Spatio-Temporal Modelling of Relationship Between Organic Carbon

Content and Land Use Using Deep Learning Approach and Several

Co-Variables: Application to the Soils of the Beni Mellal in Morocco

Sébastien Gadal

1,3 a

, Mounir Oukhattar

, Catherine Keller

and Ismaguil Hanadé Houmma

1,4 d

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

Aix-Marseille Univ., CNRS, IRD, Coll de France, INRAE, CEREGE, Technopole de l’Environnement Arbois-Méditerranée,

BP80, 13 545 Aix-en-Provence, Cedex 4, France

Department of Ecology and Geography, Institute of Environment, North-Eastern Federal University,

Republic of Sakha Yakutia, Russia

Hassan II Institute of Agronomy and Veterinary, Department of Geodesy and Topography,

Geomatics Science and Engineering, Morocco

ismaguil.hanade-houmma@etu.univ-amu.fr

Keywords: SOCS, LULC Change, Remote Sensing, Soil Analysis, Spatial Distribution, Machine Learning Classification,

Deep Learning Modelling.

Abstract: In recent decades, population growth has led to rapid urbanisation associated with a land degradation process

that threatens soil organic carbon stocks (SOCS). This paper aims to model the interrelationships between

SOCS and land use/land cover (LULC). The approach was based on the use of environmental covariates

derived from Landsat-5 TM/8 OLI images, forty soil samples, Kriging spatial interpolation method and a

Multi-layer Perceptron (MLP) model for the geo-spatialisation of SOCS. The analysis shows a high positive

autocorrelations (R

>0.75) between vegetation indices and SOCS, particularly higher for SOCS derived from

spatial modelling with MLP. On the other hand, the relationship between LULC and SOCS from the three

approaches is very variable depending on the dynamics of LULC. The autocorrelations between SOCS and

LULC units are very weak in 1985 and 2000 but significant for the year 2018. This suggests that the land use

dynamics in the area are favourable to SOCS. In general, the results show that SOCS increased in the tree

crop, unused land and forest areas but decreased in the cropland. The SOCS varied in the following order:

forest cover>unused land>cropland>urban area>tree crops. This indicates that LULC, topography and

vegetation types had an impact on SOCS distribution characteristics.

1 INTRODUCTION

As the living foundation of agricultural and forestry

production, soil is a finite and non-renewable

resource on a human lifetime scale. It is subject to

several increasing pressures that lead to tensions

between land uses (Lal et al. 2007). Changes in

agricultural production methods, the reversal of

grasslands, the loss of arable or wooded land to

urbanisation, the increase in biomass extraction, etc.,

are all developments which, if not properly

considered, could affect the quality of soils, and

https://orcid.org/0000-0002-6472-9955

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https://orcid.org/0000-0001-8455-2926

https://orcid.org/0000-0001-7838-6597

dissipate the carbon stocks they contain. Soil is a

complex system that plays a central role in

agricultural and forest ecosystems by regulating

various natural cycles such as those of greenhouse

gases. Through its agri-environmental functions, soil

is both a storage site and a sink for organic carbon and

is also a source of carbon dioxide (CO

) emissions to

the atmosphere, a high greenhouse gas, which has an

influence on climate change (Bernoux et al. 2001,

Hutchinson et al. 2007, Lal et al. 2007).

Soils are the largest terrestrial reservoirs of

organic carbon (Yang et al. 2016). They contain about

Gadal, S., Oukhattar, M., Keller, C. and Houmma, I.

Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several Co-Variables: Application to the Soils of the Beni

Mellal in Morocco.

DOI: 10.5220/0011723000003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2023), pages 15-26

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

twice as much carbon as the atmosphere and are

therefore a major compartment in the global carbon

cycle (Xiong et al. 2014). Any change, positive or

negative, in soil organic carbon stocks can represent

a sink or source of atmospheric CO

(Yang et al.

2018). These stocks can be strongly modified by

changes in practices or uses. They are also highly

dependent on climate. Changes in land use within the

agricultural sector (e.g., grassland turnover) and

between agricultural and non-agricultural uses

(afforestation, deforestation, urbanisation) impact

soil carbon stocks and aboveground biomass.

Urbanisation at the expense of generally agricultural

land or natural areas contributes to the net greenhouse

gas (GHG) emissions balance. Some previous studies

that examined the effect of LULC on SOCS found

that increasing urbanisation could lead to a loss of

SOC due to artificialisation making soils non-

permeable (Beesley 2012, Wei et al. 2014); while

others research found that some urban soils had

higher SOC contents than agricultural, grassland and

forest soils (Golubiewski 2006, Raciti et al. 2011).

Previous studies on the study area are few and have

focused particularly on climate change, ecosystem

degradation, water stress, and changes in LULC at

different times (Ait Ouhamchich et al. 2018, El

Jazouli et al. 2018, Barakat et al. 2019, Baki et al.

