Special Issue "Geographic Information Science and Spatial Analysis in Water Resources"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 May 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Heejun Chang

Department of Geography, Portland State University, 1721 SW Broadway, Portland,OR 97201, USA
Website | E-Mail
Interests: hydrology and water resources; climate change impact assessment; urban ecosystem services; spatial analysis

Special Issue Information

Dear Colleagues,

The use of geographic information systems (GIS) and spatial analysis has increased in hydrological modeling and water resources system analysis in recent years. This Special Issue invites papers addressing recent advances in hydrology and water resources using GIS and spatial analysis. Potential topics for this Special Issue include, but are not limited to, the following:

  • The role and application of GIS in hydrologic modeling and analysis
  • GIS technology for managing and understanding water resources across scales
  • Spatially explicit analysis of water resource system modeling and analysis
  • Spatial statistics in hydrologic and water resource data analysis
  • Application of GIS and spatial analysis for water quality modeling

Prof. Dr. Heejun Chang
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 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GIS
  • spatial analysis
  • spatial statistics
  • hydrologic modeling
  • water resource system
  • water quality
  • scale

Published Papers (14 papers)

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Research

Open AccessArticle Multi-Temporal Image Analysis for Fluvial Morphological Characterization with Application to Albanian Rivers
ISPRS Int. J. Geo-Inf. 2018, 7(8), 314; https://doi.org/10.3390/ijgi7080314
Received: 31 May 2018 / Revised: 25 June 2018 / Accepted: 30 June 2018 / Published: 3 August 2018
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Abstract
A procedure for the characterization of the temporal evolution of river morphology is presented. Wet and active river channels are obtained from the processing of imagery datasets. Information about channel widths and active channel surface subdivision in water, vegetation and gravel coverage classes [...] Read more.
A procedure for the characterization of the temporal evolution of river morphology is presented. Wet and active river channels are obtained from the processing of imagery datasets. Information about channel widths and active channel surface subdivision in water, vegetation and gravel coverage classes are evaluated along with channel centerline lengths and sinuosity indices. The analysis is carried out on a series of optical remotely-sensed imagery acquired by different satellite missions during the time period between 1968 and 2017. Data from the CORONA, LANDSAT and Sentinel-2 missions were considered. Besides satellite imagery, a digital elevation model and aerial ortho-photos were also used. The procedure was applied to three, highly dynamic, Albanian rivers: Shkumbin, Seman and Vjosë, showing a high potential for application in contexts with limitations in ground data availability. The results of the procedure were assessed against reference data produced by means of expert interpretation of a reference set of river reaches. The results differ from reference values by just a few percentage points (<6%). The time evolution of hydromorphological parameters is well characterized, and the results support the design of future studies aimed at the understanding of the relations between climatic and anthropogenic controls and the response of river morphological trajectories. Moreover, the high spatial and temporal resolution of the Sentinel-2 mission motivates the development of an automatic monitoring system based on a rolling application of the defined procedure. Full article
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Open AccessArticle Using Geospatial Analysis and Hydrologic Modeling to Estimate Climate Change Impacts on Nitrogen Export: Case Study for a Forest and Pasture Dominated Watershed in North Carolina
ISPRS Int. J. Geo-Inf. 2018, 7(7), 280; https://doi.org/10.3390/ijgi7070280
Received: 31 May 2018 / Revised: 10 July 2018 / Accepted: 20 July 2018 / Published: 21 July 2018
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Abstract
Many watersheds are currently experiencing streamflow and water quality related problems that are caused by excess nitrogen. Given that weather is a major driver of nitrogen transport through watersheds, the objective of this study was to predict climate change impacts on streamflow and [...] Read more.
