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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 March 2020) | Viewed by 60728

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Special Issue Editors

Dr. Jean-Francois Crétaux

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Guest Editor

CNES/ Legos, 14 Avenue Edouard Belin, 31400 Toulouse, France
Interests: satellite remote-sensing for hydrology; geodesy

Dr. Rodrigo Abarca Del Rio

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Guest Editor

Departamento de Geofísica (DGEO), Universidad de Concepción (UDEC), Casilla: 160-C, Barrió Universitario S/N, Concepción, Chile
Interests: satellite remote sensing for hydrology; data analysis; lakes; hydroclimate
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Claude Duguay

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University of Waterloo, 200 University Avenue West, Waterloo, ON N2T 2R5, Canada
Interests: remote sensing of the cryosphere and hydrosphere; observations and modelling of lake-atmosphere interactions

Special Issue Information

Dear Colleagues,

All around the world, millions of lakes dot the landscape. Scientifically, lakes are of great interest in hydrology, limnology, climatology, biogeochemistry, and geodesy. Lakes and enclosed inland seas are integrators of environmental and climatic changes occurring within their contributing basins. The factors that drive lake conditions vary widely across space and time, and lakes, in turn, impact their surrounding environments in important and diverse ways. Lakes serve as sentinels of current and changing conditions, as actors in influencing their surrounding environments, and as integrators of human and environmental activities in their contributing basins. One of the most fruitful ways that lake scientists might collaborate is via the shared tool of remote sensing, which, through existing and planned sensors, can help to extend on-the-ground measurements to regional and global contexts. Existing and forthcoming remote-sensing technologies possess great potential to accurately monitor lake-storage change, water surface-temperature, ice, and watercolor. The aim of this Special Issue is to make state-of-the-art remote-sensing technology for studying lake changes and their interaction with their environment, and the impact and feedback of the climate change.

Dr. Jean-Francois Crétaux
Dr. Rodrigo Abarca Del Rio
Prof. Claude Duguay
Guest Editors

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

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Keywords

remote sensing
climate changes
reservoirs
lake water level
lake water storage
lake ice
lake water colour

Published Papers (13 papers)

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Research

19 pages, 5524 KiB

Open AccessArticle

Monitoring Long-Term Lake Level Variations in Middle and Lower Yangtze Basin over 2002–2017 through Integration of Multiple Satellite Altimetry Datasets

by Peng Li, Hui Li, Fang Chen and Xiaobin Cai

Remote Sens. 2020, 12(9), 1448; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091448 - 02 May 2020

Cited by 21 | Viewed by 3379

Abstract

Satellite altimetry has been effectively used for monitoring lake level changes in recent years. This work focused on the integration of multiple satellite altimetry datasets from ICESat-1, Envisat and Cryosat-2 for the long-term (2002–2017) observation of lake level changes in the middle and lower Yangtze River Basin (MLYB). Inter-altimeter biases were estimated by using the gauged daily water level data. It showed that the average biases of ICESat-1 and Cryosat-2 with respect to Envisat were 6.7 cm and 3.1 cm, respectively. The satellite-derived water levels were evaluated against the gauged data. It indicated significantly high correlations between the two datasets, and the combination of three altimetry data produced precise water level time series with high temporal and spatial resolutions. A liner regression model was used to estimate the rates of lake level changes over the study period after the inter-altimeter bias adjustment was performed. The results indicated that ~79% of observed lakes (41/52) showed increasing trends in water levels with rates up to 0.203 m/y during 2002–2017. The temporal analysis of lake level variations suggested that ~60% of measured lakes (32/53) showed decreasing trends during 2002–2009 while ~66% of measured lakes (79/119) exhibited increasing trends during 2010–2017. Most of measured reservoirs displayed rapidly rising trends during the study period. The driving force analysis indicated that the temporal heterogeneity of precipitation can be mainly used to explain the observed pattern of lake level changes. The operation of reservoirs and human water consumption were also responsible for the lake level variations. This work demonstrated the potential of integrating multiple satellite altimeters for the long-term monitoring of lake levels, which can help to evaluate the impact of climate change and anthropogenic activities on regional water resources. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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21 pages, 22271 KiB