2021). However, no study has addressed the spatio-

temporal modelling of SOCS in relationship to LULC

change and environmental covariates. Such studies

are needed, given the extent of land use change in the

study area.

Remote sensing and spatial modelling based on

deep learning and machine learning are techniques

that have been more widely used in recent decades to

quantify the spatio-temporal distribution of

environmental variables such as LULC dynamics and

soil organic carbon stocks (Bae et al. 2015, Shifaw,

2018, Yang et al. 2018, Obeidat et al. 2019, Fathizad

et al. 2022). Many studies have deployed remote

sensing techniques and spatial modelling to

specifically assess LULC change and its potential

impacts on soil organic carbon sequestration (Yan et

al. 2015, Taghizadeh-Mehrjardi et al. 2017, Yang et

al. 2018). They were able to show the important role

of spatial remote sensing in modelling past and

current growth knowledge to predict the future

(Nurmiaty et al. 2014, Huong and Phuong, 2018).

Thus, some artificial intelligence models have been

used to try to predict LULC and SOCS

transformations and their potential environmental

effects. Baker (1989) and Muller and Middleton

(1994) have abbreviated the most used models to

assess LULC dynamics. Markov chain analysis and

Multi-layer Perceptron (MLP) are easy-to-use

artificial intelligence approaches to predict the spatial

characteristics of LULC and SOCS based on current

conditions (Sharjeel et al. 2016, Hazhir et al. 2018,

Emadi et al. 2020, Kılıc et al. 2022). Furthermore,

since these approaches offer the possibility to study

historical changes, we adopted them to estimate and

predict the rate of change of LULC types and the

spatial distribution of SOCS in our study area

between 1985 and 2050. Soil analyses were carried

out to characterise soil physical parameters, including

texture, density, and soil organic carbon content,

while remote sensing and spatial modelling were used

to estimate LULC changes and the spatial distribution

of SOCS.

In the USA, Pouyat et al (2006) compared the

variability of soil organic carbon stocks in six

different cities and found that urban soils had the

potential to sequester large amounts of atmospheric

. Urban greenspaces contained higher SOC

stocks than native grasslands, agricultural areas or

forests in Colorado, USA (Golubiewski, 2006).

Hutyra et al (2011) reported that above-ground

carbon stocks in Seattle's urban forests were

comparable to those in the Amazon rainforest. Kaye,

McCulley and Burke (2005) measured aboveground

net primary productivity in urban lawns and found it

to be four to five times higher than in surrounding

farmland and grasslands. In addition, Mestdagh et al

(2005) found that the SOC stock of roads, waterways

and grassy railways in urban areas accounted for 15%

of the total SOC stock in a city. Soils under

impervious surfaces in urban areas provided another

often-overlooked source of SOC (Raciti et al. 2012,

Edmonds et al. 2014).

In 2008, Morocco adopted the "Green Morocco

Plan" as its agricultural and rural development

strategy, which aims to promote Moroccan

agriculture as a driver of economic and social

development, while seeking to remove some of the

many constraints on the sustainability of this

development (Ministry of Agriculture 2008). The

main objectives consist first in removing the

constraint of spatial disparities, which are still

significant and may even increase, due to unequal

access to the means of agrarian development. The

challenge of spatial planning and agricultural

territorialisation, through development adapted to the

conditions of each region by strengthening the assets

of the various rural territories and correcting their

weaknesses, can make it possible to respond. Among

these weaknesses is the vulnerability of the land,

which a policy of sustainable management and

conservation can help to overcome. Secondly, the

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

objectives of the 'Green Morocco Plan' aim to remove

the constraint of the complex land tenure status of

Moroccan lands, which is responsible for several

forms of degradation, especially in state-owned lands

and those with community status. This reform of the

land tenure system aims to empower local actors in

the conservation and rational management of natural

resources (Ministry of Agriculture 2008).

The objective of this study is to assess the effect

of LULC change on SOCS in the different classes

(tree crops, cropland, urban area, unused land, and

forest cover) while using LULC change monitoring

which is important, especially when it results in

inefficient and rapid urbanisation policy, unregulated,

often uncontrolled urbanisation, often associated with

threats to the SOCS. Rapid urbanisation, i.e., the

growth of cities and infrastructure, extends to the

surrounding land, which is usually natural areas, and

this is usually associated with land and soil

degradation. Land degradation is implicated in

several major environmental problems such as soil

erosion, landslides, biodiversity loss, increased

atmospheric CO

concentration, desertification, and

groundwater pollution (Townshend et al. 2012).

