Index integrating soil, vegetation, climate and management qualities to

evaluate desertification in the Northwestern coast, Egypt

A. GAD, *RANIA MANSOUR

Land use Dept., National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, EGYPT

*Corresponding author

Abstract: In Egypt, the phenomenon of desertification is a geographical phenomenon that is related to the decline

or deterioration of the land's biological production capacity, which will eventually result in semi-desert

conditions, or, in other words, the loss of fertility from productive lands. An understanding of the geographical

distribution of environmentally sensitive areas (ESAs) is necessary for sustainable land use in the dry lands. The

characteristics of the research region and the Mediterranean desertification and land use (MEDALUS) approach

were used to evaluate the environmental sensitivity to desertification on the west-north coast of Egypt. Remote

sensing images, topographic data, soils, and geological data are used to calculate desertification indicators.

A hotspot of desertification risk exists on the north coast of Egypt due to soil degradation, climatic conditions,

geomorphological and topographic features, soil quality and soil uses in each area. In each of these areas, these

variables lead to varying levels and causes of soil degradation and desertification, as well as varying

environmental, economic, and social effects. The obtained data reveal that (10.6%, 82.73%) of the west north

coast are Sensitive and Very sensitive areas to desertiﬁcation, About 1.22% of the research area is the moderately

sensitive area, while the low sensitive and very low exhibit only (4.21,1.48) %. Remote sensing and GIS are

recommended to monitor sensitivity. MEDALUS factors can be modified to obtain more reliable data at the local

level.

Keywords: Desertification, Sensitivity, Quality indicators, ESAI, North coast.

Received: January 9, 2023. Revised: October 14, 2023. Accepted: November 15, 2023. Published: December 18, 2023.

1. Introduction

Desertification poses a great threat to global

eco-environmental security and human well-being

(Yue et al,2023). Desertification is the term used to

describe land degradation that takes place in

environments that are arid, semi-arid, and sub-

humid. Desertification can be defined simply as “the

making of the desert” or “the production of desert

conditions” (Verstraete, 1986). However, the

United Nations provided the first thorough definition

of the word in 1977, which took into account the

phenomenon's economic effects. It defined

desertification as “the diminution or destruction of

biological potential of land which can lead

ultimately to desert-like conditions” (United

Nations, 1977). This definition was modified in

1994, when desertification was defined as “Land

degradation in arid, semi-arid, and dry sub-humid

areas resulting from human activities and climate

variation. encompassing human influences on

climate change that go beyond monetary damage

(United Nations, 1994). Since then, this last

definition has been formally and widely applied to

numerous studies on desertification conducted all

over the world, providing a variety of perspectives

for measuring, analysing, and modelling

desertification. (Kassas, 1995; Li et al., 2016; Cui

et al., 2011; Bakr et al., 2012; Lamchin et al.,

2016; Liu et al., 2018; Xu et al., 2016; Becerril-

Pi˜na et al., 2016; Zhao et al., 2018)The arid

regions are constantly threatened by land

degradation and desertification processes, caused by

various reasons (KERTÉSZ, 2009). One of the

biggest environmental problems of our day is

desertification, according to the United Nations

Convention to Combat Desertification (UNCCD).

(Vieira et al, 2022, Yue et al,2023). Significant

economic and environmental effects of

desertification and land degradation affect 1.4 billion

people, 74% of whom are impoverished. In addition,

12 million hectares of agricultural land are lost

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annually due to desertification and drought. (United

Nations, 2015). Due to their important roles in food

productivity and the social development of

communities, arid, semi-arid, and sub-humid regions

in particular have attracted growing political and

international attention in this issue since 1970. (Li et

al., 2016; Becerril-Pi˜na et al., 2016; Liu et al.,

2018; Zhao et al., 2018). Taking into consideration

the united nation definition definitions, the current

study is adopted as: “land degradation in arid, semi-

arid, and dry sub-humid areas resulting from human

activities and climate variation which can lead to

desert-like conditions”. Currently, the rate of

desertification on the planet is 120,000 km2 per

year, and it is predicted that by 2045, this process

will have forced the displacement of over 135

million people. (Fust, 2010). The "Great Green Wall

of Africa," which aims to end land degradation by

2030, has been suggested by countries in the Sahel.

