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Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (1 September 2022) | Viewed by 48739

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Special Issue Editors

Dr. Simona Niculescu

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Guest Editor

Institut Universitaire Européen de la Mer (IUEM), Université de Brest (UBO), 29238 Brest, France
Interests: remote sensing of environment; wetlands; land cover/land use dynamics; image classification and mapping; sensor fusion; natural risk of coastal areas
Special Issues, Collections and Topics in MDPI journals

Dr. Junshi Xia

E-Mail Website
Guest Editor

Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: high-performance geo-computation; big earth data; data science
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dar Roberts

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Guest Editor

Geography Department, University of California, Santa Barbara, CA 93106, USA
Interests: imaging spectroscopy; thermal remote sensing; LiDAR; sensor fusion; spectral mixture analysis; remote sensing of wildfire; trace gas mapping; urban remote sensing; change identification; plant species mapping; vegetation drought response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental management and the preservation of biodiversity are widely considered a priority in the context of accelerating global changes affecting the physical and biological resources of our planet. This Special Issue of the journal will focus on “Coastal and Aquatic Ecosystems”. The coastal region is a transition area between terrestrial and marine ecosystems. This transition area is now considered an important component of the biosphere, both in terms of ecosystems’ diversity and in the provision of resources and services. Moreover, the coastal region is home to a significant number of distinct biological communities, including coral reefs, mangroves, salt meadows and wetlands, phanerogam meadows, and kelp forests, estuarine assemblages or coastal lagoons, forests, and grasslands. The diversity of coastal ecosystems is directly threatened by human activity. It is estimated that 60% of the world’s population lives on or near the coast, and the pressures that economic development exerts on the coastal environment are particularly high. Coastal ecosystems are undergoing permanent changes in production rates, organism abundance, and community structure.

Achieving sustainable coastal zone management poses particularly significant challenges as the pressures of a growing human population, multiple development pressures, pollution from land-based sources, and unsustainable exploitation of natural resources are particularly felt on many of the world’s coasts. Remote sensing meets this challenge by offering a wide range of standard products on environmental coastal condition, thanks in particular to various state-of-the-art sensors. The development of innovative methods based on the integration of multi-source, multi-resolution, and multi-temporal images offers promising prospects for considering the different scales of ecosystems. Consequently, the products derived from remote sensing contribute to the development of temporal and spatial indicators for better knowledge and management of coastal and aquatic ecosystems. This Special Issue calls for submissions that report original environmental research using satellite data processing—optical or radar—addressing coastal and aquatic ecosystem monitoring at different spatial and temporal scales.

Most of the papers published in this special issue were presented at the international conference EUCOMARE-2022 in the framework of the European Jean Monnet Chair European Spatial Studies of Sea and Coastal zones with the support of the ERASMUS+ Programme of the European Union.

Dr. Simona Niculescu
Dr. Junshi Xia
Dr. Dar Roberts
Guest Editors

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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 submissions that pass pre-check are 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 semimonthly 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 2700 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.

Keywords

remote sensing
coastal and aquatic ecosystems
biodiversity management

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Published Papers (13 papers)

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Editorial

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4 pages, 188 KiB

Open AccessEditorial

Editorial for Special Issue “Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management”

by Simona Niculescu, Junshi Xia and Dar Roberts

Remote Sens. 2023, 15(3), 766; https://doi.org/10.3390/rs15030766 - 29 Jan 2023

Cited by 1 | Viewed by 1559

Abstract

Most of the papers published in this Special Issue were presented at the international conference EUCOMARE-2022 in the framework of the European Jean Monnet Chair European Spatial Studies of Sea and Coastal zones with the support of the ERASMUS+ Programme of the European [...] Read more.