Therefore, studies on LULC changes and their

impacts on soil organic carbon stock are needed to

achieve environmental sustainability. Spatial and

temporal analysis of LULC trends and the

relationships between the factors that lead to these

variations would allow for better land use

management. The main stages of this study are (1)

mapping LULC in 1984, 2000 and 2018, (2)

predicting LULC in 2018, 2030 and 2050, (3) soil

sampling in the different LULC types to measure

SOCS and soil texture, (4) spatial distribution of

SOCS as a function of environmental covariates and

(5) spatial autocorrelations between SOCS and

environmental covariates.

2 MATERIALS AND METHODS

2.1 Study Area

The study area is in the Beni Mellal-Khenifra region

of Morocco. It covers an area of 252,5 km

and a

perimeter of 63,9 km

, located at the junction of the

High Atlas of Beni Mellal and Tadla plains (Figure

1). The altitude varies from 439 m in the northwest to

1709 m in the southeast of the study area.

Administratively, it covers the municipalities of Sidi

Jaber, Ouled M'barek, Adouz and Beni Mellal. The

region has significant water resources from the Atlas

Mountains. It also contains a large amount of fertile

land (Ennaji et al. 2018, Barakat et al. 2019), making

it a region with high agricultural production.

Agriculture and livestock are the main sectors of

economic activity and income. Olive and citrus are

the main tree crops in the region. The climate of the

Beni Mellal region is characteristic of a continental

climate, with an average annual rainfall in this region

of about 350-650 mm, of which almost 87% is

received from October to March. In summer, Beni

Mellal receives less rainfall than in winter. The

average annual temperature is 14,17°C, with an

average minimum temperature of 1,1°C observed in

January and an average maximum temperature of

30°C in July and August. The average annual

precipitation varies between 350-650 mm, of which

about 87% is received from October to March.

The soils in the study area belong, in order of

importance, to the following groups: Isohumous,

brown subtropical or chestnut soils are by far the most

widespread (Aghzar et al. 2002). They are found in

the Tadla plain and cover nearly 83% of the irrigated

area. These soils have a clayey or balanced texture

and are favourable to agricultural development under

irrigation. Brown calcimagnesic calcareous soils are

Figure 1: Location of the study area and soil sampling points.

Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several

Co-Variables: Application to the Soils of the Beni Mellal in Morocco

shallow, very calcareous, and stony soils, but with a

balanced texture. They are found along the wadis.

These soils occupy 11% of the soil cover of the Tadla

perimeter. The validation points presented in Figure

1 are mainly used to validate the LULC maps, and at

the same time in each validation point, soil samples

were taken to determine the soil organic carbon stock

and texture of each LULC type.

2.2 Data and Methods

2.2.1 Data, Pre-Processing and Validation

In this study, the LULC maps from 1985, 2000 and

2018 were mapped using Landsat 5 TM (Thematic

Mapper) and Landsat 8 OLI (Operational Land

Imager) satellite images (earthexplorer.usgs.gov).

The images have a ground resolution of 30 m, except

for the IR (infrared) thermal band (band 6) having a

resolution of 120 m for Landsat 5 TM images, and

the panchromatic band (band 8) having a resolution

of 15 m for Landsat 8 OLI images. All spatial data

were defined on the same coordinate system,

WGS_1984_UTM_Zone_29 N. All satellite images

were taken during the dry season (4 July 1985, 13

July 2000, and 29 June 2018) to minimise errors

caused by seasonal variations and seasonal effects of

crops. The digital terrain model (ASTER GDEM)

was taken on 23 September 2014 with a spatial

resolution of 30 m (earthexplorer.usgs.gov). The

selected temporal images were subjected to

radiometric calibration (RC) and dark object

subtraction (DOS) corrections. The raster images of

the study area were subdivided according to a

rectangular polygon created that covers the selected

study area. Then, each raster image was classified to

obtain the LULC maps. Field data and the Google

Earth platform were used to identify the LULC

classes, i.e., urban areas, forest cover, unused land,

cropland and tree crops. The supervised classification

method Spectral Angle Mapper (SAM) was used to

obtain a broad level of classification, to derive all

predefined LULC classes. The accuracy of the

generated maps (1985, 2000 and 2018) was achieved

for each land cover based on field observations and

using 2018 "natural" colour RGB composition

images from the WORLDVIEW-3 satellite with a

resolution of 30 cm by viewing on the Google Earth

platform. The Markov chain model was used to

validate the detected changes and predict future

LULC maps for 2018, 2030 and 2050. In the present

study, the Markov transition matrix was applied to

predict the 2018 LULC using the 1985 and 2000

LULC maps, and the 2000 and 2018 LULC maps

were used to determine the LULC transition matrix

for 2030 and 2050. For the model of validation, the

simulated CA-Markov 2018 LULC was compared to

the actual 2018 LULC map by image analysis.

