Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem
share announcement

Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 15235

Special Issue Editors

Dr. Jianzhong Lu

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: remote sensing of the environment; water color remote sensing; hydrological remote sensing; hydrology modelling and data assimilation; climate change and environment response; disaster monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunliang Li

E-Mail Website
Guest Editor
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: lake-catchment coupling simulation; lake-floodplain hydrological processes; wetland surface-groundwater interactions
Prof. Dr. Hui Li

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Interests: satellite altimetry; surface water hydrology; remote sensing
Dr. Zhiqiang Tan

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing; hydrological connectivity; wetland ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, remote sensing technology, with the significant development in instruments and data processing methods, has provided a sophisticated tool to understand Earth surface dynamics. The lake–wetland–watershed ecosystem is a complex system with interactions between the water and ecological community, which varies and develops with certain structures and functions through the water cycle. Therefore, water-related parameter monitoring and process-based modeling of this unique system are essential for both ecology protection and watershed management.

In this Special Issue, we invite submissions that incorporate studies on remote sensing monitoring and hydrologic or hydrodynamic modeling of lake–wetland–watershed systems to solve newly emerging environmental problems as well as to develop applications of modern monitoring and modeling technologies. This Special Issue will cover research on watershed modeling, water-related parameter retrieval from remote sensing, wetland ecology monitoring, lake–watershed coupling simulation, etc. As well as new findings, studies of the lake–wetland–watershed ecosystem that require novel approaches, the development of new tools, or improvement of existing models are welcome.

Prof. Dr. Jianzhong Lu
Prof. Dr. Yunliang Li
Prof. Dr. Hui Li
Dr. Zhiqiang Tan
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 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. Water 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 2600 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

  • watershed modeling
  • ecological monitoring
  • remote sensing of hydrology
  • water quality retrieval
  • wetland ecology
  • water cycle monitoring
  • lake–floodplain hydrological processes
  • watershed management

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 4277 KiB  
Article
An MLC and U-Net Integrated Method for Land Use/Land Cover Change Detection Based on Time Series NDVI-Composed Image from PlanetScope Satellite
by Jianshu Wang, Mengyuan Yang, Zhida Chen, Jianzhong Lu and Li Zhang
Water 2022, 14(21), 3363; https://0-doi-org.brum.beds.ac.uk/10.3390/w14213363 - 23 Oct 2022
Cited by 5 | Viewed by 2370
Abstract
Land use/land cover change (LUCC) detection based on optical remote-sensing images is an important research direction in the field of remote sensing. The key to it is to select an appropriate data source and detection method. In recent years, the continuous expansion of [...] Read more.
Land use/land cover change (LUCC) detection based on optical remote-sensing images is an important research direction in the field of remote sensing. The key to it is to select an appropriate data source and detection method. In recent years, the continuous expansion of construction land in urban areas has become the main reason for the increase in LUCC demand. However, due to the complexity and diversity of land-cover types, it is difficult to obtain high-precision classification results. In this article, a 12-month time series NDVI (Normalized Difference Vegetation Index) image of the study area was generated based on the high spatial and temporal resolution PlanetScope satellite images. According to the time series NDVI image, representative land-cover samples were selected, and the changed land samples were selected at the same time. This method could directly obtain the LUCC detection results of the study area through land-cover classification. First, Maximum Likelihood Classification (MLC), a classical machine-learning method, was used for supervised classification, and the samples needed for deep learning were selected according to the classification results. Then, the U-Net model, which can fully identify and explore the deep semantic information of the time series NDVI image, was used for land classification. Finally, this article made a comparative analysis of the two classification results. The results demonstrate that the overall classification accuracy based on time series NDVI is significantly higher than that of single-scene NDVI and mean NDVI. The LUCC detection method proposed in this article can effectively extract changed areas. The overall accuracy of the MLC and U-Net model is 79.38% and 85.26%, respectively. Therefore, the deep-learning method can effectively improve the accuracy of land-cover classification and change detection. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem)
►▼ Show Figures

Figure 1

22 pages, 2242 KiB  
Article
Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data
by Aqil Tariq, Jianguo Yan, Bushra Ghaffar, Shujing Qin, B. G. Mousa, Alireza Sharifi, Md. Enamul Huq and Muhammad Aslam
Water 2022, 14(19), 3069; https://0-doi-org.brum.beds.ac.uk/10.3390/w14193069 - 29 Sep 2022
Cited by 42 | Viewed by 4774
Abstract
Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to [...] Read more.
Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to generate flash flood maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in the river’s floodplain between the Jhelum River and Chenab rivers. A total of eight flash flood-causative physical parameters are considered for this study. Six parameters are based on remote sensing images of the Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), and Sentinel-2 Satellite, which include slope, elevation, distance from the stream, drainage density, flow accumulation, and land use/land cover (LULC), respectively. The other two parameters are soil and geology, which consist of different rock and soil formations, respectively. In the case of AHP, each of the criteria is allotted an estimated weight according to its significant importance in the occurrence of flash floods. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of drainage density was given the highest weight. The analysis shows that a distance of 2500 m from the river has values of FR ranging from 0.54, 0.56, 1.21, 1.26, and 0.48, respectively. The output zones were categorized into very low, low, moderate, high, and very high risk, covering 7354, 5147, 3665, 2592, and 1343 km2, respectively. Finally, the results show that the very high flood areas cover 1343 km2, or 6.68% of the total area. The Mangla, Marala, and Trimmu valleys were identified as high-risk zones of the study area, which have been damaged drastically many times by flash floods. It provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem)
►▼ Show Figures