Researchers now have a cutting-edge way to track

the desertification process thanks to remote sensing

technologies. Zeng et al. (2006) built an albedo-

NDVI feature space and using the findings of linear

fitting, calculated the desertification monitoring

index (DMI) to analyze the degree of desertification.

In comparison to using spectral data alone for level

division, this relatively easy method achieved

improved accuracy, and it has recently been used to

measure desertification across several locations.

(Vorovencii, 2017; Li et al., 2021).

A range of surface parameters can be used to build

DMIs based on linear and point-to-point models,

respectively, to acquire the best desertification

monitoring model while completely taking into

account vegetation cover and surface roughness.

(Wei et al., 2018). According to some studies, the

only variables causing desertification are changes in

the soil or vegetation. (An et al., 2013; Turan et al.,

2019), Indicators affecting the degree of

desertification in areas such as soil, climate,

vegetation, and management quality can be gathered

to create the Environmentally Sensitive Area Index

(ESAI). (Jiang et al., 2019; Uzuner and Dengiz,

2020).

In multiple testing regions around the Mediterranean

region, the environmental sensitivity area index

(ESAI) was verified under various environmental

conditions at both the local and regional levels.

(Basso et al. 2000; Brandt 2005). Due to its limited

vegetation cover, low drought resilience, steep

slopes, and highly erodible parent material, the

Mediterranean region is more vulnerable to

desertification due to low rainfall and harsh events.

(Ferrara et al. 1999). In dry locations, the

combination of extreme biophysical and

socioeconomic occurrences may result in an

irreversible environmental deterioration process.

(Montanarella, 2007). The Geographic Information

System (GIS) is a useful tool for storing, retrieving,

and manipulating the enormous quantity of data

required to calculate and map various quality

indexes related to desertification. (Gad and Lotfy

2006; Abdel Kawy and Belal 2011).

This current study aims to how remote sensing is

applied to the study of desertification. Therefore,

utilizing Mediterranean desertification and land use

(MDLUS) indicators in a region along Egypt's north

coast, this research was carried out with the intention

of offering an early warning approach to

desertification risk, focusing on risk management

rather than disaster management. The outcome of

this study could serve as a template for local land

managers to effectively allocate resources for

managing and preventing desertification. This

approach is thought to be transferable to other dry

regions with comparable anthropogenic and

environmental variables.

2.

Materials and methods

2.1 Location of the study area

According to Figure 1, the study region is

situated on Egypt's northwest coast between

latitudes 31°0'45 and 30°56'17 north and

longitudes 29°23'39 and 27°20'11 east.

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Figure 1: Location of the study area

Figure 2 shows that the study area consists of

six main geological units, they are El-Hagif Fm,

Gravel, Marmarica Fm, Sabkha deposits,

Stabilized Sand Dunes and undifferentiated

Quaternary Deposits

(CONOCO 1987).

Figure 2: Geology map of the study area CONOCO 1987).

2.2 Climate data

The northern region of Egypt is

currently characterized by an arid climate class,

which suggests that desertification might take

place, according to Pravalie (2016) and Pravalie

et al. (2019). The average annual temperature is

found to be between 20.12 and 21.12 C,

according to the climate data gathered from

three surface stations (Matroh, Alexandria, and

El-Dabaa). The temperature of the soil regime

varies from "Thermic" to "Hyperthermic," and

the soil moisture regime is "Torric," as

determined by Soil Survey Staff (2014). Wind

Speed at 2 Metres (m/s) varies between 3.48 to

4.07 m/s, while the total amount of precipitation

per year fluctuates between 330 and 430 mm.

An arid climate is indicated by the aridity

index, which ranges from 5.72 to 5.89.

(Pravalie et al., 2019)

.

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2.3 Remote sensing and GIS work

The primary data source used to build

the desertification monitoring model in the

research area was the Landsat 8 Operational

Land Imager remote sensing pictures, which are

accessible at https://glovis.usgs.gov. The

images used in this investigation have

paths/rows 178/39 and 179/38. The image

quality was excellent, as seen by the less than

10% total cloud cover. The visible, near-

infrared, and mid-infrared wavelengths, which

are made up of six bands and have a spatial

resolution of 30 m, were used in this

investigation. Geometric calibration and

atmospheric correction were carried out with

the impact of radiometric distortions and

atmospheric disturbances on image quality in

mind. Using ENVI 5.3 software, satellite

imageries were digitally processed. After that, a

supervised classification (maximum likelihood)

was carried out after an unsupervised

classification (ISO DATA classifier). Slope

classes and aspects have been generated from

the DEM using ArcGIS 10.8 (ESRI Co.,

Redlands, USA).