2.2.2 Spectral Angle Mapper (SAM)

The SAM method is a supervised classification

approach based on the measurement of the angular

similarity between the spectrum of each pixel in the

image and reference spectra, called endmembers

(Hunter and Power, 2002). The latter can be

measured directly in the field using a

spectroradiometer, as well as extracted from the

image. The assignment of an image pixel to a given

class is based on the value of this angle "α" which

measures the similarity or difference between the

reference spectrum vector and its image counterpart

(Girouard et al. 2004). Thus, the pixel will be

assigned to the spectral class with which it has the

most similarity, i.e., the smaller the angle "α", the

greater the similarity between the spectrum of the

evaluated pixel and the reference (Kruse et al. 1993).

In our case, the prototype spectral signatures used to

run the SAM were extracted from the image. They

represent 5 severity classes (tree crops, cropland,

unused land, urban areas, and forest cover). In this

study, after visually testing and comparing the LULC

results of six supervised classification algorithms

(Support Vector Machine, Spectral Angle Mapper,

Parallelepiped, Minimum Distance, Maximum

Likelihood and Mahalanobis Distance), the SAM

algorithm gave the best supervised classification

result. The SAM algorithm was used to classify the

five dominant LULC classes in the study area.

2.2.3 Soil Sampling and Analysis

The soil sampling sites were located by their GPS

Coordinates and selected according to the LULC

classes using the generated LULC map of 2018

(Figure 1). A total of 40 sites (8 points in tree crops

class, 11 in cropland class, 9 in unused land class, 5

in urban areas and 7 in forest cover class) were

sampled on 26/04/2019 and 03/05/2019 at a depth of

(0- 15cm) in the absence of means to sample the

entire soil profile. Each soil sampling site consisted

of 3 intact soil cores using a metal cylinder 15cm high

and 9cm in diameter for subsequent calculation of

soil bulk density from the volume of the cylinder. All

samples were dried in an oven at a temperature of

40°C for 2 days to a constant weight. The dry soil was

sieved to 2 mm to separate pebbles >2 mm. Then the

volume of the pebbles was measured to calculate the

bulk density (BD). The fraction < 2mm was

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

recovered and then crushed with an agate mortar to

obtain a finer, homogeneous fraction that will be

analysed for organic carbon content and soil texture.

BD 

   

   

(1)

Soil organic carbon (SOC) content was

determined using soil organic matter (SOM) which

was determined by the incineration method (loss on

ignition or loss on fire). Loss on ignition is a direct

measure of organic matter in the soil. The samples

are placed in a muffle furnace at 540 °C for 4 hours.

The loss by weight, after calcination, gives us the soil

organic matter (SOM).

SOC 

.



(2)

Where SOC: Soil organic carbon in % and SOM: Soil

organic matter in %. After determining the soil

organic carbon content, soil bulk density and volume

of pebbles in the samples, we calculated the soil

organic carbon stock using the following equation:

SOCS  SOC  BD  ST 





(3)

Where SOC is the organic carbon content, BD is the

bulk density and ST is the sampled thickness (15 cm).

The SOC stocks for each LULC class in the study

area were summarised according to the following

equation:

Total SOCS   SOC stock  Si





(4)

Where Si is the area of the LULC type (in km

Soil texture was determined by particle size analysis,

which consists of separating the mineral part of the

soil into categories classified according to the size of

the mineral particles smaller than 2 mm and

determining the relative proportions of these

categories (sand, silt, clay), as a percentage of the

total mineral soil mass. Textural classes were

determined according to the USDA (United States

Department of Agriculture) classification scheme

(Garcia-Gaines,

and Frankenstein, 2015).

2.2.4 Spatial Distribution of Soil Organic

Carbon Stock

Satellite images obtained by Landsat 5 TM in 1985

and 2000, Landsat 8 OLI in 2018 were used to map

the LULC, calculate vegetation indices and other

remote sensing indices. After pre-processing the

satellite images (part 2.2.1 radiometric and

atmospheric corrections), a total of 4 vegetation

indices were calculated. These indices include

Normalized Difference Vegetation Index (NDVI),

Soil Adjusted Vegetation Index (SAVI), Ratio

Vegetation Index (RVI), Enhanced vegetation index

(EVI), Principal Component Analyses (PCA) and

Minimum Noise Fraction (MNF) of spectral bands.

As well as a digital elevation model (DEM) was used

as a topography variable in the study area. In this

study, a deep learning Multi-Layer Perceptron (MLP)

model was fitted using 2018 data. To estimate

historical and future changes in SOCS, the MLP

model was applied to remote sensing data collected

for the periods 1985, 2000 and 2018, as well as field

data. MLP is the most important and commonly used

artificial neural network (ANN) structure. It is a non-

parametric estimator that can be used for SOCS

regression (Taghizadeh-Mehrjardi et al. 2017). The

basic processing elements in MLP are highly

interconnected neurons. The neurons are organised in

layers: an input layer, one or more hidden layers and

an output layer. Data is fed into the network by the

input layer, which sends this information to the

hidden layers. The data is processed by the hidden

layers and the output layer. MLP derives its capability

from the non-linear processing in the hidden layers

(Emamgholizadeh et al. 2018). In this study, the MLP

model was developed to estimate SOCS by regression

to a depth of 15cm using environmental covariates

and measured SOCS data from the 40 sampled and

analysed sites. The environmental covariates and

colour compositions images (red-green-blue RGB

and near-infrared-green-blue NirGB) were integrated

into the MLP model as images of the independent

variables, while the SOCS measurements were

integrated as the dependent image after their spatial

interpolation by the Kriging method.