Open AccessArticle

Lake Ice-Water Classification of RADARSAT-2 Images by Integrating IRGS Segmentation with Pixel-Based Random Forest Labeling

by Marie Hoekstra, Mingzhe Jiang, David A. Clausi and Claude Duguay

Remote Sens. 2020, 12(9), 1425; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091425 - 30 Apr 2020

Cited by 30 | Viewed by 3803

Abstract

Changes to ice cover on lakes throughout the northern landscape has been established as an indicator of climate change and variability, expected to have implications for both human and environmental systems. Monitoring lake ice cover is also required to enable more reliable weather forecasting across lake-rich northern latitudes. Currently, the Canadian Ice Service (CIS) monitors lakes using synthetic aperture radar (SAR) and optical imagery through visual interpretation, with total lake ice cover reported weekly as a fraction out of ten. An automated method of classification would allow for more detailed records to be delivered operationally. In this research, we present an automatic ice-mapping approach which integrates unsupervised segmentation from the Iterative Region Growing using Semantics (IRGS) algorithm with supervised random forest (RF) labeling. IRGS first locally segments homogeneous regions in an image, then merges similar regions into classes across the entire scene. Recently, these output regions were manually labeled by the user to generate ice maps, or were labeled using a Support Vector Machine (SVM) classifier. Here, three labeling methods (Manual, SVM, and RF) are applied after IRGS segmentation to perform ice-water classification on 36 RADARSAT-2 scenes of Great Bear Lake (Canada). SVM and RF classifiers are also tested without integration with IRGS. An accuracy assessment has been performed on the results, comparing outcomes with author-generated reference data, as well as the reported ice fraction from CIS. The IRGS-RF average classification accuracy for this dataset is 95.8%, demonstrating the potential of this automated method to provide detailed and reliable lake ice cover information operationally. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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25 pages, 11427 KiB

Open AccessArticle

Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations

by Jawad Ziyad, Kalifa Goïta, Ramata Magagi, Fabien Blarel and Frédéric Frappart

Remote Sens. 2020, 12(6), 967; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060967 - 17 Mar 2020

Cited by 13 | Viewed by 3187

Abstract

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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21 pages, 29828 KiB

Open AccessArticle

Compositing the Minimum NDVI for Daily Water Surface Mapping

by Xingwang Fan, Yuanbo Liu, Guiping Wu and Xiaosong Zhao

Remote Sens. 2020, 12(4), 700; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040700 - 20 Feb 2020

Cited by 18 | Viewed by 6206

Abstract

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R² = 0.914, root mean square error, RMSE = 223.7 km²), the cloud free MODIS image based result (R² = 0.824, RMSE = 356.7 km²), the recent Landsat-MODIS image fusion based result (R² = 0.765, RMSE = 403 km²), and the hydrodynamic modeling result (R² = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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27 pages, 3569 KiB

Open AccessArticle

Extending Landsat 8: Retrieval of an Orange contra-Band for Inland Water Quality Applications

by Alexandre Castagna, Stefan Simis, Heidi Dierssen, Quinten Vanhellemont, Koen Sabbe and Wim Vyverman

Remote Sens. 2020, 12(4), 637; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040637 - 14 Feb 2020

Cited by 22 | Viewed by 4961

Abstract

The Operational Land Imager (OLI) onboard Landsat 8 has found successful application in inland and coastal water remote sensing. Its radiometric specification and high spatial resolution allows quantification of water-leaving radiance while resolving small water bodies. However, its limited multispectral band set restricts the range of water quality parameters that can be retrieved. Identification of cyanobacteria biomass has been demonstrated for sensors with a band centered near 620 nm, the absorption peak of the diagnostic pigment phycocyanin. While OLI lacks such a band in the orange region, superposition of the available multispectral and panchromatic bands suggests that it can be calculated by a scaled difference. A set of 428 in situ spectra acquired in diverse lakes in Belgium and The Netherlands was used to develop and test an orange contra-band retrieval algorithm, achieving a mean absolute percentage error of 5.39% and a bias of −0.88% in the presence of sensor noise. Atmospheric compensation error propagated to the orange contra-band was observed to maintain about the same magnitude (13% higher) observed for the red band and thus results in minimal additional effects for possible base line subtraction or band ratio algorithms for phycocyanin estimation. Generality of the algorithm for different reflectance shapes was tested against a set of published average coastal and inland Optical Water Types, showing robust retrieval for all but relatively clear water types (Secchi disk depth > 6 m and chlorophyll a

