Application of Remote Sensing Technology to Water-Related Ecosystems

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Biodiversity and Functionality of Aquatic Ecosystems".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 30655

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Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
Interests: remote sensing of wetland, spatially explicit assessment of ecosystem service, and regional environmental change
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Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing for ecosystems;spatial ecosystem simulation;ecosystem risk and sustainability assessment
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing; water environment; watershed management
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Remote Sensing and Geographic Information Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: inland water carbon cycle; remote sensing of greenhouse gases
Special Issues, Collections and Topics in MDPI journals

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International Research Center of Big Data for Sustainable Development Goals, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of surface water dynamics; climate change and adaptation strategy

Special Issue Information

Dear Colleagues,

Water-related ecosystems play a critical role in the water cycle. Amongst them, wetland ecosystems are those in which water is the main component driving their functions and the services they provide. Moreover, wetland ecosystems are considered essential to achieving several Sustainable Development Goals (SDGs). SDG 6 focuses on water resources. There are eight targets under SDG 6, of which target 6.6 recognizes the importance of wetlands and other ecosystems for providing a regular supply of freshwater for domestic, agricultural and industrial usage. It is served by a single indicator, 6.6.1 – change in the extent of water-related ecosystems over time. Timely qualitative and quantitative information about water-related ecosystems is usually not available to decision makers, and to address this situation there is growing interest in the use of remote sensing technology for collecting information on water-related ecosystems for policymaking, protection, and restoration efforts.

This Special Issue is dedicated to the application of remote sensing technologies to the identification, characterization, and monitoring of four sub-indicators of SDG indicator 6.6.1 (vegetated wetlands, rivers, lakes, and artificial water bodies)—to cover the three main aspects of extent: quantity, quality, and the spatial extent or surface area. The potential topics for this Special Issue include, but are not limited to, the following:

  • Mapping, monitoring, and classification of vegetated wetlands using remote sensing on a broad scale;
  • Remote sensing of change in the spatial extent or surface area of rivers, lakes, and artificial water bodies;
  • Remote sensing of water quality of lakes and artificial water bodies;
  • Vegetated wetland species mapping and remote sensing of wetland biodiversity;
  • Estimating carbon fluxes and productivity of vegetated wetlands using remote sensing;
  • Applications of remote sensing to protection and restoration of water-related ecosystems.

Prof. Dr. Zongming Wang
Prof. Dr. Weiguo Jiang
Prof. Dr. Hongtao Duan
Prof. Dr. Zhidan Wen
Dr. Shanlong Lu
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • multi-sensors
  • vegetated wetland mapping
  • water quality
  • wetland plant species
  • biodiversity
  • sustainable development goals (SDGs)

Published Papers (9 papers)