2.4 Modeling desertification in the

studied area

Characterization of the original

MEDALUS indices

According to Kosmas et al. (1999), the indices

are the soil quality index (SQI), the climatic

quality index (CQI), the vegetation quality

index (VQI), and the management quality index

(MQI). The geometric mean algorithm of the

parameter scores was used to calculate each

index as follows:

Index



󰇟S



𝑆



𝑆



S



󰇠

/

Where n is the number of parameters, S is the

parameter score, and x is the index.

The Land Degradation Sensitivity Index

(LDSI), which is obtained for the entire study

area and is illustrated in Figure 3, is based on

these indicators, which were chosen in order to

thoroughly characterize susceptibility to land

degradation by including characteristics of

physical (climate, soil, and vegetation) and

anthropogenic (pressures related to land

management) characteristics.

The UNCCD

defined desertification as the degradation of

land in arid, semiarid, and subhumid regions;

consequently, the ESAI would be indicating the

desertification sensitivity index obtained from

the conventional MEDALUS approach,

according to Tables 1 and 2

.

Figure 3: Flowchart of desertification sensitivity assessment (DSAI)

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Table 1

:

Main characteristics of desertification categories

Classes DSI Description Reference

1 DSI <1.2

Non aﬀected areas or

very low sensitive areas

to desertiﬁcation

Gad and Lotfy,2008

2 1.2< DSI <1.3 Low sensitive areas to

desertiﬁcation

3 1.3< DSI <1.4 Medium sensitive areas

to desertiﬁcation

4 1.3> DSI <1.6 Sensitive areas to

desertiﬁcation

5 DSI >1.6 Very sensitive areas to

desertiﬁcation

Table2: Measurable parameters used in developing desertiﬁcation sensitivity indices (DSAI

)

2.4.1 Climate quality index

Four factors, including two from the

MEDALUS framework (aspect and aridity) and

two that were added in accordance with

regional specifics (rainfall and wind speed),

were used to produce this crucial indicator

(Table 3).

Due to its significance in highlighting

the availability of water for biological activity,

rainfall is regarded as a variable of significant

importance for determining the CQI (Table 4)

(Kosmas et al., 1999).

According to Salvati et al. (2013), the main

contributing factor to land degradation is climatic

aridity.

The formula "AI = P/PET" is used to

calculate the Aridity Index (AI), where P is the

amount of precipitation and PET is the amount of

potential evapotranspiration taken from the global

database created by Trabucco and Zomer (2009).

Due to its impact on the level of humidity

that affects vegetation, slope aspect is regarded

as acrucial factor. Understanding the

relationship between slope and sunlight and

evapotranspiration allows for the realization of

this impact. (Kosmas and others, 1999).

Three meteorological stations under the

National Meteorology Agency's authority

provided data on the wind speed (Table 3).Due

to the possibility for the wind deflation process

to occur in the region—especially given that it

contains the largest sandy soil areas in the

Ind.

Parameter

Data source

CQI

Rainfall

Prăvălie et al ,2017

Aridity

Wind speed

aspect

SQI

Texture

Gad and Lotfy,2008

Parental material

Slope

Soil depth

VQI

Plant cover

Mohamed,2012

drought

erosion

MQI

Land use

Mohamed,2012

grazing

policy

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nation—wind speed is a crucial component for

the process under analysis. By correlating

greater intensity values with higher wind

speeds, wind erosion was thus indirectly

approximated using this methodology (Table

4).

