E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Remote Sensing in Coastal Environments"

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

Deadline for manuscript submissions: closed (31 March 2016)

Special Issue Editors

Guest Editor
Dr. Deepak R. Mishra

Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA
Website | E-Mail
Interests: Water quality (inland waters, estuaries, coastal, and open ocean waters); Wetlands health, productivity, and carbon sequestration; Benthic habitat mapping, Cyber-innovated environmental sensing
Guest Editor
Dr. Richard W. Gould, Jr.

Head, Bio-Optical/Physical Processes and Remote Sensing Section, Naval Research Laboratory Code 7331; Building 1009; Stennis Space Center, MS 39529, USA
Website | E-Mail
Interests: remote sensing of ocean bio-optical properties; ocean color algorithm development and uncertainty analysis; optical water mass classification; ocean vertical structure from lidar; coupled bio-optical/physical processes; linking satellite and in situ data with modeling to forecast ocean properties; coastal hypoxia; harmful algal blooms

Special Issue Information

Dear Colleagues,

Coastal ecosystems are regions of remarkable primary and secondary productivity, biodiversity, and high accessibility. Apart from supporting numerous physical and biological processes, they also act as recreational, leisure, and tourism centers. Encompassing a broad range of habitat types and harboring a wealth of species and genetic diversity, coastal ecosystems perform numerous vital ecosystem functions. In addition to serving as nursery grounds for many birds and aquatic organisms, coastal ecosystems play roles in regulating: global hydrology and climate; the biological, physical, and chemical modifications of the water column, sediment, and submerged and emergent vegetation; the storage and cycling of nutrients; the filtration of pollutants from inland freshwater systems; and the protection of shorelines from erosion and storms. Consequently, there is a need for accurate, cost effective, frequent, and synoptic methods of characterizing and monitoring these complex ecosystems.

Remote sensing from in situ, airborne, and space-borne platforms satisfies the aforementioned criteria and offers large scale data acquisition at regular temporal frequencies, so as to monitor coastal environments.  This Special Issue on “Remote Sensing in Coastal Environments” is specifically aimed at addressing challenges related to assessing, quantifying, and monitoring near-shore shallow marine and open ocean processes, ecosystem productivity and biodiversity, interrelationships between vegetation and water quality, and the impact of sea level rise. Authors are encouraged to submit articles with respect to the following topics:

  • coastal mangroves, tidal wetlands (productivity, carbon flux, up-scaling techniques)
  • coupled bio-optical/physical processes (cdom/sediment dynamics, red tide, floating algae)
  • ocean properties and algorithm development (optical water mass classification; vertical structure)
  • coastal hypoxia
  • sea level rise impact on coastal environments
  • coastal and marine biodiversity and benthic habitats (corals, seagrass, benthic algae)
  • lidar and unmanned aerial vehicles (uav) in coastal research (application of structure from motion (sfm) techniques)
  • big data remote sensing and cloud computing in coastal research
  • applications of hyperspectral and/or high spatial resolution sensors
  • integrating remote sensing into coupled coastal biophysical forecast models

Deepak R. Mishra
Richard W. Gould, Jr.
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (22 papers)

View options order results:
result details:
Displaying articles 1-22
Export citation of selected articles as:

Editorial

Jump to: Research

Open AccessEditorial Preface: Remote Sensing in Coastal Environments
Remote Sens. 2016, 8(8), 665; doi:10.3390/rs8080665
Received: 12 August 2016 / Accepted: 15 August 2016 / Published: 17 August 2016
PDF Full-text (176 KB) | HTML Full-text | XML Full-text
Abstract
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp
[...] Read more.
The Special Issue (SI) on “Remote Sensing in Coastal Environments” presents a wide range of articles focusing on a variety of remote sensing models and techniques to address coastal issues and processes ranging for wetlands and water quality to coral reefs and kelp habitats. The SI is comprised of twenty-one papers, covering a broad range of research topics that employ remote sensing imagery, models, and techniques to monitor water quality, vegetation, habitat suitability, and geomorphology in the coastal zone. This preface provides a brief summary of each article published in the SI. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)

