A New Soil Degradation Method Analysis by Sentinel 2 Images

Combining Spectral Indices and Statistics Analysis: Application to

the Cameroonians Shores of Lake Chad and Its Hinterland

Sébastien Gadal

1,4

, Paul Gérard Gbetkom

1,2

and Alfred Homère Ngandam Mfondoum

Aix-Marseille Univ., CNRS, ESPACE UMR 7300, Univ. Nice Sophia Antipolis, Avignon Univ.,

13545 Aix-en-Provence, France

Laboratory of Botanic, Mycology, and Environment, University Mohammed V Rabat,

1014, 4 avenue ibn Battuta, Rabat, Morocco

StatsN’Maps, Private Consulting Firm, 19002, Dallas, Parkway, Suite 1536, Dallas, Texas 72587, U.S.A.

North-Eastern Federal University, 670000 Yakutsk, Republic of Sakha, Russian Federation

stats.n.maps.expertise@gmail.com, ngandamh@yahoo.com

Keywords: Vegetation Indices, Soil Indices, Statistics Analysis, Lake Chad, Sentinel 2.

Abstract: This paper aims to model the soil degradation risk along the Cameroonian shores of Lake Chad. The

processing is based on a statistical analysis of spectral indices of sentinel 2A satellite images. A total of four

vegetation indices such as the Greenness Index and Disease water stress index and nine soil indices such as

moisture, brightness, or organic matter content are computed and combined to characterize vegetation cover

and bare soil state, respectively. All these indices are aggregated to produce one image (independent variable)

and then regressed by individual indices (dependent variable) to retrieve correlation and determination

coefficients. Principal Component Analysis and factorial analysis are applied to all spectral indices to

summarize information, obtain factorial coordinates, and detect positive/negative correlation. The first factor

contains soil information, whereas the second factor focuses on vegetation information. The final equation of

the model is obtained by weighting each index with both its coefficient of determination and factorials

coordinates. This result generated figures cartography of five classes of soils potentially exposed to the risk

of soil degradation. Five levels of exposition risk are obtained from the "Lower" level to the "Higher": the

"Lower" and "Moderate to low" levels occupy respectively 25,214.35 hectares and 130,717.19 hectares; the

"Moderate" level spreads 137,404.34 hectares; the "High to moderate" and "Higher" levels correspond

respectively to 152,371.91 hectares and 29,175.73 hectares.

1 INTRODUCTION

The state of soil is an important parameter in the

monitoring of the land dynamic and exploitation, for

sustainable use (Jazouli et al. 2019; Chen et al. 2019).

Its degradation, which reduces the exploitation of

natural resources in general and restricts the

productivity of agricultural soils, causes significant

socio-economic impacts. In the far-northern part of

Cameroon, the shores of Lake Chad, are in the most

exposed zone to the soil degradation risks due to

environmental conditions, more severe climatic

conditions, and modes of uses and exploitation of

natural resources (National Action Plan to Combat

Desertification (PAN/LCD) 2006). It is an area

marked by degradation and decline of soil fertility,

unsuitable cultivation practices, a high extension of

barren land, erosion, runoff, and decrease of fallows,

overgrazing, and pesticide pollution (Elias

Symeonakis and Drake 2010; GIZ, 2015).

The great spatial and temporal variability of the

rainfall combined with the rain aggressiveness

constitutes major risks related to the rainfall and

accelerates the soil degradation process in this zone

(PAN/LCD, 2006). Rainfalls as violent localized

showers, and strike bared soils, prepared for sowing

and lowly protected or cleaned from their vegetation

(Seignobos and Iyébi-Mandjek 2000). This is figured

out by the presence of vast expanses of bare soils,

most of which are very sensitive to water and wind

erosion, accentuated by the dwindling vegetation

cover. Slopes are low in this environment, and the

level of soil drainage is very varied. It is moreover

based on the level of draining that, (Seignobos and

Gadal, S., Gbetkom, P. and Mfondoum, A.

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the Cameroonians Shores of Lake Chad and Its Hinterland.

DOI: 10.5220/0010521200250036

In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021), pages 25-36

ISBN: 978-989-758-503-6

Iyébi-Mandjek 2000) distinguished the well-drained

lands (terroir of Makari), the poorly drained, lands

with waterlogging (terroir of Bodo-Kouda) and the

poorly drained lands with waterlogging and fluvial

(terroir of Lake Chad). It is a periodically flooded

area, where the main activities are fishing, livestock,

agriculture, and trade, shared by a large and varied

population coming from at least four neighboring

countries (Cameroon, Chad, Niger, and Nigeria), with

consequently numerous conflicts.

Several methods are used to quantify and map soil

degradation at different spatial and temporal scales.

Universal Soil Loss Equation (Wischmeier and Smith

1978) or its modified version (Renard et al. 1997) are

used to predict soil erosion. This model depends on

the slope, the rainfall, the soil typology, topography,

the crop rotation, and the soil conservation practice.

