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Remote Sensing for Science, Education,
and Natural and Cultural Heritage

Rainer Reuter (Editor)
EARSeL, 2010

Use of Multi-temporal Satellite Data for Land-Use/LandCover Change Analyses and its Impacts on Soil Properties

in the Northern Part of Gadarif Region, Sudan
Khalid BIROa,1 , Biswajeet PRADHANa, Manfred BUCHROITHNERa,
and Franz MAKESCHINb
a
Institute for Cartography, Dresden University of Technology, 01062 Dresden, Germany
b
Institute of Soil Science and Site Ecology, Dresden University of Technology, 01062 Dresden, Germany
Abstract. Several decades of intensive dryland farming took place in the Gadarif Region, located
in the East part of Sudan. Rapid land-use/land-cover (LULC) changes occurred within this region,
mainly due to agricultural expansion, government policies and environmental calamities such as
drought. In this paper, an attempt has been made to analyse and monitor the LULC changes using
multi-temporal Landsat data of the years 1979, 1989 and 1999 and ASTER data of the year 2009.
In addition, efforts were made to discuss the impacts of LULC changes on selected soil properties.
For this, a post-classification comparison technique was used to detect LULC changes from the
satellite images. Three main LULC types were selected to investigate the properties of soil
namely: cultivated land, fallow land and woodland. Moreover, soil samples were collected from
ten sample plots for each LULC type at two depths of surface soil. For these soil samples, various
soil properties such as texture, bulk density, organic matter, pH, electrical conductivity (EC), sodium adsorption ratio (SAR), phosphorous and potassium were analysed. The results show that
there is a significant and extensive change of LULC patterns which occurred in the last three decades in the studied area. Further, laboratory tests revealed that soil properties were significantly affected by the LULC changes. It was observed that in the cultivated land bulk density increased,
whereas organic matter decreased in the cultivated land as compared to the woodland and fallow

land. The values of the physical and chemical soil properties of soil were found to be lower in the
cultivated land as compared to the woodland and the fallow land. The change of the physical and
chemical properties of the soil may be attributed to the changes in the LULC contributing to land
degradation, which in turn leads to a decline in soil productivity. Consequently, dryland-farming
systems within the study area need to be improved by developing sustainable land use practices in
order to reduce the rate of soil degradation in the future.
Keywords. Land use/land cover, change detection, remote sensing, Gadarif Region, Sudan

Introduction
Land degradation is a global problem associated with desertification, loss of biological diversity and
deforestation in drylands, which covers some 47 % of the globe’s surface. As a consequence, Africa
accounts for 65 % of the total extensive cropland degradation of the world. LULC change constitutes a major part of the land degradation and also a main cause of global environmental changes
[1-4]. Many studies have been showing that land use change and conversion can lead to deterioration in the physical and chemical properties of soils and degradation of a land [5-7].
The large modern mechanized rainfed agriculture sector has developed since 1944/45 in the Gadarif
Region, when the government projected to cultivate the cracking clays of Central Sudan, in order to
meet the food needs of army units stationed in the British colonies in East Africa. During 1960ies the
1

Corresponding Author: Khalid Biro, Institute for Cartography, Helmholtzstr. 10, 01062 Dresden, Germany; E-mail:
khalidturk76@yahoo.co.uk.

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

private sector was involved to invest in this sector, which resulted to massive vegetation cutting. Therefore, fallow lands occurred every four years, while the investors started continuously clearing new areas.
By the end of 1970ies and in the early 1980ies most of the land was used for mechanised farming [8].
This situation, coupled with mono cropping of sorghum, lack of crop rotation and inappropriate methods of soil preparation and management resulted in severe land degradation problems. Rapid physical,
chemical and biological deterioration of the soil and subsequent declines in agricultural productivity and
deterioration of the environment took place within the region [9]. This paper describes the LULC
changes in the northern part of the Gadarif Region during the period 1979 – 2009.
The Gadarif State is located in the eastern Sudan between 12º and 13ºN and 33º and 37ºE. Its
average altitude is 600 m a.s.l. (Figure 1). The region is characterized by semi-arid climatic conditions where rainfall is erratic and concentrated in only few months of the year, mainly from June to
October. The annual rainfall in the northern part is less than 500 mm. Temperature ranged from a
mean minimum of 22ºC in winter to a mean maximum of 37ºC in summer, while in the hottest
April or May it may reach 40-42ºC [10]. Soils are heavy dark cracking clays; the clay content is
very high and generally amounts to 70 to 80%. Organic matter and nitrogen contents of the soil are
low, but as there is no deficiency of other plant nutrients: the soils are moderately fertile.