Research

Jump to: Editorial

Open AccessArticle The Use of Aerial RGB Imagery and LIDAR in Comparing Ecological Habitats and Geomorphic Features on a Natural versus Man-Made Barrier Island
Remote Sens. 2016, 8(7), 602; doi:10.3390/rs8070602
Received: 17 March 2016 / Revised: 20 June 2016 / Accepted: 12 July 2016 / Published: 16 July 2016
Cited by 2 | PDF Full-text (3600 KB) | HTML Full-text | XML Full-text
Abstract
The Mississippi (MS) barrier island chain along the northern Gulf of Mexico coastline is subject to rapid changes in habitat, geomorphology and elevation by natural and anthropogenic disturbances. The purpose of this study was to compare habitat type coverage with respective elevation, geomorphic
[...] Read more.
The Mississippi (MS) barrier island chain along the northern Gulf of Mexico coastline is subject to rapid changes in habitat, geomorphology and elevation by natural and anthropogenic disturbances. The purpose of this study was to compare habitat type coverage with respective elevation, geomorphic features and short-term change between the naturally-formed East Ship Island and the man-made Sand Island. Ground surveys, multi-year remotely-sensed data, habitat classifications and digital elevation models were used to quantify short-term habitat and geomorphic change, as well as to examine the relationships between habitat types and micro-elevation. Habitat types and species composition were the same on both islands with the exception of the algal flat existing on the lower elevated spits of East Ship. Both islands displayed common patterns of vegetation succession and ranges of existence in elevation. Additionally, both islands showed similar geomorphic features, such as fore and back dunes and ponds. Storm impacts had the most profound effects on vegetation and geomorphic features throughout the study period. Although vastly different in age, these two islands show remarkable commonalities among the traits investigated. In comparison to East Ship, Sand Island exhibits key characteristics of a natural barrier island in terms of its vegetated habitats, geomorphic features and response to storm impacts, although it was established anthropogenically only decades ago. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle A Spatially Explicit, Multi-Criteria Decision Support Model for Loggerhead Sea Turtle Nesting Habitat Suitability: A Remote Sensing-Based Approach
Remote Sens. 2016, 8(7), 573; doi:10.3390/rs8070573
Received: 12 April 2016 / Accepted: 30 June 2016 / Published: 6 July 2016
Cited by 2 | PDF Full-text (5106 KB) | HTML Full-text | XML Full-text
Abstract
Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific information. Given the expansive geographic
[...] Read more.
Nesting habitat for the federally endangered loggerhead sea turtle (Caretta caretta) were designated as critical in 2014 for beaches along the Atlantic Coast and Gulf of Mexico. Nesting suitability is routinely determined based on site specific information. Given the expansive geographic location of the designated critical C. caretta nesting habitat and the highly dynamic coastal environment, understanding nesting suitability on a regional scale is essential for monitoring the changing status of the coast as a result of hydrodynamic forces and maintenance efforts. The increasing spatial resolution and temporal frequency of remote sensing data offers the opportunity to study this dynamic environment on a regional scale. Remote sensing data were used as input into the spatially-explicit, multi-criteria decision support model to determine nesting habitat suitability. Results from the study indicate that the morphological parameters used as input into the model are well suited to provide a regional level approach with the results from the optimized model having sensitivity and detection prevalence values greater than 80% and the detection rate being greater than 70%. The approach can be implemented in various geographic locations to better communicate priorities and evaluate management strategies as a result of changes to the dynamic coastal environment. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Mangroves at Their Limits: Detection and Area Estimation of Mangroves along the Sahara Desert Coast
Remote Sens. 2016, 8(6), 512; doi:10.3390/rs8060512
Received: 31 March 2016 / Revised: 27 May 2016 / Accepted: 14 June 2016 / Published: 18 June 2016
Cited by 1 | PDF Full-text (2513 KB) | HTML Full-text | XML Full-text
Abstract
The northernmost and most arid mangrove ecosystem of West Africa is found in Mauritania, in the Parc National du Banc d’Arguin (PNBA). The existing global and regional maps of Mauritania’s mangroves have little detail, and available estimates of the mangrove area differ among
[...] Read more.
