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Remote Sensing of Large Rivers

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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 October 2019) | Viewed by 44903

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Dr. Enner Alcântara

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Guest Editor

Department of Environmental Engineering, São Paulo State University (Unesp), Sao Jose dos Campos, Brazil
Interests: remote sensing; water resources management; environmental modeling; water pollution; spatial data analysis; field spectroscopy
Special Issues, Collections and Topics in MDPI journals

Dr. Edward Park

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Guest Editor

Earth Observatory of Singapore, Nanyang Technological University, 50 Nanyang Ave, blk N2-01a-15, Singapore 639798, Singapore
Interests: geomorphology; hydrology; sediment transport; human–environment interactions; remote sensing

Special Issue Information

Dear Colleagues,

Large rivers play several important roles on Earth, such as transporting eroded materials from the continents to the ocean, facilitating the transfer of nutrients through biogeochemical cycles, and sustaining complex ecosystems and high levels of biodiversity. They are also important resources (energy sources, irrigation, food and transportation) and can even be hazardous for human populations. For this reason, interest in large river research has been continuously increasing across disciplines over the last few decades. Recent advances in field techniques—e.g. to measure discharge, model flow structures, water quality monitoring or bathymetric surveying and computational methods—have further accelerated the production of knowledge on large rivers. In addition, the maintenances of hydrologic gauge stations in large river basins by regional/governmental agencies has also been supporting the growing science of large rivers at regional scales.

Despite these efforts and recent advances in technical aspects, a considerable proportion of the large rivers on our planet still remain unexplored, mainly due to their spatiotemporal complexity, which is difficult to efficiently characterize using traditional in situ methods. For example, large river basins often consist of several tributaries with distinct hydrophysical and geochemical characteristics, draining different hydroclimatic and geological settings. Therefore, to understand the dynamics of different water types in a basin, a basin-wide assessment on multiple scales is necessary. Most large rivers also contain huge floodplains that complexly interact with the main channel. To understand this process, continuous monitoring of channel–floodplain systems over a large spatial scale is necessary. Recent unprecedentedly high human impacts on fluvial systems globally are posing additional threats and complexity to large river systems by modifying their hydrogeomorphological and ecological regimes.

In this sense, remote sensing can be considered one of the most efficient and relevant means to regularly assess the spatiotemporal dynamics of various riverine environments, including channels, floodplains, lakes, reservoirs and wetlands over a large scale. For this reason, applications of remote sensing in the study of large rivers has recently been increasing substantially. This Special Issue on “Remote Sensing of Large Rivers” is dedicated to contributing to this fast-growing current trend of remote sensing applications in the large river sciences. We invite studies on recent advances in the study of large rivers solidly based on any types (active or passive) and platforms of remote sensing from multidisciplinary points of view, including water resources, fluxes or the management of large rivers, as well as review articles synthesizing the history and development of remote sensing with a focus on any component of large rivers. We particularly encourage the submission of remote sensing studies analyzing the vulnerability and responses of large fluvial systems suffering from unprecedentedly high human impacts, the highest ever recorded in recent Earth history, such as dam construction, deforestation, or sand mining activities.

Dr. Enner Alcântara
Dr. Edward Park
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

large rivers
hydrology
geomorphology
ecology
remote sensing

Published Papers (10 papers)

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Editorial

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4 pages, 196 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of Large Rivers”

by Enner Alcântara and Edward Park

Remote Sens. 2020, 12(8), 1244; https://doi.org/10.3390/rs12081244 - 14 Apr 2020

Cited by 2 | Viewed by 1835

Abstract

Large rivers play important roles on Earth, such as transporting eroded materials from the continents to the ocean, facilitating the transfer of nutrients through biogeochemical cycles, and sustaining complex ecosystems and high levels of biodiversity [...] Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

Research

Jump to: Editorial

21 pages, 2783 KiB

Open AccessArticle

An Experimental Evaluation of the Feasibility of Inferring Concentrations of a Visible Tracer Dye from Remotely Sensed Data in Turbid Rivers

by Carl J. Legleiter, Paul V. Manley II, Susannah O. Erwin and Edward A. Bulliner

Remote Sens. 2020, 12(1), 57; https://doi.org/10.3390/rs12010057 - 22 Dec 2019

Cited by 11 | Viewed by 3276

Abstract

The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially-distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity within a pair of tanks while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an optimal band ratio analysis (OBRA) algorithm to these data sets indicated strong relationships between spatially averaged reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40–60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from an sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this empirical approach can be extended to more turbid rivers than those examined previously. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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36 pages, 6894 KiB

Open AccessArticle

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

by Maurício Araújo Dias, Erivaldo Antônio da Silva, Samara Calçado de Azevedo, Wallace Casaca, Thiago Statella and Rogério Galante Negri

Remote Sens. 2020, 12(1), 43; https://doi.org/10.3390/rs12010043 - 20 Dec 2019

Cited by 12 | Viewed by 4368

Abstract

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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22 pages, 6363 KiB

Open AccessArticle

Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

by Donghwan Kim, Hyongki Lee, Chi-Hung Chang, Duong Du Bui, Susantha Jayasinghe, Senaka Basnayake, Farrukh Chishtie and Euiho Hwang

Remote Sens. 2019, 11(22), 2684; https://doi.org/10.3390/rs11222684 - 17 Nov 2019

Cited by 11 | Viewed by 3411

Abstract

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q (

{\hat{Q}}_{E L Q}

) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of

{\hat{Q}}_{E L Q}

decreased by 1504/1338 m³/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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15 pages, 4778 KiB

Open AccessArticle

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

by Davi de C. D. Melo and Augusto Getirana

Remote Sens. 2019, 11(21), 2487; https://doi.org/10.3390/rs11212487 - 24 Oct 2019

Cited by 7 | Viewed by 2975

Abstract

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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22 pages, 5699 KiB

Open AccessArticle

Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

by Nariane Bernardo, Alisson do Carmo, Edward Park and Enner Alcântara

Remote Sens. 2019, 11(19), 2283; https://doi.org/10.3390/rs11192283 - 30 Sep 2019

Cited by 18 | Viewed by 3090

Abstract

Suspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tietê River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m³, turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since K_d is not susceptible to the saturation issue encountered when using remote sensing reflectance (R_rs), we estimate SPM concentrations via K_d. K_d was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using K_d(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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22 pages, 25281 KiB

Open AccessArticle

Impacts of Climate Change on Lake Fluctuations in the Hindu Kush-Himalaya-Tibetan Plateau

by Xiankun Yang, Xixi Lu, Edward Park and Paolo Tarolli

Remote Sens. 2019, 11(9), 1082; https://doi.org/10.3390/rs11091082 - 07 May 2019

Cited by 16 | Viewed by 5495

Abstract

Lakes in the Hindu Kush-Himalaya-Tibetan (HKHT) regions are crucial indicators for the combined impacts of regional climate change and resultant glacier retreat. However, they lack long-term systematic monitoring and thus their responses to recent climatic change still remain only partially understood. This study investigated lake extent fluctuations in the HKHT regions over the past 40 years using Landsat (MSS/TM/ETM+/OLI) images obtained from the 1970s to 2014. Influenced by different regional atmospheric circulation systems, our results show that lake changing patterns are distinct from region to region, with the most intensive lake shrinking observed in northeastern HKHT (HKHT Interior, Tarim, Yellow, Yangtze), while the most extensive expansion was observed in the western and southwestern HKHT (Amu Darya, Ganges Indus and Brahmaputra), largely caused by the proliferation of small lakes in high-altitude regions during 1970s–1995. In the past 20 years, extensive lake expansions (~39.6% in area and ~119.1% in quantity) were observed in all HKHT regions. Climate change, especially precipitation change, is the major driving force to the changing dynamics of the lake fluctuations; however, effects from the glacier melting were also significant, which contributed approximately 31.9–40.5%, 16.5–39.3%, 12.8–29.0%, and 3.3–6.1% of runoff to lakes in the headwaters of the Tarim, Amu Darya, Indus, and Ganges, respectively. We consider that the findings in this paper could have both immediate and long-term implications for dealing with water-related hazards, controlling glacial lake outburst floods, and securing water resources in the HKHT regions, which contain the headwater sources for some of the largest rivers in Asia that sustain 1.3 billion people. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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29 pages, 15279 KiB

