The Prediction and Assessment of the Impacts of Soil Sealing on Agricultural Land in the North Nile Delta (Egypt) Using Satellite Data and GIS Modeling

Hendawy, Ehab; Belal, Abdel-Aziz; Mohamed, Elsayed Said; Elfadaly, Abdelaziz; Murgante, Beniamino; Aldosari, Ali; Lasaponara, Rosa

doi:10.3390/su11174662

Cited by 35 publications

(31 citation statements)

References 50 publications

(23 reference statements)

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“…4). These results correspond well to the findings of (Wahren et al 2009, Abu-Hashim 2011Elkhrachy 2015;Hendawy et al 2019) who reported that land use has a positive impact on soil infiltration. Moreover, the variation in infiltration capacity is largely related to land management and human activity as well as soil characteristics.…”

Section: Discussionsupporting

confidence: 91%

“…The results show that land use has significant impacts on infiltration rate and soils holding capacity as these results associated with human activities in two directions positive and negative impact. The positive impact was noted in the area demonstrated by good agricultual practice and management, meanwhile the negative impacts were noted in the area characterized by urban sprawl (Hendawy et al 2019;Abu-Hashim 2011). That the paved surfaces and rooftops did not allow water to penetrate in soil causing direct surface runoff, and these approaches showed with the illustrated results that the highest value of the direct surface runoff was distinguished around urban areas where it ranged between 40-50 and 50-58 mm.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

2019

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Background: Evaluation of surface runoff is an essential factor in the precision water and soil conservation management through their main extreme impacts on soil properties. The natural resource conservation service curve number model (NRCS-CN) model is used to estimate the magnitude of runoff. Collected topographic data is used to explain the effects of slope variation on water retention and surface runoff. Twenty-eight soil profiles are prepared in Nile delta, Egypt to cover different geomorphic units and hydrological soil groups in the study area. Results: The results revealed that the highest value of surface runoff was distinguished close to the urban area and ranges between 40 and 50 mm. In urban areas, the surfaces are paved and there are no infiltration of water. Consequently, the runoff water directly flows to the storm channels. Runoff values ranging between 30 and 40 mm occurred at the north of the study area. The sloping surface and the nature of the clay soil contributed to generate more runoff than do lowland areas. Conclusion: The study presented and tested the hydric runoff estimation based-model on the integrating of hydric balance parameters. The GIS tools analyze and compose these parameters to perform an indirect method for the quantity of water that results in direct surface runoff flow. This method helps to gain clear imaging of the surface runoff risks in the study area.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

2019

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“…Soil function is generally threatened by increased food demands, human influence, and its activities (such as the continuous irrigation with low quality water), as well as land use and climate change [97,98]. This may lead to physical and chemical degradation processes and negatively affect soil sustainability [99,100]. Monitoring land use/land cover changes is fundamental for sustainable irrigation water management plans.…”

Section: Discussionmentioning

confidence: 99%

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

Abdel-Fattah

Abd-Elmabod

Aldosari

et al. 2020

Water

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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.

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“…Therefore, integrated GIS and geostatistical analyses can be useful for assessing the spatial variation of soil properties and predicting them in unsampled locations. For example, the use of variogram analyses can capture and accurately map the complex spatial relationships between soil data layers [21][22][23]. Kriging is one of the most commonly used interpolation methods [24], and it can suitably support precise farming based on the identification of homogeneous sub-sets of similar yield-limiting factors [25,26].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

et al. 2021

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Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

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The Prediction and Assessment of the Impacts of Soil Sealing on Agricultural Land in the North Nile Delta (Egypt) Using Satellite Data and GIS Modeling

Cited by 35 publications

References 50 publications

Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

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

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

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