The northernmost and most arid mangrove ecosystem of West Africa is found in Mauritania, in the Parc National du Banc d’Arguin (PNBA). The existing global and regional maps of Mauritania’s mangroves have little detail, and available estimates of the mangrove area differ among studies. We assessed the use of automated Remote Sensing classification techniques to calculate the extent and map the distribution of the mangrove patches located at Cap Timiris, PNBA, using QuickBird and GeoEye imagery. It was possible to detect the northernmost contiguous mangrove patches of West Africa with an accuracy of 87% ± 2% using the Maximum Likelihood algorithm. The main source of error was the low spectral difference between mangroves and other types of terrestrial vegetation, which resulted in an erroneous classification between these two types of land cover. The most reliable estimate for the mangrove area obtained in this study was 19.48 ± 5.54 ha in 2011. Moreover, we present a special validation procedure that enables a detailed and reliable validation of the land cover maps. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Submerged Kelp Detection with Hyperspectral Data
Remote Sens. 2016, 8(6), 487; doi:10.3390/rs8060487
Received: 31 March 2016 / Revised: 30 May 2016 / Accepted: 2 June 2016 / Published: 8 June 2016
Cited by 1 | PDF Full-text (5802 KB) | HTML Full-text | XML Full-text
Abstract
Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool
[...] Read more.
Submerged marine forests of macroalgae known as kelp are one of the key structures for coastal ecosystems worldwide. These communities are responding to climate driven habitat changes and are therefore appropriate indicators of ecosystem status and health. Hyperspectral remote sensing provides a tool for a spatial kelp habitat mapping. The difficulty in optical kelp mapping is the retrieval of a significant kelp signal through the water column. Detecting submerged kelp habitats is challenging, in particular in turbid coastal waters. We developed a fully automated simple feature detection processor to detect the presence of kelp in submerged habitats. We compared the performance of this new approach to a common maximum likelihood classification using hyperspectral AisaEAGLE data from the subtidal zones of Helgoland, Germany. The classification results of 13 flight stripes were validated with transect diving mappings. The feature detection showed a higher accuracy till a depth of 6 m (overall accuracy = 80.18%) than the accuracy of a maximum likelihood classification (overall accuracy = 57.66%). The feature detection processor turned out as a time-effective approach to assess and monitor submerged kelp at the limit of water visibility depth. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast
Remote Sens. 2016, 8(6), 477; doi:10.3390/rs8060477
Received: 12 April 2016 / Revised: 20 May 2016 / Accepted: 27 May 2016 / Published: 4 June 2016
Cited by 2 | PDF Full-text (9770 KB) | HTML Full-text | XML Full-text
Abstract
We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual
[...] Read more.
We examined the influence of abiotic drivers on inter-annual and phenological patterns of aboveground biomass for Marsh Cordgrass, Spartina alterniflora, on the Central Georgia Coast. The linkages between drivers and plant response via soil edaphic factors are captured in our graphical conceptual model. We used geospatial techniques to scale up in situ measurements of aboveground S. alterniflora biomass to landscape level estimates using 294 Landsat 5 TM scenes acquired between 1984 and 2011. For each scene we extracted data from the same 63 sampling polygons, containing 1222 pixels covering about 1.1 million m2. Using univariate and multiple regression tests, we compared Landsat derived biomass estimates for three S. alterniflora size classes against a suite of abiotic drivers. River discharge, total precipitation, minimum temperature, and mean sea level had positive relationships with and best explained biomass for all dates. Additional results, using seasonally binned data, indicated biomass was responsive to changing combinations of variables across the seasons. Our 28-year analysis revealed aboveground biomass declines of 33%, 35%, and 39% for S. alterniflora tall, medium, and short size classes, respectively. This decline correlated with drought frequency and severity trends and coincided with marsh die-backs events and increased snail herbivory in the second half of the study period. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images
Remote Sens. 2016, 8(6), 459; doi:10.3390/rs8060459
Received: 8 January 2016 / Revised: 20 May 2016 / Accepted: 24 May 2016 / Published: 28 May 2016
Cited by 2 | PDF Full-text (3030 KB) | HTML Full-text | XML Full-text
Abstract
Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The
