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Remote Sensing for the Study of the Changes in Wetlands

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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: 15 January 2025 | Viewed by 7733

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Special Issue Editors

Dr. Simona Niculescu

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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
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Dr. Heidi Van Deventer

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Council for Scientific and Industrial Research (CSIR), University of Pretoria, Pretoria 0001, South Africa
Interests: freshwater ecosystem typing; freshwater essential biodiversity variables; change detection and monitoring of estuarine and freshwater ecosystems; using hyperspectral; multispectral; radar sensors and time-series analysis

Prof. Dr. Dar Roberts

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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
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Dr. Junshi Xia

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Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: high-performance geo-computation; big earth data; data science
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Special Issue Information

Dear Colleagues,

Wetlands are important and valuable ecosystems, providing a range of ecosystem services that are considered critical buffers against climate change, yet they remain threatened worldwide (IPBES, 2019). Among estuarine and freshwater inland wetlands, coastal wetlands along the transition zone between the freshwater and estuarine realms represent very interesting areas for this Remote Sensing Special Issue (SI). The coastline is a high-stakes, vulnerable area, and contains coastal wetlands under pressure from both anthropogenic and climate change stresses. The Ramsar Convention (Ramsar Convention Secretariat 2022) considers mangroves, salt marshes, seagrass beds, coral reefs, beaches, estuaries, and coastal water bodies less than 6 m deep to be coastal wetlands. In addition, other forested wetlands such as floodplain and riverine and swamp forests are freshwater habitats interspersed or fringing estuarine habitats. These coastal wetlands represent a wealth of valuable, but highly fragile ecosystems, yet despite the essential ecosystem services they provide they remain threatened with increasing degradation, risking their persistence (Millennium Ecosystem Assessment, 2005). On the current time scale, tidal wetlands are biologically productive ecosystems with high biodiversity, providing multiple benefits to the ecosystem; however, the advantages of these wetlands are not fully recognized or even precisely known. We know that these wetlands are an important contributing factor in mitigating the impact of floods, delaying the effects of drought, but they also facilitate biological production for fishing and shellfish farming, create reservoirs of biodiversity, improve water quality, regulate the water cycle, store carbon in the mangrove soil, and maintain green areas at the periphery of urban areas.

Earth observation plays a critical role in informing changes to the extent, integrity and connectivity of these wetlands, of which targets for measuring these changes are currently in discussion for Goal A of the post-2020 Global Biodiversity Framework of the Convention of Biological Diversity (CBD, 2021). In addition, Earth observation is key to the monitoring of essential biodiversity variables, such as changes in composition, integrity and structure (Turak et al., 2017). Since no global monitoring system is in place for reporting on changes in coastal wetlands to the CBD or the Sustainable Development Goals (SDGs), this SI is focused on providing evidence from Earth observation technologies to quantify changes in coastal ecosystems for global reporting to targets. One of the major challenges is to distinguish natural dynamics in these systems from artificial and climate change impacts.

For this SI we invite you to submit your research on the use of Earth observation technologies to respond to the challenge of quantifying changes in wetlands for global reporting, including estuarine, coastal and freshwater wetlands.

References

CBD (Secretariat of the Convention on Biological Diversity. (2021). First draft of the post-2020 global biodiversity framework. Available at: https://www.cbd.int/doc/c/abb5/591f/2e46096d3f0330b08ce87a45/wg2020-03-03-en.pdf.
Turak, E. et al., (2017). Essential Biodiversity Variables for measuring change in global freshwater biodiversity. Biological Conservation, 213(Part B): 272–279. https://doi.org/10.1016/j.biocon.2016.09.005.

Dr. Simona Niculescu
Dr. Heidi Van Deventer
Prof. Dr. Dar Roberts
Dr. Junshi Xia
Guest Editors

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Keywords

earth observation monitoring of wetlands
essential biodiversity variables
time-series analysis to distinguish natural dynamics from artificial and climate change impacts
wetlands, including lacustrine and palustrine biome wetlands, in the estuarine, coastal and freshwater realms

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Published Papers (6 papers)

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Research

17 pages, 17604 KiB

Open AccessArticle

Remote Sensing for Mapping Natura 2000 Habitats in the Brière Marshes: Setting Up a Long-Term Monitoring Strategy to Understand Changes

by Thomas Lafitte, Marc Robin, Patrick Launeau and Françoise Debaine

Remote Sens. 2024, 16(15), 2708; https://doi.org/10.3390/rs16152708 - 24 Jul 2024

Viewed by 449

Abstract

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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25 pages, 8689 KiB

Open AccessArticle

Assessment of Atmospheric Correction Algorithms for Sentinel-3 OLCI in the Amazon River Continuum

by Aline M. Valerio, Milton Kampel, Vincent Vantrepotte, Victoria Ballester and Jeffrey Richey

Remote Sens. 2024, 16(14), 2663; https://doi.org/10.3390/rs16142663 - 20 Jul 2024

Viewed by 779

Abstract

Water colour remote sensing is a valuable tool for assessing bio-optical and biogeochemical parameters across the vast extent of the Amazon River Continuum (ARC). However, accurate retrieval depends on selecting the best atmospheric correction (AC). Four AC processors (Acolite, Polymer, C2RCC, OC-SMART) were [...] Read more.

