E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Remote Sensing of Inland Waters and Their Catchments"

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

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Guest Editor
Privat. Doz. Dr. habil. Angela Lausch

Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Permoserstr.15, D-04318 Leipzig, Germany
Website | E-Mail
Interests: Remote sensing; scaling approaches; linked open data; semantic web; data science approaches; spectral abiotic and biotic traits; spectral trait and trait variation concepts; spatial-temporal process-pattern interactions; vegetation; biodiversity ecosystem health; land-use intensity using RS approaches; essential biodiversity variables (EBV)
Guest Editor
Dr. Jan Bumberger

Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research (UFZ), Permoserstr.15, D-04318 Leipzig, Germany
Website | E-Mail
Interests: Scalable sensor network technologies; calibration and validation of remote sensing data; high frequency electromagnetic and optical spectral monitoring; data management; signal processing of multiparametric and cross-domain data; data-driven information extraction
Guest Editor
Prof. Dr. Natascha Oppelt

Remote Sensing and Environmental Modelling Lab, Dept. of Geography, Kiel University, Ludewig-Meyn-Str. 14, 21098 Kiel, Germany
Website | E-Mail
Phone: +49 431 880-3330
Interests: Remote sensing; imaging spectroscopy; sensors; remote sensing and GIS applications for terrestrial and aquatic ecosystem research
Guest Editor
Dr. Karsten Rinke

Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Brückstrasse 3a, 39114 Magdeburg, Germany
Website | E-Mail
Interests: water quality of lakes and reservoirs; eutrophication and algal blooms; reservoir operation; water quality management; modelling of lake ecosystems; climate change

Special Issue Information

Dear Colleagues,

This issue should address two interwoven and inseparable complex issues, i.e., land-use intensity in catchments and water quality and ecosystem health in the associated aquatic ecosystems.

Changing land use and intensity, human pressures or the stages involved in growing food are defined as the extent of land that is used. Clearance and homogenization of land and vegetation, the planting of trees, the drainage of wetlands or the sealing of surfaces, the implementation of organic fertilisers, the (in)direct application of pesticides, fungicides, human waste, such as pharmaceuticals or contamination with plastics, oils, salts or waste heat.

Land use intensity is also an indicator of the degree of land development in an area, and reflects the effects and environmental impacts generated by that development. Land-use intensity in catchment areas has a substantial effect on aquatic ecosystems. There are only a handful of indicators that measure land-use intensity and no indicators for understanding their complex interactions with water quality and ecosystem state.

Good water quality and sufficient water quantity are necessary for achieving the Sustainable Development Goals for human and ecosystem health, food security and water security. Therefore, it is of major concern that water pollution has worsened since the 1990s in the majority of inland waters and rivers in all regions of the world. It is imperative that actions to protect and restore water quality are linked to the efforts to achieve the Sustainable Development Goals (SDGs) and the Post 2015 Development Agenda. Eutrophication of inland waters, essentially driven by nutrient pollution from urban areas or agriculture, is a world-wide problem leading to deteriorating water quality, harmful algal blooms, and severe problems in drinking water production. Severe pathogen pollution already affects around one third of all river stretches in Latin America, Africa and Asia. In addition to the health risks from contaminated drinking water, many people are also at risk of disease by using polluted surface waters for bathing, cleaning clothes and for other household activities. The number of rural people at risk in this way may extend to hundreds of millions globally.

It is therefore the goal of this Special Issue to record water quality and ecosystem health using remote sensing and its methodological requirements and approaches, as well as investigating critical terrestrial parameters in the catchment using remote sensing and in situ measurements.

To compile existing research using remote sensing techniques in the field of water body/catchment mapping, we would like to invite you to submit articles on recent research with respect to the following topics:

  • Exploring the relationships between bio-optical properties and biogeochemical parameters of inland water systems
  • New developments in assessment and monitoring of water quality and status indicators of inland waters (trophic state, harmful algal blooms) using remote sensing
  • Linking remote sensing of inland waters with water resources management and modelling
  • Linkage RS and in situ data
  • Linkage air and space-borne RS and  wireless sensor networks
  • Standardization of water RS monitoring
  • Assessment of catchment characteristics using remote sensing in the context of water quality and aquatic ecosystem research
  • Remote sensing for quantifying land use change and intensity in catchments
  • Modelling the interactions of land-surface structures in catchments, land-use change/intensity, water quality and aquatic ecosystem health
  • Semantic Web for linkage catchment management and water remote sensing informations

Privat Dozent Dr. Angela Lausch
Dr. Jan Bumberger
Prof. Dr. Natascha Oppelt
Dr. Karsten Rinke
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 papers will be 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 1800 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