< 1.6

mg m

^{- 3}

). The algorithm was further validated with 79 matchups against the Ocean and Land Colour Imager (OLCI) orange band for 10 globally distributed lakes. The retrieved band is shown to convey information independent from the adjacent bands under variable phycocyanin concentrations. An example application using Landsat 8 imagery is provided for a known cyanobacterial bloom in Lake Erie, US. The method is distributed in the ACOLITE atmospheric correction code. The contra-band approach is generic and can be applied to other sensors with overlapping bands. Recommendations are also provided for development of future sensors with broad spectral bands with the objective to maximize the accuracy of possible spectral enhancements. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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24 pages, 11917 KiB

Open AccessArticle

Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data

by Justin Murfitt and Claude R. Duguay

Remote Sens. 2020, 12(3), 382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030382 - 25 Jan 2020

Cited by 17 | Viewed by 4461

Abstract

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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19 pages, 7019 KiB

Open AccessArticle

Spatial Non-Uniformity of Surface Temperature of the Dead Sea and Adjacent Land Areas

by Pavel Kishcha, Boris Starobinets, Rachel T. Pinker, Pavel Kunin and Pinhas Alpert

Remote Sens. 2020, 12(1), 107; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010107 - 28 Dec 2019

Cited by 2 | Viewed by 3298

Abstract

Pronounced spatial non-uniformity has been obtained of daytime sea surface temperature (SST) of the Dead Sea and of land surface temperature (LST) over areas adjacent to the Dead Sea. This non-uniformity was observed in the summer months, under uniform solar radiation. Our findings are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002–2016) on board the Terra and Aqua satellites. MODIS data showed that, on average for the 15-year study period, daytime SST over the eastern part of the lake (Te) exceeded by 5 °C that over the western part (Tw). This SST non-uniformity (observed in the absence of surface heat flow from land to sea at the eastern side) was accompanied by spatial non-uniform distribution of land surface temperature (LST) over areas adjacent to the Dead Sea. Specifically, LST over areas adjacent to the eastern side exceeded by 10 °C that over areas adjacent to the western side. Our findings of spatial non-uniformity of SST/LST based on MODIS data were supported by Meteosat Second Generation LST records. Regional atmospheric warming led to a decrease in spatial non-uniformity of SST during the study period. Temperature difference between Te and Tw steadily decreased at the rate of 0.32 °C decade⁻¹, based on MODIS/Terra data, and 0.54 °C decade⁻¹, based on MODIS/Aqua data. Our simulations of monthly skin temperature distribution over the Dead Sea by the Weather Forecast and Research (WRF) model contradict satellite observations. The application to modeling of the observed SST/LST spatial non-uniformity will advance our knowledge of atmospheric dynamics over hypersaline lakes. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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18 pages, 7611 KiB

Open AccessArticle

Long-Term Water Surface Area Monitoring and Derived Water Level Using Synthetic Aperture Radar (SAR) at Altevatn, a Medium-Sized Arctic Lake

by Hannah Vickers, Eirik Malnes and Kjell-Arild Høgda

Remote Sens. 2019, 11(23), 2780; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232780 - 25 Nov 2019

Cited by 17 | Viewed by 5314

Abstract

Monitoring water storage in lakes and reservoirs is critical to water resource management, especially in a changing climate. Satellite microwave remote sensing offers a weather and light-independent solution for mapping water cover over large scales. We have used 13 years of synthetic aperture radar (SAR) data from three different sensors (Sentinel-1, RADARSAT-2, and Envisat advanced synthetic aperture radar (ASAR)) to develop a method for mapping surface water cover and thereby estimating the lake water extent (LWE). The method uses the unsupervised K-means clustering algorithm together with specific post-processing techniques to create binary maps of the water area. We have specifically tested and validated the method at Altevatn, a medium-sized arctic lake in Northern Norway, by using in-situ measurements of the water level. The multi-sensor SAR LWE time series were used in conjunction with the water level measurements to derive the lake hypsometry while at the same time quantifying the accuracy of our method. For Altevatn lake we estimated LWE with a root mean squared error (RMSE) of 0.89 km² or 1.4% of the mean LWE, while the inferred lake water level (LWL) was associated with an RMSE of 0.40 m, or 2.5% of the maximum annual variation. We foresee that there is potential to further develop the algorithm by generalizing its use to other lakes worldwide and automating the process such that near real-time monitoring of LWE may be possible. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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19 pages, 5330 KiB