Figure 1. Location of the study area.
1. Data and methodology
1.1. Remote sensing data and image processing

Multi-temporal Landsat data of the years 1979, 1989 and 1999 and ASTER data for the year 2009
with a path/row of 171/50 (Table 1) were used in this study. ASTER data was ordered in level 1B
quality, which is radiometrically and geometrically corrected [11]. The images were selected in the
same season to minimize the influence of seasonal variations on the result. Ancillary data on the different land use/land cover features and their location points were recorded using a GPS instrument.
Table 1. Remote sensing data used in this study
Satellite
Landsat-3 MSS
Landsat-5 TM
Landsat-7 ETM+
ASTER

306

Acquisition date
05.Nov. 1979
16.Dec. 1989
21.Nov. 1999
17.Jan. 2009

Spectral resolution

1 – 4 bands
1 – 7 bands
1 – 8 bands
1 – 3 bands

Ground resolution
57 m
30 m
30 m
15 m

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

The Landsat MSS, TM and ETM+ were geo-referenced to the ASTER image of 2009 using approximately 20 – 30 well-distributed GCPs. Second-order polynomial equations were used, and,
like for the MSS image, maximum RMS error was 0.7 pixels. Using the nearest neighbour method
the images were resampled to a pixel size of 30 m × 30 m. Atmospheric correction was not applied
in this study, because the post-classification comparison approach adopted for LULC change analysis compensates for variation in atmosphere conditions and vegetation phenology between dates
since each LULC classification is independently mapped [12,13].
1.2. Classification scheme and land use/cover classes
A classification scheme (Table 2), based on the land cover and land use classification system developed by Anderson et al. (1976) was adopted [14]. Training areas were selected based on image interpretation and extensive field survey. Supervised classification using maximum likelihood classification (MLC) was employed to classify the individual images independently [15].

1.3. Classification accuracy assessment
Accuracy assessment determines the quality of the information derived from remotely sensed data.
It is most reliable when using reference data collected on the ground or from aerial photographs at
or near the time of satellite overpasses [16]; however, in the African countries, such data is rarely
available for retrospective remote sensing data analyses [17]. In this study the accuracy of the 1979,
1989 and 1999 classified images was assessed by visual interpretation of the unclassified satellite
images [18]. In the present study a total of 300, 352, 347 and 360 pixels were selected for the LULC
maps of 1979, 1989, 1999 and 2009 respectively. Error matrices as cross-tabulations of the classified data vs. the reference data were used to assess the classification accuracy. Overall accuracy,
user’s and producer’s accuracies, and the Kappa statistic were then derived from the error matrices.
A multi-date post-classification comparison (PCC) change detection algorithm was used to determine the LULC changes in four intervals: 1979–1989, 1989–1999, 1999–2009, and 1979–2009. It
is the most commonly used method for LULC change detection [19,20].
Table 2. Description of the land use/cover classes identified in the study area
Classes
Cultivated land

Woodland

Fallow land
Bare land
Settlement

Water

Descriptions
Areas currently under crop or land being prepared for raising crops. Physical
boundaries are broadly defined to encompass the main areas of agricultural activity
and not based on the exact field boundaries.
All wooded areas with 5–20% tree canopy cover, where the canopy is composed of
single stemmed and woody plants greater than 5m in height. This class also includes
shrubs and grass bushes cover, mainly of perennial species.
Cultivated areas which left to be recovered after long period of cultivation. Shrubs,
wood bushes and grasses occur in this class. Fallow age usually 4 – 10 years.
Non-vegetated areas such as bare rocks or areas with very little vegetation cover,
where soil exposure is clearly apparent.
An area covering houses, manmade structures, such as towns and villages.
Reservoirs and water ponds.

1.4. Soil sampling and analysis
Soil samples were collected from three LULC types: cultivated land, fallow land and woodland. The
soil was sampled at two different depths: 0-5 cm and 5-15 cm from 10 sample plots for each LULC
type. A bulk soil sample from each plot was collected, air-dried and passed through a 2 mm sieve.