Open AccessArticle

Defining the Limits of Spectrally Based Bathymetric Mapping on a Large River

by Carl J. Legleiter and Ryan L. Fosness

Remote Sens. 2019, 11(6), 665; https://doi.org/10.3390/rs11060665 - 19 Mar 2019

Cited by 36 | Viewed by 4335

Abstract

Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images [...] Read more.

d_{m a x}

, directly from an image and identifying optically deep areas that exceed

d_{m a x}

. Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of

d_{m a x}

by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep (

d > d_{m a x}

) classes and fit a logistic regression model to estimate the probability of optically deep water,

P r (O D)

. Applying a

P r (O D)

threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River,

d_{m a x}

reached as high as 9.5 m at one site, with accurate depth retrieval (

R^{2} = 0.94

) in areas with

d < d_{m a x}

. As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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21 pages, 16364 KiB

Open AccessArticle

Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River

by Arthur W. Sichangi, Lei Wang and Zhidan Hu

Remote Sens. 2018, 10(9), 1385; https://doi.org/10.3390/rs10091385 - 31 Aug 2018

Cited by 50 | Viewed by 10200

Abstract

A novel approach has been developed to estimating river discharge solely using satellite-derived parameters. The temporal river width observations from Moderate Resolution Imaging Spectroradiometer (MODIS), made at two stream segments a distance apart, are plotted to identify the time lag. The river velocity estimate is then computed using the time lag and distance between the width measurement locations, producing a resultant velocity of 0.96 m/s. The estimated velocity is comparable to that computed from in situ gauge-observed data. An empirical relationship is then utilized to estimate river depth. In addition, the channel condition values published in tables are used to estimate the roughness coefficient. The channel slope is derived from the digital elevation model averaged over a river section approximately 516 km long. Finally, the temporal depth changes is captured by adjusting the estimated depth to the Envisat satellite altimetry -derived water level changes, and river width changes from Landsat ETM+. The newly developed procedure was applied to two river sites for validation. In both cases, the river discharges were estimated with reasonable accuracy (with Nash–Sutcliffe values >0.50). The performance evaluation of discharge estimation using satellite-derived parameters was also analyzed. Since the methodology for estimating discharge is solely dependent on global satellite datasets, it represents a promising technique for use on rivers worldwide. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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14 pages, 17587 KiB

Open AccessArticle

Remotely Sensed Analysis of Channel Bar Morphodynamics in the Middle Yangtze River in Response to a Major Monsoon Flood in 2002

by Zhaoyang Wang, Hui Li and Xiaobin Cai

Remote Sens. 2018, 10(8), 1165; https://doi.org/10.3390/rs10081165 - 24 Jul 2018

Cited by 15 | Viewed by 4254

Abstract

Channel bars are a major depositional feature in channels, and are considered as an important part of the morphodynamics of an alluvial river. The long-term morphodynamics of bars have been intensively investigated. However, relatively little is known about the response of channel bars to a major river flood, which is considered to be the predominant force in shaping bar morphology. This is especially the case for the monsoon-affected Yangtze River, where fluvial geomorphic work is largely carried out during monsoon floods. In this study, multi-temporal satellite images and river stage data were used to examine the morphodynamics of four large channel bars in the middle Yangtze River in response to a major monsoon flood in 2002. Based on bar surface areas estimated with Landsat images at different river stages, a rating curve was developed for each of the four bars, which was used to estimate bar volume through an integral process. Our study shows that two of the bars tended to be stable, while the other two experienced severe erosion during the flood. The results reveal that the flood caused a total bar surface area decrease of 1,655,100 m² (or 8.30%), and a total bar volume decline of 5.89 × 10⁶ m³ (or 6.10%) between the river stages of 20.81 m and 25.75 m. The volume decrease is equivalent to a sediment loss of approximately 8.25 × 10⁶ metric tons, based on an average bulk density of 1.4 metric tons per cubic meter. The results imply that channel bars in the middle Yangtze River can also be large sediment sources rather than depositional areas during the flood. The decrease of sediment load in the middle of Yangtze River was found to be responsible for the dramatic morphodynamics of channel bars, which could last for a long period of time, depending on the operation of the Three Gorges Dam, which opened in 2003. Hence, we suggest making management efforts to protect the bars from further erosion. Full article

(This article belongs to the Special Issue Remote Sensing of Large Rivers)

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