Figure 2: Flow chart of the working methodology.

Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several

Co-Variables: Application to the Soils of the Beni Mellal in Morocco

3 RESULTS AND DISCUSSION

3.1 LULC Change Analysis

The LULC maps (1985, 2000, 2018) obtained by the

SAM supervised classification method of Landsat 5

TM and Landsat 8 OLI data are presented in Figure

3, along with their projections to 2018, 2030, and

2050 obtained by the CA-Markov geosimulation

model. These mapping results show current and

future changes in five dominant LULC classes in the

study area. These mapped LULC classes include five

LULC types: Unused Land (Ivory colour), Forest

Land (Purple colour), Urban Area (Midnight Blue

colour), Cropland (Beige colour), and Tree Crops

(Green colour).

Statistical analyses of the LULC maps (Table 1

and Figure 3) revealed that tree crops have increased

significantly in the study area. They have evolved

from 11,6% (29,3 km

) in 1985 to 18% (45,5 km

) in

2018 with a positive rate of change of 55,3%.

According to the projection in 2050, tree crops would

be 18,2% (46 km

) with a rate of change of 1,1%

between 2018 and 2050. This transition is linked to

the Moroccan Ministry of Agriculture's tree sector

support program (Green Morocco Plan), which aims

to remove the constraint of the complex land tenure

status of Moroccan lands, responsible for several

forms of degradation, particularly in state lands and

those with community status. This reform of the land

tenure system aims to empower local actors in the

conservation and rational management of natural

resources.

On the other hand, the statistics provided in Table

1 show both regressive and progressive changes in the

LULC natural units in the study area. We can see a

slight increase in forest cover from 6,4% (16,2 km

)

in 1985 to 6,8% (17,1 km

) in 2018. The cropland has

decreased remarkably, from 68,9% (173,7 km

) in

1985 to 37,6% (95 km

) in 2018. Based on this

regressive trend, cropland in the study area could

decrease to 33,9% (85,5 km

) in 2050 with a negative

rate of change of -10% between 2018 and 2050

according to CA-Markov based projections.

Comparing the area gains and losses between

cropland and each of the other LULC classes over this

period, the most significant conversion of cropland

was to urban areas and unused land. This main

conversion of cropland contributing to the decline of

fertile soils in the study area could be explained by

the rapid increase in urbanisation and drought. A true

conversion of cropland to urban areas around the city

of Beni Mellal was also observed between 1985 and

2018, which would be due to urban and suburban

growth and expansion. Urban areas increased from

1,8% (4,5 km

) in 1985 to 10,7% (27,1 km

) in 2018

with a positive change rate of 502,2%. Simulations

based on CA-Markov show that urban areas would

reach 16,3% (41,1 km

) with a rate of change of

51,7% between 2018 and 2050. This increase in urban

areas during the study period (1985-2018) is related

to the conversion of a portion of cropland into built-

up areas due to urban sprawl and expansion of

economic development activities. At the same time,

unused land increased from 11,3% (28,5 km

) in 1985

to 26,9% (67,8 km

) in 2018 with a positive rate of

predicted.

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

Table 1: Change area in different LULC categories between 1985 and 2050.

Type of Land

Use

Surface

1985 2000 2018 2030 2050

(km

) (%) (km

) (%)

Tree Crops 29,3 11,6 23,2 9,2 45,5 18 46 18,2 46 18,2

Croplan

173,7 68,9 139,4 55,3 95 37,6 86,4 34,2 85,5 33,9

Urban Area 4,5 1,8 11,6 4,6 27,1 10,7 36,6 14,5 41,1 16,3

Unused Lan

28,5 11,3 67,5 26,8 67,8 26,9 61,7 24,4 59,2 23,5

Forest Cove

16,2 6,4 10,5 4,2 17,1 6,8 21,8 8,6 20,7 8,2

Total 252,2 100 252,2 100 252,5 100 252,5 100 252,5 100

Type of Land

Use

Change

1985-2000 1985-2018 2018-2030 2018-2050

(km

) (%) (km

) (%)

Tree Crops -6,1 -20,8 16,2 55,3 0,5 1,1 0,5 1,1

Croplan

-34,3 -19,7 -78,7 -45,3 -8,6 -9,1 -9,5 -10

Urban Area 7,1 157,8 22,6 502,2 9,5 35,1 14 51,7

Unused Lan

39 136,8 39,3 137,9 -6,1 -9 -8,6 -12,7

Forest Cove

-5,7 -35,2 0,9 5,6 4,7 27,5 3,6 21,1

change of 137,9%. This change would reach 23,5%

(59,2 km

) in 2050 with a rate of change of -12,7%.

