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Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements

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

Deadline for manuscript submissions: closed (30 April 2017) | Viewed by 97323

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Special Issue Editors

Prof. Dr. Hongjie Xie

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Guest Editor

Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: remote sensing of water cycle; cryosphere; and polar regions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xianwei Wang

E-Mail Website
Guest Editor

School of Geography and Planning Sun Yat-sen University 135 West Xingang Road, Guangzhou, 510275 China
Interests: remote sensing, GIS, surface hydrology, lakes, flood mapping and risk management, cryosphere science, snow and ice

Special Issue Information

Dear Colleagues,

Water, one of the most important natural resources, supports people’s daily life, maintains the ecosystems that people rely on, provides transportation, recreation, ecotourism, and much more. Pressures on water resources and disasters are rising primarily due to unequal distribution, urbanization, extreme and frequent drought and flooding, pollution, deforestation, and also partly due to poor knowledge about the distribution of water recourses and poor management of water resources and usage. Remote sensing provides critical data sources for mapping water resources and changes, while GIS provides the best tool for water resource and flood risk management, presentation, visualization and publication education. This Special Issue calls for the best practices, cutting-edge research and applications of remote sensing and GIS use for water resources mapping, management, visualization, public education and outreaches. The submission of cutting edge research on remote sensing water quality, pollution, drought, flooding and other research that is related to water resource and flood risk management, is also encouraged for this Special Issue.

Prof. Dr. Hongjie Xie
Prof. Dr. Xianwei Wang
Guest Editors

Manuscript Submission Information

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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.

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Keywords

remote sensing
GIS
water resources mapping
water resources management
eduation and outreaches
water quality
drought and flooding

Published Papers (13 papers)

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Editorial

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11 pages, 230 KiB

Open AccessEditorial

A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management

by Xianwei Wang and Hongjie Xie

Water 2018, 10(5), 608; https://doi.org/10.3390/w10050608 - 07 May 2018

Cited by 97 | Viewed by 20222

Abstract

Water is one of the most critical natural resources that maintain the ecosystem and support people’s daily life. Pressures on water resources and disaster management are rising primarily due to the unequal spatial and temporal distribution of water resources and pollution, and also partially due to our poor knowledge about the distribution of water resources and poor management of their usage. Remote sensing provides critical data for mapping water resources, measuring hydrological fluxes, monitoring drought and flooding inundation, while geographic information systems (GIS) provide the best tools for water resources, drought and flood risk management. This special issue presents the best practices, cutting-edge technologies and applications of remote sensing, GIS and hydrological models for water resource mapping, satellite rainfall measurements, runoff simulation, water body and flood inundation mapping, and risk management. The latest technologies applied include 3D surface model analysis and visualization of glaciers, unmanned aerial vehicle (UAV) video image classification for turfgrass mapping and irrigation planning, ground penetration radar for soil moisture estimation, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) satellite rainfall measurements, storm hyetography analysis, rainfall runoff and urban flooding simulation, and satellite radar and optical image classification for urban water bodies and flooding inundation. The application of those technologies is expected to greatly relieve the pressures on water resources and allow better mitigation of and adaptation to the disastrous impact of droughts and flooding. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

Research

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3924 KiB

Open AccessArticle

Exploring Jeddah Floods by Tropical Rainfall Measuring Mission Analysis

by Ahmet Emre Tekeli

Water 2017, 9(8), 612; https://doi.org/10.3390/w9080612 - 16 Aug 2017

Cited by 10 | Viewed by 6555

Abstract

Estimating flash floods in arid regions is a challenge arising from the limited time preventing mitigation measures from being taken, which results in fatalities and property losses. Here, Tropical Rainfall Measuring Mission (TRMM) Multi Satellite Precipitation Analysis (TMPA) Real Time (RT) 3B2RT data are utilized in estimating floods that occurred over the city of Jeddah located in the western Kingdom of Saudi Arabia. During the 2000–2014 period, six floods that were effective on 19 days occurred in Jeddah. Three indices, constant threshold (CT), cumulative distribution functions (CDFs) and Jeddah flood index (JFI), were developed using 15-year 3-hourly 3B42RT. The CT calculated, as 10.37 mm/h, predicted flooding on 14 days, 6 of which coincided with actual flood-affected days (FADs). CDF thresholds varied between 87 and 93.74%, and JFI estimated 28 and 20 FADs where 8 and 7 matched with actual FADs, respectively. While CDF and JFI did not miss any flood event, CT missed the floods that occurred in the heavy rain months of January and December. The results are promising despite that only rainfall rates, i.e., one parameter out of various flood triggering mechanisms, i.e., soil moisture, topography and land use, are used. The simplicity of the method favors its use in TRMM follow-on missions such as the Global Precipitation Measurement Mission (GPM). Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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2776 KiB