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) measurements. K-means classification identified four optical water types (OWTs) that are affected by the ARC. Two OWTs showed seasonal differences in the Lower Amazon River, influenced by the increase in suspended sediment concentration with river discharge. The other OWTs in the Amazon River Plume are dominated by phytoplankton or by a mixture of optically significant constituents. The Quality Water Index Polynomial method used to assess the quality of in situ and orbital

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had a high failure rate when the Apparent Visible Wavelength was >580 nm for in situ

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. OC-SMART

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products showed better spectral quality compared to

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derived from other AC processors evaluated in this study. These results improve our understanding of remotely sensing very turbid waters, such as those in the Amazon River Continuum. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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21 pages, 13915 KiB

Open AccessArticle

Improved Classification of Coastal Wetlands in Yellow River Delta of China Using ResNet Combined with Feature-Preferred Bands Based on Attention Mechanism

by Yirong Li, Xiang Yu, Jiahua Zhang, Shichao Zhang, Xiaopeng Wang, Delong Kong, Lulu Yao and He Lu

Remote Sens. 2024, 16(11), 1860; https://doi.org/10.3390/rs16111860 - 23 May 2024

Viewed by 664

Abstract

The Yellow River Delta wetlands in China belong to the coastal wetland ecosystem, which is one of the youngest and most characteristic wetlands in the world. The Yellow River Delta wetlands are constantly changed by inland sediment and the influence of waves and storm surges, so the accurate classification of the coastal wetlands in the Yellow River Delta is of great significance for the rational utilization, development and protection of wetland resources. In this study, the Yellow River Delta sentinel-2 multispectral data were processed by super-resolution synthesis, and the feature bands were optimized. The optimal feature-band combination scheme was screened using the OIF algorithm. A deep learning model attention mechanism ResNet based on feature optimization with attention mechanism integration into the ResNet network is proposed. Compared with the classical machine learning model, the AM_ResNet model can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. The overall accuracy was 94.61% with a Kappa of 0.93, and they were improved by about 6.99% and 0.1, respectively, compared with the best-performing Random Forest Classification in machine learning. The results show that the method can effectively improve the classification accuracy of the wetlands in the Yellow River Delta. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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30 pages, 9009 KiB

Open AccessArticle

Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site

by Gregory S. Norris, Armand LaRocque, Brigitte Leblon, Myriam A. Barbeau and Alan R. Hanson

Remote Sens. 2024, 16(6), 1049; https://doi.org/10.3390/rs16061049 - 15 Mar 2024

Cited by 2 | Viewed by 1026

Abstract

Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1–95.2%; (2) pixel-based classifiers outperformed object-based classifiers by 1.3–2.0%; (3) input variables extracted from the August images were more important than those extracted from the June and July images; (4) certain raw reflectances, vegetation indices, and textural features were among the most important variables; and (5) classes that changed temporally were mapped with user’s and producer’s validation accuracies of 86.7–100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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25 pages, 36769 KiB

Open AccessArticle

Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020

by Guangqing Zhai, Jiaqiang Du, Lijuan Li, Xiaoqian Zhu, Zebang Song, Luyao Wu, Fangfang Chong and Xiya Chen

Remote Sens. 2024, 16(3), 567; https://doi.org/10.3390/rs16030567 - 1 Feb 2024

Cited by 1 | Viewed by 1176

Abstract

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 10⁵ hm², with the predominant decline occurring in the 0–1 hm² and 1–3 hm² size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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28 pages, 7457 KiB

Open AccessArticle

Monitoring and Mapping Floods and Floodable Areas in the Mekong Delta (Vietnam) Using Time-Series Sentinel-1 Images, Convolutional Neural Network, Multi-Layer Perceptron, and Random Forest

by Chi-Nguyen Lam, Simona Niculescu and Soumia Bengoufa

Remote Sens. 2023, 15(8), 2001; https://doi.org/10.3390/rs15082001 - 10 Apr 2023

Cited by 7 | Viewed by 2270

Abstract

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta. Full article

(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)

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