Many watersheds are currently experiencing streamflow and water quality related problems that are caused by excess nitrogen. Given that weather is a major driver of nitrogen transport through watersheds, the objective of this study was to predict climate change impacts on streamflow and nitrogen export. A forest and pasture dominated watershed in North Carolina Piedmont region was used as the study area. A physically-based Soil and Water Assessment Tool (SWAT) model parameterized using geospatial data layers and spatially downscaled temperature and precipitation estimates from eight different General Circulation Models (GCMs) were used for this study. While temperature change predictions are fairly consistent across the GCMs for the study watershed, there is significant variability in precipitation change predictions across the GCMs, and this leads to uncertainty in the future conditions within the watershed. However, when the downscaled GCM projections were taken as a model ensemble, the results suggest that both high and low emission scenarios would result in an average increase in streamflow of 14.1% and 12.5%, respectively, and a decrease in the inorganic nitrogen export by 12.1% and 8.5%, respectively, by the end of the century. The results also show clear seasonal patterns with streamflow and nitrogen loading both increasing in fall and winter months by 97.8% and 50.8%, respectively, and decreasing by 20.2% and 35.5%, respectively, in spring and summer months by the end of the century. Full article
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Open AccessArticle The Impact of the Parameterisation of Physiographic Features of Urbanised Catchment Areas on the Spatial Distribution of Components of the Water Balance Using the WetSpass Model
ISPRS Int. J. Geo-Inf. 2018, 7(7), 278; https://doi.org/10.3390/ijgi7070278
Received: 22 May 2018 / Revised: 18 July 2018 / Accepted: 19 July 2018 / Published: 21 July 2018
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Abstract
An analysis was conducted of the activity of individual homogeneous Hydrological Response Units (HRUs) and their impact on the components of the spatially distributed water balance based on the example of two urbanised catchments of the city of Poznań (Western Poland). Water balance [...] Read more.
An analysis was conducted of the activity of individual homogeneous Hydrological Response Units (HRUs) and their impact on the components of the spatially distributed water balance based on the example of two urbanised catchments of the city of Poznań (Western Poland). Water balance was developed using the WetSpass model and GIS spatial data, based on hydrometeorological data from the reference period of 1961–2000 including projected land usage changes and precipitation changes expected by 2025 in the city. The catchments were parameterised with reference to land usage, soil permeability, terrain declivities and the level of groundwater waters in summer and winter. The dependence between HRUs and their impact on components of the water balance was determined. Water balance forecasts have shown two-way changes in the components of approximately 12% of the catchments. A significant increase of surface runoff (an increase of 20–30 mm/HRU) at the expense of effective infiltration reduction (by 15–20 mm/HRU) was determined for arable land intended for development. An increase of infiltration and evapotranspiration at the expense of the surface runoff reduction is forecast for areas designed for urban afforestation. The tendency of increase of atmospheric precipitation within the city until 2025 was indicated by changes in the water balance components. Changes in the landscape resulting from urban expansion may lead to detrimental hydrological effects: accumulation of surface runoffs and occurrence of local flash flooding, as confirmed by the simulations carried out using the WetSpass model. The results may contribute to a more accurate understanding of the impact of urban landscape modification patterns on the water balance at the regional and local scale. Full article
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Open AccessArticle Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods
ISPRS Int. J. Geo-Inf. 2018, 7(6), 211; https://doi.org/10.3390/ijgi7060211
Received: 5 March 2018 / Revised: 11 May 2018 / Accepted: 27 May 2018 / Published: 2 June 2018
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Abstract
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses [...] Read more.
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer. Full article
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Open AccessArticle Examining the Stream Threshold Approaches Used in Hydrologic Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(6), 201; https://doi.org/10.3390/ijgi7060201
Received: 17 April 2018 / Revised: 15 May 2018 / Accepted: 27 May 2018 / Published: 29 May 2018
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Abstract
The stream threshold is a user-defined and important parameter that directly affects the drainage network and basin boundaries that would be obtained as a result of hydrological analysis. There have been several approaches developed for the stream threshold. According to the approaches treated [...] Read more.