Soil particle size fractions were determined by the hydrometer method after dispersion with
sodium hexametaphosphate solution. Dry bulk density (BD) was calculated by dividing the oven
dry mass at 105° C by the volume of the core. Soil pH was measured on a 1:1 soil to water ratio
suspensions, while organic matter (OM) was determined according to the Walkley and Black
307

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

method [21]. Available Phosphorus (P) was determined using the Olsen sodium bicarbonate extraction [22], while ammonium acetate was used to extract and determine available potassium (K) in the
soil [23]. Electrical conductivity (EC) was determined in (1:5) soil:water suspensions [24].
2. Results and discussion
2.1. Classification accuracy assessment and LULC mapping
An overall LULC classification accuracy level for the four images varies ranging from 86 to 92%,
with Kappa indexes of agreement ranging from 76 to 88% (Table 3). The image-processing approach was judged to have been effective in producing compatible LULC data over time, irrespective of the differences in spatial, spectral and radiometric resolution of the satellite data.
According to (Figure 2) cultivated land, woodland and fallow land were the dominate LULC
classes for the maps of 1979 and 1989. The map of 2009 illustrates that the predominant LULC
classes were cultivated land, fallow land and settlement. However, significant spatial expansion in
cultivated land and the large decrease in woodland were observed in the 1989 LULC map. The area
of cultivated land increased to 80.47%, while woodland area was reduced to 7.78%. A slight increase of 6.87% in the spatial extent of settlement and bareland was also observed. Spatial analysis
of the 1999 LULC map reveals increases in woodland and settlement areas, which occupied 19.33

and 5.31% respectively, while the cultivated area decreased to 69.87%. Nevertheless considerable
spatial expansion in cultivated land and the rapid decrease in woodland were observed in the 2009
LULC map. General patterns of LULC identified in all four images were largely dominated by cultivated land, woodland and fallow land areas (Figure 3). A significant increase of the cultivated land
indicates that deforestation, due to the expansion of the mechanized rain-fed agriculture, has been a
continuing trend in the study area.

Figure 2. Land use/land cover map for the year 1979, 1989, 1999 and 2009.

308

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

Table 3. Summary of classification accuracies (%) for 1979, 1989, 1999, and 2009
1979
LULC

1989

1999

2009

‫٭‬Prod.

‫٭٭‬Use.

Prod.

Use.

Prod.

Use.

Prod.

Use.

87
90
83
81
85

89
81
81
85
85

95
76
72
70
77

90
81
86
88
94

92
83
72
81
100

91
82
87
90
100

92
100
85
86
97

94
85
80
97
92

100

100

100

100

Cultivated land
Woodland
Fallow land
Bareland
Settlement
Water
Overall accuracy
Kappa statistic

86
86
78

89
76

90
82

100
92
88

‫٭‬Prod. = Producer’s accuracy, ‫٭٭‬Use. = User’s accuracy
1200

1000
Cultivated land

Area (km²)

800

Woodland
Fallow land

600
Bareland
Settlement

400

Water
200

0
1979

1989

1999

2009 Year

Figure 3. Areas of LULC classes in the study area

2.2. Impacts of LULC change on soil properties
2.2.1. 2.2.1 Soil texture and bulk density
Within the different LULC types, clay content in the surface layers (0-5 and 5-15 cm) varied from
58.73 to 64.89%, while silt fractions ranged from 26.92 to 36.99% (Table 4). Clay content were
higher in the cultivated land and fallow land compared to woodland at the layer 0-5 cm, however
there was no significant difference (p > 0.05) between the three LULC types at the depth of 5-15 cm.
Table 4 show that sand and silt fractions differed significantly across LULC types. There was a significant difference (p > 0.05) for bulk density values between the three LULC types (Table 4).
Table 4. Soil particle size distribution and soil Bulk density (BD) of the main LULC types (mean values, n = 10, SE in
parenthesis, different letters within one row indicate significant difference at P < 0.05)
Soil Properties
Sand (%)
Silt (%)
Clay (%)
BD (g/cm³)

Depth (cm)

Cultivated Land

Fallow Land

Woodland

0–5
5 – 15
0–5
5 – 15
0–5
5 – 15
0–5
5 – 15

7.96 (1.85) c
8.45 (2.06) bc
27.61 (0.42) a
26.92 (0.59) a
64.42 (1.59) bc
64.62 (1.67) a
1.56 (0.007) c
1.72 (0.045) c