Further, (Ali and Saîdati 2003) have used

sedimentology and magnetic measurements to

identify sediment source areas, assess spatial

variations in sediment levels, and classify these zones

depending on their degree of spatial reworking.

Another method was proposed by Daniel et al, 2018

to map the soil degradation, by collecting field

samples and performing an unsupervised Iterative

Self-Organizing Data Analysis Technique

(ISODATA) classification on the combination of

sentinel-2 data image and airborne orthoimages. The

United Nations Convention to Combat

Desertification quantifies soil organic carbon and

extract indicators as soil productivity and land cover

using MODIS NDVI data, to map the proportion of

land degraded over the world (support by

Conservation International, Lund Université, NASA,

and Global Environment Facility).

All the above-described methods include several

ancillary data and field samples of the study area and

need to consider the topography of the field. But the

ancillary data are not available in the total to the

extent of our study area and the distinct types of soil

topology and topography are not easy to distinguish

due to the spatial resolution of the image used. So, we

need to develop a new remote sensing approach only

based, on the soil and the vegetation spectral indices

which can allow identifying areas exposed to the risk

of soil degradation.

Indeed, remote sensing enables collecting and

integrating data for a continuous and repeated

observation of the phenomenon on large surfaces

(Begni et al. 2005). The reflectance of some objects

such as soil and vegetation is a good indicator of

changes in the environment (Gbetkom et al. 2018) and

can be used to calculate spectral indices useful for the

study of soil degradation. Previous models have been

developed on the topic. It is the case of Ngandam et al.

(2016) who use the linear and the multiple regressions,

and the principal component analyses to assess the

status of soil degradation in Far-North Cameroon.

Following this last work, the statistical methods will be

supplemented in this work by other statistical

treatments such as factor analysis, to highlight the level

of correlation between the selected indices. Thus, the

indices such as the Normalized difference vegetation

index (NDVI), Modified Soil Adjusted Vegetation

Index (MSAVI2), Normalized Difference Greenness

Index (NDGI), Disease water stress index (DSWI) are

used in this study to quantify vegetation cover and

provide information respectively on chlorophyll

activity, the density of vegetation cover, vegetation

greenery and plant water stress. On the other hand, soil

characteristics are highlighted through spectral indices.

Those used for that purpose in this study are moisture

stress index (MSI); texture index (TI); colour index

(IC); brightness index (BI); cuirass index (CI); topsoil

surface particles index (GSI); crusting index (CI);

redness index (RI); and salinity index (NDSI).

The mapping of soil degradation from indices is

sometimes limited to a simple combination of the index

in the form of a band-colored composition (Soufiane

Maimouni and Bannari 2011) or to an approach that

associates spectral indices with different classification

methods (Chikhaoui et al. 2007). On the other hand,

(Ngandam et al. 2016), cross indices and model soil

degradation by weighting indexes and neo-bands using

the coefficient of determination resulting from the

linear regression between each index and the weighted

sum image. In their approach, (Pandey et al. 2013)

cross-spectral indices to land cover maps but, index

maps are reclassified according to the level of severity

of land degradation and associated with land use and

land cover map. Therefore, this paper explores another

modeling approach to assess soil degradation.

Specifically, in three steps, it highlights soil properties

through spectral indices. After that, it proceeds to a

statistical analysis of the indices contents to withdraw

their correlation trends. Finally, the two steps above

propose an overall model to predict soil degradation

risk.

2 METHODOLOGY

2.1 The Study Area: Cameroonian Part

of Lake Chad and Hinterland

The study area is located in the Far north

administrative region of Cameroon and shares

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

borders with the Republic of Chad to the north and

east, the Federal Republic of Nigeria to the west, and

the rest of the country to the south (Figure 1). It is

located between latitude 12 °N to 13 °N and meridian

14 °E to 15 °E. It is a semi-arid region with a Sudano-

Sahelian climate, characterized by a rainy season

from June to October and a dry season that runs from

November to May. The annual rainfall totals around

400 mm, the temperature range is 7.7 °C, and the

average monthly temperature is 28 °C.

Figure 1: Localisation of the study area.

2.2 An Approach based on Sentinel 2

Images

Two Sentinel 2 satellite images acquired on April 29,

2017, were used. They have 13 bands, but only six

of them were staked, i.e., Bands 2, 3, 4, 8, 11, and 12.

2.3 Soil Risk of Degradation Model

Design

2.3.1 Indices Modeling

The Vegetation Index’s. The use of vegetation

indices has several objectives, such as the estimation

of the green vegetable mass, the forecast of harvests,

the description of the phonological state of the soil

cover, the inventory of crops by segmentation of

indices, and the evolution of vegetation cover at the

continental scale (Caloz and Collet 2011). For

example, the ability of the NDVI to detect the

presence, density, and condition of vegetation was

successfully used by Eklundh and Olsson 2003, to

observe a regreening of the Sahel between 1982 and

Figure 2: Flowchart of the methodology.

