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Remote Sensing and GIS in Freshwater Environments

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 11771

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Dr. Michael Nones

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Department of Hydrology and Hydrodynamics, Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland
Interests: hydraulics; sediment transport; floods; remote sensing; hydro-morphodynamics
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Dr. Maria Nicolina Papa

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Department of Civil Engineering, University of Salerno, 84084 Fisciano (SA), Italy
Interests: river management; remote sensing; debris flows

Dr. Paolo Paron

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Department of Water Resources and Ecosystem, IHE Delft, Insititute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands
Interests: remote sensing; drones; fluvial dynamics; sediment dynamics at the basin scale; field work in developing countries
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, geographical information systems), the use of remotely sensed information for monitoring freshwater hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support, for example, flood risk and river management applications, as well as the restoration of wetlands and management of natural and man-made reservoirs.

This Special Issue aims to gather researchers, practitioners and water managers working on problems related to the management of freshwater ecosystems using remote sensing and GIS tools, and to show recent advancements in this research field.

Research topics covered by the Issue can include, but are not limited to:

Monitoring of fluvial and coastal landforms and hydro-morphodynamic processes at different spatial and temporal scales using remote sensing
Drones monitoring and mapping freshwater environments, and the impact of humans on their dynamics.
Remote sensing and GIS tools for multitemporal estimations of water quality and ecological status in freshwater systems.
Hydro-morphological and sediment transport modelling supported by remotely sensed data and GIS tools.
Remote sensing for monitoring and managing infrastructures in water systems.
GIS as a decision-support tool for water managers.
Data assimilation: development of new techniques and algorithms, as well as innovative applications of existing methodologies.

Prof. Dr. Michael Nones
Dr. Maria Nicolina Papa
Dr. Paolo Paron
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

GIS in water management
hydro-morpho-biodynamics of freshwater systems
data assimilation and big data
uncrewed aerial vehicles for monitoring freshwater systems
water quality
monitoring of natural and non-natural matter from above

Published Papers (7 papers)

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Research

21 pages, 5892 KiB

Open AccessArticle

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

by Wonjin Jang, Jinuk Kim, Jin Hwi Kim, Jae-Ki Shin, Kangmin Chon, Eue Tae Kang, Yongeun Park and Seongjoon Kim

Remote Sens. 2024, 16(2), 315; https://doi.org/10.3390/rs16020315 - 12 Jan 2024

Viewed by 1355

Abstract

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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19 pages, 59754 KiB

Open AccessArticle

Characterization of Active Riverbed Spatiotemporal Dynamics through the Definition of a Framework for Remote Sensing Procedures

by Marta Crivellaro, Alfonso Vitti, Guido Zolezzi and Walter Bertoldi

Remote Sens. 2024, 16(1), 184; https://doi.org/10.3390/rs16010184 - 01 Jan 2024

Cited by 1 | Viewed by 1253

Abstract

The increasing availability and quality of remote sensing data are changing the methods used in fluvial geomorphology applications, allowing the observation of hydro-morpho-biodynamics processes and their spatial and temporal variations at broader and more refined scales. With the advent of cloud-based computing, it is nowadays possible to reduce data processing time and increase code sharing, facilitating the development of reproducible analyses at regional and global scales. The consolidation of Earth Observation mission data into a single repository such as Google Earth Engine (GEE) offers the opportunity to standardize various methods found in literature, in particular those related to the identification of key geomorphological parameters. This work investigates different computational techniques and timeframes (e.g., seasonal, annual) for the automatic detection of the active river channel and its multi-temporal aggregation, proposing a rational integration of remote sensing tools into river monitoring and management. In particular, we propose a quantitative analysis of different approaches to obtain a synthetic representative image of river corridors, where each pixel is computed as a percentile of the bands (or a combination of bands) of all available images in a given time span. Synthetic images have the advantage of limiting the variability of individual images, thus providing more robust results in terms of the classification of the main components of the riverine ecosystem (sediments, water, and riparian vegetation). We apply the analysis to a set of rivers with analogous bioclimatic conditions and different levels of anthropic pressure, using a combination of Landsat and Sentinel-2 data. The results show that synthetic images derived from multispectral indexes (such as NDVI and MDWI) are more accurate than synthetic images derived from single bands. In addition, different temporal reduction statistics affect the detection of the active channel, and we suggest using the 90th percentile instead of the median to improve the detection of vegetated areas. Individual representative images are then aggregated into multitemporal maps to define a systematic and easily replicable approach for extracting active river corridors and their inherent spatial and temporal dynamics. Finally, the proposed procedure has the potential to be easily implemented and automated as a tool to provide relevant data to river managers. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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21 pages, 27352 KiB

Open AccessArticle

Can Water-Detection Indices Be Reliable Proxies for Water Discharges in Mid-Sized Braided Rivers Using Coarse-Resolution Landsat Archives?

by Peng Gao, Barbara Belletti, Hervé Piégay, Yuchi You and Zhiwei Li

Remote Sens. 2024, 16(1), 137; https://doi.org/10.3390/rs16010137 - 28 Dec 2023

Viewed by 696

Abstract

The use of water detection (WD) indices to infer daily discharge (Q_d) has a great potential to enrich needed hydrological data for understanding fluvial processes driving the morphological changes of braided rivers. However, no consensus has been reached on which one stands out for use in mid-sized braided rivers. In this study, we compared the physical characteristics of three most commonly used WD indices, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Moisture Index (NDMI), for two mid-sized braided reach segments in the Qinghai-Tibet Plateau, China, that have different morphological structures. Relying on the Google Earth Engine web interface, we calculated the total mean water width (WWt) based on the detected surface-water areas (A_s) and braiding index (BI), as well as the mean values (m) of these indices over about four decades at the braided corridor scale (cs) (mNDWIcs, mMNDWIcs, and mNDMIcs). We then examined different responses of these indices to water and non-water features and their best threshold values for characterizing channel structures. Our analyses demonstrated that (1) NDWI and MNDWI perform well for detecting braided channel structures with the threshold of zero; (2) WWt is generally better correlated to Q_d in a linear style than WD indices do, particularly when calculated from MNDWI; and (3) among WD indices calculated at the braided corridor scale, mMNDWIcs shows a better relationship with Q_d than mNDMIcs does. Finally, we provided mechanisms that may explain these differences in terms of photometric discrepancies in calculating WWt and WD indices and the impact of image resolution on their calculations. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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24 pages, 56935 KiB

Open AccessArticle

Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery

by Daniele Rossi, Guido Zolezzi, Walter Bertoldi and Alfonso Vitti

Remote Sens. 2023, 15(14), 3622; https://doi.org/10.3390/rs15143622 - 20 Jul 2023

Cited by 2 | Viewed by 1369

Abstract

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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22 pages, 19821 KiB

Open AccessArticle

Spatio-Temporal Evolution of Inland Lakes and Their Relationship with Hydro-Meteorological Factors in Horqin Sandy Land, China

by Yiran Zhang, Xin Tong, Tingxi Liu, Limin Duan, Lina Hao, Vijay P. Singh, Tianyu Jia and Shuo Lun

Remote Sens. 2023, 15(11), 2719; https://doi.org/10.3390/rs15112719 - 24 May 2023

Viewed by 1066

Abstract

In the inland closed area of Horqin Sandy Land, China, lakes are the most important source of water, and they maintain the regional hydrological balance and ecosystem health. Clarifying long-term continuous changes of inland lake surface area and water storage in the sandy land is thus of great significance to the management of water resources in arid and semi-arid areas. This study estimated changes in the surface area and water storage of small lakes in the sandy land during 1984–2021 using a multiple index threshold method and an empirical equation based on Shuttle Radar Topography Mission (SRTM) DEM (digital elevation model) data and Landsat 5/7/8 images. Hydro-meteorological variables were also incorporated to explore their potential relationship with changes in the lake elements. The lakes in the sandy land resemble stars or dots, with distinct inhomogeneity. Permanent lakes account for the majority of the total lake area, mostly distributed in the center and east of the study area, whereas most seasonal lakes are small water bodies, with surface areas of 0.1–0.5 km² and irregularly distributed. Lake surface area and water storage underwent frequent changes during the 38 years, and could be divided into three characteristic fluctuation phases. From 1984 to 1999, the lake surface area fluctuated greatly, and the water storage increased by approximately 0.3732 Gt, with an average expansion rate of 0.0572 Gt/y. Specifically, lakes with a surface area >2 km² primarily accounted for the expansion. From 1999 to 2004, the lake area shrank sharply to the lowest point, and the water storage capacity decreased by approximately 0.4003 Gt. From 2004 to 2021, the lake surface area and water storage tended to be stable. Annual fluctuations of lake surface area and water storage were mostly affected by precipitation and evapotranspiration, followed by vapor pressure deficit, wet day frequency, and temperature, which have significant periodicity and hysteresis. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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21 pages, 78301 KiB