Based on the geometric mean of the sub-

indicators' score values, the CQI indicator was

calculated using the following formula:

𝐶𝑄𝐼  󰇛Aridity Index  aspect  Rainfall

 Wind speed󰇜

/

2.4.2 Soil quality index

In the arid, semi-arid, and dry zones,

soil is the most important component of

terrestrial ecosystems, notably due to its impact

on biomass production. Water availability and

erosion resistance can be related to soil quality

parameters for mapping ESAs (Briggs et al.,

1992; Basso et al., 1998). In the current

analysis, four soil parameters—namely, parent

material, soil texture, soil depth, and slope

gradient—were taken into account. On the

basis of (OSS, 2004), which was derived from

the Medalus project methodology (European

Commission, 1999), weighting factors were

applied to each category of the attributes that

were taken into consideration. The allocated

indexes for the various categories of each

parameter are shown in Table 3-B. The

following algorithm was used to calculate the

soil quality index (SQI), and categories are

provided in Table 4.

𝑆𝑄𝐼  󰇛𝐼





𝐼





I





I



󰇜

/

Where: The indices of the parent material (I

p

),

the soil texture (I

t

), the soil depth (I

d

), and the

slope gradient (I

s

)

2.4.3 Vegetation quality index (VQI)

The allocated indexes for the various

categories of each parameter are shown in

Table 3-C. The following equation was used to

calculate the Vegetation Quality Index (VQI),

which was then divided into groups according

to Table 4.

VQI  󰇛I





𝐼



𝐼



󰇜

/

Where; the indices of erosion protection (I

ep

),

drought resistance (I

dr

), and vegetation cover

(NDVI) (I

nd

).

2.4.4 Management quality index

The Management Quality Index, which

can be analysed from a two-point perspective (i.e.,

anthropogenic activity intensity (in crop-lands,

pastures, natural, mining, and recreation areas),

and agricultural policies aiming to improve

restrictive environmental conditions), is one

method of evaluating anthropogenic stress on the

environment (Kosmas et al., 1999).

The following equation was used to

construct the management quality index:

𝑀𝑄𝐼  󰇛𝐼



I



I



󰇜

/

In this case, I

l

stands for "land use," I

g

for

"grazing intensity," and I

p

for "policy."

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Table 3: Characteristics of the parameters (classes and corresponding weights) used for obtaining

the (CQI), SQI, VQI and MQI

Indicator

Measurable Parameter Classes

Description

Score

A

(CQI)

Rainfall (mm)

1

650

–

2

280–650

1.5

3

280

–

Aridity Index

(mm/mm)

1

Humid ( 0.65)

1

2

Dry sub-humid (0.5–0.65)

1.5

3

Semi-arid ( 0.5)

–

Aspect

1

N, NE, NW, V, ﬂat areas

1

2

S, SE, SW, E

2

Wind speed

(m/s)

1

4.2

1

2

4.2–5.2

1.5

3

5.2

2

B

(SQI)

Texture

1

Loamy sand, Sandy loam,

Balanced

1

2

Loamy clay, Clayey sand, Sandy

clay

1.33

3

Clay, Clay loam

1.66

4

Sandy to very Sandy

2

Parental material

1

Coherent: Limestone, dolomite,

non-friable sandstone, hard

limestone layer

1

2

conglomerates, unconsolidated

Moderately coherent: Marine

limestone, friable sandstone

1.5

3

Soft to friable: Calcareous clay,

clay, sandy formation, alluvium

and colluvium

2

Slope

1

Gentle,

1

2

Not very gentle

1.33

3

Abrupt

1.66

4

Very abrupt

2

Depth

1

Soil thickness is more than 1 m

1

2

Soil thickness ranges from

1.33

3

Soil thickness ranges from

1.66

4

Soil thickness 0.15 m

2

C

VQI

Plant cover

1

NDVI >0.95

1

2

NDVI 0.65-0.95

1.2

3

NDVI 0.35-0.65

1.5

4

NDVI <0.35

2

Drought

1

Gardens, orchards, rangelands

1

2

Permanent grassland, annual

crops and grasslands

1.5

3

Bare land

2

Erosion

1

High

1

2

Moderate

1.33

3

Low

1.66

4

Very Low

2

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D

(MQI)