Table 1: Characteristics of the vegetation indices.

Indices Algorithm Goal References

For the chlorophyll

activity: NDVI

NDVI = (NIR – R) / (NIR + R)

(Rouse et al. 1973)

Used to evaluate the chlorophyll activity

of plants and also for the monitoring of

the state of the vegetation cover.

(Martín-Sotoca et al. 2018); (E.

Symeonakis and Drake 2004); Pang

et al. 2017; (Farooq Ahmad 2012).

For the density of

vegetation cover:

MSAVI2

MSAVI 2 =

(Qi et al. 1994)

Description of the vegetation density

and reduces the effects of soil, in

particular when the canopy is sparse

especially in arid and semi-arid

environments.

(Qi et al. 1994); (Ngandam et al.

2016); (Farooq Ahmad 2012).

For the

characterization of

the plant water

stress: DSWI

DSWI = (NIR+G) / (SWIR+R)

(Apan et al. 2003)

Used to describe the variation of the

water content of foliage.

Pu 2008; X. Li et al. 2014; Apan et

al. 2003.

For the recognition

of the vegetation

greenery: NDGI

NDGI = (G-R) / (G+R)

(Chamard et al. 1991)

Used to estimate the biomass of vegeta-

tion and measure the hydric potential of

the leaves at the level of the canopy

(Romero et al. 2018); (Gao et al.

2017); (H. Li et al. 2015); (Rallo et

al. 2014); (Sun, Li, and Li 2011).

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the

Cameroonians Shores of Lake Chad and Its Hinterland

2003, due to the spatial increase in vegetation cover.

The following indices were therefore used in this

study to analyze the chlorophyll activity, the density

of vegetation cover, the vegetation greenery, and

plant water stress.

The visual comparison of vegetation index's

efficiency to discriminate and quantify canopy

density shows a more accurate representation using

MSAVI2. Unlike the NDVI, the MSAVI2 offers a

sensitive distinction between bare soils and green

areas in less vegetated regions. Also, this index

attributes low values to aquatic spaces in contrast to

the DSWI and NDGI indices. These observations are

consistent with the results of previous works that

showed the potential of MSAVI2 to map the state of

the vegetation cover in arid environments (Ngandam

et al. 2016); (Farooq Ahmad 2012). The four indices

distinguish vegetated areas from bare soils. However,

the use of soil indices in addition to vegetation indices

is essential to characterize the bare spaces. So, nine

soil indices are computed and combined.

The Soil Indices. Escadafal and Huete 1991 use the

soil color index to distinguish surface materials from

soils according to the saturation of their color.

Chikhaoui et al. 2005 characterize the state of land

degradation in Morocco through the Land

degradation index (LDI).

The following indices were therefore used in this

study to highlight the mineralogical composition of

soils, to assess the organic matter content of soils, and

the physical state of soils in terms of moisture and

compactness. Moreover, parameters such as color,

brightness, texture, and moisture characterize the

absorption properties of the soil constituents and are

important for mapping soil conditions, particularly in

arid environments.

The indices that characterize the soils using the

reflectance curves and the spectral properties of the

soil constituents (MSI, BI, crust Index, TI, cuirass

Index, RI, color Index, GSI, NDSI). Cuirass and crust

indices show that compact soils are mostly present in

the southern part of the study area where soils are

Table 2: Characteristics of the soil index’s.

Indices Algorithm Goal References

The moisture

stress: MSI

MSI = SWIR1/NIR

(Yongnian et al, 2004)

Used to evaluate the spatial extend of less soil

moisture, due to the higher level of

evapotranspiration.

(Elhag and Bahrawi 2017) ;

(Welikhe et al. 2017).

The texture

analysis: TI

TI =

(SWIR1-

SWIR2)/(SWIR1+SWIR2)

(Madeira Netto 1991)

The texture index is calculated to evaluate the

content or percentage of sand, silt, and clay in soil

composition, and appreciate the level of the

mineral alteration of rock.

(Madeira Netto 1991); (Oliveira et

al. 2016); Houssa et al, 1996.

The soil color: CI

CI = (R-V)/ (R+V)

(Escadafal and Huete

1991)

This index is used to extract information

concerning the organic matter content and

mineralogical composition of the soil.

(Soufiane Maimouni and Bannari

2011).

The soil

brightness: BI

BI =

(Kauth and Thomas 1976)

The role of the brightness index is to identify the

reflectance of soil and to highlight the vegetal

cover of bare areas.

(Bannari et al. 1996); (Soufiane

Maimouni and Bannari 2011).

The soil Cuirass:

CI = 3*G-R-100

(Pouchin 2001)

It aims is to dissociate vegetated coverings from

mineralized surfaces.

Okaingni et al. 2010; Stéphane et al.

2016.