Figure 1

11 pages, 3178 KiB  
Article
Study of Identification and Classification Models of Urban Black and Odorous Water Based on Field Measurements of Spectral Data
by Yaming Zhou, Bin Meng, Nan Wang, Shoujing Yin, Aiping Feng, Huan Zhao, Li Zhu and Rong Zhang
Water 2022, 14(8), 1254; https://0-doi-org.brum.beds.ac.uk/10.3390/w14081254 - 13 Apr 2022
Cited by 1 | Viewed by 1453
Abstract
Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to [...] Read more.
Urban Black and Odorous Water (BOW) has become an environmental problem in many cities in China. The use of satellite remote sensing technology to identify BOW is still in its infancy, and there are many problems that need further solutions. In order to monitor BOW by satellite, between 2016 and 2017, the reflectance of remote sensing and some other parameters of 173 samples were collected from multiple field water experiments first. The samples were located at the major BOW in the urban areas of four Chinese cities, and the differences in remote sensing reflectance of severe BOW (SBOW), moderate BOW (MBOW), and general water (GW) were analyzed. Based on field measurements of spectral data, six remote sensing classification or identification models of BOW were compared in terms of their correct identification rate and reliability. The results show that compared with the GW in the study area, the urban BOW has the lowest reflectance. The peaks and valleys were not obvious in the visible band, especially the remote sensing reflectance of heavy BOW, which fluctuated very little in the visible band. Compared with the other five models, the H Index model had the best identification correctness and reliability. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem)
►▼ Show Figures

Figure 1

14 pages, 4494 KiB  
Article
An Operational Model for Remote Estimating Absorption of Optical Activity Constituents
by Zhifeng Yu, Shoujing Yin, Xiaohong Yuan, Yaming Zhou, Nan Wang, Bin Meng and Bin Zhou
Water 2022, 14(7), 1154; https://0-doi-org.brum.beds.ac.uk/10.3390/w14071154 - 03 Apr 2022
Viewed by 1842
Abstract
The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [...] Read more.
The need for an accurate model that can derive the absorption coefficient of optical activity constituents from both marine water and coastal water remains necessary. This study aimed to develop an algorithm for determining the absorption coefficients for both phytoplankton and non-phytoplankton pigments [aph(λ) and adg(λ), respectively]. This algorithm included two portions: (1) the total absorption coefficients at the blue and green bands were computed using a neural network technology-based, quasi-analytical algorithm; and (2) the relationship between the adg(λ) coefficient and the coefficient of total absorption was analyzed. This algorithm was evaluated with both in-situ observations and remote-sensed satellite data. The results showed that the algorithm could produce acceptable results in the retrievals of adg(λ) and aph(λ) in both turbid and clear waters. The results also indicated that the proposed algorithm was effective for distinguishing between adg(λ) and aph(λ) from the total coefficients of absorption, even though more independent assessments using in-situ and remote-sensed data are required to further improve the approach. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem)
►▼ Show Figures

Figure 1

25 pages, 4544 KiB  
Article
The Ecological Compensation Mechanism in a Cross-Regional Water Diversion Project Using Evolutionary Game Theory: The Case of the Hanjiang River Basin, China
by Kai Zhu, Yuan Zhang, Min Wang and Hai Liu
Water 2022, 14(7), 1151; https://0-doi-org.brum.beds.ac.uk/10.3390/w14071151 - 03 Apr 2022
Cited by 20 | Viewed by 3659
Abstract
As a vital method to resolve conflicts between water use in upstream and downstream areas and solve the problem of transboundary water pollution, watershed ecological compensation is widely used worldwide. It is necessary to analyze the influencing factors of watershed ecological compensation from [...] Read more.
As a vital method to resolve conflicts between water use in upstream and downstream areas and solve the problem of transboundary water pollution, watershed ecological compensation is widely used worldwide. It is necessary to analyze the influencing factors of watershed ecological compensation from the perspective of how different governments interact with each other. However, the previous literature has paid less attention to the special situation of cross-regional water diversion projects, the changing processes of governmental behavior, and the interventions by the central government. Therefore, when taking the upstream and downstream governments and the central government in the basin of a cross-regional water diversion project as research objects, it is important to study their behavior and influencing factors to improve the ecological compensation system in the basin. This paper first analyzes the interactions among upstream, downstream, and central governments in the basin, based on evolutionary game theory. Second, the evolutionary game models before and after the interventions by the central government were developed separately, and the effects of different contexts on the dynamic evolutionary process were analyzed. Finally, taking the Hanjiang River Basin as an example, which is where the water source area of China’s South-to-North Water Diversion Middle Project is located, the opportunity cost of protecting the water environment in the upstream areas of this basin was estimated by establishing an econometric regression model using data on water quality and gross domestic product. The results show that (1) the initial probabilities of governments affect their final behaviors; (2) even without the supervision of the central government, it is still possible for upstream and downstream governments to reach the desired state spontaneously; (3) the supervision of the central government can promote upstream and downstream governments to reach a stable state faster; and (4) the current level of compensation from the central government is significantly lower than the opportunity cost of protecting the water environment for upstream governments in the Hanjiang River Basin. This paper can provide helpful insights for improving the ecological compensation system in the basin, which helps promote cooperation in water environment protection. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Process Analysis and Lake-Wetland-Watershed Ecosystem)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop