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Remote Sensing
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Applications of Remote Sensing in Limnology
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Applications of Remote Sensing in Limnology

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 35125

Special Issue Editor

Dr. Gustavious Paul Williams

E-Mail Website
Guest Editor
Civil and Construction Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: remote sensing; water quality; statistical analysis; aquatic chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data to support limnology have traditionally been gathered by in-lake sampling which means these data are spatially sparse and may not be representative of the larger system. Lakes and reservoir often exhibit localized temporally varying systems that are difficult to characterize or understand using point samples. Satellite-based remote sensing has a long tradition in limnology studies and has often used to address these shortcomings. However, remote sensing applications have been somewhat limited due to limited temporal coverage and the need for higher resolution for applications to lakes and reservoirs.

Remote sensing technology has seen significant changes and advances: there has been an explosion in the number of earth observation satellites; unmanned aerial and aquatic vehicles have moved from curiosities to standard tools; and a number of new sensors that are applicable to limnology have become affordable and useable. These new sensors include technologies such as multi-spectral and hyperspectral imagers, fluorescence and polarization imagers, radar and lidar systems, and various active optical systems such as those based on LED lasers. These remote sensors have been deployed on systems ranging from shoreline installations, to shipboard  and unmanned aquatic vehicle mounts, to low-elevation unmanned aerial systems, to traditional manned aerial platforms, to satellites. Autonomous and unmanned deployment platforms have proliferated providing a variety of new collection strategies, with unmanned aquatic and aerial systems in common use. Combined with this revolution in remote sensing sensors and platforms, have been parallel advances in data exploitation methods, analysis techniques, and tools for presenting these massive new data streams in a way to support better understanding or management of lakes and reservoirs. The need for advanced analysis techniques systems has increased due to the ability to gather large spatial data sets and extremely high temporal and spatial resolution. These new sensors, platforms, and methods have expanded the scope of remote sensing in limnology studies allowing applications to processes and problems previously not in the purview of remote sensing techniques.

Many of these new technologies, platforms, and sensors have a host of supporting research, but literature on applications of these new sensors, data gathering methods, or data analysis techniques are less common. These advances bring new challenges – for example imaging sensors mounted on boats or low-altitude drones may have spatial resolutions of 1 cm/pixel with the ability to fly hourly or even more often – research on efficient, effective data collection is needed. In the previous era, more data was always better, now that is not clear, and it is an open challenge to determine appropriate data requirements.   

This Special Issue will highlight limnology applications of these technological advances in remote sensing and the related challenges. Manuscripts related to any aspect of remote sensing to limnology are invited, including new applications for existing sensors, expansion of traditional remote sensing techniques through the use of new platforms; new analysis methods or techniques such as statistic methods, machine learning or other mathematical approaches; multi-sensor approaches – including data from multiple platforms; data collection methods; and research into systems for displaying or interpreting the resulting data.

Submissions are encouraged over a broad range of topics, suggestions include:

  • The use of non-traditional remote sensing platforms for limnology;
  • Limnology application of new sensors, data, or analysis methods;
  • Studies exploring the appropriate spatial or temporal resolution for limnology data:
  • Useful approaches for remote sensing data collection, especially focusing on new sensors or platforms;
  • Uncertainty and accuracy of remote sensing techniques for limnology;
  • Comparison of novel and traditional remote sensing methods for lake and reservoir processes;
  • Design of reservoir monitoring using multiple platforms and sensors;
  • Characterization of the advantages and limitations of new sensors, platforms, and methods compared to traditional approaches;
  • Methods to characterize and describe temporal lake and reservoir processes;

Relevant case studies which highlight novel or innovative applications are welcome and encouraged if they demonstrate new sensors, platforms, methods, analysis techniques, or applications in limnology.

Dr. Gustavious Paul Williams
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • limnology
  • remote sensing
  • lakes and reservoirs
  • aerial platforms
  • statistical methods
  • water quality
  • reservoir management

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Published Papers (7 papers)