The Topsoil

Grain Size: GSI

GSI =(R-B)/(R+V+B)

(Xiao et al. 2006)

GSI or topsoil grain size index is an index

appropriated to characterize the texture of the soil

surface depending on the soil reflectance curve.

(Jieying Xiao, Shen, and Ryutaro

2014); (Ngandam et al. 2016).

The soil crusting:

CI= (R – B) / (R + B)

(Karnieli 1997)

Is used to detect and map from satellite imagery

different lithological morphological units. It is

also able to

reveal poor infiltration, reduced air exchange

between the soil and the atmosphere

(Karnieli 1997).

The soil redness:

RI = R²/B*G

(Mathieu et al. 1998)

Used as one of the indicators to evaluate the

mineralogy of soils, including the iron content.

(Ray et al. 2014); (Escadafal and

Huete 1991); (Mandal 2016).

The soil salinity:

NDSI

NDSI = (R-NIR) / (R+NIR)

(Khan et al. 2005)

Is used to identify soils affected by salinity, and to

show the spatial extent of salinity prevalent in our

study area.

(Azabdaftari and Sunar 2016);

(Chandana’ et al. 2004); (Asfaw, et

al, 2018); Gorji et al, 2015; Allbed et

al, 2014; (Narmada, et al, 2015).

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

completely bare. The low values of the color index

coincide with the high values of the redness index and

correspond to the densely vegetated Lake Chad

littoral spaces, which are therefore rich in organic

matter. On the other hand, spaces with a low redness

index have high values of brightness, MSI, and NDSI,

which indicates low soil moisture and a prominent

level of drought and soil salinity. Furthermore, in the

southern part of the study area, where the levels of

cuirass and crust are already high, the soil texture is

also dominated by the presence of coarse particles

considering the results of the texture indices and GSI.

2.3.2 Statistical Patterns

The model being developed also depends on the

statistical information withdrawn from the indices.

This includes linear regression, factor analysis, and

principal component analysis were calculated.

Linear Regression.

By adding all the indices

used, we obtain a new image that summarizes all

the information provided by each index. The

image obtained will serve as the independent

variable for the linear modeling between indices.

Figure 3: Correlation between synthetic image and index’s.

Table 3: Statistics relations between synthetic image and indices.

Indices Correlation Coefficient Determination Coefficient P values

Threshold Test

NDVI

-0,119 R²=0,014 P < 0,0001 Important

MSAVI2

0,081 R²=0,007 P < 0,0001 Important

DSWI

-0,747 R²=0,558 P < 0,0001 Important

NDGI

-0,769 R²=0,592 P < 0,0001 Important

MSI

0,743 R²=0,552 P < 0,0001 Important

0,953 R²=0,909 P < 0,0001 Important

Crust index

0,662 R²=0,438 P < 0,0001 Important

-0,606 R²=0,367 P < 0,0001 Important

Cuirass index

0,942 R²=0,887 P < 0,0001 Important

Redness index

-0,827 R²=0,684 P < 0,0001 Important

Colour index

0,662 R²=0,438 P < 0,0001 Important

GSI

0,685 R²=0,470 P < 0,0001 Important

NDSI

0,119 R²=0,014 P < 0,0001 Important

-0.4

0.1

0.6

2000 4000 6000

NDVI

synthetic image

y = 0,2537-7,9483E-06*x

(R²=0,014)

-0.8

-0.3

0.2

0.7

2000 7000

MSAVI2

synthetic image

y = 0,2033+8,5536E-06*x

(R²=0,007)

0.5

1.5

2.5

3.5

2000 4000 6000

DSWI

synthetic image

y = 1,3331-6,0103E-05*x

(R²=0,558)

-0.2

0.1

0.3

2500 4500 6500

NDGI

synthetic image

y = 9,0742E-02-2,1518E-

05*x

(R²=0,592)

0.5

1.5

2000 4000 6000

MSI

synthetic image

y = 0,5416+8,9365E-05*x

(R²=0,552)

500

1500

2500

3500

2000 4000 6000

brightness index

synthetic image

y = 201,7408+0,4089*x

(R²=0,909)

0.1

0.2

0.3

0.4

2500 4500 6500

Crust index

synthetic image

y = 0,1436+1,3516E-05*x

(R²=0,438)

0.1

0.2

0.3

2500 4500 6500

texture index

synthetic image

y = 0,2144-1,4896E-05*x

(R²=0,367)

500

1500

2500

2000 4000 6000

cuirass index

synthetic image

y = 284,8433+0,2923*x

(R²=0,887)

0.0000005

0.0000015

0.0000025

2500 4500 6500

redness index

synthetic image

y = 2,5755E-06-1,8635E-

10*x

(R²=0,684)

0.1

0.2

0.3

0.4

2500 4500 6500

colour index

synthetic image

y = 0,1436+1,3516E-05*x

(R²=0,438)

0.1

0.2

2500 4500 6500

GSI

synthetic image

y = 8,3203E-02+1,0307E-

05*x

(R²=0,470)

-0.8

-0.3

0.2

2000 4000 6000

NDSI

synthetic image

y = -0,2537+7,9483E-06*x

(R²=0,014)

Each correlation is defined by an

equation where:

y = dependent variable

x = independent variable

R² = coefficient of determination

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the

Cameroonians Shores of Lake Chad and Its Hinterland

Table 4: Factorial coordinates of indices.