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

Research

Jump to: Editorial

33 pages, 5879 KiB

Open AccessArticle

A Multi-Satellite Mapping Framework for Floating Kelp Forests

by Lianna Gendall, Sarah B. Schroeder, Peter Wills, Margot Hessing-Lewis and Maycira Costa

Remote Sens. 2023, 15(5), 1276; https://doi.org/10.3390/rs15051276 - 25 Feb 2023

Cited by 7 | Viewed by 3849

Abstract

Kelp forests provide key habitat on the Pacific Coast of Canada; however, the long-term changes in their distribution and abundance remain poorly understood. With advances in satellite technology, floating kelp forests can now be monitored across large-scale areas. We present a methodological framework using an object-based image analysis approach that enables the combination of imagery from multiple satellites at different spatial resolutions and temporal coverage, to map kelp forests with floating canopy through time. The framework comprises four steps: (1) compilation and quality assessment; (2) preprocessing; (3) an object-oriented classification; and (4) an accuracy assessment. Additionally, the impact of spatial resolution on the detectability of floating kelp forests is described. Overall, this workflow was successful in producing accurate maps of floating kelp forests, with global accuracy scores of between 88% and 94%. When comparing the impact of resolution on detectability, lower resolutions were less reliable at detecting small kelp forests in high slope areas. Based on the analysis, we suggest removing high slope areas (11.4%) from time series analyses using high- to medium-resolution satellite imagery and that error, in this case up to 7%, be considered when comparing imagery at different resolutions in low–mid slope areas through time. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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27 pages, 8698 KiB

Open AccessArticle

Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

by Guanyao Xie and Simona Niculescu

Remote Sens. 2022, 14(18), 4437; https://doi.org/10.3390/rs14184437 - 6 Sep 2022

Cited by 20 | Viewed by 5078

Abstract

Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finistère with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH/VV, and finally, heading in VV and VH polarizations. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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26 pages, 40427 KiB

Open AccessArticle

Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling

by Wendy Diruit, Anthony Le Bris, Touria Bajjouk, Sophie Richier, Mathieu Helias, Thomas Burel, Marc Lennon, Alexandre Guyot and Erwan Ar Gall

Remote Sens. 2022, 14(13), 3124; https://doi.org/10.3390/rs14133124 - 29 Jun 2022

Cited by 23 | Viewed by 3771

Abstract

Intertidal macroalgal habitats are major components of temperate coastal ecosystems. Their distribution was studied using field sampling and hyperspectral remote mapping on a rocky shore of Porspoder (western Brittany, France). Covers of both dominating macroalgae and the sessile fauna were characterized in situ at low tide in 24 sampling spots, according to four bathymetric levels. A zone of ca. 17,000 m² was characterized using a drone equipped with a hyperspectral camera. Macroalgae were identified by image processing using two classification methods to assess the representativeness of spectral classes. Finally, a comparison of the remote imaging data to the field sampling data was conducted. Seven seaweed classes were distinguished by hyperspectral pictures, including five different species of Fucales. The maximum likelihood (MLC) and spectral angle mapper (SAM) were both trained using image-derived spectra. MLC was more accurate to classify the main dominating species (Overall Accuracy (OA) 95.1%) than SAM (OA 87.9%) at a site scale. However, at sampling points scale, the results depend on the bathymetric level. This study evidenced the efficiency and accuracy of hyperspectral remote sensing to evaluate the distribution of dominating intertidal seaweed species and the potential for a combined field/remote approach to assess the ecological state of macroalgal communities. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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15 pages, 5360 KiB

Open AccessArticle

Shifts in Salt Marsh Vegetation Landcover after Debris Flow Deposition

by Germán D. Silva, Dar A. Roberts, Joseph P. McFadden and Jennifer Y. King

Remote Sens. 2022, 14(12), 2819; https://doi.org/10.3390/rs14122819 - 12 Jun 2022

Cited by 4 | Viewed by 3089

Abstract

On 9 January 2018, Carpinteria Salt Marsh Reserve received a large quantity of sediment following debris flows in Montecito, California. Because disturbances potentially impact the ecosystem services and functions that wetlands provide, an understanding of how the ecosystem responded to the debris flows is important for the management of salt marsh systems. However, a lack of field data before and after this disturbance makes this task impossible to complete by field methods alone. To address this gap, we used Sentinel-2 satellite imagery to calculate landcover fractions and spectral indices to produce maps of landcover before, during, and after the debris flow using a random forest classifier. Change detection showed that vegetation extent in November 2020 approached pre-debris flow conditions. While total vegetated area experienced little net change (0.15% decrease), there was a measurable change in the areal extent of vegetation type, with high marsh vegetation transitioning to mid marsh vegetation in regions that initially showed an increase in bare soil cover. These results are uniquely quantifiable using remote sensing techniques and show that disturbance due to debris flows may affect ecosystem function via plant community change. These impacts will need to be taken into consideration when managing wetlands prone to depositional events. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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18 pages, 3609 KiB