The stream threshold is a user-defined and important parameter that directly affects the drainage network and basin boundaries that would be obtained as a result of hydrological analysis. There have been several approaches developed for the stream threshold. According to the approaches treated in this study, the stream threshold is determined based on (1) the flow accumulation statistics: (a) 1% of the maximum flow accumulation value and (b) the mean flow accumulation value; (2) the flow accumulation values at the cells including the beginning points of the line features representing the existing streams; and (3) the adjacency and direction relationships between the cells including the beginning points of the lines in the drainage network to be derived. In this paper, a new approach as an integrant of the last approach was introduced (not only the adjacency and direction relationships between the cells including the beginning points, but also the adjacency and direction relationships amongst all of the cells, as well as including the lines considered). An experiment was conducted in accordance with the purpose of comparing and evaluating these approaches. According to the experimental results, it was found that the last two approaches differed from the first three approaches in terms of providing clues to the user regarding the quality and the quantity of the drainage network to be derived. Full article
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Open AccessArticle Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling
ISPRS Int. J. Geo-Inf. 2018, 7(2), 64; https://doi.org/10.3390/ijgi7020064
Received: 28 November 2017 / Revised: 7 February 2018 / Accepted: 17 February 2018 / Published: 19 February 2018
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Abstract
Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this [...] Read more.
Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. We selected ten watersheds in the USA and analyzed if water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R2) and greater decreases in residual SAC (rSAC). We compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R2 and decreases in rSAC after performing spatial regressions. In this study, we found a generally linear relationship between the spatial model outcomes (R2 and rSAC) and the degree of SAC in each water quality variable. We suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed. Full article
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Open AccessArticle Analysis of Hydrological Sensitivity for Flood Risk Assessment
ISPRS Int. J. Geo-Inf. 2018, 7(2), 51; https://doi.org/10.3390/ijgi7020051
Received: 30 November 2017 / Revised: 30 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
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Abstract
In order for the Indian government to maximize Integrated Water Resource Management (IWRM), the Brahmaputra River has played an important role in the undertaking of the Pilot Basin Study (PBS) due to the Brahmaputra River’s annual regional flooding. The selected Kulsi River—a part [...] Read more.
In order for the Indian government to maximize Integrated Water Resource Management (IWRM), the Brahmaputra River has played an important role in the undertaking of the Pilot Basin Study (PBS) due to the Brahmaputra River’s annual regional flooding. The selected Kulsi River—a part of Brahmaputra sub-basin—experienced severe floods in 2007 and 2008. In this study, the Rainfall-Runoff-Inundation (RRI) hydrological model was used to simulate the recent historical flood in order to understand and improve the integrated flood risk management plan. The ultimate objective was to evaluate the sensitivity of hydrologic simulation using different Digital Elevation Model (DEM) resources, coupled with DEM smoothing techniques, with a particular focus on the comparison of river discharge and flood inundation extent. As a result, the sensitivity analysis showed that, among the input parameters, the RRI model is highly sensitive to Manning’s roughness coefficient values for flood plains, followed by the source of the DEM, and then soil depth. After optimizing its parameters, the simulated inundation extent showed that the smoothing filter was more influential than its simulated discharge at the outlet. Finally, the calibrated and validated RRI model simulations agreed well with the observed discharge and the Moderate Imaging Spectroradiometer (MODIS)-detected flood extents. Full article
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Open AccessArticle Inverse Parametrization of a Regional Groundwater Flow Model with the Aid of Modelling and GIS: Test and Application of Different Approaches
ISPRS Int. J. Geo-Inf. 2018, 7(1), 22; https://doi.org/10.3390/ijgi7010022
Received: 15 November 2017 / Revised: 30 December 2017 / Accepted: 6 January 2018 / Published: 12 January 2018
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Abstract
The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying [...] Read more.
The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R2) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results. Full article
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Open AccessArticle Reconstruction of River Boundaries at Sub-Pixel Resolution: Estimation and Spatial Allocation of Water Fractions
ISPRS Int. J. Geo-Inf. 2017, 6(12), 383; https://doi.org/10.3390/ijgi6120383
Received: 4 October 2017 / Revised: 7 November 2017 / Accepted: 20 November 2017 / Published: 24 November 2017
