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Remote Sensing of Water Resources Monitoring, Parametrization and Modeling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 89459

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Special Issue Editors

Prof. Dr. Assefa M. Melesse

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Guest Editor

Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL, USA
Interests: remote sensing; watershed modeling; climate change impact; sediment dynamics; river basin management
Special Issues, Collections and Topics in MDPI journals

Dr. Omid Rahmati

E-Mail Website
Guest Editor

Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
Interests: environmental sustainability; geospatial modeling; watershed management; natural disasters
Special Issues, Collections and Topics in MDPI journals

Dr. Zahra Kalantari

E-Mail Website
Guest Editor

Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden
Interests: regional and global hydrology; water resources; water-related disasters; sustainable urban and rural development
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ravinesh Deo

E-Mail Website
Guest Editor

School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Interests: metaheuristic algorithm; deep learning; artificial intelligence in renewable energy; smart electricity grids; energy loads or demand model; energy informatics or economics; green or cleaner energy solutions; energy generation; utilization; conversion; storage; transmission; management; and sustainability; sources such as mechanical; thermal; nuclear; chemical; electromagnetic; magnetic; electricity; solar; bio; hydro; wind; geothermal; tidal and ocean energy; fossil fuels and nuclear resources
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Dr. Khabat Khosravi

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Guest Editor

School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Interests: watershed hydrology; flood modeling; river engineering and sediment transport; natural hazards; groundwater modeling and vulnerability assessment; GIS and machine learning in soil and water science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data integrated with advanced machine learning algorithms serve as a cost-efficient alternative to in situ data collection at numerous spatial and temporal scales. Numerous models can be proposed to effectively monitor problems with a special focus on the conservation and management of water resources. The goal of this Special Issue of Remote Sensing is to collect papers (original research articles and review papers) to give insights about the use of RS/GIS-based techniques for monitoring, modeling, and managing water resources and water-related processes such as flooding, drought, land subsidence, sediment transport, and changing the morphology of rivers. In addition, the combined use of both optical and thermal multi-temporal imageries can be thus used to analyze aquatic environments at both global and regional scales. Therefore, articles that explore, evaluate, or implement the use of thermal images to monitor submarine groundwater discharge (SGD) and assess groundwater–lake interactions are welcome.

This open access Special Issue invites high-quality and innovative scientific papers describing cutting-edge research on the application of remote sensing methods from any platform (satellite, aircraft, drones, etc.) to the study of water-related issues. Potential topics include, but are not limited to, the following:

Monitoring water quality (rivers, lakes, etc.) using remote sensing techniques
Remote sensing-based modeling of submarine groundwater discharge (SGD)
Thermal anomalies mapping in waterbodies
Application of remote sensing methods in monitoring and modeling of water-related disasters (land subsidence, flood, drought, etc.) caused by the mismanagement of water resources
Modeling energy and water fluxes

This Special Issue will benefit natural and social water and sustainability scientists, engineers, managers, and other stakeholders with an interest in the remote sensing of water resources.

Prof. Assefa M. Melesse
Dr. Omid Rahmati
Dr. Zahra Kalantari
Dr. Ravinesh C Deo
Dr. Khabat Khosravi
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. 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

Soil moisture
Flood extent mapping
Water quality
Evapotranspiration
Machine learning in water resources engineering
Thermal anomalies in waterbodies
Land subsidence

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Published Papers (12 papers)

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Research

26 pages, 758 KiB

Open AccessArticle

Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies

by Philipp M. Maier, Sina Keller and Stefan Hinz

Remote Sens. 2021, 13(4), 718; https://doi.org/10.3390/rs13040718 - 16 Feb 2021

Cited by 23 | Viewed by 5018

Abstract

Information about the chlorophyll a concentration of inland water bodies is essential for water monitoring. This study focuses on estimating chlorophyll a with remote sensing data, and machine learning (ML) approaches on the real-world SpecWa dataset. We adapt and apply a one-dimensional convolutional neural network (1D CNN) as a deep learning architecture for the first time to address this estimation. Since such a DL approach requires a large amount of data for its training, we rely on simulation data generated by the Water Color Simulator (WASI). This simulation is prepared accordingly and includes a knowledge-based water composition with two origins of the chlorophyll a concentration. Therefore, the training data is independent of the real-world SpecWa dataset, which is challenging for any ML approach. We define two spectral downsampling approaches as a pre-processing step, representing the hyperspectral EnMAP satellite mission (SR-EnMAP) and the multispectral Sentinel-2 mission (SR-Sentinel). Subsequently, we train a Random Forest, an artificial neural network, a band-ratio approach, and the 1D CNN on the WASI-generated simulation training dataset. Finally, all ML models are evaluated on the real SpecWa dataset. For both downsampled data, the 1D CNN outperforms the other ML models. On the finer resolved SR-EnMAP data it achieves an