  • Water quality
  • Bio-optical modelling
  • Atmospheric correction
  • Optical complexity
  • In-situ–RS coupling
  • Validation and Calibration strategies
  • Land-use-intensity
  • Catchment structure and management
  • Aquatic wireless sensor networks
  • Standardization
  • Semantic web in water and catchment cycle

Published Papers (11 papers)

View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:

Research

Open AccessArticle
Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks
Remote Sens. 2019, 11(14), 1674; https://doi.org/10.3390/rs11141674
Received: 19 May 2019 / Revised: 8 July 2019 / Accepted: 12 July 2019 / Published: 14 July 2019
PDF Full-text (1990 KB) | HTML Full-text | XML Full-text
Abstract
Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality [...] Read more.
Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Figure 1

Open AccessArticle
High Temporal Resolution Monitoring of Suspended Matter Changes from GOCI Measurements in Lake Taihu
Remote Sens. 2019, 11(8), 985; https://doi.org/10.3390/rs11080985
Received: 15 March 2019 / Revised: 11 April 2019 / Accepted: 23 April 2019 / Published: 25 April 2019
PDF Full-text (10540 KB) | HTML Full-text | XML Full-text
Abstract
The Tiaoxi River is the main source of water for Lake Taihu and can result in plumes in the lake after heavy precipitation events. These plumes have played a crucial role in the water quality changes within the lake. High temporal resolution GOCI [...] Read more.
The Tiaoxi River is the main source of water for Lake Taihu and can result in plumes in the lake after heavy precipitation events. These plumes have played a crucial role in the water quality changes within the lake. High temporal resolution GOCI (Geostationary Ocean Color Imager) data were used to study the spatial distribution of the total suspended matter concentration in Lake Taihu after heavy precipitation events in the Tiaoxi River Basin via an empirical model. The plumes were analyzed after two heavy precipitation events in 2011 and 2013 using 16 GOCI images, which indicated that the Tiaoxi River had a great influence on the spatial distributions of total suspended matter and algal blooms. It was concluded that the main factors affecting the plumes in the Tiaoxi River were precipitation intensity, runoff, and total suspended matter concentration. Human activity, such as sand excavation also played a crucial role in sediment discharge. The results of this study demonstrate that the visualization of GOCI data makes it possible to use remote sensing technology to continuously monitor an inland water environment on an hourly scale, which is of great significance for studying the diffusion and evolution of river plumes. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
Shrinkage of Nepal’s Second Largest Lake (Phewa Tal) Due to Watershed Degradation and Increased Sediment Influx
Remote Sens. 2019, 11(4), 444; https://doi.org/10.3390/rs11040444
Received: 14 January 2019 / Revised: 12 February 2019 / Accepted: 18 February 2019 / Published: 21 February 2019
PDF Full-text (61620 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial [...] Read more.
Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial damming and sedimentation is disputed in the literature due to the lack of a historical time series. In this paper, we present an analysis of the lake’s evolution from 1926 to 2018 and model the 50-year trajectory of shrinkage. The area of Phewa Lake expanded from 2.44 ± 1.02 km2 in 1926 to a maximum of 4.61 ± 0.07 km2 in 1961. However, the lake area change was poorly constrained prior to a 1957–1958 map. The contemporary lake area was 4.02 ± 0.07 km2 in April 2018, and expands seasonally by ~0.18 km2 due to the summer monsoon. We found no evidence to support a lake area of 10 km2 in 1956–1957, despite frequent reporting of this value in the literature. Based on the rate of areal decline and sediment influx, we estimate the lake will lose 80% of its storage capacity in the next 110–347 years, which will affect recreational use, agricultural irrigation, fishing, and a one-megawatt hydroelectric power facility. Mitigation of lake shrinkage will require addressing landslide activity and sediment transport in the watershed, as well as urban expansion along the shores. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
Indirect Assessment of Sedimentation in Hydropower Dams Using MODIS Remote Sensing Images
Remote Sens. 2019, 11(3), 314; https://doi.org/10.3390/rs11030314
Received: 27 December 2018 / Revised: 23 January 2019 / Accepted: 31 January 2019 / Published: 5 February 2019
PDF Full-text (2858 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this study, we used moderate resolution imaging spectroradiometer (MODIS) satellite images to quantify the sedimentation processes in a cascade of six hydropower dams along a 700-km transect in the Paranapanema River in Brazil. Turbidity field measurement acquired over 10 years were used [...] Read more.
In this study, we used moderate resolution imaging spectroradiometer (MODIS) satellite images to quantify the sedimentation processes in a cascade of six hydropower dams along a 700-km transect in the Paranapanema River in Brazil. Turbidity field measurement acquired over 10 years were used to calibrate a turbidity retrieval algorithm based on MODIS surface reflectance products. An independent field dataset was used to validate the remote sensing estimates showing fine accuracy (RMSE of 9.5 NTU, r = 0.75, N = 138). By processing 13 years of MODIS images since 2000, we showed that satellite data can provide robust turbidity monitoring over the entire transect and can identify extreme sediment discharge events occurring on daily to annual scales. We retrieved the decrease in the water turbidity as a function of distance within each reservoir that is related to sedimentation processes. The remote sensing-retrieved turbidity decrease within the reservoirs ranged from 2 to 62% making possible to infer the reservoir type and operation (storage versus run-of-river reservoirs). The reduction in turbidity assessed from space presented a good relationship with conventional sediment trapping efficiency calculations, demonstrating the potential use of this technology for monitoring the intensity of sedimentation processes within reservoirs and at large scale. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Figure 1