FACTORIAL ANALYSIS PRINCIPAL COMPONENT ANALYSIS

F1 F2 F1 F2

NDVI 0,053

-0,976

NDVI 0,056

0,974

MSAVI2 -0,177

-0,960

MSAVI2 -0,172

0,961

DSWI

0,921

0,139 DSWI

0,927

-0,151

NDGI

0,964

0,159 NDGI

0,958

-0,169

MSI

-0,900

-0,075 MSI

-0,912

0,085

-0,871

-0,133 BI

-0,889

0,143

Crust Index

-0,899

-0,203 Crust Index

-0,906

0,219

TI 0,581

-0,761

TI 0,584

0,764

Cuirass Index

-0,701

0,399 Cuirass Index

-0,731

-0,428

0,641

-0,541 RI

0,664

0,574

Colour Index

-0,899

-0,203 Colour Index

-0,906

0,219

GSI

-0,922

-0,185 GSI

-0,925

0,198

NDSI -0,053

0,976

NDSI -0,056

-0,974

The purpose is to highlight the potential regressions

between the synthetic image of the indices used here

as an explanatory variable, and each of the vegetation

and soil indices used as variables to explain.

It thus appears that five indices are negatively

correlated to the synthetic image. These are the NDVI

(-0.119), the DSWI (-0.747), NDGI (-0.769), TI

(-0.606), and the redness index which has the most

negative coefficient of correlation (-0.827).

Moreover, the high values of the coefficient of

determination of all the soil indices except the NDSI

show their influence on the synthetic image (Table 3).

The other correlations are positive with values

ranging from 0.119 for the NDSI to 0.953 for the

brightness index.

The index most strongly determined by the

synthesis image is the brightness index with an R²

equal to 0.909. The other soil indices have an R² with

values that vary in the interval [0.037-0.385]. For

vegetation indices, the R² values are contained

between 0.007 and 0.592.

Descriptive Statistics. For this step, we use the

factorial analysis which is a fundamental tool of

statistical analysis of data tables that do not have a

particular structure (Baccini 2010, Palm 1993). It is

usually combined with the Principal Component

Analysis (PCA) that is an extremely powerful tool for

synthesizing information (Guerrien 2003), to reduce

dimensional space (two for example) to obtain the

most relevant summary of the initial data. The output

graphs are supported by characteristic numerical

values, useful to ease the interpretation of the results.

The graphs to be interpreted are, the geographical

representations and the tables which make it possible

to see the connections and the oppositions between

the studied characteristics, according to the factors

used for illustration.

Factors “one” and “two” condense the most

information and explain 85.62% of the common

variability of the characteristics measured for the

factor analysis and 87.54% for the PCA (Figure 4).

Moreover, for each method, factor one with more

than 54% of the information is more important than

factor two that contains a little more than 31%. For

each factor, the best-revealed indices are displayed in

bold and the opposition of the indices is measured by

the signs of the values (Table 4). For both methods,

the first factor opposes DSWI, greenery, and redness

indices, with MSI, brightness, crusting, cuirass, color,

and GSI indices. On the second factor the NDVI,

MSAVI2, and texture indices are opposed to the

NDSI index. The degrees of opposition and their

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

disposition are illustrated by the graph of correlations

between variables and factors (Figure 4).

Figure 4: Correlations between variables and factors.

The symmetrical opposition of the first factor

indices shows that in the studied area when the

greenery is high and the DSWI is also high, the

brightness and the crusting of the soils decrease, the

soils are wetter, darken, and the granulometry of

topsoil is dominated by small particles. One can

understand that the clear soils are much encrusted,

dry, formed of particles of coarse size, and

characterized by a high brightness. The correlation of

the redness and texture indices with the four

vegetation indices informs on the fact that red soils

(hydromorphic and vertisol soils with significant iron

content) and sandy soils are mostly present in the

vegetated areas and as the soils are battleships

vegetation cover decreases. Also, the opposition

between NDVI and NDSI reflects the fact that the

decline in chlorophyll activity is followed by an

increase in soil salinity. Besides, the influence of soil

salinity on plant quality and health is also observed

by the proximity of NDSI with NDGI and DSWI in

the correlation circle.

The first factor informs more about soil-related

information and opposes five soil indices (MSI, BI,

crust index, color index, GSI) to two vegetation

indices, NDGI and the DSWI. Factor “two”

concentrates vegetation information by contrasting

the other two main vegetation indices (NDVI and

MSAVI2) with the salinity index (NDSI).

