&lt;title&gt;Development of a GIS simulation model and the use of remote sensing for monitoring and prediction of soil salinity and waterlogging in the Nile delta (Egypt)&lt;/title&gt;

Goossens, Rudi; Dapper, Morgan De; Ghabour, T. K.; Badawi, Mohamed El; Gad, A.

doi:10.1117/12.154682

Cited by 5 publications

(6 citation statements)

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“…Furthermore, AlaviPanah and Goossens [84] discovered that adding the thermal band to the best Landsat TM visible-NIR band combination has excellent promise for distinguishing saline soil from gypsiferous soil in an Iranian case study. This research corroborated the findings of Goossens et al [85] who found that the TM thermal band plays a critical role in distinguishing gypsiferous from salty soils. Furthermore, Furby et al [86] claimed that multispectral satellite sensors confounded reflectance because nonsaline soils were mistaken with bare, excessively saline places.…”

Section: Resultssupporting

confidence: 91%

Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery

et al. 2022

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Salinity systems are well known as extreme environmental systems that occur either naturally or by certain human activities, in arid and semiarid regions, which may harm crop production. Soil salinity identification is essential for soil management and reclamation projects. Information derived from space data acquisition systems (e.g., Landsat, ASTER) is considered as one of the most rapid techniques in mapping Salt-Affected Soil (SAfSoil). The current study tested the previously proposed salinity indices on the northern Nile Delta region, Egypt. The results indicated that most of the indices were not suitable to detect the SAfSoil in the area, due to the interaction between the bare soils, salts, and urbanization. To resolve this issue, the current work suggested a new index for detecting and monitoring the SAfSoil in the Nile Delta region. The newly proposed index takes into consideration plant health, the salt crust at the surface of the soils, as well as urbanization. It facilitates the mapping processes of SAfSoil in the area compared to any other previously proposed index. In this respect, multi-temporal Landsat-7 and 8 satellite data, acquired in 2002, 2016, and 2021, were used. The new index was prepared using the 2002 data and verified using the 2016 and 2021 data. Field measurements and data collected during 2002, 2016, and 2021 were utilized as ground truth data to assess the accuracy of the results obtained from the proposed index. The evaluation of the results indicated that the accuracy assessment for 2002, 2016, and 2021 images was 94.58, 96.08, and 95.68%, respectively. Finally, the effectiveness of using remote sensing in detecting and mapping SAfSoil is outlined.

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Section: Resultssupporting

confidence: 91%

Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery

et al. 2022

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“…Therefore, there is a difference between geomorphology and physiography. Physiography is based on geomorphology and can provide a good basis for explaining geomorphology by interpreting aerospace images using physiographic analysis, which identifies a certain physiographic process, which in turn, provides an important clue for delineating soil patterns after predicting some certain soil properties (Goosen 1967).…”

Section: Physiographic Units Of the Study Areamentioning

confidence: 99%

“…Therefore, there is a difference between geomorphology and physiography. Physiography is based on geomorphology and can provide a good basis for explaining geomorphology through the aerospace images interpretation, using physiographic analysis, that identifies a certain physiogaphic process, which in turn, provides an important element clue for delineating soil patterns after predicting some certain soil properties (Goosen 1967). In the study area, identification in terms of physiograpic units was generally based on specifying some geomorphic units, which reflect most of the landscape forms and their genesis.…”

Section: Physiographic Units Of the Study Areamentioning

confidence: 99%

“…The approach to the problem of delineating saline soils using remote sensing data and geographical information system (GIS) techniques has been proved efficient in many recent studies (Dwivedi 1992;Dwivedi and Sreenivas 1998;Iqbal and Mehdi 2008;Khan et al 2005;Rao et al 1991;Sharma and Bhargawa 1988). Buces et al (2005) described the method of soil salinity mapping using a combined spectral response index for bare soil and vegetation The combination of remote sensing with GIS is very promising, especially for the monitoring of soil salinization (Goossens et al 1993). An approach using remote sensing indicators, different salinity indices (SI), vegetation indices and supervised classification, which are based on spectral characteristics of different kind of surfaces, has been applied for this study as digital image processing (DIP) and GIS techniques (Goossens et al 1993;Naseri 1998;Steven et al 1992).…”

Section: Soil Map Of the Study Areamentioning

confidence: 99%

“…Buces et al (2005) described the method of soil salinity mapping using a combined spectral response index for bare soil and vegetation The combination of remote sensing with GIS is very promising, especially for the monitoring of soil salinization (Goossens et al 1993). An approach using remote sensing indicators, different salinity indices (SI), vegetation indices and supervised classification, which are based on spectral characteristics of different kind of surfaces, has been applied for this study as digital image processing (DIP) and GIS techniques (Goossens et al 1993;Naseri 1998;Steven et al 1992). However, the work has been accomplished with the use of the functionality of Arc GIS and IDRISI for windows (Eastman 1995;Khan and Sato 2001;Metternicht and Zinck 2003;Khan et al 2005) through its special modules and mathematical operator functions for remote sensing data processing and analysis.…”

Section: Soil Map Of the Study Areamentioning

confidence: 99%

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An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications

AbdelRahman

2023

Rend. Fis. Acc. Lincei

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Land degradation (LD) poses a major threat to food security, livelihoods sustainability, ecosystem services and biodiversity conservation. The total area of arable land in the world is estimated at 7616 million acres or only 24% of the total area of the land surface, and currently about half of this area is cultivated. The productivity of arable land depends mainly on soil formation and management. Soil contains mineral components, organic matter and microorganism in a natural dynamic balance whose components have been formed in thousands and millions of years (geologic ages). Disruption of this balance due to human pressures and poor land use leads to soil degradation in few years. Scientists differed in setting a precise definition of the concept and dimensions of land degradation, but many agreed on the general concept of land degradation: ‘quantitative or qualitative change in the properties of soil that reduces the land's potential for production’ and the more comprehensive definition that land degradation is Change in the characteristics of the Earth. Leading to the creation of more desert or drier conditions, due to the decline or deterioration of the load of biological energy for a period of time does not have to be continuous, but temporary. Additionally, organic farming might be a substitute to prevent the deterioration of our soil. The emphasis of the current review is on how land degradation affects agriculture and the environment, as well as how we can manage our lands to prevent further degradation. Therefore, this review will focus in explain the land degradation definition and concept, and presenting two case studies from Egypt and India.

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Modeling and Prediction of Groundwater Level Fluctuations Using Geoinformatics and Artificial Neural Networks in Al Ain City, UAE

Elhaj

Issa

Alshamsi

et al. 2023

Water Science and Technology Library

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<title>Development of a GIS simulation model and the use of remote sensing for monitoring and prediction of soil salinity and waterlogging in the Nile delta (Egypt)</title>

Cited by 5 publications

References 0 publications

Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery

Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery

An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications

Modeling and Prediction of Groundwater Level Fluctuations Using Geoinformatics and Artificial Neural Networks in Al Ain City, UAE

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