Open AccessArticle

An Examination of Soil Moisture Estimation Using Ground Penetrating Radar in Desert Steppe

by Yizhu Lu, Wenlong Song, Jingxuan Lu, Xuefeng Wang and Yanan Tan

Water 2017, 9(7), 521; https://doi.org/10.3390/w9070521 - 22 Jul 2017

Cited by 24 | Viewed by 6617

Abstract

Ground penetrating radar (GPR) is a new technique of rapid soil moisture measurement, which is an important approach to measure soil moisture at the intermediate scale. To test the applicability of GPR method for soil moisture in desert steppe, we used the common-mid point (CMP) method and fixed offset (FO) method to evaluate the influence factors and the accuracy of GPR measurement with gravimetric soil moisture measurements. The experiments showed that Topp’s equation is more suitable than Roth’s equation for processing the GPR data in desert steppe and the soil moisture measurements by GPR had high accuracy by either CMP method or FO method. To a certain extent, the vegetation coverage affects the measurement precision and the soil moisture profile. The precipitation can reduce the effective sampling depth of the ground wave from 0.1 m to 0.05 m. The results revealed that GPR has the advantages of high measurement accuracy, easy movement, simple operation, and no damage to the soil layer structure. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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2860 KiB

Open AccessArticle

Optimal Combinations of Non-Sequential Regressors for ARX-Based Typhoon Inundation Forecast Models Considering Multiple Objectives

by Huei-Tau Ouyang, Shang-Shu Shih and Ching-Sen Wu

Water 2017, 9(7), 519; https://doi.org/10.3390/w9070519 - 14 Jul 2017

Cited by 4 | Viewed by 3512

Abstract

Inundation forecast models with non-sequential regressors are advantageous in efficiency due to their rather fewer input variables required to be processed. This type of model is nevertheless rare mainly because of the difficulty in finding the proper combination of regressors for the model to perform accurate prediction. A novel methodology is proposed in this study to tackle the problem. The approach involves integrating a Multi-Objective Genetic Algorithm (MOGA) with forecast models based on ARX (Auto-Regressive model with eXogenous inputs) to transfer the search for the optimal combination of non-sequential regressors into an optimization problem. An innovative approach to codifying any combinations of model regressors into binary strings is developed and employed in MOGA. The Pareto optimal sets of three types of models including linear ARX (LARX), nonlinear ARX with Wavelet function (NLARX-W), and nonlinear ARX with Sigmoid function (NLARX-S) are searched for by the proposed methodology. The results show that the optimal models acquired through this approach have good inundation forecasting capabilities in every aspect in terms of accuracy, time shift error, and error distribution. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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Open AccessFeature PaperArticle

Improvement to the Huff Curve for Design Storms and Urban Flooding Simulations in Guangzhou, China

by Cuilin Pan, Xianwei Wang, Lin Liu, Huabing Huang and Dashan Wang

Water 2017, 9(6), 411; https://doi.org/10.3390/w9060411 - 08 Jun 2017

Cited by 34 | Viewed by 6395

Abstract

The storm hyetograph is critical in drainage design since it determines the peak flooding volume in a catchment and the corresponding drainage capacity demand for a return period. This study firstly compares the common design storms such as the Chicago, Huff, and Triangular curves employed to represent the storm hyetographs in the metropolitan area of Guangzhou using minute-interval rainfall data during 2008–2012. These common design storms cannot satisfactorily represent the storm hyetographs in sub-tropic areas of Guangzhou. The normalized time of peak rainfall is at 33 ± 5% for all storms in the Tianhe and Panyu districts, and most storms (84%) are in the 1st and 2nd quartiles. The Huff curves are further improved by separately describing the rising and falling limbs instead of classifying all storms into four quartiles. The optimal time intervals are 1–5 min for deriving a practical urban design storm, especially for short-duration and intense storms in Guangzhou. Compared to the 71 observed storm hyetographs, the Improved Huff curves have smaller RMSE and higher NSE values (6.43, 0.66) than those of the original Huff (6.62, 0.63), Triangular (7.38, 0.55), and Chicago (7.57, 0.54) curves. The mean relative difference of peak flooding volume simulated with SWMM using the Improved Huff curve as the input is only 2%, −6%, and 8% of those simulated by observed rainfall at the three catchments, respectively. In contrast, those simulated by the original Huff (−12%, −43%, −16%), Triangular (−22%, −62%, −38%), and Chicago curves (−17%, −19%, −21%) are much smaller and greatly underestimate the peak flooding volume. The Improved Huff curve has great potential in storm water management such as flooding risk mapping and drainage facility design, after further validation. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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12919 KiB