[...] Read more.
Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Spatial Assessment of the Bioclimatic and Environmental Factors Driving Mangrove Tree Species’ Distribution along the Brazilian Coastline
Remote Sens. 2016, 8(6), 451; doi:10.3390/rs8060451
Received: 18 March 2016 / Revised: 16 May 2016 / Accepted: 18 May 2016 / Published: 27 May 2016
Cited by 2 | PDF Full-text (5251 KB) | HTML Full-text | XML Full-text
Abstract
Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of
[...] Read more.
Brazil has one of the largest mangrove surfaces worldwide. Due to a wide latitudinal distribution, Brazilian mangroves can be found within a large range of environmental conditions. However, little attention has been given to the description of environmental variables driving the distribution of mangrove species in Brazil. In this study, we present a novel and unprecedented description of environmental conditions for all mangroves along the Brazilian coast focusing on species limits. We apply a descriptive statistics and data-driven approach using Self-Organizing Maps and we combine data from terrestrial and marine environmental geodatabases in a Geographical Information System. We evaluate 25 environmental variables (21 bioclimatic variables, three sea surface temperature derivates, and salinity). The results reveal three groups of correlated variables: (i) air temperature derivates and sea surface temperature derivates; (ii) air temperature, potential evapotranspiration and precipitation derivates; and (iii) precipitation derivates, aridity and salinity. Our results unveil new locations of extreme values of temperature and precipitation. We conclude that Rhizophora harrisonii and Rhizophora racemosa are more limited by precipitation and aridity and that they do not necessarily follow a latitudinal gradient. Our data also reveal that the lowest air temperatures of the coldest month are not necessarily found at the southernmost limits of mangroves in Brazil; instead they are localized at the Mesoregion of Vale do Itajaí. However, the minimum sea surface temperature drops gradually with higher latitudes in the Brazilian southern hemisphere and is probably a better indicator for the decrease of species at the latitudinal limits of mangroves than the air temperature and precipitation. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Impact of Satellite Remote Sensing Data on Simulations of Coastal Circulation and Hypoxia on the Louisiana Continental Shelf
Remote Sens. 2016, 8(5), 435; doi:10.3390/rs8050435
Received: 30 March 2016 / Revised: 16 May 2016 / Accepted: 17 May 2016 / Published: 23 May 2016
Cited by 2 | PDF Full-text (5317 KB) | HTML Full-text | XML Full-text
Abstract
We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall
[...] Read more.
We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for solar penetration. Improvements in the model results in comparison with in situ observations occurred when the two types of satellite data were included. Without inclusion of the satellite-derived surface salinity flux, realistic monthly variability in the model salinity fields was observed, but important inter-annual variability was missed. Without inclusion of the satellite-derived light attenuation, model bottom water temperatures were too high nearshore due to excessive penetration of solar irradiance. In general, these salinity and temperature errors led to model stratification that was too weak, and the model failed to capture observed spatial and temporal variability in water-column vertical stratification. Inclusion of the satellite data improved temperature and salinity predictions and the vertical stratification was strengthened, which improved prediction of bottom-water dissolved oxygen. The model-predicted area of bottom-water hypoxia on the Louisiana shelf, an important management metric, was substantially improved in comparison to observed hypoxic area by including the satellite data. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Monitoring the Topography of a Dynamic Tidal Inlet Using UAV Imagery
Remote Sens. 2016, 8(5), 387; doi:10.3390/rs8050387
Received: 31 December 2015 / Revised: 8 April 2016 / Accepted: 27 April 2016 / Published: 6 May 2016
Cited by 5 | PDF Full-text (12381 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned Aerial Vehicles (UAVs) are being increasingly used to monitor topographic changes in coastal areas. Compared to Light Detection And Ranging (LiDAR) data or Terrestrial Laser Scanning data, this solution is low-cost and easy to use, while allowing the production of a Digital
[...] Read more.