R^{2} = 81.9 %

R M S E = 12.4

μg L⁻¹, and

M A E = 6.7

μg L⁻¹. Besides, the 1D CNN’s performance decreases on the SR-Sentinel data to

R^{2} = 62.4 %

. When focusing on the individual water bodies of the SpecWa dataset, the most significant differences exist between natural and artificial water bodies. We discover that the applied models estimate the chlorophyll a concentration of most natural water bodies satisfyingly. In sum, the newly DL approach can estimate the chlorophyll a values of unknown inland water bodies successfully, although it is trained on an entire simulation dataset. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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25 pages, 4422 KiB

Open AccessArticle

Hydroclimatic Extremes Evaluation Using GRACE/GRACE-FO and Multidecadal Climatic Variables over the Nile River Basin

by Zemede M. Nigatu, Dongming Fan, Wei You and Assefa M. Melesse

Remote Sens. 2021, 13(4), 651; https://doi.org/10.3390/rs13040651 - 11 Feb 2021

Cited by 32 | Viewed by 4648

Abstract

Hydroclimatic extremes such as droughts and floods triggered by human-induced climate change are causing severe damage in the Nile River Basin (NRB). These hydroclimatic extremes are not well studied in a holistic approach in NRB. In this study, the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow on mission (GRACE-FO) derived indices and other standardized hydroclimatic indices are computed for developing monitoring and evaluation methods of flood and drought. We evaluated extreme hydroclimatic conditions by using GRACE/GRACE-FO derived indices such as water storage deficits Index (WSDI); and standardized hydroclimatic indices (i.e., Palmer Drought Severity Index (PDSI) and others). This study showed that during 1950–2019, eight major floods and ten droughts events were identified based on standardized-indices and GRACE/GRACE-FO-derived indices. Standardized-indices mostly underestimated the drought and flood severity level compared to GRACE/GRACE-FO derived indices. Among standardized indices PDSI show highest correlation (r² = 0.72) with WSDI. GRACE-/GRACE-FO-derived indices can capture all major flood and drought events; hence, it may be an ideal substitute for data-scarce hydro-meteorological sites. Therefore, the proposed framework can serve as a useful tool for flood and drought monitoring and a better understanding of extreme hydroclimatic conditions in NRB and other similar climatic regions. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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20 pages, 6358 KiB

Open AccessArticle

Scrutinizing Relationships between Submarine Groundwater Discharge and Upstream Areas Using Thermal Remote Sensing: A Case Study in the Northern Persian Gulf

by Aliakbar Nazari Samani, Mohsen Farzin, Omid Rahmati, Sadat Feiznia, Gholam Abbas Kazemi, Giles Foody and Assefa M. Melesse

Remote Sens. 2021, 13(3), 358; https://doi.org/10.3390/rs13030358 - 21 Jan 2021

Cited by 2 | Viewed by 3197

Abstract

Nutrient input through submarine groundwater discharge (SGD) often plays a significant role in primary productivity and nutrient cycling in the coastal areas. Understanding relationships between SGD and topo-hydrological and geo-environmental characteristics of upstream zones is essential for sustainable development in these areas. However, these important relationships have not yet been completely explored using data-mining approaches, especially in arid and semi-arid coastal lands. Here, Landsat 8 thermal sensor data were used to identify potential sites of SGD at a regional scale. Relationships between the remotely-sensed sea surface temperature (SST) patterns and geo-environmental variables of upland watersheds were analyzed using logistic regression model for the first time. The accuracy of the predictions was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) metric. A highly accurate model, with the AUC-ROC of 96.6%, was generated. Moreover, the results indicated that the percentage of karstic lithological formation and topographic wetness index were key variables influencing SGD phenomenon and spatial distribution in the northern coastal areas of the Persian Gulf. The adopted methodology and applied metrics can be transferred to other coastal regions as a rapid assessment procedure for SGD site detection. Moreover, the results can help planners and decision-makers to develop efficient environmental management strategies and the design of comprehensive sustainable development policies. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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23 pages, 9051 KiB

Open AccessArticle

Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

by Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal, Asish Saha, Rabin Chakrabortty, Assefa M. Melesse and Amirhosein Mosavi

Remote Sens. 2020, 12(21), 3568; https://doi.org/10.3390/rs12213568 - 31 Oct 2020

Cited by 153 | Viewed by 7710

Abstract

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km² (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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20 pages, 7443 KiB

Open AccessArticle

Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River

by Dan Li, Baosheng Wu, Bowei Chen, Chao Qin, Yanjun Wang, Yi Zhang and Yuan Xue

Remote Sens. 2020, 12(17), 2737; https://doi.org/10.3390/rs12172737 - 24 Aug 2020

Cited by 28 | Viewed by 6322

Abstract

Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R²) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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30 pages, 7180 KiB