Open AccessArticle
Decline in Transparency of Lake Hongze from Long-Term MODIS Observations: Possible Causes and Potential Significance
Remote Sens. 2019, 11(2), 177; https://doi.org/10.3390/rs11020177
Received: 12 December 2018 / Revised: 9 January 2019 / Accepted: 12 January 2019 / Published: 18 January 2019
Cited by 1 | PDF Full-text (3685 KB) | HTML Full-text | XML Full-text
Abstract
Transparency is an important indicator of water quality and the underwater light environment and is widely measured in water quality monitoring. Decreasing transparency occurs throughout the world and has become the primary water quality issue for many freshwater and coastal marine ecosystems due [...] Read more.
Transparency is an important indicator of water quality and the underwater light environment and is widely measured in water quality monitoring. Decreasing transparency occurs throughout the world and has become the primary water quality issue for many freshwater and coastal marine ecosystems due to eutrophication and other human activities. Lake Hongze is the fourth largest freshwater lake in China, providing water for surrounding cities and farms but experiencing significant water quality changes. However, there are very few studies about Lake Hongze’s transparency due to the lack of long-term monitoring data for the lake. To understand long-term trends, possible causes and potential significance of the transparency in Lake Hongze, an empirical model for estimating transparency (using Secchi disk depth: SDD) based on the moderate resolution image spectroradiometer (MODIS) 645-nm data was validated using an in situ dataset. Model mean absolute percentage and root mean square errors for the validation dataset were 27.7% and RMSE = 0.082 m, respectively, which indicates that the model performs well for SDD estimation in Lake Hongze without any adjustment of model parameters. Subsequently, 1785 cloud-free images were selected for use by the validated model to estimate SDDs of Lake Hongze in 2003–2017. The long-term change of SDD of Lake Hongze showed a decreasing trend from 2007 to 2017, with an average of 0.49 m, ranging from 0.57 m in 2007 to 0.42 m in 2016 (a decrease of 26.3%), which indicates that Lake Hongze experienced increased turbidity in the past 11 years. The loss of aquatic vegetation in the northern bays may be mainly affected by decreases of SDD. Increasing total suspended matter (TSM) concentration resulting from sand mining activities may be responsible for the decreasing trend of SDD. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Figure 1

Open AccessArticle
Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties
Remote Sens. 2018, 10(10), 1655; https://doi.org/10.3390/rs10101655
Received: 3 September 2018 / Revised: 5 October 2018 / Accepted: 9 October 2018 / Published: 18 October 2018
Cited by 2 | PDF Full-text (4633 KB) | HTML Full-text | XML Full-text
Abstract
The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to [...] Read more.
The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s−1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Figure 1