Unmanned Aerial Vehicles (UAVs) are being increasingly used to monitor topographic changes in coastal areas. Compared to Light Detection And Ranging (LiDAR) data or Terrestrial Laser Scanning data, this solution is low-cost and easy to use, while allowing the production of a Digital Surface Model (DSM) with a similar accuracy. Three campaigns were carried out within a three-month period at a lagoon-inlet system (Bonne-Anse Bay, La Palmyre, France), with a flying wing (eBee) combined with a digital camera. Ground Control Points (GCPs), surveyed by the Global Navigation Satellite System (GNSS) and post-processed by differential correction, allowed georeferencing DSMs. Using a photogrammetry process (Structure From Motion algorithm), DSMs and orthomosaics were produced. The DSM accuracy was assessed against the ellipsoidal height of a GNSS profile and Independent Control Points (ICPs) and the root mean square discrepancies were about 10 and 17 cm, respectively. Compared to traditional topographic surveys, this solution allows the accurate representation of bedforms with a wavelength of the order of 1 m and a height of 0.1 m. Finally, changes identified between both main campaigns revealed erosion/accretion areas and the progradation of a sandspit. These results open new perspectives to validate detailed morphological predictions or to parameterize bottom friction in coastal numerical models. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques
Remote Sens. 2016, 8(5), 377; doi:10.3390/rs8050377
Received: 30 December 2015 / Revised: 16 April 2016 / Accepted: 20 April 2016 / Published: 4 May 2016
Cited by 2 | PDF Full-text (14320 KB) | HTML Full-text | XML Full-text
Abstract
We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite
[...] Read more.
We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (aph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The aph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved aph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum aph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing
Remote Sens. 2016, 8(4), 321; doi:10.3390/rs8040321
Received: 31 December 2015 / Revised: 28 March 2016 / Accepted: 31 March 2016 / Published: 12 April 2016
Cited by 1 | PDF Full-text (11107 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing reflectance (Rrs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring
[...] Read more.
Remote sensing reflectance (Rrs) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, 2003; summer and winter, 2006/2007; and spring and autumn, 2007). Specifically, we analyzed classification features of Rrs spectra and obtained spatio-temporal characteristics of reflectance and bio-optical properties in the coastal waters. Yellow Sea waters were classified into the following four typical regions based on their spatial distribution characteristics: middle of the Yellow Sea (MYS), north Yellow Sea (NYS), coastal Shandong (CS), and Jiangsu shoal (JS), and five water type categories consisting of Classes A–E were used to represent water colors from clear to very turbid. Application of this classification scheme to Medium Resolution Imaging Spectrometer (MERIS) imagery revealed seasonal variations in the data, which suggests that the water types have both significant temporal and spatial distributions. In particular, the area of Class E waters in the Jiangsu shoal tended to gradually shrink in summer and expand in winter. The spatio-temporal variability was due to the influence of various environmental factors such as currents, tidal activity, fresh water discharges, monsoon winds, and typhoons. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Combining L- and X-Band SAR Interferometry to Assess Ground Displacements in Heterogeneous Coastal Environments: The Po River Delta and Venice Lagoon, Italy
Remote Sens. 2016, 8(4), 308; doi:10.3390/rs8040308
Received: 30 December 2015 / Revised: 26 February 2016 / Accepted: 21 March 2016 / Published: 6 April 2016
Cited by 7 | PDF Full-text (6890 KB) | HTML Full-text | XML Full-text
Abstract
From leveling to SAR-based interferometry, the monitoring of land subsidence in coastal transitional environments significantly improved. However, the simultaneous assessment of the ground movements in these peculiar environments is still challenging. This is due to the presence of relatively small built-up zones and
[...] Read more.