Consequently, the main characteristics of the soils

derived from the correlations circles between indices

(variables) and the factors are the organic matter

content, humidity, and the physical state of the soils

for the first factor. The second factor is the state and

density of the vegetation cover.

However, the comparison of results obtained

between the linear regression and the factorial

analysis requires a few remarks. This concerns the

consistency of negative correlations between

MSAVI2 and NDSI on one hand and positive

correlations between MSAVI2 and NDVI on the

other. Both are valid for linear regression and

descriptive statistics and can then explain why the

NDSI is not close to other soil indices. We notice the

low representativeness of the cuirass, redness, and

texture indices and their low correlation with the

other indices. One can also note that in the correlation

circle of the PCA, all the variables are far from the

center than they are in the factorial’s analysis one.

The oppositions between the (variable) indices

remain the same for the PCA as for the factorial

analysis only their signs concerning the first-factor

change.

2.3.3 The Equation Proposed for the Model

The model’s equation proposed here is designed to

balance all the information obtained from the

statistical analysis performed with the indices and

based on previous work approaches. The adopted

approach is to weigh the index maps with their

coefficient of determination which serves us to

highlight the individual contribution of each index to

the final map of soil degradation. Also, we consider

for each index of its highest values of factorial

coordinates obtained through the factorial analysis

and the PCA to preserve the best information

provided by each of these methods of analysis. This

information is combined to compose the following

equation:

NDVI

MSAVI

DSWI

NDGI

MSI

CRUST

CUIRA

COLOU

GSI

NDSI

-1.0

-0.8

-0.5

-0.3

0.0

0.3

0.5

0.8

1.0

-1.0 -0.8 -0.5 -0.3 0.0 0.3 0.5 0.8 1.0

F2 (31,11 %)

F1 (54,51 %)

NDVI

MSAVI

DSWI

NDGI

MSI

CRUST

CUIRA

GSI

NDSI

-1.0

-0.8

-0.5

-0.3

0.0

0.3

0.5

0.8

1.0

-1.0 -0.8 -0.5 -0.3 0.0 0.3 0.5 0.8 1.0

F2 (31,78 %)

F1 (55,76 %)

ndvi*(xmax+Ymax)*R² + msavi2*(xmax+Ymax)*R² + dswi*(xmax+Ymax)*R² + ndgi*(xmax+Ymax)*R²+

msi*(xmax+Ymax)*R² + bi*(xmax+Ymax)*R² + crust index*(xmax+Ymax)*R² + ti*(xmax+Ymax)*R² + cuirass

index*(xmax+Ymax)*R² + ri*(xmax+Ymax)*R² + colour index*(xmax+Ymax)*R² + gsi*(xmax+Ymax)*R² +

ndsi*

(

xmax+Ymax

)

*R² = RISK OF SOILS DEGRADATION

ndvi(17,09+0,64) 0,014 + msavi2(16,81+2,06) 0,007 + dswi(2,57+10,73) 0,558 + ndgi(2,88+11,23) 0,592 +

msi(1,45+10,49) 0,552 + bi(2,44+10,23) 0,909 + crust index(3,73+10,47) 0,438 + ti(13,32+6,76) 0,367 + cuirass

index(7,30+8,41) 0,887 + ri(9,79+7,46) 0,684 + colour index(3,73+10,47) 0,438 + gsi(3,23+10,74) 0,470 +

ndsi

(

17,09+0,64

)

0,014 = RISK OF SOILS DEGRADATION

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the

Cameroonians Shores of Lake Chad and Its Hinterland

Table 5: Classification of degradation levels.

INDICES

EXPOSITIONS LEVELS

LOWER HIGHER

NDVI (high to low

chlorophyll activity)

0,501 - 0,861 0,285 - 0,501 0,070 - 0,285 -0,145 - 0,070 -0,502 - -0,145

MSAVI2 (high to low

vegetation density)

0,425 - 0,925 0,041 - 0,425 -0,342 - 0,041 -0,726 - -0,342 -2,012 - -0,726

DSWI (high to low

vegetation water stress)

1,516 - 6,787 1,252 - 1,516 0,988 - 1,252 0,724 - 0,988 0 - 0,724

NDGI (high to low

vegetation greenery)

0,039 - 0,370 -0,040 - 0,039 -0,120 - -0,040 -0,200 - -0,120 -0,355 - -0,200

MSI (high to low soil

moisture)

0 - 0,657 0,657 - 1,038 1,038 - 1,419 1,419 - 1,622 1,622 - 5,746

BI (low to high soil

brightness)

0 - 1394,708 1394,708 - 3280,037 3280,037 - 5165,367 5165,367 - 7050,696 7050,696 - 15541,476

CRUST INDEX (low to

high soil crusting)

-0,113 - 0,141 0,141 - 0,205 0,205 - 0,269 0,269 - 0,333 0,333 - 0,607

TI (low to high soil texture) -0,578 - 0,007 0,007 - 0,078 0,078 - 0,149 0,149 - 0,220 0,220 - 0,428

