scholarly journals Exploring the Spatial Variation of the Relationship between Land Use and Water Quality in a Drainage Basin Using Geographically Weighted Regression

2020 ◽  
Vol 12 (2) ◽  
pp. 147-168
Author(s):  
Samuel Azua ◽  
Taiye Oluwafemi Adewuyi ◽  
Lazarus Mustapha Ojigi ◽  
Omafuvwe Joseph Mudiare
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Geographically Weighted Regression ◽  
Drainage Basin ◽  
Water Quality Management ◽  
Mixed Forest ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Sample Points ◽  
The Relationship

The focus of this study is to determine the relationship between land use and water quality in the River Mu drainage basin for effective water quality management. Various land uses in the study area were identified and mapped using Landsat 8 OLI of 2016. Water samples were also collected from 112 sample sites using Stratified Random Sampling methods. The samples were analysed in terms of physicochemical parameters using standard methods. The results of land use and water quality parameters were regressed using Geographically Weighted Regression (GWR) to determine whether there exist spatially varying relationships. The results revealed that the local R2 values varied between 0.0 and 0.5, indicating a weak relationship between land use and water pollution, except for mixed forest and pH which recorded local R2 values of 0.7 towards the western region of the study area. This shows that the relationship between the two variables varied spatially across the drainage basin. The one-sample Kolmogorov Smirmov test-p<0.05 revealed that there were significant differences in pH (0.00), EC (0.00), turbidity (0.001), TDS (0.048), DO (0.003), NH4+ (0.002), Ca2+ (0.00), Cl- (0.036), Fe3+ (0.00) and Cr2+ (0.039) across the different sample points, whereas K+ (0.134), PO43- (0.715) and NO3- (0.501) were not significantly different across the different sample points. The study recommended that the procedure for water management be localized to sub-catchment and basin levels, to provide adequate attention to each sub-catchment depending on the level and nature of pollution identified.

scholarly journals LAND USE ANALYSIS ON LAND SURFACE TEMPERATURE IN URBAN AREAS USING A GEOGRAPHICALLY WEIGHTED REGRESSION AND LANDSAT 8 IMAGERY, A CASE STUDY: TEHRAN, IRAN

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 117-122 ◽  
Author(s):  
A. Karimi ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Urban Areas ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Spatial Factors

Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R<sup>2</sup> value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial,military, transportation, and roads areas have the highest surface temperature.

scholarly journals Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

2021 ◽  
Vol 13 (15) ◽  
pp. 2962
Author(s):  
Jingyi Wang ◽  
Huaqiang Du ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Geographically Weighted Regression ◽  
Local Variation ◽  
Zhejiang Province ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Bamboo Forest ◽  
Prediction Ability ◽  
Gwr Model ◽  
Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

scholarly journals Multivariate Analysis for Assessing Irrigation Water Quality: A Case Study of the Bahr Mouise Canal, Eastern Nile Delta

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2537 ◽  
Author(s):  
Mohamed K. Abdel-Fattah ◽  
Sameh Kotb Abd-Elmabod ◽  
Ali A. Aldosari ◽  
Ahmed S. Elrys ◽  
Elsayed Said Mohamed
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Multivariate Analysis ◽  
Sustainable Agriculture ◽  
Irrigation Water ◽  
Class Ii ◽  
Class I ◽  
Landsat 8 ◽  
Irrigation Water Quality

Water scarcity and suitable irrigation water management in arid regions represent tangible challenges for sustainable agriculture. The current study aimed to apply multivariate analysis and to develop a simplified water quality assessment using principal component analysis (PCA) and the agglomerative hierarchical clustering (AHC) technique to assess the water quality of the Bahr Mouise canal in El-Sharkia Governorate, Egypt. The proposed methods depended on the monitored water chemical composition (e.g., pH, water electrical conductivity (ECiw), Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, and SO42−) during 2019. Based on the supervised classification of satellite images (Landsat 8 Operational Land Imager (OLI)), the distinguished land use/land cover types around the Bahr Mouise canal were agriculture, urban, and water bodies, while the dominating land use was agriculture. The water quality of the Bahr Mouise canal was classified into two classes based on the application of the irrigation water quality index (IWQI), while the water quality was classified into three classes using the PCA and AHC methods. Temporal variations in water quality were investigated, where the water qualities in winter, autumn, and spring (January, February, March, April, November, and December) were classified as class I (no restrictions) based on IWQI application, and the water salinity, sodicity, and/or alkalinity did not represent limiting factors for irrigation water quality. On the other hand, in the summer season (May, June, July, August, and October), the irrigation water was classified as class II (low restrictions); therefore, irrigation processes during summer may lead to an increase in the alkalinity hazard. The PCA classifications were compared with the IWQI results; the PCA classifications had similar assessment results during the year, except in September, while the water quality was assigned to class II using the PCA method and class I by applying the IWQI. Furthermore, the normalized difference vegetation index (NDVI) around the Bahr Mouise canal over eight months and climatic data assisted in explaining the fluctuations in water quality during 2019 as a result of changing the crop season and agriculture management. Assessments of water quality help to conserve soil, reduce degradation risk, and support decision makers in order to obtain sustainable agriculture, especially under water irrigation scarcity and the limited agricultural land in such an arid region.

scholarly journals A Study on the Relationship between Land Use Change and Water Quality of the Mitidja Watershed in Algeria Based on GIS and RS

2020 ◽  
Vol 12 (9) ◽  
pp. 3510 ◽  
Author(s):  
Dechao Chen ◽  
Acef Elhadj ◽  
Hualian Xu ◽  
Xinliang Xu ◽  
Zhi Qiao
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
River Basin ◽  
Agricultural Land ◽  
Vegetation Coverage ◽  
Water Quality Control ◽  
Urban Settlement ◽  
The Relationship

Many catchments in northern Algeria, including the coastal Mitidja Basin in the north central part of the country have been negatively affected by the deterioration of water quality in recent years. This study aims to discover the relationship between land use change and its impact on water quality in the coastal Mitidja river basin. Based on the data of land use and water quality in 2000, 2010 and 2017, the relationship between land use change and surface water quality index in the Mitidja Watershed was discussed through GIS and statistical analysis. The results show that the physical and chemical properties of the Mitidja river basin have obvious spatial heterogeneity. The water quality of upstream was better than that of downstream. There was a significant spatial relationship between the eight water quality indicators and three land use types, including urban residential land, agricultural land and vegetation. In most cases, settlements and agricultural land are the dominant factors leading to river pollution, and higher vegetation coverage helps to improve water quality. The regression model revealed that percentage of urban settlement area was a predictor for NH4-N, BOD5, COD, SS, PO4-P, DO and pH, while vegetation was a predictor for NO3-N. The analysis also showed that during this period, urban settlement areas increased sharply, which has a significant impact on water quality variables. Agricultural land only had a significant positive correlation with PO4-P. The results provide an effective way to evaluate river water quality, control water pollution and land use management by landscape pattern.

scholarly journals Evaluating the relationship between temporal changes in land use and resulting water quality

2018 ◽  
Vol 234 ◽  
pp. 480-486 ◽  
Author(s):  
Buddhi Wijesiri ◽  
Kaveh Deilami ◽  
Ashantha Goonetilleke
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Temporal Changes ◽  
The Relationship
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