Open AccessArticle

Comparing the Use of Red-Edge and Near-Infrared Wavelength Ranges for Detecting Submerged Kelp Canopy

by Brian Timmer, Luba Y. Reshitnyk, Margot Hessing-Lewis, Francis Juanes and Maycira Costa

Remote Sens. 2022, 14(9), 2241; https://doi.org/10.3390/rs14092241 - 7 May 2022

Cited by 9 | Viewed by 3796

Abstract

Kelp forests are commonly classified within remote sensing imagery by contrasting the high reflectance in the near-infrared spectral region of kelp canopy floating at the surface with the low reflectance in the same spectral region of water. However, kelp canopy is often submerged below the surface of the water, making it important to understand the effects of kelp submersion on the above-water reflectance of kelp, and the depth to which kelp can be detected, in order to reduce uncertainties around the kelp canopy area when mapping kelp. Here, we characterized changes to the above-water spectra of Nereocystis luetkeana (Bull kelp) as different canopy structures (bulb and blades) were submerged in water from the surface to 100 cm in 10 cm increments, while collecting above-water hyperspectral measurements with a spectroradiometer (325–1075 nm). The hyperspectral data were simulated into the multispectral bandwidths of the WorldView-3 satellite and the Micasense RedEdge-MX unoccupied aerial vehicle sensors and vegetation indices were calculated to compare detection limits of kelp with a focus on differences between red edge and near infrared indices. For kelp on the surface, near-infrared reflectance was higher than red-edge reflectance. Once submerged, the kelp spectra showed two narrow reflectance peaks in the red-edge and near-infrared wavelength ranges, and the red-edge peak was consistently higher than the near-infrared peak. As a result, kelp was detected deeper with vegetation indices calculated with a red-edge band versus those calculated with a near infrared band. Our results show that using red-edge bands increased detection of submerged kelp canopy, which may be beneficial for estimating kelp surface-canopy area and biomass. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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24 pages, 5845 KiB

Open AccessArticle

Assessment of RTK Quadcopter and Structure-from-Motion Photogrammetry for Fine-Scale Monitoring of Coastal Topographic Complexity

by Stéphane Bertin, Pierre Stéphan and Jérôme Ammann

Remote Sens. 2022, 14(7), 1679; https://doi.org/10.3390/rs14071679 - 31 Mar 2022

Cited by 10 | Viewed by 2532

Abstract

Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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24 pages, 11448 KiB

Open AccessArticle

Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

by Huachang Qiu, Zhaoning Gong, Kuinan Mou, Jianfang Hu, Yinghai Ke and Demin Zhou

Remote Sens. 2022, 14(4), 927; https://doi.org/10.3390/rs14040927 - 14 Feb 2022

Cited by 6 | Viewed by 2453

Abstract

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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30 pages, 33778 KiB

Open AccessArticle

Classification of Land-Water Continuum Habitats Using Exclusively Airborne Topobathymetric Lidar Green Waveforms and Infrared Intensity Point Clouds

by Mathilde Letard, Antoine Collin, Thomas Corpetti, Dimitri Lague, Yves Pastol and Anders Ekelund

Remote Sens. 2022, 14(2), 341; https://doi.org/10.3390/rs14020341 - 12 Jan 2022

Cited by 13 | Viewed by 3076

Abstract

Coastal areas host highly valuable ecosystems that are increasingly exposed to the threats of global and local changes. Monitoring their evolution at a high temporal and spatial scale is therefore crucial and mostly possible through remote sensing. This article demonstrates the relevance of topobathymetric lidar data for coastal and estuarine habitat mapping by classifying bispectral data to produce 3D maps of 21 land and sea covers at very high resolution. Green lidar full waveforms are processed to retrieve tailored features corresponding to the signature of those habitats. These features, along with infrared intensities and elevations, are used as predictors for random forest classifications, and their respective contribution to the accuracy of the results is assessed. We find that green waveform features, infrared intensities, and elevations are complimentary and yield the best classification results when used in combination. With this configuration, a classification accuracy of 90.5% is achieved for the segmentation of our dual-wavelength lidar dataset. Eventually, we produce an original mapping of a coastal site under the form of a point cloud, paving the way for 3D classification and management of land and sea covers. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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18 pages, 29888 KiB