Open AccessArticle

Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data

by Xiaohan Liu, Zhongping Lee, Yunlin Zhang, Junfang Lin, Kun Shi, Yongqiang Zhou, Boqiang Qin and Zhaohua Sun

Remote Sens. 2019, 11(19), 2226; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192226 - 25 Sep 2019

Cited by 34 | Viewed by 4041

Abstract

The Secchi disk depth (Z_SD, m) has been used globally for many decades to represent water clarity and an index of water quality and eutrophication. In recent studies, a new theory and model were developed for Z_SD, which enabled its semi-analytical remote sensing from the measurement of water color. Although excellent performance was reported for measurements in both oceanic and coastal waters, its reliability for highly turbid inland waters is still unknown. In this study, we extend this model and its evaluation to such environments. In particular, because the accuracy of the inherent optical properties (IOPs) derived from remote sensing reflectance (R_rs, sr⁻¹) plays a key role in determining the reliability of estimated Z_SD, we first evaluated a few quasi-analytical algorithms (QAA) specifically tuned for turbid inland waters and determined the one (QAA_TI) that performed the best in such environments. For the absorption coefficient at 443 nm (a(443), m⁻¹) ranging from ~0.2 to 12.5 m⁻¹, it is found that the QAA_TI-derived absorption coefficients agree well with field measurements (r² > 0.85, and mean absolute percentage difference (MAPD) smaller than ~39%). Furthermore, with QAA_TI-derived IOPs, the MAPD was less than 25% between the estimated and field-measured Z_SD (r² > 0.67, Z_SD in a range of 0.1–1.7 m). Furthermore, using matchup data between R_rs from the Medium Resolution Imaging Spectrometer (MERIS) and in-situ Z_SD, a similar performance in the estimation of Z_SD from remote sensing was obtained (r² = 0.73, MAPD = 37%, Z_SD in a range of 0.1–0.9 m). Based on such performances, we are confident to apply the Z_SD remote sensing scheme to MERIS measurements to characterize the spatial and temporal variations of Z_SD in Lake Taihu during the period of 2003–2011. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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28 pages, 8824 KiB

Open AccessArticle

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

by Ethan D. Kyzivat, Laurence C. Smith, Lincoln H. Pitcher, Jessica V. Fayne, Sarah W. Cooley, Matthew G. Cooper, Simon N. Topp, Theodore Langhorst, Merritt E. Harlan, Christopher Horvat, Colin J. Gleason and Tamlin M. Pavelsky

Remote Sens. 2019, 11(18), 2163; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11182163 - 17 Sep 2019

Cited by 23 | Viewed by 4480

Abstract

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km² of open surface water were mapped from 23,380 km² of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km² (40 m²) to 15 km². Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km² in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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20 pages, 27763 KiB

Open AccessArticle

Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products

by Tejas Bhagwat, Igor Klein, Juliane Huth and Patrick Leinenkugel

Remote Sens. 2019, 11(17), 1974; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11171974 - 22 Aug 2019

Cited by 18 | Viewed by 4759

Abstract

Globally, the number of dams increased dramatically during the 20th century. As a result, monitoring water levels and storage volume of dam-reservoirs has become essential in order to understand water resource availability amid changing climate and drought patterns. Recent advancements in remote sensing data show great potential for studies pertaining to long-term monitoring of reservoir water volume variations. In this study, we used freely available remote sensing products to assess volume variations for Lake Mead, Lake Powell and reservoirs in California between 1984 and 2015. Additionally, we provided insights on reservoir water volume fluctuations and hydrological drought patterns in the region. We based our volumetric estimations on the area–elevation hypsometry relationship, by combining water areas from the Global Surface Water (GSW) monthly water history (MWH) product with corresponding water surface median elevation values from three different digital elevation models (DEM) into a regression analysis. Using Lake Mead and Lake Powell as our validation reservoirs, we calculated a volumetric time series for the GSW_MWH–DEM_{median elevation} combinations that showed a strong linear ‘area (W_A) – elevation (W_H)’ (R² > 0.75) hypsometry. Based on ‘W_A-W_H’ linearity and correlation analysis between the estimated and in situ volumetric time series, the methodology was expanded to reservoirs in California. Our volumetric results detected four distinct periods of water volume declines: 1987–1992, 2000–2004, 2007–2009 and 2012–2015 for Lake Mead, Lake Powell and in 40 reservoirs in California. We also used multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) for San Joaquin drainage in California to assess regional links between the drought indicators and reservoir volume fluctuations. We found highest correlations between reservoir volume variations and the SPEI at medium time scales (12–18–24–36 months). Our work demonstrates the potential of processed, open source remote sensing products for reservoir water volume variations and provides insights on usability of these variations in hydrological drought monitoring. Furthermore, the spatial coverage and long-term temporal availability of our data presents an opportunity to transfer these methods for volumetric analyses on a global scale. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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17 pages, 5769 KiB