CUIRASS INDEX (low to

high soil cuirass)

-100 - 1703,341 1703,341 - 2439,784 2439,784 - 3176,226 3176,226 - 3912,669 3912,669061 - 366881

RI (high to low soils

redness)

2,297e-006 - 1,403e-005 1,731e-006 - 2,297e-006 1,166e-006 - 1,731e-006 6,005e-007 - 1,166e-006 3,366e-009 - 6,005e-007

COLOR INDEX (low to

high soil color)

304,098 - 833,117 833,117 - 1588,966 1588,966 - 2344,815 2344,815 - 3100,664 3100,664 - 16903,046

GSI (low to high grain size) -0,085 - 0,042 0,042 - 0,088 0,088 - 0,133 0,133 - 0,189 0,189 - 0,416

NDSI (low to high soils

salinity)

-0,861 - -0,506 -0,506 - -0,291 -0,291 - -0,075 -0,075 - 0,140 0,140 - 0,497

3 RESULTS

3.1 Map of Exposition Soils Degree to

Agents and Degradation Factors

The result of this modeling is a map of exposition

soils degree to agents and degradation factors. The

potential soil exposition state is classified on the map

below in five levels of exposition risk from the

"Lower" level to the "Higher" (Table 5). The diversity

of land cover explains the nature and the state of the

soils, justifies the heterogeneity of the map, and

explains the need to have a high number of classes to

represent all the levels of exposition risk.

In the absence of field truth data, the different

exposition levels are obtained by performing a

standard deviation threshold of the image histogram.

The standard deviation threshold method allows

visualizing how much the attribute values of a class

vary compared to the mean, by using mean values and

standard deviations from the mean.

The "Lower" and "Moderate to low" levels cover

the permanent open water areas of the lake, the

marshland, and vegetated areas of the immediate

shores, a portion of the intermediate shores, and

occupy respectively 25,214.35 hectares and

130,717.19 hectares (Figure 5). The "Moderate" level

of exposition spreads sparsely over the bare areas of

the outer shores and the hinterland over an area of

137,404.34 hectares. The "High to moderate" and

"Higher" levels dominate the outer shores and the

hinterland. With 152,371.91 hectares, the "High to

moderate" level represents the most widespread state

of exposition of our study area. The "Higher" level

occupies 29,175.73 hectares. The main difficulty now

is to be able to identify for each level of exposition

the most influential indices.

Figure 5: Map of soils exposition risk to degradation.

GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management

Table 6: Areas of degradation level by index.

LEVEL OF

DEGRADATION

NDVI MSAVI2 DSWI NDGI MSI BI

CRUST

INDEX

CUIRASS

INDEX

COLOUR

INDEX

GSI NDSI

Lower 47347,15 116895,5 12232,53 51141,44 56785,6 20022,04 44050,68 436,12 34703,46 12455,87 42013,35 9303 45373,94

Moderate to low 65710,8 307273,49 43821,77 77454,57 73233,53 131856,39 75252,17 189099,23 114273,9 30834,54 103409,8 36286,48 66397,32

Moderate 310022,07 9348,34 65791,41 132378,86 122002,11 314392,22 169496,11 161940,13 166788,83 66436,07 134027,8 77362,84 310755,2

High to moderate 10351,48 15298,62 142606,5 213842,42 190128,43 8575,23 179840,75 61304,64 132939,34 196491,09 178548,4 227054,85 10428,33

Higher 41426,95 26042,49 210406,3 41,15 32708,78 15,46 6218,72 62076,68 26152,92 168640,87 16859,07 124851,26 41903,57

TOTAL 474858,45 474858,44 474858,5 474858,44 474858,45 474861,34 474858,43 474856,8 474858,45 474858,44 474858,4 474858,43 474858,4

The method adopted to answer this concern is

inspired by (Ngandam et al. 2016), which consists of

classifying the indices by class of degradation and

identifying the influence of the indices according to

their spatial distribution by class (Table 6). For the

"Higher" level, the top five indices with the largest

spatial distributions are in decreasing order, DSWI

(210,406.3 hectares), RI (168,640.87 hectares), GSI

(124,851.26 hectares), TI (62,076.68 hectares), and

NDSI (41,903.57 hectares). As a result, the "Higher"

level is explained by bare soils where vegetation has

completely disappeared, the low rate of iron in the soil,

the coarse texture of the surface particles, and high

salinity. For the "High to moderate" level, the GSI,

NDGI, RI, MSI, and crust index with respectively

227,054.85 hectares, 213,842.42 hectares, 196,491.09

hectares, 190,128.43 hectares, 179,840.75 hectares are

the most indicative. This means that the soils of this

class are also characterized by the coarse texture of the

particles on the surface, but also by the weak greenery

of the vegetation, an important crusting, and low

moisture and iron contents.