Open AccessArticle

Comparison of IMERG Level-3 and TMPA 3B42V7 in Estimating Typhoon-Related Heavy Rain

by Ren Wang, Jianyao Chen and Xianwei Wang

Water 2017, 9(4), 276; https://doi.org/10.3390/w9040276 - 22 Apr 2017

Cited by 31 | Viewed by 5422

Abstract

Typhoon-related heavy rain has unique structures in both time and space, and use of satellite-retrieved products to delineate the structure of heavy rain is especially meaningful for early warning systems and disaster management. This study compares two newly-released satellite products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG final run) and the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA 3B42V7) with daily rainfall observed by ground rain gauges. The comparison is implemented for eight typhoons over the coastal region of China for a two-year period from 2014 to 2015. The results show that all correlation coefficients (CCs) of both IMERG and TMPA for the investigated typhoon events are significant at the 0.01 level, but they tend to underestimate the heavy rainfall, especially around the storm center. The IMERG final run exhibits an overall better performance than TMPA 3B42V7. It is also shown that both products have a better applicability (i.e., a smaller absolute relative bias) when rain intensities are within 20–40 and 80–100 mm/day than those of 40–80 mm/day and larger than 100 mm/day. In space, they generally have the best applicability within the range of 50–100 km away from typhoon tracks, and have the worst applicability beyond the 300-km range. The results are beneficial to understand the errors of satellite data in operational applications, such as storm monitoring and hydrological modeling. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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7333 KiB

Open AccessArticle

Large Differences between Glaciers 3D Surface Extents and 2D Planar Areas in Central Tianshan

by Xianwei Wang, Huijiao Chen and Yaning Chen

Water 2017, 9(4), 282; https://doi.org/10.3390/w9040282 - 17 Apr 2017

Cited by 5 | Viewed by 5676

Abstract

Most glaciers in China lie in high mountainous environments and have relatively large surface slopes. Common analyses consider glaciers’ projected areas (2D Area) in a two-dimensional plane, which are much smaller than glacier’s topographic surface extents (3D Area). The areal difference between 2D planar areas and 3D surface extents exceeds −5% when the glacier’s surface slope is larger than 18°. In this study, we establish a 3D model in the Muzart Glacier catchment using ASTER GDEM data. This model is used to quantify the areal difference between glaciers’ 2D planar areas and their 3D surface extents in various slope zones and elevation bands by using the second Chinese Glacier Inventory (CGI2). Finally, we analyze the 2D and 3D area shrinking rate between 2007 and 2013 in Central Tianshan using glaciers derived from Landsat images by an object-based classification approach. This approach shows an accuracy of 89% when it validates by comparison of glaciers derived from Landsat and high spatial resolution GeoEye images. The extracted glaciers in 2007 also have an agreement of 89% with CGI2 data in the Muzart Glacier catchment. The glaciers’ 3D area is 34.2% larger than their 2D area from CGI2 in the Muzart Glacier catchment and by 27.9% in the entire Central Tianshan. Most underestimation occurs in the elevation bands of 4000–5000 m above sea level (a.s.l.). The 3D glacier areas reduced by 30 and 115 km² between 2007 and 2013 in the Muzart Glacier catchment and Central Tianshan, being 37.0% and 27.6% larger than their 2D areas reduction, respectively. The shrinking rates decrease with elevation increase. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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9019 KiB

Open AccessArticle

Physically, Fully-Distributed Hydrologic Simulations Driven by GPM Satellite Rainfall over an Urbanizing Arid Catchment in Saudi Arabia

by Hatim O. Sharif, Muhammad Al-Zahrani and Almoutaz El Hassan

Water 2017, 9(3), 163; https://doi.org/10.3390/w9030163 - 24 Feb 2017

Cited by 28 | Viewed by 6444

Abstract

A physically-based, distributed-parameter hydrologic model was used to simulate a recent flood event in the city of Hafr Al Batin, Saudi Arabia to gain a better understanding of the runoff generation and spatial distribution of flooding. The city is located in a very arid catchment. Flooding of the city is influenced by the presence of three major tributaries that join the main channel in and around the heavily urbanized area. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) rainfall product was used due to lack of detailed ground observations. To overcome the heavy computational demand, the catchment was divided into three sub-catchments with a variable model grid resolution. The model was run on three subcatchments separately, without losing hydrologic connectivity among the sub-catchments. Uncalibrated and calibrated satellite products were used producing different estimates of the predicted runoff. The runoff simulations demonstrated that 85% of the flooding was generated in the urbanized portion of the catchments for the simulated flood. Additional model simulations were performed to understand the roles of the unique channel network in the city flooding. The simulations provided insights into the best options for flood mitigation efforts. The variable model grid size approach allowed using physically-based, distributed models—such as the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model used in this study—on large basins that include urban centers that need to be modeled at very high resolutions. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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4844 KiB

Open AccessArticle

Improvements to Runoff Predictions from a Land Surface Model with a Lateral Flow Scheme Using Remote Sensing and In Situ Observations

by Jong Seok Lee and Hyun Il Choi

Water 2017, 9(2), 148; https://doi.org/10.3390/w9020148 - 22 Feb 2017

Cited by 6 | Viewed by 4675

Abstract

Like most land surface models (LSMs) coupled to regional climate models (RCMs), the original Common Land Model (CoLM) predicts runoff from net water at each computational grid without explicit lateral flow (LF) schemes. This study has therefore proposed a CoLM+LF model incorporating a set of lateral surface and subsurface runoff computations controlled by topography into the existing terrestrial hydrologic processes in the CoLM to improve runoff predictions in land surface parameterizations. This study has assessed the new CoLM+LF using Earth observations at the 30-km resolution targeted for mesoscale climate applications, especially for surface and subsurface runoff predictions in the Nakdong River Watershed of Korea under study. Both the baseline CoLM and the new CoLM+LF are implemented in a standalone mode using the realistic surface boundary conditions (SBCs) and meteorological forcings constructed from remote sensing products and in situ observations, mainly by geoprocessing tools in a Geographic Information System (GIS) for the study domain. The performance of the CoLM and the CoLM+LF simulations are evaluated by the comparison of daily runoff results from both models with observations during 2009 at the Jindong stream gauge station in the study watershed. The proposed CoLM+LF, which can simulate the effect of runoff travel time over a watershed by an explicit lateral flow scheme, more effectively captures seasonal variations in daily streamflow than the baseline CoLM. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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Open AccessArticle

Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery

by Fan Yang, Jianhua Guo, Hai Tan and Jingxue Wang

Water 2017, 9(2), 144; https://doi.org/10.3390/w9020144 - 21 Feb 2017

Cited by 34 | Viewed by 6715

Abstract

The extraction of urban water bodies from high‐resolution remote sensing images, which has been a hotspot in researches, has drawn a lot of attention both domestic and abroad. A challenging issue is to distinguish the shadow of high‐rise buildings from water bodies. To tackle this issue, we propose the automatic urban water extraction method (AUWEM) to extract urban water bodies from high‐resolution remote sensing images. First, in order to improve the extraction accuracy, we refine the NDWI algorithm. Instead of Band2 in NDWI, we select the first principal component after PCA transformation as well as Band1 for ZY‐3 multi‐spectral image data to construct two new indices, namely NNDWI1, which is sensitive to turbid water, and NNDWI2, which is sensitive to the water body whose spectral information is interfered by vegetation. We superimpose the image threshold segmentation results generated by applying NNDWI1 and NNDWI2, then detect and remove the shadows in the small areas of the segmentation results using object‐oriented shadow detection technology, and finally obtain the results of the urban water extraction. By comparing the Maximum Likelihood Method (MaxLike) and NDWI, we find that the average Kappa coefficients of AUWEM, NDWI and MaxLike in the five experimental areas are about 93%, 86.2% and 88.6%, respectively. AUWEM exhibits lower omission error rates and commission error rates compared with the NDWI and MaxLike. The average total error rates of the three methods are about 11.9%, 18.2%, and 22.1%, respectively. AUWEM not only shows higher water edge detection accuracy, but it also is relatively stable with the change of threshold. Therefore, it can satisfy demands of extracting water bodies from ZY‐3 images. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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Open AccessArticle

