparent along oceanfront areas; shoreline dynamics in sheltered water bodies have also
[...] Read more.
Coastlines are constantly changing due to both natural and anthropogenic forces, and climate change and associated sea level rise will continue to reshape coasts in the future. Erosion is not only apparent along oceanfront areas; shoreline dynamics in sheltered water bodies have also gained greater attention. Additional estuarine shoreline studies are needed to better understand and protect coastal resources. This study uses a point-based approach to analyze estuarine shoreline change and associated parameters, including fetch, wave energy, elevation, and vegetation, in the Neuse River Estuary (NRE) at two contrasting scales, Regional (whole estuary) and Local (estuary partitioned into eight sections, based on orientation and exposure). With a mean shoreline-change rate of –0.58 m yr−1, the majority (93%) of the NRE study area is eroding. Change rates show some variability related to the land-use land-cover classification of the shoreline. Although linear regression analysis at the Regional Scale did not find significant correlations between shoreline change and the parameters analyzed, trends were determined from Local Scale data. Specifically, erosion rates, fetch, and wave exposure increase in the down-estuary direction, while elevation follows the opposite trend. Linear regression analysis between mean fetch and mean shoreline-change rates at the Local Scale provide a first-order approach to predict shoreline-change rates. The general trends found in the Local Scale data highlight the presence of underlying spatial patterns in shoreline-change rates within a complex estuarine system, but Regional Scale analysis suggests shoreline composition also has an important influence. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Remote Sensing of Shallow Coastal Benthic Substrates: In situ Spectra and Mapping of Eelgrass (Zostera marina) in the Gulf Islands National Park Reserve of Canada
Remote Sens. 2011, 3(5), 975-1005; doi:10.3390/rs3050975
Received: 28 February 2011 / Revised: 5 May 2011 / Accepted: 6 May 2011 / Published: 16 May 2011
Cited by 10 | PDF Full-text (1204 KB) | HTML Full-text | XML Full-text
Abstract
Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal
[...] Read more.
Eelgrass (Zostera marina) is a keystone component of inter- and sub-tidal ecosystems. However, anthropogenic pressures have caused its populations to decline worldwide. Delineation and continuous monitoring of eelgrass distribution is an integral part of understanding these pressures and providing effective coastal ecosystem management. A proposed tool for such spatial monitoring is remote imagery, which can cost- and time-effectively cover large and inaccessible areas frequently. However, to effectively apply this technology, an understanding is required of the spectral behavior of eelgrass and its associated substrates. In this study, in situ hyperspectral measurements were used to define key spectral variables that provide the greatest spectral separation between Z. marina and associated submerged substrates. For eelgrass classification of an in situ above water reflectance dataset, the selected variables were: slope 500–530 nm, first derivatives (R’) at 566 nm, 580 nm, and 602 nm, yielding 98% overall accuracy. When the in situ reflectance dataset was water-corrected, the selected variables were: 566:600 and 566:710, yielding 97% overall accuracy. The depth constraint for eelgrass identification with the field spectrometer was 5.0 to 6.0 m on average, with a range of 3.0 to 15.0 m depending on the characteristics of the water column. A case study involving benthic classification of hyperspectral airborne imagery showed the major advantage of the variable selection was meeting the sample size requirements of the more statistically complex Maximum Likelihood classifier. Results of this classifier yielded eelgrass classification accuracy of over 85%. The depth limit of eelgrass spectral detection for the AISA sensor was 5.5 m. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Open AccessArticle A Multi-Sensor Approach to Examining the Distribution of Total Suspended Matter (TSM) in the Albemarle-Pamlico Estuarine System, NC, USA
Remote Sens. 2011, 3(5), 962-974; doi:10.3390/rs3050962
Received: 24 April 2011 / Revised: 1 May 2011 / Accepted: 5 May 2011 / Published: 13 May 2011
Cited by 13 | PDF Full-text (482 KB) | HTML Full-text | XML Full-text
Abstract
For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have
[...] Read more.
