Application of GIS Models and Remote Sensing in Water Quality Evaluation, Land and Coastal Zone Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 5371

Special Issue Editors


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Guest Editor
Shibaura Institute of Technology, Tokyo, Japan
Interests: remote sensing; GIS; sustainable development goals; climate change; water quality; flood modeling; coastal management

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Guest Editor
Yamaguchi University (Center for Remote and Application of Satellite Remote Sensing, YUCRAS), Ube City, Japan
Interests: remote sensing; GIS; coastal management; climate change; ocean modelling

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Guest Editor
Institute for Integrated Management of Material Fluxes and of Resources, United Nations University (UNU-FLORES), Dresden, Germany
Interests: climate change adaptation; decentralized wastewater management; environmental change & sustainable development; water pollution; water resources management

Special Issue Information

Dear Colleagues,

One hundred and ninety-three country leaders committed to achieving the United Nations’ Sustainable Development Goals (SDGs) to realize a better quality of life and a sustainable environment by 2030. Remote sensing technology (RS) and Geographic Information Systems (GIS) take an essential role in achieving the UN’s targets, especially Goal 6 (water), Goal 14 (life below water), and Goal 15 (life on land). Water quality may accelerate other goals, especially in land and coastal zone management. However, some countries lack the resources to collect data, causing some obstacles in achieving the SDGs. Cutting-edge research on the application and integration of RS and GIS widely open the opportunity for the researchers to develop policy recommendation to achieve the SDGs, especially in water quality, land and coastal zone management.

This Special Issue will focus on applying remote sensing technology and Geographic Information Systems in water quality, land and coastal zone management. We provide a platform to showcase the latest research in the field. The aim is to compile the studies highlighting the RS and GIS application to propose a policy recommendation for achieving sustainability in water quality and land and coastal zone management. The study collected in this Special Issue will contribute important information to our current knowledge, directing policy recommendations to achieve the SDGs. We welcome original research articles, study cases and reviews in several research areas focusing on water quality, land and coastal zone management (but are not limited to):

  • Remote sensing and GIS application in the coastal zone area;
  • Remote sensing and GIS application on natural water-related disasters;
  • Remote sensing and GIS application in fishing grounds;
  • GIS application on water ground modeling;
  • SDG water, land and coast related.

Dr. Andi Besse Rimba
Prof. Dr. Takahiro Osawa
Dr. Saroj Kumar Chapagain
Guest Editors

Manuscript Submission Information

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Keywords

  • sustainable development goals (SDGs)
  • remote sensing (RS)
  • geographic information systems (GIS)
  • water quality
  • land management
  • coastal zone management

Published Papers (3 papers)