Open AccessArticle

Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

by Romulus Costache, Quoc Bao Pham, Ema Corodescu-Roșca, Cătălin Cîmpianu, Haoyuan Hong, Nguyen Thi Thuy Linh, Chow Ming Fai, Ali Najah Ahmed, Matej Vojtek, Siraj Muhammed Pandhiani, Gabriel Minea, Nicu Ciobotaru, Mihnea Cristian Popa, Daniel Constantin Diaconu and Binh Thai Pham

Remote Sens. 2020, 12(9), 1422; https://doi.org/10.3390/rs12091422 - 30 Apr 2020

Cited by 51 | Viewed by 10040

Abstract

The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km². The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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15 pages, 4303 KiB

Open AccessArticle

A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

by Edward Park, Eder Merino, Quinn W. Lewis, Eric O. Lindsey and Xiankun Yang

Remote Sens. 2020, 12(8), 1353; https://doi.org/10.3390/rs12081353 - 24 Apr 2020

Cited by 10 | Viewed by 4887

Abstract

Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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22 pages, 5406 KiB

Open AccessArticle

Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions

by Jude Lubega Musuuza, David Gustafsson, Rafael Pimentel, Louise Crochemore and Ilias Pechlivanidis

Remote Sens. 2020, 12(5), 811; https://doi.org/10.3390/rs12050811 - 3 Mar 2020

Cited by 14 | Viewed by 5470

Abstract

The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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30 pages, 5231 KiB

Open AccessArticle

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

by Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad and Anuar Ahmad

Remote Sens. 2020, 12(2), 266; https://doi.org/10.3390/rs12020266 - 13 Jan 2020

Cited by 237 | Viewed by 18501

Abstract

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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21 pages, 7335 KiB

Open AccessArticle

Accurate Simulation of Ice and Snow Runoff for the Mountainous Terrain of the Kunlun Mountains, China

by Yongchao Duan, Tie Liu, Fanhao Meng, Ye Yuan, Min Luo, Yue Huang, Wei Xing, Vincent Nzabarinda and Philippe De Maeyer

Remote Sens. 2020, 12(1), 179; https://doi.org/10.3390/rs12010179 - 3 Jan 2020

Cited by 12 | Viewed by 4687

Abstract

While mountain runoff provides great potential for the development and life quality of downstream populations, it also frequently causes seasonal disasters. The accurate modeling of hydrological processes in mountainous areas, as well as the amount of meltwater from ice and snow, is of great significance for the local sustainable development, hydropower regulations, and disaster prevention. In this study, an improved model, the Soil Water Assessment Tool with added ice-melt module (SWATAI) was developed based on the Soil Water Assessment Tool (SWAT), a semi-distributed hydrological model, to simulate ice and snow runoff. A temperature condition used to determine precipitation types has been added in the SWATAI model, along with an elevation threshold and an accumulative daily temperature threshold for ice melt, making it more consistent with the runoff process of ice and snow. As a supplementary reference, the comparison between the normalized difference vegetation index (NDVI) and the quantity of meltwater were conducted to verify the simulation results and assess the impact of meltwater on the ecology. Through these modifications, the accuracy of the daily flow simulation results has been considerably improved, and the contribution rate of ice and snow melt to the river discharge calculated by the model increased by 18.73%. The simulation comparison of the flooding process revealed that the accuracy of the simulated peak flood value by the SWATAI was 77.65% higher than that of the SWAT, and the temporal accuracy was 82.93% higher. The correlation between the meltwater calculated by the SWATAI and the NDVI has also improved significantly. This improved model could simulate the flooding processes with high temporal resolution in alpine regions. The simulation results could provide technical support for economic benefits and reasonable reference for flood prevention. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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27 pages, 9387 KiB

Open AccessArticle

Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery

by Lifei Wei, Can Huang, Zhengxiang Wang, Zhou Wang, Xiaocheng Zhou and Liqin Cao

Remote Sens. 2019, 11(20), 2402; https://doi.org/10.3390/rs11202402 - 16 Oct 2019

Cited by 41 | Viewed by 4575

Abstract

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R² = 0.978), and test dataset (adjusted_R² = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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27 pages, 6583 KiB

Open AccessArticle

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

by Duie Tien Bui, Khabat Khosravi, Himan Shahabi, Prasad Daggupati, Jan F. Adamowski, Assefa M. Melesse, Binh Thai Pham, Hamid Reza Pourghasemi, Mehrnoosh Mahmoudi, Sepideh Bahrami, Biswajeet Pradhan, Ataollah Shirzadi, Kamran Chapi and Saro Lee

Remote Sens. 2019, 11(13), 1589; https://doi.org/10.3390/rs11131589 - 4 Jul 2019

Cited by 142 | Viewed by 11327

Abstract

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Monitoring, Parametrization and Modeling)

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