The increase in unused land is linked to the

conversion of agricultural land to urbanised areas in

the future (public facilities, industrial areas, urban

areas, and rural areas) and could also be explained by

the droughts during the last decades.

3.2 Organic Carbon Stocks and Soil

Texture of LULC Types

Analytical data on SOC stocks and texture of soils

sampled in different LULC classes in the study area

are presented in figures 4 and 7. Summary statistics

in terms of min, max and mean for the analysed

parameters (SOC and SOCS) are provided in Figure

Soil organic matter and soil organic carbon is rich

in forests and unused or infertile land because: (1)

Forests and unused soils are less disturbed than

agricultural soils. (2) Forests and unused soils evolve

slowly. Nevertheless, their fertility is limited and

strongly dependent on the natural flows of elements

and organic matter. (3) Forests and unused soils are

generally the poorest soils chemically or those with

physical properties most unfavourable to agriculture.

In contrast to agricultural soils, they are not worked

or are only lightly worked. This results in a high

accumulation of organic matter in the litter and

surface soil horizons.

Traditionally, SOC in forest ecosystems is

usually considered in the regional assessment. As

shown in this study (Figure 4), the organic carbon

content at a depth of 0-15 cm in forest soils (4,9% -

10,4%) was higher than that reported for urban soils

in the Tadla plain (3,4% - 7,2%), where the city of

Beni Mellal is located. This showed that forest soils

can store large amounts of organic carbon in the soil.

Soil organic carbon contents measured under

different types of LULC varied in the following

order: forest cover > unused land > urban areas >

cropland > tree crops. A variation in SOC is very

noticeable in urban soils mainly due to human

activities that often change the parameters of these

soils.

SOC stocks were calculated for each sample in

the study area using the organic matter, bulk density,

and pebble volume values in the sample. In Figure 4,

SOCS values in soils sampled at a depth of 15cm

covering the whole study area ranged from 4,7 to 11,7

kg/m

. The SOCS in the forest cover ranged from 8,1

to 11,7 kg/m

, with an average of 9,9 kg/m

. In

cropland, the SOCS varied between 5,8 and 10,1

kg/m

with an average of 7,9 kg/m

, while the SOCS

stocks in tree crops varied between 4,7 and 8,9 kg/m

and an average of 6,4 kg/m

. Unused land had SOC

stocks between 7,6 and 10,1 kg/m

with an average of

8,9 kg/m

. At the same time, urban areas had SOC

values between 5,8 and 9,2 kg/m

with an average

value of 7,4 kg/m

. From these results of the sampled

soils, we notice that the SOC stocks measured under

different types of LULC varied in the following

order: forest cover > unused land > cropland > urban

areas > tree corps.

3.2.1 SOCS of the Study Period

The SOC stocks for 1985, 2000, 2018, 2030 and 2050

were calculated for each LULC type in the study area

using the average SOCS values and the area of each

LULC type. In Table 2, the SOCS values have been

summed separately for each date and LULC type.

These SOCS have changed slightly in the forest cover

Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several

Co-Variables: Application to the Soils of the Beni Mellal in Morocco

Figure 4: Variation in SOC and SOCS by LULC type.

Table 2: Change in SOCS by LULC type between the years 1985, 2000, 2018, 2030 and 2050.

Type of Land Use

SOCS (kg/m

) SOCS Change (kg/m

)

1985 2000 2018 2030 2050 1985-2018 2018-2030 2018-2050

Tree Crops 187,5 148,5 291,2 294,4 294,4 103,7 3,2 3,2

Cro

lan

1337,5 1073,4 731,5 665,3 658,4 -606 -66,2 -73,1

Urban Area 33,3 85,8 200,5 270,8 304,1 167,2 70,3 103,6

Unused Lan

253,7 600,8 603,4 549,1 526,9 349,7 -54,3 -76,5

Forest Cove

160,4 104 169,3 215,8 204,9 8,9 46,5 35,6

class from 160,4 kg/m

in 1985 to 169,3 kg/m

2018 with a positive rate of change of 8,9 kg/m

According to the projections in 2050, the SOCS

would expect 204,9 kg/m

. At the same time there is

a net increase in these stocks in the tree crops class

from 187,5 kg/m

in 1985 to 291,2 kg/m

in 2018,

with a rate of change of 103,7 kg/m

. In 2050, these

stocks are projected to be 294,4 kg/m

. This slight

increase in SOCS in the tree crop and forestry sectors

is mainly due to the planting of fruit trees in the plains

and afforestation in the forests that have been carried

out under the two strategies (the Green Morocco Plan

and the National Watershed Management Plan).