The following indices: brightness (314,392.22

hectares), salinity (310,755.2 hectares), chlorophyll

(310,022.07 hectares), crust (169,496.11 hectares)

and cuirass (166,788.83 hectares) are the most

influential for the "Moderate" level. The soils of this

class are clear and salty, weakly covered with

vegetation, and compact on their surfaces.

A good vegetation cover of the soil, the fine texture

of the soil particles, dark soils, weakly cuirassed,

characterizes the "Moderate to low" level that covers

the open waters of the lake, marshland areas, and part

of the immediate and intermediate shores and which

contain organic matter in significant quantities.

Indeed, in this class, the MSAVI2 with 307,273.49

hectares is the most widespread index followed by TI

189,099.23 hectares, BI 131,856.39 hectares, cuirass

index 114,273.9 hectares, and color index 103,409.8

hectares. In the "Lower" class, which occupies open

water and marshland, the influence of vegetation

indices is the most important (MSAVI2 116,895.5

hectares, NDGI 51,141.44 hectares, NDVI 47,347.15

hectares), the soil moisture is high (MSI 56,785.6

hectares), and their salinity rate is low (the NDSI

45,373.94 hectares).

3.2 Validation of Results

A confusion matrix was used to validate the results

obtained by a comparison with the existing map of the

land degradation status of the far north region of

Cameroon, provided by (Ngandam et al. 2016). A

subset containing the main characteristic of the study

area was used, i.e., the permanent open water, the

marshland, the immediate shores, the external shores,

and the hinterland.

So, the confusion matrix performed provided the

information for verification and accuracy assessment

between our results with the ground truth map. The

overall accuracy which represents in percent the

number of correctly classified values divided by the

total numbers of values is 54.3%, and the kappa

coefficient which assesses how much better the

classification is than a random classification has a

value of 40.49%.

4 CONCLUSION

The present work was based on laboratory tests

applied to sentinel 2A satellite images. The purpose

was to model the risk of soil degradation in Sahelian

regions by combining spectral indices with statistical

analyses. The results are highly correlated to some

factors as the phenological season of satellite image

acquisition, the quality of the images, the formula of

the indices used, and the applied statistical treatments.

Also, statistical analysis was applied to the

resulting image giving on one hand the correlation

and determination coefficients of each index, and on

the other hand, the factorial axes which summarize

more information. All indices are considered

statistically significant (P-value < 0.0001). The first

two factors of PCA and factorial analysis explain

A New Soil Degradation Method Analysis by Sentinel 2 Images Combining Spectral Indices and Statistics Analysis: Application to the

Cameroonians Shores of Lake Chad and Its Hinterland

respectively 87.54 % and 85.62 % of the common

variability of the characteristics measured. The first

factor contains the soil information, and the second

factor focuses information on vegetation. This final

equation of the model is obtained by index weighting

with the respective values of the coefficient of

determination, which oscillates between 0.007 for the

MSAVI2 and 0.909 for the brightness index. Among

the most serious levels of degradation, the "High to

moderate" level is the most widespread with

15,271.91 hectares, followed by the "Moderate" level

with 137,404.34 hectares, and the "Higher" level,

which occupies an area of 29,175.73 hectares.

However, we apply our methodology to images of

a specific month of the year (April). So, the challenge

now is the adaptation of the model to previous years

and other periods of the year. Moreover, the lack of

consideration of urban areas is a limit for this work

because the elements that constitute the habitat

(example of aluminum roofs) necessarily influence

the results of the calculation of certain indices.

At last, whatever performing decorrelation

analysis as a method of unlinking indices, all of them

is calculated on satellite images from the same sensor.

Consequently, they have a basic dependent relation

because of their origin same spectral characteristics.

For this reason, it should be interesting in further

analysis to perform the whole analysis on multisource

satellite images (SPOT or MODIS), to assess the

statistic behavior and decorrelation, while an index of

one source and another of the other source is used as

the independent and dependent variable.

Moreover, the method adopted in this study to

evaluate the contribution of the different indices to

each degree of degradation brought interesting

results. However, the presence on our images of open

waters and marshland to a certain extent brings out a

new constraint to consider. The low values of

vegetation indices of NDVI and MSAVI2 appear in

open water rather than appearing in bare spaces.

Without this class of occupation, these two indices

would have better contributed to characterize the

classes of strong degradation as the DSWI did. To

overcome this difficulty, one of the ways of

improving the model will be to classify indices as

functions of the distinct levels of degradation, using

the spectral windows obtained from the spectral

signature of these indices.

The imbalance between the number of vegetation

index and the number of soil index is to be

considered, through a readjustment that will allow

integrating new parameters including climatic like the

temperature of the surface, precipitations, albedo, or

evapotranspiration. Other elements such as

topography and hydrographic network distribution

are also to be considered.

ACKNOWLEDGMENTS

The authors are grateful to European Space Agency

(ESA) and the Copernicus program for the Sentinel 2

satellite images direct access. We thank all those who

contributed to this article.

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