From leveling to SAR-based interferometry, the monitoring of land subsidence in coastal transitional environments significantly improved. However, the simultaneous assessment of the ground movements in these peculiar environments is still challenging. This is due to the presence of relatively small built-up zones and infrastructures, e.g., coastal infrastructures, bridges, and river embankments, within large natural or rural lands, e.g., river deltas, lagoons, and farmland. In this paper we present a multi-band SAR methodology to integrate COSMO-SkyMed and ALOS-PALSAR images. The method consists of a proper combination of the very high-resolution X-band Persistent Scatterer Interferometry (PSI), which achieves high-density and precise measurements on single structures and constructed areas, with L-band Short-Baseline SAR Interferometry (SBAS), properly implemented to raise its effectiveness in retrieving information in vegetated and wet zones. The combined methodology is applied on the Po River Delta and Venice coastland, Northern Italy, using 16 ALOS-PALSAR and 31 COSMO-SkyMed images covering the period between 2007 and 2011. After a proper calibration of the single PSI and SBAS solution using available GPS records, the datasets have been combined at both the regional and local scales. The measured displacements range from ~0 mm/yr down to −35 mm/yr. The results reveal the variable pattern of the subsidence characterizing the more natural and rural environments without losing the accuracy in quantifying the sinking of urban areas and infrastructures. Moreover, they allow improving the interpretation of the natural and anthropogenic processes responsible for the ongoing subsidence. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Potential of High Spatial and Temporal Ocean Color Satellite Data to Study the Dynamics of Suspended Particles in a Micro-Tidal River Plume
Remote Sens. 2016, 8(3), 245; doi:10.3390/rs8030245
Received: 13 December 2015 / Revised: 26 February 2016 / Accepted: 7 March 2016 / Published: 16 March 2016
Cited by 8 | PDF Full-text (8017 KB) | HTML Full-text | XML Full-text
Abstract
Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning
[...] Read more.
Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors in terms of spectral, spatial and temporal resolutions to (i) estimate the seawater reflectance signal and then SPM concentrations and (ii) monitor the dynamics of SPM in the Rhône River plume characterized by moderately turbid surface waters in a micro-tidal sea. Consistent remote-sensing reflectance (Rrs) values are retrieved in the red spectral bands of these four satellite sensors (median relative difference less than ~16% in turbid waters). By applying a regional algorithm developed from in situ data, these Rrs are used to estimate SPM concentrations in the Rhône river plume. The spatial resolution of OLI provides a detailed mapping of the SPM concentration from the downstream part of the river itself to the plume offshore limits with well defined small-scale turbidity features. Despite the low temporal resolution of OLI, this should allow to better understand the transport of terrestrial particles from rivers to the coastal ocean. These details are partly lost using MODIS coarser resolutions data but SPM concentration estimations are consistent, with an accuracy of about 1 to 3 g·m−3 in the river mouth and plume for spatial resolutions from 250 m to 1 km. The MODIS temporal resolution (2 images per day) allows to capture the daily to monthly dynamics of the river plume. However, despite its micro-tidal environment, the Rhône River plume shows significant short-term (hourly) variations, mainly controlled by wind and regional circulation, that MODIS temporal resolution failed to capture. On the contrary, the high temporal resolution of SEVIRI makes it a powerful tool to study this hourly river plume dynamics. However, its coarse resolution prevents the monitoring of SPM concentration variations in the river mouth where SPM concentration variability can reach 20 g·m−3 inside the SEVIRI pixel. Its spatial resolution is nevertheless sufficient to reproduce the plume shape and retrieve SPM concentrations in a valid range, taking into account an underestimation of about 15%–20% based on comparisons with other sensors and in situ data. Finally, the capabilities, advantages and limits of these satellite sensors are discussed in the light of the spatial and temporal resolution improvements provided by the new and future generation of ocean color sensors onboard the Sentinel-2, Sentinel-3 and Meteosat Third Generation (MTG) satellite platforms. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary
Remote Sens. 2016, 8(3), 244; doi:10.3390/rs8030244
Received: 16 December 2015 / Revised: 29 February 2016 / Accepted: 4 March 2016 / Published: 15 March 2016
Cited by 2 | PDF Full-text (7824 KB) | HTML Full-text | XML Full-text
Abstract
Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of
[...] Read more.
Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data
Remote Sens. 2016, 8(3), 226; doi:10.3390/rs8030226
Received: 20 January 2016 / Revised: 29 February 2016 / Accepted: 7 March 2016 / Published: 10 March 2016
Cited by 2 | PDF Full-text (5522 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400–1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans),
