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Remote Sensing
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Geospatial Techniques for Urban Water Management
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Geospatial Techniques for Urban Water Management

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 10898

Special Issue Editor

Dr. Reda Amer

E-Mail Website
Guest Editor
Earth and Space Sciences, College of Arts and Sciences, Lamar University, Beaumont, TX, USA
Interests: Dr. Amer’s research interests have primarily focused on integrating remote sensing, geographic information systems, (GIS), and near-surface geophysical technologies to address a wide range of geological, hydrological, and environmental problems. Dr. Amer is involved in interdisciplinary research in a wide range of fields, such as mineral exploration, water quality and water resources management, urban flood resilience, land use/land cover change, land subsidence, and coastal restoration.

Special Issue Information

Dear Colleagues,

Urban water refers to all water that occurs in the urban environment and includes surface water, groundwater, water provided for potable use, sewage, drainage, stormwater, flood risk, wetlands, waterways, and estuaries in urban landscapes. Growing population and urbanization have led to problems associated with water quality, storm water management, flood control, environmental health, and related ecosystem impacts. Remote sensing and geographic information systems (GIS) techniques provide great opportunities and potential to assist in dealing with a wide range of issues facing water management in urban areas.

This Special Issue on “Geospatial Techniques for Urban Water Management” is specifically designed to highlight applied research currently being performed using satellite imagery, aerial photography, drone imaging, GIS-based mapping, spatial analysis, artificial neural networks, machine learning, and web-based applications to better understand and solve problems of urban water management. Manuscripts in the areas of urban waterways, water quality, pollution, stormwater, flooding and flood risk management, and other research related to wetlands, estuaries, and coastal water quality, are encouraged for this Special Issue.

Dr. Reda Amer
Guest Editor

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. Remote Sensing 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 2700 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

  • impacts of land use/land cover change on water quality
  • urbanization and water quality
  • coastal water quality (nutrients, algal blooms, hypoxia)
  • urban stormwater management
  • flood risk assessment
  • wetlands restoration

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

21 pages, 4728 KiB  
Article
A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone
by Angela Cotugno, Virginia Smith, Tracy Baker and Raghavan Srinivasan
Remote Sens. 2021, 13(19), 3806; https://doi.org/10.3390/rs13193806 - 23 Sep 2021
Cited by 6 | Viewed by 4664
Abstract
As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built [...] Read more.
As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone. Full article
(This article belongs to the Special Issue Geospatial Techniques for Urban Water Management)
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23 pages, 20357 KiB  
Article
Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest
by Morteza Esfandiari, Ghasem Abdi, Shabnam Jabari, Heather McGrath and David Coleman
Remote Sens. 2020, 12(19), 3206; https://doi.org/10.3390/rs12193206 - 1 Oct 2020
Cited by 24 | Viewed by 5190
Abstract
Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, [...] Read more.
Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen’s kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data. Full article
(This article belongs to the Special Issue Geospatial Techniques for Urban Water Management)
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