Special Issue "Mapping and Monitoring of Wetlands"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 3626

Special Issue Editor

Dr. Thomas Gumbricht
E-Mail Website
Guest Editor
Department of Physical Geography, Stockholm University, Stockholm, Sweden
Karttur AB, Stockholm, Sweden
Interests: remote sensing big data; sustainable management; wetlands; hydrology; global models; systems theory

Special Issue Information

Dear Colleagues,

Wetlands are key hubs for biological production and biodiversity, regulating both the water cycle and biogeochemical cycles. The mapping and monitoring of wetlands can be done using remote sensing imagery, terrain data, and/or hydrological modeling. On a detailed scale, high-resolution datasets (<5 m), mainly from LiDAR and optical sensors, are often used for local wetland studies, including textural and object-based image analysis which require iterative calibration and validation. Medium-resolution datasets (~5–200 m), including terrain, optical, thermal, and microwave (SAR) data, have been applied for digital wetland mapping since the 1970s. At this scale, wetlands often act as transitory ecotones and are usually classified in a pixel-wise manner. Most efforts have been directed towards estimating different wetland attributes, rather than delineating wetlands as such. A few attributes (e.g., open water and terrain forms) can be mapped using automated algorithms, even at a global scale. Mapping wetland categories across larger regions is more difficult, and the few hitherto efforts have required extensive user interactions. At a coarser scale, additional datasets from microwave brightness temperature and gravity sensors are available. Routinely monitored properties at this scale include climate, geomorphology, freeze/thaw, soil moisture, inundation, land cover, and vegetation and its phenology. These are often downscaled to support local studies, and are more seldom used for direct mapping and monitoring of wetlands over large regions. Thus, despite an explosive growth in sensors and data availability, combined with strong developments in algorithms, there are gaps in the knowledge about the extent and function of wetlands at different scales. New approaches and ideas for mapping and monitoring wetlands across space and time are needed—ideally using methods that are transparent to wetland functional traits and can be used for understanding the effects on wetlands in times of change.

Dr. Thomas Gumbricht
Guest Editor

Manuscript Submission Information

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Keywords

  • Wetlands
  • Mapping
  • Monitoring
  • Modeling
  • Multi-source
  • Hybrid
  • Landform
  • Hydrology
  • Biophysical indicators
  • Global
  • Transparent

Published Papers (3 papers)

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Research

Article
Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery
Water 2022, 14(2), 244; https://doi.org/10.3390/w14020244 - 15 Jan 2022
Cited by 2 | Viewed by 899
Abstract
Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this [...] Read more.
Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)
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Article
A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme
Water 2021, 13(24), 3601; https://doi.org/10.3390/w13243601 - 15 Dec 2021
Cited by 2 | Viewed by 1091
Abstract
Due to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using [...] Read more.
Due to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using remote sensing. At the same time, advances in artificial intelligence and machine learning, particularly deep learning models, have provided opportunities to advance wetland classification methods. However, the developed deep and very deep algorithms require a higher number of training samples, which is costly, logistically demanding, and time-consuming. As such, in this study, we propose a Deep Convolutional Neural Network (DCNN) that uses a modified architecture of the well-known DCNN of the AlexNet and a Generative Adversarial Network (GAN) for the generation and classification of Sentinel-1 and Sentinel-2 data. Applying to an area of approximately 370 sq. km in the Avalon Peninsula, Newfoundland, the proposed model with an average accuracy of 92.30% resulted in F-1 scores of 0.82, 0.85, 0.87, 0.89, and 0.95 for the recognition of swamp, fen, marsh, bog, and shallow water, respectively. Moreover, the proposed DCNN model improved the F-1 score of bog, marsh, fen, and swamp wetland classes by 4%, 8%, 11%, and 26%, respectively, compared to the original CNN network of AlexNet. These results reveal that the proposed model is highly capable of the generation and classification of Sentinel-1 and Sentinel-2 wetland samples and can be used for large-extent classification problems. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)
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Article
Modelling, Characterizing, and Monitoring Boreal Forest Wetland Bird Habitat with RADARSAT-2 and Landsat-8 Data
Water 2021, 13(17), 2327; https://doi.org/10.3390/w13172327 - 25 Aug 2021
Viewed by 876
Abstract
Earth observation technologies have strong potential to help map and monitor wildlife habitats. Yellow Rail, a rare wetland obligate bird species, is a species of concern in Canada and provides an interesting case study for monitoring wetland habitat with Earth observation data. Yellow [...] Read more.
Earth observation technologies have strong potential to help map and monitor wildlife habitats. Yellow Rail, a rare wetland obligate bird species, is a species of concern in Canada and provides an interesting case study for monitoring wetland habitat with Earth observation data. Yellow Rail has highly specific habitat requirements characterized by shallowly flooded graminoid vegetation, the availability of which varies seasonally and year-to-year. Polarimetric Synthetic Aperture Radar (SAR) in combination with optical data should, in theory, be a great resource for mapping and monitoring these habitats. This study evaluates the use of RADARSAT-2 data and Landsat-8 data to characterize, map, and monitor Yellow Rail habitat in a wetland area within the mineable oil sands region. Specifically, we investigate: (1) The relative importance of polarimetric SAR and Landsat-8 data for predicting Yellow Rail habitat; (2) characterization of wetland habitat with polarimetric SAR data; (3) yearly trends in available habitat; and (4) predictions of potentially suitable habitat across northeastern Alberta. Results show that polarimetric SAR using the Freeman–Durden decomposition and polarization ratios were the most important predictors when modeling the Yellow Rail habitat. These parameters also effectively characterize this habitat based on high congruence with existing descriptions of suitable habitat. Applying the prediction model across all wetland areas showed accurate predictions of occurrence (validated on field occurrence data), and high probability habitats were constrained to very specific wetland areas. Using the RADARSAT-2 data to monitor yearly changes to Yellow Rail habitat was inconclusive, likely due to the different image acquisition times of the 2014 and 2016 images, which may have captured seasonal, rather than inter-annual, wetland dynamics. Polarimetric SAR has proved to be very useful for capturing the specific hydrology and vegetation structure of the Yellow Rail habitat, which could be a powerful technology for monitoring and conserving wetland species habitat. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)
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