Open AccessArticle

Research on Intelligent Crack Detection in a Deep-Cut Canal Slope in the Chinese South–North Water Transfer Project

by Qingfeng Hu, Peng Wang, Shiming Li, Wenkai Liu, Yifan Li, Weiqiang Lu, Yingchao Kou, Fupeng Wei, Peipei He and Anzhu Yu

Remote Sens. 2022, 14(21), 5384; https://doi.org/10.3390/rs14215384 - 27 Oct 2022

Cited by 1 | Viewed by 2065

Abstract

The Chinese South–North Water Transfer Project is an important project to improve the freshwater supply environment in the Chinese interior and greatly alleviates the water shortage in the Chinese North China Plain; its sustainable, healthy, and safe operation guarantees ecological protection and economic development. However, due to the special expansive soil and deep excavation structure, the first section of the South–North Water Transfer Project canal faces serious disease risk directly manifested by cracks in the slope of the canal. Currently, relying on manual inspection not only consumes a lot of human resources but also unnecessarily repeats and misses many inspection areas. In this paper, a monitoring method combining depth learning and Uncrewed Aerial Vehicle (UAV) high-definition remote sensing is proposed, which can detect the cracks of the channel slope in time and accurately and can be used for long-term health inspection of the South–North Water Transfer Project. The main contributions are as follows: (1) aiming at the need to identify small cracks in reinforced channels, a ground-imitating UAV that can obtain super-clear resolution remote-sensing images is introduced to identify small cracks on a complex slope background; (2) to identify fine cracks in massive images, a channel crack image dataset is constructed, and deep-learning methods are introduced for the intelligent batch identification of massive image data; (3) to provide the geolocation of crack-extraction results, a fast field positioning method for non-modeled data combined with navigation information is investigated. The experimental results show that the method can achieve a 92.68% recall rate and a 97.58% accuracy rate for detecting cracks in the Chinese South–North Water Transfer Project channel slopes. The maximum positioning accuracy of the method is 0.6 m, and the root mean square error is 0.21 m. It provides a new technical means for geological risk identification and health assessment of the South–North Water Transfer Central Project. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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15 pages, 3354 KiB

Open AccessArticle

Assessment of Human-Induced Effects on Sea/Brackish Water Chlorophyll-a Concentration in Ha Long Bay of Vietnam with Google Earth Engine

by Nguyen Hong Quang, Minh Nguyen Nguyen, Matt Paget, Janet Anstee, Nguyen Duc Viet, Michael Nones and Vu Anh Tuan

Remote Sens. 2022, 14(19), 4822; https://doi.org/10.3390/rs14194822 - 27 Sep 2022

Cited by 2 | Viewed by 2305

Abstract

Chlorophyll-a is one of the most important water quality parameters that can be observed by satellite imagery. It plays a significant function in the aquatic environments of rapidly developing coastal cities such as Ha Long City, Vietnam. Urban population growth, coal mining, and tourist activities have affected the water quality of Ha Long Bay. This work uses Sentinel-2/Multispectral Instrument (MSI) imagery data to a calibrated ocean chlorophyll 2-band (OC-2) model to retrieve chlorophyll-a (chl-a) concentration in the bay from 2019 to 2021. The variability of chlorophyll-a during seasons over the study area was inter-compared. The chlorophyll-a concentration was mapped by analyzing the time series of water cover on the Google Earth Engine platform. The results show that the OC-2 model was calibrated well to the conditions of the study areas. The calibrated model accuracy increased nearly double compared with the uncalibrated OC-2 model. The seasonal assessment of chl-a concentration showed that the phytoplankton (algae) developed well in cold weather during fall and winter. Spatially, algae grew densely inside and in the surroundings of aquaculture, urban, and tourist zones. In contrast, coal mining activities did not result in algae development. We recommend using the Sentinel-2 data for seawater quality monitoring and assessment. Future work might focus on model calibration with a longer time simulation and more in situ measured data. Moreover, manual atmospheric correction of optical remote sensing is crucial for coastal environmental studies. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Freshwater Environments)

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