[...] Read more.
The purpose of this investigation was to determine the seasonal relationships (dry vs. rainy) between reflectance (400–1000 nm) and leaf pigment contents (chlorophyll-a (chl-a), chlorophyll-b (chl-b), total carotenoids (tcar), chlorophyll a/b ratio) in three mangrove species (Avicennia germinans (A. germinans), Laguncularia racemosa (L. racemosa), and Rhizophora mangle (R. mangle)) according to their condition (stressed vs. healthy). Based on a sample of 360 leaves taken from a semi-arid forest of the Mexican Pacific, it was determined that during the dry season, the stressed A. germinans and R. mangle show the highest maximum correlations at the green (550 nm) and red-edge (710 nm) wavelengths (r = 0.8 and 0.9, respectively) for both chl-a and chl-b and that much lower values (r = 0.7 and 0.8, respectively) were recorded during the rainy season. Moreover, it was found that the tcar correlation pattern across the electromagnetic spectrum was quite different from that of the chl-a, the chl-b, and chl a/b ratio but that their maximum correlations were also located at the same two wavelength ranges for both seasons. The stressed L. racemosa was the only sample to exhibit minimal correlation with chl-a and chl-b for either season. In addition, the healthy A. germinans and R. mangle depicted similar patterns of chl-a and chl-b, but the tcar varied depending on the species. The healthy L. racemosa recorded higher correlations with chl-b and tcar at the green and red-edge wavelengths during the dry season, and higher correlation with chl-a during the rainy season. Finally, the vegetation index Red Edge Inflection Point Index (REIP) was found to be the optimal index for chl-a estimation for both stressed and healthy classes. For chl-b, both the REIP and the Vogelmann Red Edge Index (Vog1) index were found to be best at prediction. Based on the results of this investigation, it is suggested that caution be taken as mangrove leaf pigment contents from spectroscopy data have been shown to be sensitive to seasonality, species, and condition. The authors suggest potential reasons for the observed variability in the reflectance and pigment contents relationships. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters
Remote Sens. 2016, 8(3), 211; doi:10.3390/rs8030211
Received: 26 December 2015 / Revised: 22 February 2016 / Accepted: 29 February 2016 / Published: 5 March 2016
Cited by 9 | PDF Full-text (6538 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate
[...] Read more.
Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water
Remote Sens. 2016, 8(3), 200; doi:10.3390/rs8030200
Received: 28 September 2015 / Revised: 4 February 2016 / Accepted: 17 February 2016 / Published: 1 March 2016
Cited by 6 | PDF Full-text (14105 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances;
[...] Read more.
Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R2 = 0.977, p < 0.001) and OLI (R2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803 ± 0.47 kg in 2009, while it was 752.3 ± 0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Fuzzy Classification for Shoreline Change Monitoring in a Part of the Northern Coastal Area of Java, Indonesia
Remote Sens. 2016, 8(3), 190; doi:10.3390/rs8030190
Received: 10 December 2015 / Revised: 25 January 2016 / Accepted: 18 February 2016 / Published: 27 February 2016
Cited by 5 | PDF Full-text (7882 KB) | HTML Full-text | XML Full-text
Abstract
This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images
[...] Read more.
This study presents an unsupervised fuzzy c-means classification (FCM) to observe the shoreline positions. We combined crisp and fuzzy methods for change detection. We addressed two perspectives of uncertainty: (1) uncertainty that is inherent to shoreline positions as observed from remote sensing images due to its continuous variation over time; and (2) the uncertainty of the change results propagating from object extraction and implementation of shoreline change detection method. Unsupervised FCM achieved the highest kappa (κ) value when threshold (t) was set at 0.5. The highest κ values were 0.96 for the 1994 image. For images in 2013, 2014 and 2015, the κ values were 0.95. Further, images in 2003, 2002 and 2000 obtained 0.93, 0.90 and 0.86, respectively. Gradual and abrupt changes were observed, as well as a measure of change uncertainty for the observed objects at the pixel level. These could be associated with inundations from 1994 to 2015 at the northern coastal area of Java, Indonesia. The largest coastal inundations in terms of area occurred between 1994 and 2000, when 739 ha changed from non-water and shoreline to water and in 2003–2013 for 200 ha. Changes from water and shoreline to non-water occurred between 2000 and 2002 (186 ha) and in 2013–2014 (65 ha). Urban development in flood-prone areas resulted in an increase of flood hazards including inundation and erosion leading to the changes of shoreline position. The proposed methods provided an effective way to present shoreline as a line and as a margin with fuzzy boundary and its associated change uncertainty. Shoreline mapping and monitoring is crucial to understand the spatial distribution of coastal inundation including its trend. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Automatic Sun Glint Removal of Multispectral High-Resolution Worldview-2 Imagery for Retrieving Coastal Shallow Water Parameters