These two strategies are carried out mainly to combat

the degradation of natural resources (soil, water,

forests, etc.) and to combat erosion to increase

organic carbon stocks in the soil.

Similarly, we can notice a strong increase of

SOCS in unused land from 253,7 kg/m

in 1985 to

603,4 kg/m

with a rate of change of 349,7 kg/m

This increase could be explained by the increase of

unused land surfaces during this period due to the

drought. However, there is a strong decrease in the

SOCS of cropland from 1337,5 kg/m

in 1985 to

731,5 kg/m

in 2018 with a negative rate of change of

-606 kg/m

. Based on this regressive trend, cropland

SOCS could decrease to 658,4 kg/m

in 2050 with a

negative rate of change of -73,1 kg/m

. This decrease

is generally due to the extension of urbanisation, the

increase of informal settlements on the outskirts of

the city of Beni Mellal (Adouz, M'ghila, Ourbiaa...),

and to drought and water stress which convert the

surfaces of croplands to unused lands.

Moreover, in urban soils, SOCS has increased

from 33,3 kg/m

in 1985 to 200,5 kg/m

in 2018 with

a positive variation of 167,2 kg/m

. In 2050,

according to projections, these stocks in urban soils

would reach 304,1 kg/m

. According to Pouyat et al

in 2006, urban soils have the potential to sequester

large amounts of atmospheric CO

. Similarly,

Golubiewski in 2006 found that urban green spaces

contained higher stocks of SOC than native

grasslands, agricultural or forested areas in Colorado,

USA.

The results show that there is a significant

difference between the SOCS of different types of

LULC. LULC play a dominant role in influencing

SOC content and stock because surface soil

disturbance, litterfall and decomposition vary with

LULC, resulting in a difference in SOCS according to

land use. The SOCS of forest soils is very high

compared to other LULC due to the intense

topography and forest cover, which slows down soil

erosion and organic carbon decomposition, resulting

in forest soils having SOCS and SOC above their

potential capacity. However, the SOCS of tree crops

and cropland is very low compared to forest cover and

unused land due to intensive agricultural cultivation

(tillage), which accelerates soil erosion and

decomposition of soil organic carbon (Lal, 2005,

Laganière et al. 2010).

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

3.2.2 Soil Organic Carbon Trends for 1985,

2000, 2018, 2030 and 2050

To spatially distribute the content of SOC stocks

between 1985 and 2050, spatial regression maps of

these stocks from different environmental covariates

(DEM, NDVI, EVI, RVI, SAVI, LULC, MNF, PCA,

RGB, NirGB) were generated using a deep learning

approach with MLP (Figure 5). The maps were

plotted at the same scale and with geometric intervals

to facilitate comparisons. The results indicated that

SOC stocks showed high spatial variability in the

higher elevations of the study area. From the results

obtained concerning the stock of SOC, we deduce that

the important factors affecting this stock are the

topography of the place, the uses, and the types of

soil. In addition, the fact that photosynthesis allows

through biomass to store carbon in the soil, which we

see in Figure 5 concerning the forest where there is an

increase in the stock of SOC according to the years

1985, 2000, 2018 and 2050. Similarly, SOCS are

more sensitive to growth and increasing population

density (Lal and Augustin, 2011, Liu et al. 2016).

Urbanisation and buildings in the study area degrade

soils, cementing them and making them

impermeable, and thus they cannot absorb carbon

circulating in the biosphere. Thus, agricultural soils

and unused land transformed over time into built-up

areas could increase the loss of soil carbon sinks. This

indicates that land use has had an impact on the

distribution characteristics of SOC in the topsoil (0-

15 cm) of the study area (Figure 5).

The spatial distribution of SOCS (in 1985, 2000,

2018, 2030 and 2050) is shown in Figure 5 and shows

that high SOCS levels generally correspond to

forests. The spatial distribution of SOCS is almost

similar for each date across the different covariates

and LULC types. Overall, SOCS stocks in the south-

east are higher than those in the north-west, east, and

west of the study area, respectively. The results show

small pockets of SOCS highest in the central and

extreme south-eastern corner at a depth of 0-15 cm,

which corresponds to unused land in the Tadla plain

and forest cover in the mountains. The eastern and

western corners of the study site have the lowest

SOCS value as it is covered by urban areas, cropland,

and tree crops. This indicates that forests and

grasslands are more efficient in storing SOC than

other types of LULC. Most of the study area has

average SOCS values. The analysis suggests that,

mainly in the topsoil (0-15 cm depth), the spatial

distribution pattern of SOCS was highly variable due

to small-scale variations in supply, redistribution, and

stabilisation. The central part of the study area is

represented by a low SOCS.