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Research

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18 pages, 6740 KiB  
Article
Assessment of Water Ecosystem Integrity (WEI) in a Transitional Brazilian Cerrado–Atlantic Forest Interface
by Allita R. Santos, Mariana A. G. A. Barbosa, Talyson Bolleli, Phelipe S. Anjinho, Rhayane Roque and Frederico F. Mauad
Water 2023, 15(4), 775; https://doi.org/10.3390/w15040775 - 16 Feb 2023
Cited by 1 | Viewed by 1631
Abstract
Although healthy ecosystems are vital to sustaining human society, the Brazilian Cerrado and Atlantic Forest biomes have suffered from disorderly human development and the intense use of natural resources. Thus, cost-effective studies are needed to develop tools to assess environmental conservation and the [...] Read more.
Although healthy ecosystems are vital to sustaining human society, the Brazilian Cerrado and Atlantic Forest biomes have suffered from disorderly human development and the intense use of natural resources. Thus, cost-effective studies are needed to develop tools to assess environmental conservation and the integrity of water courses to inform decisions for ensuring their recovery where ecosystem maintenance is deficient. This study sought to develop a methodology in which the Watershed Habitat Evaluation and Biotic Integrity Protocol (WHEBIP) and Rapid Assessment Protocol for Habitat Diversity (RAP) could be used in an integrated, adaptive manner to evaluate the Water Ecosystem Integrity (WEI) in courses of rivers and streams in tropical regions of the Brazilian Cerrado–Atlantic Forest interface undergoing intense agricultural exploitation. Accordingly, a spatial assessment using geographic information systems was followed by a field visit to apply the methodology. A preliminary assessment of the soil conditions in the Lobo Reservoir Hydrographic Basin was conducted, identifying stretches of rivers and streams that were suitable for payment for environmental services and for recovery from the impact of anthropic activities. Such activities were present in 50.23% of the basin’s total area, and intensive degradation was found in stretches of the water courses, primarily where the head springs of the Itaqueri River and Lobo Stream, the principal tributaries of the Lobo Reservoir, lie. Native vegetation, Brazilian Cerrado, and reforestation occupy a total of 38.5% of the basin, comprising areas of intense conservation activity by the Brazilian government. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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21 pages, 9238 KiB  
Article
A Deep Learning Method Based on Two-Stage CNN Framework for Recognition of Chinese Reservoirs with Sentinel-2 Images
by Guodongfang Zhao, Ping Yao, Li Fu, Zhibin Zhang, Shanlong Lu and Tengfei Long
Water 2022, 14(22), 3755; https://doi.org/10.3390/w14223755 - 18 Nov 2022
Cited by 2 | Viewed by 1569
Abstract
The development of effective and comprehensive methods for mapping and monitoring reservoirs is essential for the utilization of water resources and flood control. Remote sensing has the great advantages of broad spatial coverage and regular revisit to meet the demand of large-scale and [...] Read more.
The development of effective and comprehensive methods for mapping and monitoring reservoirs is essential for the utilization of water resources and flood control. Remote sensing has the great advantages of broad spatial coverage and regular revisit to meet the demand of large-scale and long-term tasks of earth observation. Although there already exist some methods for coarse-grained identification of reservoirs at region-level in remote sensing images, it remains a challenge to recognize and localize reservoirs accurately with insufficiency of object details and samples annotated. This study focuses on the fine-grained identification and location of reservoirs with a two-stage CNN framework method, which is comprised of a coarse classification between aquatic and land areas of image patches and a fine detection of reservoirs in aquatic patches with precise geographical coordinates. Moreover, a NIR RCNN detection network is proposed to make use of the multi-spectral characteristics of Sentinel-2 images. To verify the effectiveness of our proposed method, we construct a reservoir and dam dataset of 36 Sentinel-2 images which are sampled in various provinces across China and annotated at the instance level by manual work. The experimental results in the test set show that the two-stage CNN method achieves an average recall of 80.83% nationwide, and the comparison between reservoirs recognized by the proposed model and those provided by the China Institute of Water Resources and Hydropower Research verifies that the model reaches a recall of about 90%. Both the indicator evaluation and visualization of identification results have shown the applicability of the proposed method to reservoir recognition in remote sensing images. Being the first attempt to make a fine-grained identification of reservoirs at the instance level, the two-stage CNN framework, which can automatically identify and localize reservoirs in remote sensing images precisely, shows the prospect to be a useful tool for large-scale and long-term reservoir monitoring. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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19 pages, 2734 KiB  
Article
Poyang Lake Wetland Classification Using Time-Series ENVISAT ASAR Data and Beijing-1 Imagery
by Fang Ding, Lin Wang, Iryna Dronova and Kun Cao
Water 2022, 14(20), 3344; https://doi.org/10.3390/w14203344 - 21 Oct 2022
Viewed by 1814
Abstract
Beijing-1 and ENVISAT ASAR images were used to classify wetland aquatic macrophytes in terms of their plant functional types (PFTs) over the Poyang Lake region, China. Speckle noise filtering, systematic sensor calibration within the same polarization or between different polarizations, and accurate geo-registration [...] Read more.
Beijing-1 and ENVISAT ASAR images were used to classify wetland aquatic macrophytes in terms of their plant functional types (PFTs) over the Poyang Lake region, China. Speckle noise filtering, systematic sensor calibration within the same polarization or between different polarizations, and accurate geo-registration were applied to the time-series SAR data. As a result, time-series backscattering data, which is described as permittivity curves in this paper, were obtained. In addition, time-series indices, described as phenological curves, were derived from Beijing-1 time-series images in the classification experiment. Based on these two curves, a rule-based classification strategy was developed to extract wetland information from the combined SAR and optical data. In the rule-based wetland classification method, DEM data, submersion time index, temporal Beijing-1 images, time-series normalized difference vegetation index (TSNDVI) images, principal component analysis (PCA), and temporal ratio of ASAR time-series images were used. In addition, a decision tree-based method was used to map the wetlands. Conclusions include the following: (1) after the preprocessing of ASAR data, it was possible to satisfactorily separate different aquatic plant functional types; (2) hydrophytes from different PFTs exhibited distinct phenological, structural, moisture, and roughness characteristics due to the impact of the annual inundation of Poyang Lake wetland; and (3) more accurate results were obtained with the rule-based method than the decision tree (DT) method. Producer’s and user’s accuracy calculated from test samples in the classification results indicate that the DT method can potentially be used for mapping aquatic PFTs, with overall producer’s accuracy exceeding 80% and higher user’s accuracy for aquatic bed wetland PFTs. A comparison of producer’s and user’s accuracy from the rule-based classification increased from 3 to 12% and 7 to 26%, respectively, for different aquatic PFTs. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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20 pages, 7349 KiB  
Article
Remote Sensing Estimation of Long-Term Total Suspended Matter Concentration from Landsat across Lake Qinghai
by Weibang Li, Qian Yang, Yue Ma, Ying Yang, Kaishan Song, Juan Zhang, Zhidan Wen and Ge Liu
Water 2022, 14(16), 2498; https://doi.org/10.3390/w14162498 - 13 Aug 2022
Cited by 2 | Viewed by 2427
Abstract
Total suspended matter (TSM) is one of the most widely used water quality parameters, which can influence the light transmission process, planktonic algae, and ecological health. A comprehensive field expedition aiming at water quality assessment was conducted for Lake Qinghai in September 2019. [...] Read more.
Total suspended matter (TSM) is one of the most widely used water quality parameters, which can influence the light transmission process, planktonic algae, and ecological health. A comprehensive field expedition aiming at water quality assessment was conducted for Lake Qinghai in September 2019. The in-situ measurements were used to support the calibration and validation of TSM concentration using Landsat images. A regional empirical model was established using the top-of-atmosphere (TOA) radiance of Landsat image data at the red band with a wavelength range of 640–670 nm. The coefficient of determination (R2), mean relative error (MRE), and root mean square error (RMSE) of the TSM estimation model were 0.81, 17.91%, and 0.61 mg/L, respectively. The model was further applied to 87 images during the periods from 1986 to 2020. A significant correlation was found between TSM concentration and daily wind speed (r = 0.74, p < 0.01, n = 87), which revealed the dominance of wind speed on TSM concentration. In addition, hydrological changes also had a significant influence on TSM variations of lake estuaries. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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17 pages, 3638 KiB  
Article
Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)
by Romeu Gerardo and Isabel P. de Lima
Water 2022, 14(15), 2284; https://doi.org/10.3390/w14152284 - 22 Jul 2022
Cited by 11 | Viewed by 2699
Abstract
Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of [...] Read more.
Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is often affected by water pollution. The study approach uses spatiotemporal multispectral data from the Sentinel-2 satellite and from 2021 and investigates the potential of remote sensing-based vegetation and water indices (Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), Green Red Vegetation Index (GRVI), Normalized Difference Water Index (NDWI)) for detecting duckweeds’ infestation and its severity. The NDAVI was identified as the vegetation index (VI) that better depicted the presence of duckweeds in the surface of the water course; however, results obtained for the other VIs are also encouraging, with NDVI showing a response that is very similar to NDAVI. Results are promising regarding the ability of remote sensing products to provide insight into the behavior of Lemna minor and to identify problematic sections along small watercourses. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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18 pages, 4733 KiB  
Article
Artificial and Natural Water Bodies Change in China, 2000–2020
by Yong Wang, Shanlong Lu, Feng Zi, Hailong Tang, Mingyang Li, Xinru Li, Chun Fang and Harrison Odion Ikhumhen
Water 2022, 14(11), 1756; https://doi.org/10.3390/w14111756 - 30 May 2022
Cited by 3 | Viewed by 2797
Abstract
Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional [...] Read more.
Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China’s surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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16 pages, 4532 KiB  
Article
Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images
by Hailong Tang, Shanlong Lu, Muhammad Hasan Ali Baig, Mingyang Li, Chun Fang and Yong Wang
Water 2022, 14(9), 1454; https://doi.org/10.3390/w14091454 - 2 May 2022
Cited by 22 | Viewed by 3673
Abstract
Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random [...] Read more.
Surface water is a highly dynamical object on the earth’s surface. At present, satellite remote sensing is the most effective way to accurately depict the temporal and spatial variation characteristics of surface water on a large scale. In this study, a region-adaptive random forest algorithm is designed on the Google Earth Engine (GEE) for automatic surface water mapping by using data from multi-sensors such as Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-1 SAR images as source data, and China as a case study region. The visual comparison of the mapping results with the original images under different landform areas shows that the extracted water body boundary is consistent with the water range in the image. The cross-validation with the JRC GSW validation samples shows a very high precision that the average producer’s accuracy and average user’s accuracy of water is 0.933 and 0.998, respectively. The average overall accuracy and average kappa is 0.966 and 0.931, respectively. The independent verification results of lakes with different areas also prove the high accuracy for our method, with a maximum average error of 3.299%. These results show that the method is an ideal way for large-scale surface water mapping with a high spatial–temporal resolution. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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24 pages, 5723 KiB  
Article
Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery
by Huaxin Liu, Qigang Jiang, Yue Ma, Qian Yang, Pengfei Shi, Sen Zhang, Yang Tan, Jing Xi, Yibo Zhang, Bin Liu and Xin Gao
Water 2022, 14(1), 82; https://doi.org/10.3390/w14010082 - 3 Jan 2022
Cited by 10 | Viewed by 2625
Abstract
The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due [...] Read more.
The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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Review