Open AccessArticle

An Object-Based Classification Method to Detect Methane Ebullition Bubbles in Early Winter Lake Ice

by Prajna Lindgren, Guido Grosse, Franz J. Meyer and Katey Walter Anthony

Remote Sens. 2019, 11(7), 822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070822 - 05 Apr 2019

Cited by 10 | Viewed by 3632

Abstract

Thermokarst lakes in the Arctic and Subarctic release carbon from thawing permafrost in the form of methane and carbon dioxide with important implications for regional and global carbon cycles. Lake ice impedes the release of gas during the winter. For instance, bubbles released from lake sediments become trapped in downward growing lake ice, resulting in vertically-oriented bubble columns in the ice that are visible on the lake surface. We here describe a classification technique using an object-based image analysis (OBIA) framework to successfully map ebullition bubbles in airborne imagery of early winter ice on an interior Alaska thermokarst lake. Ebullition bubbles appear as white patches in high-resolution optical remote sensing images of snow-free lake ice acquired in early winter and, thus, can be mapped across whole lake areas. We used high-resolution (9–11 cm) aerial images acquired two and four days following freeze-up in the years 2011 and 2012, respectively. The design of multiresolution segmentation and region-specific classification rulesets allowed the identification of bubble features and separation from other confounding factors such as snow, submerged and floating vegetation, shadows, and open water. The OBIA technique had an accuracy of >95% for mapping ebullition bubble patches in early winter lake ice. Overall, we mapped 1195 and 1860 ebullition bubble patches in the 2011 and 2012 images, respectively. The percent surface area of lake ice covered with ebullition bubble patches for 2011 was 2.14% and for 2012 was 2.67%, representing a conservative whole lake estimate of bubble patches compared to ground surveys usually conducted on thicker ice 10 or more days after freeze-up. Our findings suggest that the information derived from high-resolution optical images of lake ice can supplement spatially limited field sampling methods to better estimate methane flux from individual lakes. The method can also be used to improve estimates of methane ebullition from numerous lakes within larger regions. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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22 pages, 20304 KiB

Open AccessArticle

A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume

by Jonathan W. Chipman

Remote Sens. 2019, 11(2), 158; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020158 - 16 Jan 2019

Cited by 31 | Viewed by 7398

Abstract

Lakes in arid regions play an important role in regional water cycles and are a vital economic resource, but can fluctuate widely in area and volume. This study demonstrates the use of a multisensor satellite remote sensing method for the comprehensive monitoring of lake surface areas, water levels, and volume for the Toshka Lakes in southern Egypt, from lake formation in 1998 to mid-2017. Two spectral water indices were used to construct a daily time-series of surface area from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), validated by higher-resolution Landsat images. Water levels were obtained from analysis of digital elevation models from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), validated with ICESat Geoscience Laser Altimeter System (GLAS) laser altimetry. Total lake volume peaked at 26.54 × 10⁹ m³ in December 2001, and declined to 0.76 × 10⁹ m³ by August 2017. Evaporation accounted for approximately 86% of the loss, and groundwater recharge accounted for 14%. Without additional inflows, the last remaining lake will likely disappear between 2020 and 2022. The Enhanced Lake Index, a water index equivalent to the Enhanced Vegetation Index, was found to have lower noise levels than the Normalized Difference Lake Index. The results show that multi-platform satellite remote sensing provides an efficient method for monitoring the hydrology of lakes. Full article

(This article belongs to the Special Issue Lake Remote Sensing)

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