The multi-date (1985, 2000 and 2018) spatial

autocorrelations of SOCS with environmental

covariates derived from satellite images are presented

in Figure 6. In this analysis, SOCS derived from

spatial interpolation techniques (IDW and Kriging)

and MLP were autocorrelated with spectral indices

(NDVI, EVI, RVI, SAVI), image transformation

indices (MNF, PCA), LULC units and spectral bands

(RGB and NirGB). The analysis shows very high

autocorrelations between the vegetation indices and

the SOCS. However, these autocorrelations are

particularly higher for the SOCS derived from spatial

modelling

with

MLP

than

the

SOCS

derived

from

Figure 5: Predicted soil organic carbon stocks (SOCS) (in kg/m

predicted, (E) 2030 and (F) 2050.

Spatio-Temporal Modelling of Relationship Between Organic Carbon Content and Land Use Using Deep Learning Approach and Several

Co-Variables: Application to the Soils of the Beni Mellal in Morocco

Figure 6: Spatial autocorrelations between SOCS and environmental covariates.

Figure 7: Variation in the percentage of Sand, Silt and Clay by LULC type.

spatial interpolation techniques. This suggests that

spatial modelling approaches to SOCS with deep

learning algorithms from environmental covariates

and in situ measurements are more accurate than uni-

variate spatial interpolation techniques based on field

samples. Similarly, significant, and positive spatial

autocorrelations were observed with the topographic

variable, but very high negative autocorrelations were

observed with the spectral bands and SOCS for all

three dates. In contrast, the relationship between

LULC units and SOCS from the three approaches is

particularly variable depending on the multi-date

dynamics of LULC. The autocorrelations between

SOCS and LULC units are very weak in 1985 and

2000 but significant for the year 2018. This suggests

that the land use dynamics in the area have favoured

SOCS.

3.2.3 Soil Texture

Texture indicates the relative abundance of different

particle sizes in the soil: sand, silt, or clay. Texture

determines how easily the soil can be worked, how

much water and air it contains, and how quickly water

can enter and move through the soil. Soil texture is

very stable over time and less affected by LULC. In

addition, soil aggregates are a factor responsible for

SOC stabilisation (micro-aggregates protect the SOC

in the long term and the renewal of macro-aggregates

is a crucial process that influences SOC stabilisation)

(Six et al. 2004). Therefore, the texture analysis of the

sampled soils was performed by adopting the USDA

classification (Garcia-Gaines,

and Frankenstein,

2015). The forty sites sampled for each type of LULC

have clay values ranging from 0,3% to 41,7%, sand

values ranging from 5,6% to 51,4%, and silt values

ranging from 22,5% to 94,1% (Figure 7). In the study

area, the textural class of the studied soils is clay

loam for the LULC classes of tree crops and urban

areas and Silt-clay loam for the classes of cropland,

unused land, and forest cover. The results of the

particle size analyses projected onto the USDA

triangular diagram showed that all LULC types have

a loamy soil texture which is a moderately fine

texture of fine sands and silts.

4 CONCLUSIONS

In this study, a multi-date geospatial approach to soil

organic carbon stocks in the Beni Mellal region of

Morocco and its relationship with LULC dynamics

and environmental covariates was developed by

applying two methods: a spatial interpolation method

on in situ measurements and a MLP model trained on

ten biophysical variables. For the three dates

considered (1985, 2000 and 2018), the results

obtained show highly significant spatial

autocorrelations

>0,75) between the SOCS from

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

the multivariate modelling with the MLP better than

those obtained between the SOCS from the spatial

interpolation techniques (IDW and Kriging). On the

other hand, spatial autocorrelations between LULC

units and SOCS are highly variable across years. For

the earliest years (1985, 2000), very low

autocorrelations were found between the SOCS and

LULC units, but the most the recent year 2018 was

distinguished by significant positive correlation

values between the SOCS from the MLP modelling

and the LULC units. Indeed, land use change in the

study area between 1985 and 2018 was (11,6% to

18%), (68,9% to 37,6%), (1,8% to 10,7%), (11,3% to

26,9%) and (6,4% to 6,8%) for tree crops, cropland,

urban area, unused land, and forest cover

respectively. In general, urbanisation linked to

population growth has also had a significant impact

on LULC change and has tended to implicitly reduce

soil carbon sequestration. However, according to the

SOCS results, tree crops, unused land and forest

cover mainly tend to be more resistant to land

degradation. Furthermore, it should be noted that this

study was conducted at a very small spatial scale with

few samples. A large-scale comparative evaluation of

multivariate SOCS geospatial approaches based on

deep learning or machine learning and spatial

interpolation techniques is one of the perspectives for

future studies to refine the different conclusions from

this approach.

ACKNOWLEDGEMENTS

We would like to thank the Faculty of Science and

Technology of Beni Mellal, for having given us the

means (transport and soil analysis laboratory) to carry

out this work. We would also like to thank Professor

A. Barakat for his advice during the realisation of this

study.

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