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Research

16 pages, 10343 KiB  
Article
Water Quality Estimation Using Gaofen-2 Images Based on UAV Multispectral Data Modeling in Qinba Rugged Terrain Area
by Dianchao Han, Yongxiang Cao, Fan Yang, Xin Zhang and Min Yang
Water 2024, 16(5), 732; https://doi.org/10.3390/w16050732 - 29 Feb 2024
Viewed by 550
Abstract
This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized [...] Read more.
This study presents an innovative method for large-scale surface water quality assessment in rugged terrain areas, specifically tailored for regions like the Qinba Mountains. The approach combines the use of high-resolution (10 cm) multispectral data acquired by unmanned aerial vehicles (UAVs) with synchronized ground sampling and 1 m resolution multispectral imagery from China’s Gaofen-2 satellite. By integrating these technologies, the study aims to capitalize on the convenience and synchronized observation capabilities of UAV remote sensing, while leveraging the broad coverage of satellite remote sensing to overcome the limitations of each individual technique. Initially, a multispectral estimation model is developed for key water quality parameters, including chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), utilizing data from UAVs and coordinated ground samples. Subsequently, a comparison is made between the spectral band ratios (R/G and NIR/G) obtained from the UAV data and those from the Gaofen-2 satellite data, revealing a substantial similarity. Ultimately, this integrated methodology is successfully employed in monitoring water quality across a vast area, particularly along the midstream of the Hanjiang River in the Qinba Mountain region. The results underscore the feasibility, advantages, improved efficiency, and enhanced accuracy of this approach, making it particularly suitable for large-scale water quality monitoring in mountainous terrain. Furthermore, this method reduces the burden associated with traditional ground-based spectral acquisitions, paving the way for a more practical and cost-effective solution in monitoring vast water bodies. Full article
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31 pages, 16179 KiB  
Article
Assessment and Mapping of Riverine Flood Susceptibility (RFS) in India through Coupled Multicriteria Decision Making Models and Geospatial Techniques
by Ravi Kumar, Manish Kumar, Akash Tiwari, Syed Irtiza Majid, Sourav Bhadwal, Netrananda Sahu and Ram Avtar
Water 2023, 15(22), 3918; https://doi.org/10.3390/w15223918 - 09 Nov 2023
Viewed by 2899
Abstract
Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are [...] Read more.
Progressive environmental and climatic changes have significantly increased hydrometeorological threats all over the globe. Floods have gained global significance owing to their devastating impact and their capacity to cause economic and human loss. Accurate flood forecasting and the identification of high-risk areas are essential for preventing flood impacts and implementing strategic measures to mitigate flood-related damages. In this study, an assessment of the susceptibility to riverine flooding in India was conducted utilizing Multicriteria Decision making (MCDM) and an extensive geospatial database was created through the integration of fourteen geomorphological, meteorological, hydroclimatic, and anthropogenic factors. The coupled methodology incorporates a Fuzzy Analytical Hierarchy Process (FAHP) model, which utilizes Triangular Fuzzy Numbers (TFN) to determine the Importance Weights (IWs) of various parameters and their subclasses based on the Saaty scale. Based on the determined IWs, this study identifies proximity to rivers, drainage density, and mean annual rainfall as the key factors that contribute significantly to the occurrence of riverine floods. Furthermore, as the Geographic Information System (GIS) was employed to create the Riverine Flood Susceptibility (RFS) map of India by overlaying the weighted factors, it was found that high, moderate, and low susceptibility zones across the country span of 15.33%, 26.30%, and 31.35% of the total area of the country, respectively. The regions with the highest susceptibility to flooding are primarily concentrated in the Brahmaputra, Ganga, and Indus River basins, which happen to encompass a significant portion of the country’s agricultural land (334,492 km2) potentially posing a risk to India’s food security. Approximately 28.13% of built-up area in India falls in the highly susceptible zones, including cities such as Bardhaman, Silchar, Kharagpur, Howrah, Kolkata, Patna, Munger, Bareilly, Allahabad, Varanasi, Lucknow, and Muzaffarpur, which are particularly susceptible to flooding. RFS is moderate in the Kutch-Saurashtra-Luni, Western Ghats, and Krishna basins. On the other hand, areas on the outskirts of the Ganga, Indus, and Brahmaputra basins, as well as the middle and outer portions of the peninsular basins, show a relatively low likelihood of riverine flooding. The RFS map created in this research, with an 80.2% validation accuracy assessed through AUROC analysis, will function as a valuable resource for Indian policymakers, urban planners, and emergency management agencies. It will aid them in prioritizing and executing efficient strategies to reduce flood risks effectively. Full article
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14 pages, 2207 KiB  
Article
Cloud-Based Machine Learning for Flood Policy Recommendations in Makassar City, Indonesia
by Andi Besse Rimba, Andi Arumansawang, I Putu Wira Utama, Saroj Kumar Chapagain, Made Nia Bunga, Geetha Mohan, Kuncoro Teguh Setiawan and Takahiro Osawa
Water 2023, 15(21), 3783; https://doi.org/10.3390/w15213783 - 29 Oct 2023
Viewed by 998
Abstract
Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data [...] Read more.
Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data sources on a cloud-based platform, integrating the physical factors of a flood (i.e., land use data and digital elevation model—DEM—data) with the local government’s urban land use plan and existing drainage networks. The research aims to map the inundation area, identify the most vulnerable land cover, slope, and elevation, and assess the efficiency of Makassar’s drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Manggala sub-districts results in severe flooding, encompassing a total area of 35.28 km2. The most affected land use type is cultivation land, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km2, is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions. Full article
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