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18 pages, 4511 KiB  
Review
Satellite Detection of Surface Water Extent: A Review of Methodology
by Jiaxin Li, Ronghua Ma, Zhigang Cao, Kun Xue, Junfeng Xiong, Minqi Hu and Xuejiao Feng
Water 2022, 14(7), 1148; https://doi.org/10.3390/w14071148 - 2 Apr 2022
Cited by 33 | Viewed by 10072
Abstract
Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources [...] Read more.
Water is an imperative part of the Earth and an essential resource in human life and production. Under the effects of climate change and human activities, the spatial and temporal distribution of water bodies has been changing, and the shortage of water resources is becoming increasingly serious worldwide. Therefore, the monitoring of water bodies is indispensable. Remote sensing has the advantages of real time, wide coverage, and rich information and has become a brand-new technical means to quickly obtain water information. This study summarizes the current common methods of water extraction based on optical and radar images, including the threshold method, support vector machine, decision tree, object-oriented extraction, and deep learning, as well as the advantages and disadvantages of each method. These methods were applied to the Huai River Basin in China and Nam Co on the Qinghai-Tibet Plateau. The extraction results show that all the aforementioned approaches can obtain reliable results. Among them, the threshold segmentation method based on normalized difference water index is more robust than others. In the water extraction process, there are still many problems that restrict the accuracy of the results. In the future, researchers will continue to search for more automatic, extensive, and high-precision water extraction methods. Full article
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
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