For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have demonstrated that TSM or suspended sediments can be successfully mapped using MODIS 250 m data for relatively large water bodies, MODIS 250 m data is of more limited use for smaller estuaries and bays or aquatic systems with complex shoreline geometry. To adequately examine TSM in the Albemarle-Pamlico Estuarine System (APES) of North Carolina, the large-scale synoptic view of MODIS and the higher spatial resolution of other sensors are required. MODIS, Landsat 7 ETM+ and FORMOSAT-2 remote sensing instrument (RSI) data were collected on 8 November, 24 November and 10 December, 2010. Using TSM images (mg/L) derived from MODIS 250 m band 1 (620–670 nm) data, Landsat 7 ETM+ 30 m band 3 (630–690 nm) and FORMOSAT-2 RSI 8 m band 3 (630−690 nm) atmospherically corrected images were calibrated to TSM for select areas of the APES. There was a significant linear relationship between both Landsat 7 ETM+ (r2 = 0.87, n = 599, P < 0.001) and FORMOSAT-2 RSI (r2 = 0.95, n = 583, P < 0.001) reflectance images and MODIS-derived TSM concentrations, thus providing consistent estimates of TSM at 250, 30 and 8 m pixel resolutions. This multi-sensor approach will support a broad range of investigations on the water quality of the APES and help guide sampling schemes of future field campaigns. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle MERIS Retrieval of Water Quality Components in the Turbid Albemarle-Pamlico Sound Estuary, USA
Remote Sens. 2011, 3(4), 684-707; doi:10.3390/rs3040684
Received: 1 February 2011 / Revised: 14 March 2011 / Accepted: 18 March 2011 / Published: 1 April 2011
Cited by 12 | PDF Full-text (802 KB) | HTML Full-text | XML Full-text
Abstract
Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water,
[...] Read more.
Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water, and air/water interface. One incorporated a very simple atmospheric correction and modified quasi-single-scattering approximation (QSSA) for estimating the spectral Gordon’s parameter, and the second estimated WQCs directly from the top of atmosphere satellite radiance without atmospheric corrections. A modified version of the Global Meteorological Database for Solar Energy and Applied Meteorology (METEONORM) was used to estimate directional atmospheric transmittances. The study incorporated in situ Chl data from the Ferry-Based Monitoring (FerryMon) program collected in the Neuse River Estuary (n = 633) and Pamlico Sound (n = 362), along with Medium Resolution Imaging Spectrometer (MERIS) satellite imagery collected (2006–2009) across the APES; providing quasi-coinciding samples for Chl algorithm development and validation. Results indicated a coefficient of determination (R2) of 0.70 and mean-normalized root-mean-squares errors (NRMSE) of 52% in the Neuse River Estuary and R2 = 0.44 (NRMSE = 75 %) in the Pamlico Sound—without atmospheric corrections. The simple atmospheric correction tested provided on performance improvements. Algorithm performance demonstrated the potential for supporting long-term operational WQCs satellite monitoring in the APES. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Airborne Remote Sensing of a Biological Hot Spot in the Southeastern Bering Sea
Remote Sens. 2011, 3(3), 621-637; doi:10.3390/rs3030621
Received: 27 January 2011 / Revised: 7 March 2011 / Accepted: 14 March 2011 / Published: 21 March 2011
Cited by 7 | PDF Full-text (95 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Intense, ephemeral foraging events within localized hot spots represent important trophic transfers to top predators in marine ecosystems, though the spatial extent and temporal overlap of predators and prey are difficult to observe using traditional methods. The southeastern Bering Sea has high marine
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Intense, ephemeral foraging events within localized hot spots represent important trophic transfers to top predators in marine ecosystems, though the spatial extent and temporal overlap of predators and prey are difficult to observe using traditional methods. The southeastern Bering Sea has high marine productivity along the shelf break, especially near marine canyons. At a hot spot located near Bering Canyon, we observed three foraging events over a 12 day period in June 2005. These were located by aerial surveys, quantified by airborne lidar and visual counts, and characterized by ship-based acoustics and net catches. Because of the high density of seabirds, the events could be seen in images from space-based synthetic aperture radar. The events developed at the shelf slope, adjacent to passes between the Aleutian Islands, persisted for 1 to 8 days, then abruptly disappeared. Build-up and break down of the events occurred on 24 hr time scales, and diameters ranged from 10 to 20 km. These events comprised large concentrations of euphausiids, copepods, herring, other small pelagic fishes, humpback whales, Dall’s porpoise, short-tailed shearwaters, northern fulmars, and other pelagic seabirds. The lidar and acoustic remote sensing data demonstrated that prey densities inside the events were several times higher than those outside, indicating the importance of including events in forage fish surveys. This implies a need for either very intensive traditional surveys covering large expanses or for adaptive surveys guided by remote sensing. To our knowledge, this is the first time that an Alaskan hot spot was monitored with the combination of airborne and satellite remote sensing. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Open AccessArticle Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area
Remote Sens. 2011, 3(3), 460-483; doi:10.3390/rs3030460
Received: 22 December 2010 / Revised: 1 February 2011 / Accepted: 21 February 2011 / Published: 1 March 2011
Cited by 13 | PDF Full-text (697 KB) | HTML Full-text | XML Full-text
Abstract
The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process.