Land use

1

Agricultural lands

1

2

Rangelands

1.3

3

Poor and degraded

1.66

4

Bare lands

2

Grazing

1

Low

1

2

Moderate

1.5

3

High

2

Policy

1

Complete: >75 % of the area

under protection

1

2

Partial: 25–75 % of the area

under protection

1.5

3

Incomplete: <25 % of the area

under protection

2

Table 4: Classification of CQI, SQI, VQI and MQI

Parameter Class Description Range

CQI

1 High quality <1.15

2 Moderate quality 1.15 to 1.81

3 Low quality >1.81

SQI

1 High quality <1.13

2 Moderate quality 1.13 to 1.45

3 Low quality >1.64

VQI

1 High quality < 1.2

2 Moderate quality 1.2 -1.4

3 Low quality 1.4-1.6

4 Very Low quality >1.6

MQI

1 High quality 1-1.25

2 Moderate quality 1.26-1.5

3 Low quality >1.5

3. Result and discussion

3.1 Climate Quality Index (CQI)

The investigated area is an arid region where it

receives very little annual precipitation. The major

meteorological characteristics that lead to

desertification processes are the rainfall, wind speed

aspect in addition to the aridity. The result illustrate

100 % of the total area characterized by moderate

climatic index where it fills within score range 1.15

and 1.81. It occupies all the area which extens

4584.78 Km2 , as shown in Table 5 and Figure 4.

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Figure 4: Climate Quality Index Map

3.2 Soil Quality Index (SQI)

To determine the type of parent material, the

geologic map was utilized. (Figure 5(A)).this figure

shows the study area constitutes only two classes

(i.e. Moderate coherent and friable).The friable class

is located along the coast covering an area of 1.13–

1.45a km, % . as deduced from the GIS system.

The topography is represented by slope gradient

(Figure. 5(B)) which iscategorized on the basis of

the Digital Elevation Model (DEM). The result

shows that most of area is assigned by strongly

sloping and ranged from 0 to 20 %. This means that

the slope in the study area falls into 6 categories

according to the FAO classification which are

described to flat to very gently sloping, gently

sloping, sloping strongly sloping and moderately

sloping. The soil texture was assessed on basis of the

texture analyses, figure 5 (C) illustrates that soil

texture of the study area is ranged between coarse

and very like to average. Depth of the study area

varies between shallow, moderate and very deep

soils as illustrated by figure 5 (D).

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Figure 5: Spatial representation of the selected SQI measurable parameters

Water availability and erosion resistance are two

signs of soil quality that can be used to

mapEnvironmental Sensitivity Areas (ESAs).

Simple soil characteristics including soil texture,

parent material, soil depth, and slope gradient can be

used to analyse these qualities. The soil quality

index (Table 5 and Figure 6) shows that 11.74%

(538.43 km

2

) of the examined area, located in the

northern half, is distinguished by high soil quality.

About 3616.59 Km

2

or 78.88% of the total area is

covered by the moderate soil quality index. About

9.37% (429.76 Km

2

) of the entire area is occupied

by the low soil quality index. The depth, the daring

condition, the parent material, and the slope are the

main limiting criteria of soil quality in the northern

section of the research area.

A

B

C

D

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Figure 6: Soil Quality Index Map

3.3 Vegetation quality index

A reliable indicator for determining the North

West Coast's classes of desertification susceptibility

was discovered to be the Vegetation Quality Index

value. The current study takes into consideration soil

erosion control, drought resistance, and plant cover

type as VQI indicators. The NDVI values, which

indicate vegetation cover, were calculated using

remotely sensed Landsat photos. Adapted NDVI

ratings ranged from 1 to 2, depending on how

intense the vegetation index was. The data obtained

showed that the study region, which made up around

1.72 and 98.27% of the overall study area (4505.29

Km2, 79.01 K m2, respectively), is typified by poor

to extremely low vegetation quality index.

According to Table 5 and Figure 7, the moderate

vegetation quality is predominantly found in the

eastern section and covers 0.48 Km2, or 0.01% of the

research area.

Figure 7: Vegetation Index Map

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3.4 Management quality index

In the present study, grazing intensity, policy,

and land use factors that were undoubtedly

significant in managing the process of desertification

were used to suggest the management quality index.

The findings show that the research area falls into

the moderate and low management quality index

categories. It has been determined that (4269.83km2,

or 93.13% of the study area) falls into the low MQI

category. (314.95 Km2, which makes up 6.87% of

the total area), has a mediocre quality index as

illustrated by Figure 8 and Table 5. It should be

noted that the study area suffers from poor land

resource management.

Figure 8: Management Index Map

Table 5: Areas of quality indices classes of Climate, Soil, Vegetation and Management

Indicator Class Quality

description Score range Total area

Km2 %

CQI

1 High 1.15 0.00 0.00

2 Moderate 1.15–1.81 4584.78 100.00

3 Low 1.81 0.00 0.00

SQI

1 High 1.13 538.43 11.74

2 Moderate 1.13–1.45 3616.59 78.88

3 Low 1.45 429.76 9.37

VQI

1 High < 1.2 0.00 0.00

2 Moderate 1.2 -1.4 0.48 0.01

3 Low 1.4-1.6 79.01 1.72

4 Very low >1.6 4505.29 98.27

MQI

1 High quality 1-1.25 0.00 0.00

2 Moderate quality 1.26-1.5 314.95 6.87

3 Low quality >1.5 4269.83 93.13

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3.5 Desertification sensitive index

To calculate the for desertification ESI, it was

necessary to take into account the integration of the

climate, soil characteristics, vegetation cover, and

management rates. Indicating varying levels of

sensitivity to desertification, the values obtained are

distributed spatially (Figure 9). The results of this

study, which are presented in Table 6, showed that

the bulk of the research region, which is located in

the south and west, is vulnerable to the processes of

desertification, which are typified by severe to

extremely severe ESI. These classes represent,

respectively, 10.36% and 82.73% (474.91 km2 and

3793.22 km2) of the total area. On the other hand,

due to the vegetation cover and land usage, the

eastern half of the region is very low to low

susceptible to desertification. They occupied 67.84

Km2 and 192.86 Km2, or 1.48% and 4.21%,

respectively, of the total area. Due to the moderately

poor soil quality and management, only 1.22% of

the land (55.96 km2) is considered to be Medium

Sensitive to Desertification.

Table 6: The area, expressed in absolute and percentage (% of the total study area without sea)

values, which corresponds to (DSI), north coast, Egypt

Indicator Class Quality description Score range Total area (km2) Total area

(%)

DSI

1 very low sensitive areas to

desertiﬁcation <1.2 67.84 1.48

2 Low sensitive areas to

desertiﬁcation 1.2<DSI<1.3 192.86 4.21

3 Medium sensitive areas to

desertiﬁcation 1.3<DSI<1.4 55.96 1.22

4 Sensitive areas to desertiﬁcation 1.4<DSI<1.6 474.91 10.36

5 Very sensitive areas to

desertiﬁcation >1.6 3793.22 82.73

Figure9: Environmentally sensitive areas (ESA’s) for

north coast, Egypt

International Journal of Environmental Engineering and Development

DOI: 10.37394/232033.2023.1.23

A. Gad, Rania Mansour

E-ISSN: 2945-1159

262

Volume 1, 2023

4. Conclusions

Desertification is an important geographical

phenomena affecting arid regions. The case study

discussed in the current article is situated on Egypt's

northwestern coast. It is undeniable that identifying

the spatial patterns of desertification-prone

environmentally sensitive arid regions (ESAs) is a

crucial first step in managing sustainable land use.

The current paper deals with a case study related to a

methodology intended to map and appraise

environmentally sensitive areas at risk of

desertification. Environmentally sensitive arid areas

(ESAs) were calculated using the MEDALUS model

in conjunction with field and laboratory

investigations. The results indicate that Very

Sensitive Areas to Desertification make up 82.73%

of the examined area, and Sensitive Areas to

Desertification make up 10.36%. Thus, neither the

Medium nor the Low vulnerable zones to

desertification exceed 1.22–5.69%, respectively.

This makes it possible for the land user to recognize

and postpone sensitive risk states, supporting the

activities required to counter risky desertification

processes. The strategy adopted in the current paper

is transferable to other Nile basin agricultural

regions, the intervening zone of the desert, and

prospective oases along the coast. The authors

unequivocally advise improving soil and vegetation

cover by introducing less water-demanding species,

as well as establishing strong stakeholder

involvement in order to control wind erosion to

mitigate desertification in the study area, particularly

in the southern part.

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International Journal of Environmental Engineering and Development

DOI: 10.37394/232033.2023.1.23

A. Gad, Rania Mansour

E-ISSN: 2945-1159

265

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