Measuring Spatiotemporal Features of Land Subsidence, Groundwater Drawdown, and Compressible Layer Thickness in Beijing Plain, China

by Yongyong Li, Huili Gong, Lin Zhu and Xiaojuan Li

Water 2017, 9(1), 64; https://doi.org/10.3390/w9010064 - 22 Jan 2017

Cited by 28 | Viewed by 8288

Abstract

Beijing is located on multiple alluvial-pluvial fans with thick Quaternary unconsolidated sediments. It has suffered serious groundwater drawdown and land subsidence due to groundwater exploitation. This study aimed to introduce geographical distribution measure methods into land subsidence research characterizing, geographically, land subsidence, groundwater drawdown, and compressible layer thickness. Therefore, we used gravity center analysis and standard deviational ellipse (SDE) methods in GIS to statistically analyze their concentration tendency, principle orientation, dispersion trend, and distribution differences in 1995 (1999), 2007, 2009, 2011, and 2013. Results show that they were all concentrated in Chaoyang District of Urban Beijing. The concentration trend of land subsidence was consistent with that of groundwater drawdown. The principle orientation of land subsidence was SW–NE, which was more similar with that of the static spatial distribution of the compressible layer. The dispersion tendency of land subsidence got closer to that of the compressible layer with its increasing intensity. The spatial distribution difference between land subsidence and groundwater drawdown was about 0.2, and that between land subsidence and compressible layer thickness it decreased from 0.22 to 0.07, reflecting that the spatial distribution pattern of land subsidence was increasingly close to that of the compressible layer. Results of this study are useful for assessing the distribution of land subsidence development and managing groundwater resources. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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Open AccessArticle

Monitoring of the Spatio-Temporal Dynamics of the Floods in the Guayas Watershed (Ecuadorian Pacific Coast) Using Global Monitoring ENVISAT ASAR Images and Rainfall Data

by Frédéric Frappart, Luc Bourrel, Nicolas Brodu, Ximena Riofrío Salazar, Frédéric Baup, José Darrozes and Rodrigo Pombosa

Water 2017, 9(1), 12; https://doi.org/10.3390/w9010012 - 01 Jan 2017

Cited by 19 | Viewed by 6395

Abstract

The floods are an annual phenomenon on the Pacific Coast of Ecuador and can become devastating during El Niño years, especially in the Guayas watershed (32,300 km²), the largest drainage basin of the South American western side of the Andes. As limited information on flood extent in this basin is available, this study presents a monitoring of the spatio-temporal dynamics of floods in the Guayas Basin, between 2005 and 2008, using a change detection method applied to ENVISAT ASAR Global Monitoring SAR images acquired at a spatial resolution of 1 km. The method is composed of three steps. First, a supervised classification was performed to identify pixels of open water present in the Guayas Basin. Then, the separability of their radar signature from signatures of other classes was determined during the four dry seasons from 2005 to 2008. In the end, standardized anomalies of backscattering coefficient were computed during the four wet seasons of the study period to detect changes between dry and wet seasons. Different thresholds were tested to identify the flooded areas in the watershed using external information from the Dartmouth Flood Observatory. A value of −2.30 ± 0.05 was found suitable to estimate the number of inundated pixels and limit the number of false detection (below 10%). Using this threshold, monthly maps of inundation were estimated during the wet season (December to May) from 2004 to 2008. The most frequently inundated areas were found to be located along the Babahoyo River, a tributary in the east of the basin. Large interannual variability in the flood extent is observed at the flood peak (from 50 to 580 km²), consistent with the rainfall in the Guayas watershed during the study period. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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Open AccessArticle

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

by Alberto-Jesús Perea-Moreno, María-Jesús Aguilera-Ureña, José-Emilio Meroño-De Larriva and Francisco Manzano-Agugliaro

Water 2016, 8(12), 584; https://doi.org/10.3390/w8120584 - 08 Dec 2016

Cited by 16 | Viewed by 5897

Abstract

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management. Full article

(This article belongs to the Special Issue Applications of Remote Sensing/GIS in Water Resources and Flooding Risk Managements)

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