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Abstract
Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation [...] Read more.
Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-band WorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classification. Full article
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Open AccessArticle Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters
ISPRS Int. J. Geo-Inf. 2017, 6(11), 360; https://doi.org/10.3390/ijgi6110360
Received: 13 October 2017 / Revised: 9 November 2017 / Accepted: 14 November 2017 / Published: 15 November 2017
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Abstract
Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal [...] Read more.
Coastal waters are one of the most vulnerable resources that require effective monitoring programs. One of the key factors for effective coastal monitoring is the use of remote sensing technologies that significantly capture the spatiotemporal variability of coastal waters. Optical properties of coastal waters are strongly linked to components, such as colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), and suspended solids (SS) concentrations, which are essential for the survival of a coastal ecosystem and usually independent of each other. Thus, developing effective remote sensing models to estimate these important water components based on optical properties of coastal waters is mandatory for a successful coastal monitoring program. This study attempted to evaluate the performance of empirical predictive models (EPM) and neural networks (NN)-based algorithms to estimate Chl-a and SS concentrations, in the coastal area of Hong Kong. Remotely-sensed data over a 13-year period was used to develop regional and local models to estimate Chl-a and SS over the entire Hong Kong waters and for each water class within the study area, respectively. The accuracy of regional models derived from EPM and NN in estimating Chl-a and SS was 83%, 93%, 78%, and 97%, respectively, whereas the accuracy of local models in estimating Chl-a and SS ranged from 60–94% and 81–94%, respectively. Both the regional and local NN models exhibited a higher performance than those models derived from empirical analysis. Thus, this study suggests using machine learning methods (i.e., NN) for the more accurate and efficient routine monitoring of coastal water quality parameters (i.e., Chl-a and SS concentrations) over the complex coastal area of Hong Kong and other similar coastal environments. Full article
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Open AccessArticle Spatial Analysis of Linear Structures in the Exploration of Groundwater
ISPRS Int. J. Geo-Inf. 2017, 6(11), 335; https://doi.org/10.3390/ijgi6110335
Received: 21 September 2017 / Revised: 18 October 2017 / Accepted: 25 October 2017 / Published: 2 November 2017
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Abstract
The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding), Principal Component Analysis (PCA), [...] Read more.
The analysis of linear structures on major geological formations plays a crucial role in resource exploration in the Inner Niger Delta. Highlighting and mapping of the large lithological units were carried out using image fusion, spectral bands (RGB coding), Principal Component Analysis (PCA), and band ratio methods. The automatic extraction method of linear structures has permitted the obtaining of a structural map with 82,659 linear structures, distributed on different stratigraphic stages. The intensity study shows an accentuation in density over 12.52% of the total area, containing 22.02% of the linear structures. The density and nodes (intersections of fractures) formed by the linear structures on the different lithologies allowed to observe the behavior of the region’s aquifers in the exploration of subsoil resources. The central density, in relation to the hydrographic network of the lowlands, shows the conditioning of the flow and retention of groundwater in the region, and in-depth fluids. The node areas and high-density linear structures, have shown an ability to have rejections in deep (pores) that favor the formation of structural traps for oil resources. Full article
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Open AccessArticle Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring
ISPRS Int. J. Geo-Inf. 2017, 6(10), 301; https://doi.org/10.3390/ijgi6100301
Received: 17 August 2017 / Revised: 21 September 2017 / Accepted: 24 September 2017 / Published: 27 September 2017
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Abstract
Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes [...] Read more.
Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes a more cost-effective technique, employing freely available Landsat images. Despite its extensive spectrum and robust discrimination capability, Landsat data are normally of medium spatial resolution and, as such, fail to delineate smaller hydrological features. Based on Multivariate Mutual Information (MMI), the Landsat images were fused with Digital Surface Model (DSM) on the spatial domain. Each coinciding pixel would then contain not only rich indices but also intricate topographic attributes, derived from its respective sources. The proposed data fusion ensures robust, precise, and observer-invariable extraction of water surfaces and their branching, while eliminating spurious details. Its merit was demonstrated by effective discrimination of flooded regions from natural rivers for flood monitoring. The assessments we completed suggest improved extraction compared to traditional methods. Compared with manual digitizing, this method also exhibited promising consistency. Extraction using Dempster–Shafer fusion provided a 91.81% F-measure, 93.09% precision, 90.74% recall, and 98.25% accuracy, while using Majority Voting fusion resulted in an 84.91% F-measure, 75.44% precision, 97.37% recall, and 97.21% accuracy. Full article
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Open AccessArticle Analysis of Groundwater Nitrate Contamination in the Central Valley: Comparison of the Geodetector Method, Principal Component Analysis and Geographically Weighted Regression
ISPRS Int. J. Geo-Inf. 2017, 6(10), 297; https://doi.org/10.3390/ijgi6100297
Received: 28 July 2017 / Revised: 15 September 2017 / Accepted: 18 September 2017 / Published: 26 September 2017
Cited by 3 | PDF Full-text (23689 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is a ubiquitous groundwater problem found in various parts of the valley. Heavy irrigation and application of fertilizer over the last several decades have caused groundwater nitrate contamination in several domestic, public [...] Read more.
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is a ubiquitous groundwater problem found in various parts of the valley. Heavy irrigation and application of fertilizer over the last several decades have caused groundwater nitrate contamination in several domestic, public and monitoring wells in the CV above EPA’s Maximum Contamination level of 10 mg/L. Source variables, aquifer susceptibility and geochemical variables could affect the contamination rate and groundwater quality in the aquifer. A comparative study was conducted using Geodetector (GED), Principal Component Analysis (PCA) and Geographically Weighted Regression (GWR) to observe which method is most effective at revealing environmental variables that control groundwater nitrate concentration. The GED method detected precipitation, fertilizer, elevation, manure and clay as statistically significant variables. Watersheds with percent of wells above 5 mg/L of nitrate were higher in San Joaquin and Tulare Basin compared to Sacramento Valley. PCA grouped cropland, fertilizer, manure and precipitation as a first principal component, suggesting similar construct of these variables and existence of data redundancy. The GWR model performed better than the OLS model, with lower corrected Akaike Information Criterion (AIC) values, and captured the spatial heterogeneity of fertilizer, precipitation and elevation for the percent of wells above 5 mg/L in the CV. Overall, the GED method was more effective than the PCA and GWR methods in determining the influence of explanatory variables on groundwater nitrate contamination. Full article
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Open AccessArticle Determination of 3D Displacements of Drainage Networks Extracted from Digital Elevation Models (DEMs) Using Linear-Based Methods
ISPRS Int. J. Geo-Inf. 2017, 6(8), 234; https://doi.org/10.3390/ijgi6080234
Received: 15 June 2017 / Revised: 14 July 2017 / Accepted: 2 August 2017 / Published: 4 August 2017
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Abstract
This study describes a new method developed to determine the 3D positional displacements of the drainage networks extracted from Digital Elevation Models (DEMs). The proposed method establishes several stages for data preparation. The displacements are derived by means of linestring-based assessment methods, which [...] Read more.
This study describes a new method developed to determine the 3D positional displacements of the drainage networks extracted from Digital Elevation Models (DEMs). The proposed method establishes several stages for data preparation. The displacements are derived by means of linestring-based assessment methods, which can be applied in 2D and 3D. Also, we propose the use of several tools (maps, aggregation of results, new indices, etc.) in order to obtain a wider assessment of positional accuracy, or a time change analysis. This approach supposes a novelty in drainage network studies both in the application of line-based methods and its expansion to 3D data. The method has been tested using a sample of channels extracted from DEMs of two different dates of a zone of about 600 square kilometers using as reference linestrings those extracted from another more recent DEM which had higher spatial accuracy and higher spatial resolution. The results have demonstrated the viability of the method proposed because of the obtaining of 3D displacement vectors, which showed the general and particular behavior of the channels selected. Full article
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