6.10 (0.25) bc
7.03 (0.40) ab
32.40 (1.05) b
28.10 (1.13) ab
61.51 (1.03) ab
64.89 (1.10) a
1.37 (0.007) b
1.56 (0.007) b

4.28 (0.54) ac
3.58 (0.30) a
36.99 (1.61) c
32.97 (0.95) c
58.73 (1.22) a
63.45 (1.02) a
1.33 (0.005) a
1.42 (0.010) a

309

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

2.2.2. Soil pH and organic matter (OM)
Soil pH value was slightly higher for the cultivated land compared to the woodland. This is attributed to the reduction of organic matter and the ploughing processes of cultivated fields. A trend of
increased organic matter content for the fallow land and woodland was observed in the both depths
of soil. Obviously, the conversion of woodland into the cultivated land has led to a drop in soil organic matter contents.
2.2.3. Electrical conductivity (EC) and sodium adsorption ratio (SAR)
EC values in the soils of fallow land were found to be significantly different (p < 0.05) from the
soils of cultivated land and woodland (Table 5). Also, the Statistics given in Table 5 indicated that
LULC types differed significantly regarding the SAR of soils. The high values of EC and SAR in
the fallow land compared to the cultivated land and woodland is attributed to the leaching of soil
bases and subsequent reduction in soil carbonate. Sodium is well known as a dispersing agent in
soils. Thus, alkaline conditions and the presence of sodium contribute to the degradation of vertisols.
2.2.4. Available phosphorus and exchangeable potassium
The data in (Table 5) reveals that, the available phosphorus was higher in the fallow land than that
for the cultivated land and woodland. High phosphorus content in fallow land can be attributed to
fewer uptakes by the grasses and shrubs, which usually dominated in fallow areas. However, the
low phosphorus contents under woodland systems with a relatively high-tree density could be attributed to high plant uptake and possibly to higher sequestrations of phosphorus in tree biomass,
because of the comparatively larger number of trees per unit area and massive root network in these
systems. The exchangeable potassium content decreased with the soil depth for all LULC types (Table 5). Soil potassium was significantly different (p < 0.05) across the three LULC classes. The
higher amount of potassium in the first layer is attributed to the increases of potassium fixation with
the increasing soil pH. Also, potassium still exists in soil, although organic matter decreases with
erosion.
Table 5. Selected soil chemical properties of the main LULC types (mean values, n = 10, SE in parenthesis, different
letters within one row indicate significant difference at P < 0.05)
Soil Properties

Depth (cm)

Cultivated Land

Fallow Land

Woodland

pH

0–5

7.39 (0.019) bc

7.33 (0.015) b

7.23 (0.032) a

5 – 15
0–5
5 – 15
0–5
5 – 15
0–5
5 – 15
0–5
5 – 15
0–5
5 – 15

7.38 (0.019) bc
0.93 (0.034) a
0.92 (0.038) a
147 (11.74) b
156 (7.25) b
1.80 (0.13) b
1.70 (0.15) b
1.06 (0.10) a
0.96 (0.16) a
0.60 (0.03) b
0.51 (0.04) b

7.33 (0.016) b
1.22 (0.014) c
1.20 (0.022) c
188 (13.44) bc
171 (8.77) bc
2.00 (0.15) bc
2.30 (0.34) bc
6.10 (1.10) c
5.76 (1.29) c
0.74 (0.60) bc
0.57 (0.04) bc

7.25 (0.027) a
1.13 (0.028) b
1.08 (0.022) b
138 (11.44) ab
142 (4.58) ab
1.40 (0.16) ab
1.40 (0.16) ab
1.44 (0.07) b
1.44 (0.30) b
0.44 (0.02) a
0.43 (0.03) ab

OM (%)
EC (µS)
SAR
P (mg/kg)
K (meq/kg)

3. Conclusions
In the present study the analysis of LULC changes over three decades revealed a highly dynamic
interchange of land use, driven by competition for land between forest, agriculture and urbanisation.
The land degradation which appeared in the study area particularly in agriculture is a result of rapid
LULC changes. Development of sustainable land use practices in the dryland-farming of the study
area need to be improved in order to reduce the amount of soil degradation in the future.
310

Khalid BIRO, et al.: Use of Multi-temporal Satellite Data for Land-Use/Land-Cover Change Analyses

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