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Research

21 pages, 3301 KiB  
Article
Warm Arctic Proglacial Lakes in the ASTER Surface Temperature Product
by Adrian Dye, Robert Bryant, Emma Dodd, Francesca Falcini and David M. Rippin
Remote Sens. 2021, 13(15), 2987; https://doi.org/10.3390/rs13152987 - 29 Jul 2021
Cited by 6 | Viewed by 3703
Abstract
Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is [...] Read more.
Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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20 pages, 5273 KiB  
Article
Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China
by Bazel Al-Shaibah, Xingpeng Liu, Jiquan Zhang, Zhijun Tong, Mingxi Zhang, Ahmed El-Zeiny, Cheechouyang Faichia, Muhammad Hussain and Muhammad Tayyab
Remote Sens. 2021, 13(9), 1603; https://doi.org/10.3390/rs13091603 - 21 Apr 2021
Cited by 21 | Viewed by 5539
Abstract
Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, [...] Read more.
Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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15 pages, 5444 KiB  
Article
A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks
by Martino E. Malerba, Nicholas Wright and Peter I. Macreadie
Remote Sens. 2021, 13(2), 319; https://doi.org/10.3390/rs13020319 - 18 Jan 2021
Cited by 35 | Viewed by 11405
Abstract
Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the [...] Read more.
Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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30 pages, 13480 KiB  
Article
Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs
by Izaak Cooper, Rollin H. Hotchkiss and Gustavious Paul Williams
Remote Sens. 2021, 13(1), 35; https://doi.org/10.3390/rs13010035 - 24 Dec 2020
Cited by 7 | Viewed by 3301
Abstract
Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters [...] Read more.
Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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17 pages, 7404 KiB  
Article
Remote Sensing of Lake Sediment Core Particle Size Using Hyperspectral Image Analysis
by Hamid Ghanbari, Olivier Jacques, Marc-Élie Adaïmé, Irene Gregory-Eaves and Dermot Antoniades
Remote Sens. 2020, 12(23), 3850; https://doi.org/10.3390/rs12233850 - 24 Nov 2020
Cited by 11 | Viewed by 3570
Abstract
Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems [...] Read more.
Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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21 pages, 4214 KiB  
Article
MODIS-Based Remote Estimation of Absorption Coefficients of an Inland Turbid Lake in China
by Qiao Chu, Yuchao Zhang, Ronghua Ma, Minqi Hu and Yuanyuan Jing
Remote Sens. 2020, 12(12), 1940; https://doi.org/10.3390/rs12121940 - 16 Jun 2020
Cited by 8 | Viewed by 2996
Abstract
Optical complexity and various properties of Case 2 waters make it essential to derive inherent optical properties (IOPs) through an appropriate method. Based on field measured data of Lake Chaohu between 2009 and 2018, the quasi-analytical algorithm (QAA) was modified for the particular [...] Read more.
Optical complexity and various properties of Case 2 waters make it essential to derive inherent optical properties (IOPs) through an appropriate method. Based on field measured data of Lake Chaohu between 2009 and 2018, the quasi-analytical algorithm (QAA) was modified for the particular scenario of that lake to derive absorption coefficients based on the moderate-resolution imaging spectroradiometer (MODIS) bands. By changing the reference wavelength to longer ones and building a relationship between the value of spectral power for particle backscattering coefficient (Y), suspended particulate matter (SPM), and above-surface remote-sensing reflectance (Rrs), we improved the accuracy of the retrieval of total absorption coefficients. The absorption coefficients of gelbstoff and non-algal particulates (adg) and absorption coefficients of phytoplankton (aph) in Lake Chaohu were also derived by changing important parameters according to Lake Chaohu. The derived aph tend to be bigger than measured aph in this study, while derived adg tend to be smaller than measured data. We also used the corrected MODIS surface reflectance product (MOD09/MYD09) to calculate the aph(443), aph(645), and aph(678) by the model proposed in this study. It shows that in summer and autumn, aph tended to be higher in the northwestern part of Lake Chaohu, and were relatively lower in the spring and winter, which is similar to previous studies. Overall, our study provides an algorithm that is effectively used in the case of Lake Chaohu and applicable to the data obtained by MODIS, which can be used for further study to investigate the change law of absorption coefficients in long time series by applying MODIS data. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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21 pages, 9354 KiB  
Article
Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality
by Juhua Luo, Ruiliang Pu, Ronghua Ma, Xiaolong Wang, Xijun Lai, Zhigang Mao, Li Zhang, Zhaoliang Peng and Zhe Sun
Remote Sens. 2020, 12(11), 1866; https://doi.org/10.3390/rs12111866 - 9 Jun 2020
Cited by 17 | Viewed by 3219
Abstract
Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to [...] Read more.
Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to manage the water quality (WQ) of lakes. In this study, an automatic approach for extracting the pen aquaculture area was developed based on Landsat data. The approach integrates five algorithms, including grey transformation, discrete wavelet transform, fast Fourier transform, singular value decomposition and k-nearest neighbor classification. It was successfully applied in the automatic mapping of the pen aquaculture areas in Lake Yangcheng from 1990 to 2016. The overall accuracies were greater than 92%. The result indicted that the practice of pen aquaculture experienced five stages, with the general area increasing in the beginning and decreasing by the end of the last stage. Meanwhile, the changes of nine WQ parameters observed from 2000 to 2016, such as ammonia (NH3-N), pH, total nitrogen (TN), total phosphorus (TP), chlorophyll a, biochemical oxygen demand (BOD), chemiluminescence detection of permanganate index (CODMn), Secchi disk depth (SDD) and dissolved oxygen (DO), were analyzed in the lake sectors of Lake Yangcheng, and then their relationships were explored with the percentage of pen aquaculture area. The result suggested that the percentage of pen aquaculture area exhibits significantly positive correlations with NH3-N, TN, TP, chlorophyll a, BOD and CODMn, but significantly negative correlations with SDD and DO. The experimental results may offer an important implication for managing similar shallow lakes with pen aquaculture expansion and water pollution problems. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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