[...] Read more.
The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m) and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively) covering a large (>260 km2) traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
Open AccessArticle Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007
Remote Sens. 2011, 3(1), 42-64; doi:10.3390/rs3010042
Received: 28 October 2010 / Revised: 24 December 2010 / Accepted: 31 December 2010 / Published: 6 January 2011
Cited by 44 | PDF Full-text (1857 KB) | HTML Full-text | XML Full-text
Abstract
Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine
[...] Read more.
Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine ecosystem processes. The capacity to accurately and consistently map and monitor these benthic habitats is critical to developing and implementing management applications. This paper presents a method for integrating field survey data and high spatial resolution, multi-spectral satellite image data to map bathymetry and seagrass in shallow coastal waters. Using Quickbird 2 satellite images from 2004 and 2007, acoustic field survey data were used to map bathymetry using a linear and ratio algorithm method; benthic survey field data were used to calibrate and validate classifications of seagrass percentage cover and seagrass species composition; and a change detection analysis of seagrass cover was performed. The bathymetry mapping showed that only the linear algorithm could effectively and accurately predict water depth; overall benthic map accuracies ranged from 57–95%; and the change detection produced a reliable change map and showed a net decrease in seagrass cover levels, but the majority of the study area showed no change in seagrass cover level. This study demonstrates that multiple spatial products (bathymetry, seagrass and change maps) can be produced from single satellite images and a concurrent field survey dataset. Moreover, the products were produced at higher spatial resolution and accuracy levels than previous studies in Moreton Bay. The methods are developed from previous work in the study area and are continuing to be implemented, as well as being developed to be repeatable in similar shallow coastal water environments. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Open AccessArticle Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida
Remote Sens. 2010, 2(12), 2713-2728; doi:10.3390/rs2122713
Received: 12 October 2010 / Revised: 22 November 2010 / Accepted: 30 November 2010 / Published: 7 December 2010
Cited by 16 | PDF Full-text (447 KB) | HTML Full-text | XML Full-text
Abstract
Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean.
[...] Read more.
Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. There are satellite-derived products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance daily product (MOD09GQ) Band 1 (620–670 nm) which are now routinely available at 250 m spatial resolution and corrected for atmospheric effect. This study shows this product to be useful to estimate turbidity in Tampa Bay, Florida, after rainfall events (R2 = 0.76, n = 34). Within Tampa Bay, Hillsborough Bay (HB) and Old Tampa Bay (OTB) presented higher turbidity compared to Middle Tampa Bay (MTB) and Lower Tampa Bay (LTB). Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)
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Open AccessLetter Nearshore Water Quality Estimation Using Atmospherically Corrected AVIRIS Data
Remote Sens. 2011, 3(2), 257-269; doi:10.3390/rs3020257
Received: 11 January 2011 / Revised: 8 February 2011 / Accepted: 9 February 2011 / Published: 11 February 2011
Cited by 4 | PDF Full-text (441 KB) | HTML Full-text | XML Full-text
Abstract
The objective of the research is to characterize the surface spectral reflectance of the nearshore waters using atmospheric correction code—Tafkaa for retrieval of the marine water constituent concentrations from hyperspectral data. The study area is the nearshore waters of New York/New Jersey considered
[...] Read more.
The objective of the research is to characterize the surface spectral reflectance of the nearshore waters using atmospheric correction code—Tafkaa for retrieval of the marine water constituent concentrations from hyperspectral data. The study area is the nearshore waters of New York/New Jersey considered as a valued ecological, economic and recreational resource within the New York metropolitan area. Comparison of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) measured radiance and in situ reflectance measurement shows the effect of the solar source and atmosphere in the total upwelling spectral radiance measured by AVIRIS. Radiative transfer code, Tafkaa was applied to remove the effects of the atmosphere and to generate accurate reflectance (R(0)) from the AVIRIS radiance for retrieving water quality parameters (i.e., total chlorophyll). Chlorophyll estimation as index of phytoplankton abundance was optimized using AVIRIS band ratio at 675 nm and 702 nm resulting in a coefficient of determination of R2 = 0.98. Use of the radiative transfer code in conjunction with bio optical model is the main tool for using ocean color remote sensing as an operational tool for monitoring of the key nearshore ecological communities of phytoplankton important in global change studies. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Ecosystem)

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