Open AccessArticle

Satellite–Derived Topography and Morphometry for VHR Coastal Habitat Mapping: The Pleiades–1 Tri–Stereo Enhancement

by Dorothée James, Antoine Collin, Antoine Mury and Rongjun Qin

Remote Sens. 2022, 14(1), 219; https://doi.org/10.3390/rs14010219 - 4 Jan 2022

Cited by 7 | Viewed by 3125

Abstract

The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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23 pages, 12116 KiB

Open AccessEditor’s ChoiceArticle

Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2

by Alyson Le Quilleuc, Antoine Collin, Michael F. Jasinski and Rodolphe Devillers

Remote Sens. 2022, 14(1), 133; https://doi.org/10.3390/rs14010133 - 29 Dec 2021

Cited by 41 | Viewed by 6363

Abstract

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D^® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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17 pages, 17272 KiB

Open AccessArticle

Easily Implemented Methods of Radiometric Corrections for Hyperspectral–UAV—Application to Guianese Equatorial Mudbanks Colonized by Pioneer Mangroves

by Marion Jaud, Guillaume Sicot, Guillaume Brunier, Emma Michaud, Nicolas Le Dantec, Jérôme Ammann, Philippe Grandjean, Patrick Launeau, Gérard Thouzeau, Jules Fleury and Christophe Delacourt

Remote Sens. 2021, 13(23), 4792; https://doi.org/10.3390/rs13234792 - 26 Nov 2021

Cited by 5 | Viewed by 2740

Abstract

Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations) is a custom, mini-UAV (unmanned aerial vehicle) platform (<20 kg), equipped with a light push broom hyperspectral sensor combined with a navigation module measuring position and orientation. Because of the particularities of UAV surveys (low flight altitude, small spatial scale, and high resolution), dedicated pre-processing methods have to be developed when reconstructing hyperspectral imagery. This article presents light, easy-implementation, in situ methods, using only two Spectralon^® and a field spectrometer, allowing performance of an initial calibration of the sensor in order to correct “vignetting effects” and a field standardization to convert digital numbers (DN) collected by the hyperspectral camera to reflectance, taking into account the time-varying illumination conditions. Radiometric corrections are applied to a subset of a dataset collected above mudflats colonized by pioneer mangroves in French Guiana. The efficiency of the radiometric corrections is assessed by comparing spectra from Hyper-DRELIO imagery to in situ spectrometer measurements above the intertidal benthic biofilm and mangroves. The shapes of the spectra were consistent, and the spectral angle mapper (SAM) distance was 0.039 above the benthic biofilm and 0.159 above the mangroves. These preliminary results provide new perspectives for quantifying and mapping the benthic biofilm and mangroves at the scale of the Guianese intertidal mudbanks system, given their importance in the coastal food webs, biogeochemical cycles, and the sediment stabilization. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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18 pages, 4309 KiB

Open AccessArticle

Spatiotemporal Trends of Bora Bora’s Shoreline Classification and Movement Using High-Resolution Imagery from 1955 to 2019

by Emma Gairin, Antoine Collin, Dorothée James, Tehani Maueau, Yoann Roncin, Lucas Lefort, Franck Dolique, Matthieu Jeanson and David Lecchini

Remote Sens. 2021, 13(22), 4692; https://doi.org/10.3390/rs13224692 - 20 Nov 2021

Cited by 13 | Viewed by 4353

Abstract

Coastal urbanisation is a widespread phenomenon throughout the world and is often linked to increased erosion. Small Pacific islands are not spared from this issue, which is of great importance in the context of climate change. The French Polynesian island of Bora Bora was used as a case study to investigate the historical evolution of its coastline classification and position from 1955 to 2019. A time series of very high-resolution aerial imagery was processed to highlight the changes of the island’s coastline. The overall length of natural shores, including beaches, decreased by 46% from 1955 to 2019 while human-made shores such as seawalls increased by 476%, and as of 2019 represented 61% of the coastline. This evolution alters sedimentary processes: the time series of aerial images highlights increased erosion in the vicinity of seawalls and embankments, leading to the incremental need to construct additional walls. In addition, the gradual removal of natural shoreline types modifies landscapes and may negatively impact marine biodiversity. Through documenting coastal changes to Bora Bora over time, this study highlights the impacts of human-made structures on erosional processes and underscores the need for sustainable coastal management plans in French Polynesia. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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