Remote Sens. 2016, 8(1), 37; doi:10.3390/rs8010037
Received: 23 September 2015 / Revised: 16 December 2015 / Accepted: 21 December 2015 / Published: 5 January 2016
Cited by 5 | PDF Full-text (2495 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping
[...] Read more.
Remote sensing of coastal areas requires multispectral satellite images with a high spatial resolution. In this sense, WorldView-2 is a very high resolution satellite, which provides an advanced multispectral sensor with eight narrow bands, allowing the proliferation of new environmental monitoring and mapping applications in shallow coastal ecosystems. These challenges need the accurate determination of the water radiance, which is not often valued compared to other sources such as atmosphere and specular water reflection (sun glint). In this context, the atmospheric correction and the glinting removal have demonstrated to be critical steps in the preprocessing chain of high resolution images. In this work, the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is used to compensate the atmospheric effects and to compute part of the deglinting algorithm using the modeled direct normalized irradiance. This paper describes a novel automatic deglinting procedure, integrated in the Radiative Transfer Modeling (RTM) inversion of the shallow water environments, which allows computing the water Inherent Optical Properties (IOPs), bathymetry and seafloor albedo contributions. The proposed methodology has demonstrated a proper performance for environmental monitoring in shallow water areas. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments
Remote Sens. 2015, 7(12), 16756-16777; doi:10.3390/rs71215852
Received: 25 August 2015 / Revised: 12 November 2015 / Accepted: 24 November 2015 / Published: 9 December 2015
Cited by 4 | PDF Full-text (5208 KB) | HTML Full-text | XML Full-text
Abstract
Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ
[...] Read more.
Most ocean color algorithms are designed for optically deep waters, where the seafloor has little or no effect on remote sensing reflectance. This can lead to inaccurate retrievals of inherent optical properties (IOPs) in optically shallow water environments. Here, we investigate in situ hyperspectral bottom reflectance signatures and their separability for coral reef waters, when observed at the spectral resolutions of MODIS and SeaWiFS sensors. We use radiative transfer modeling to calculate the effects of bottom reflectance on the remote sensing reflectance signal, and assess detectability and discrimination of common coral reef bottom classes by clustering modeled remote sensing reflectance signals. We assess 8280 scenarios, including four IOPs, 23 depths and 45 bottom classes at MODIS and SeaWiFS bands. Our results show: (i) no significant contamination (Rrscorr < 0.0005) of bottom reflectance on the spectrally-averaged remote sensing reflectance signal at depths >17 m for MODIS and >19 m for SeaWiFS for the brightest spectral reflectance substrate (light sand) in clear reef waters; and (ii) bottom cover classes can be combined into two distinct groups, “light” and “dark”, based on the modeled surface reflectance signals. This study establishes that it is possible to efficiently improve parameterization of bottom reflectance and water-column IOP retrievals in shallow water ocean color models for coral reef environments. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Open AccessArticle A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh
Remote Sens. 2015, 7(12), 16480-16503; doi:10.3390/rs71215837
Received: 27 July 2015 / Accepted: 25 November 2015 / Published: 5 December 2015
Cited by 3 | PDF Full-text (3005 KB) | HTML Full-text | XML Full-text
Abstract
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground
[...] Read more.
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass. Full article
(This article belongs to the Special Issue Remote Sensing in Coastal Environments)
Figures

Journal Contact

MDPI AG
Remote Sensing Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Remote Sensing Edit a special issue Review for Remote Sensing
loading...
Back to Top