Open AccessArticle
Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry
Remote Sens. 2018, 10(10), 1566; https://doi.org/10.3390/rs10101566
Received: 10 July 2018 / Revised: 21 September 2018 / Accepted: 25 September 2018 / Published: 1 October 2018
Cited by 3 | PDF Full-text (11430 KB) | HTML Full-text | XML Full-text
Abstract
Increasing flood hazards worldwide due to the intensification of hydrological events and the development of adaptation-mitigation strategies are key challenges that society must address. To minimize flood damages, one of the crucial factors is the identification of flood prone areas through fluvial hydraulic [...] Read more.
Increasing flood hazards worldwide due to the intensification of hydrological events and the development of adaptation-mitigation strategies are key challenges that society must address. To minimize flood damages, one of the crucial factors is the identification of flood prone areas through fluvial hydraulic modelling in which a detailed knowledge of the terrain plays an important role for reliable results. Recent studies have demonstrated the suitability of the Reduced Cost Aerial Precision Photogrammetry (RC-APP) technique for fluvial applications by accurate-detailed-reliable Digital Terrain Models (DTMs, up to: ≈100 point/m2; vertical-uncertainty: ±0.06 m). This work aims to provide an optimal relationship between point densities and vertical-uncertainties to generate more reliable fluvial hazard maps by fluvial-DTMs. This is performed through hydraulic models supported by geometric models that are obtained from a joint strategy based on Structure from Motion and Cloth Simulation Filtering algorithms. Furthermore, to evaluate vertical-DTM, uncertainty is proposed as an alternative approach based on the method of robust estimators. This offers an error dispersion value analogous to the concept of standard deviation of a Gaussian distribution without requiring normality tests. This paper reinforces the suitability of new geomatic solutions as a reliable-competitive source of accurate DTMs at the service of a flood hazard assessment. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types
Remote Sens. 2018, 10(8), 1273; https://doi.org/10.3390/rs10081273
Received: 1 July 2018 / Revised: 7 August 2018 / Accepted: 9 August 2018 / Published: 13 August 2018
Cited by 5 | PDF Full-text (8243 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning [...] Read more.
Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
In-Channel 3D Models of Riverine Environments for Hydromorphological Characterization
Remote Sens. 2018, 10(7), 1005; https://doi.org/10.3390/rs10071005
Received: 31 March 2018 / Revised: 10 June 2018 / Accepted: 19 June 2018 / Published: 25 June 2018
PDF Full-text (5361 KB) | HTML Full-text | XML Full-text
Abstract
Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by [...] Read more.
Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System
Remote Sens. 2018, 10(6), 853; https://doi.org/10.3390/rs10060853
Received: 3 May 2018 / Revised: 22 May 2018 / Accepted: 28 May 2018 / Published: 1 June 2018
PDF Full-text (3527 KB) | HTML Full-text | XML Full-text
Abstract
Here, we demonstrate how a combination of three multivariate statistic techniques can identify key environmental factors affecting the seasonal and spatial variability of chlorophyll-a (Chl-a) in a productive tropical estuarine-lagoon system. Remote estimation of Chl-a was carried out using a NIR-Red model based [...] Read more.
Here, we demonstrate how a combination of three multivariate statistic techniques can identify key environmental factors affecting the seasonal and spatial variability of chlorophyll-a (Chl-a) in a productive tropical estuarine-lagoon system. Remote estimation of Chl-a was carried out using a NIR-Red model based on MODIS bands, which is highly consistent with the in situ measurement of Chl-a with root mean square error (RMSE) of 15.24 mg m−3 and 13.43 mg m−3 for two independent datasets used for the model’s calibration and validation, respectively. Our findings suggest that the river discharges and hydraulic residence time of the lagoons promote a stronger effect on the spatial variability of Chl-a in the coastal lagoons, while wind, solar radiation and temperature have a secondary importance. The results also indicate a slight seasonal variability of Chl-a in Mundaú lagoon, which are different the from Manguaba lagoon. The multivariate approach was able to fully understand the relative importance of key environmental factors on the spatiotemporal variability of Chl-a of the aquatic ecosystem, providing a powerful tool for reducing dimensionality and analyzing large amounts of satellite-derived Chl-a data. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Open AccessArticle
Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road
Remote Sens. 2018, 10(4), 595; https://doi.org/10.3390/rs10040595
Received: 9 March 2018 / Revised: 7 April 2018 / Accepted: 9 April 2018 / Published: 12 April 2018
Cited by 5 | PDF Full-text (26538 KB) | HTML Full-text | XML Full-text
Abstract
In the context of global warming, the changes in major lakes and their responses to the influence factors in arid and semi-arid regions along the Silk Road are especially important for the sustainable development of local water resources. In this study, the areas [...] Read more.
In the context of global warming, the changes in major lakes and their responses to the influence factors in arid and semi-arid regions along the Silk Road are especially important for the sustainable development of local water resources. In this study, the areas of 24 lakes were extracted using MODIS NDVI data, and their spatial-temporal characteristics were analyzed. In addition, the relationship between lake areas and the influence factors, including air temperature, precipitation, evapotranspiration, land use and land cover change (LULCC) and population density in the watersheds, were investigated. The results indicated that the areas of most lakes shrank, and the total area decreased by 22,189.7 km2 from 2001 to 2016, except for those of the lakes located on the Qinghai-Tibetan Plateau. The air temperature was the most important factor for all the lakes and increased at a rate of 0.113 °C/a during the past 16 years. LULCC and the increasing population density markedly influenced the lakes located in the middle to western parts of this study area. Therefore, our results connecting lake area changes in the study region highlight the great challenge of water resources and the urgency of implementation of the green policy in the One Belt and One Road Initiative through international collaboration. Full article
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top