沙漠研究 19-1, 319- 322 (2009 )Journal of Arid Land Studies“Desert Technology IX ”
Refereed Paper

Mapping Long-Term Variability of Vegetation Greenness and Sand Dunes around
Watering Points in the Rangelands of Dahar and El Ouara (Tunisia) during thePeriod 1975-2000 through Remote Sensing Data
Bouajila ESSIFI1, Mohamed OUESSAR*2, Mohamed Chedly RABIA1
Abstract: This study investigates the ability of satellite remote sensing and GIS tools to map
temporal and spatial patterns of sands dunes, vegetation greenness variability and theirinterrelationships around water points in the rangelands of Dahar and El Ouara (Tataouine-Tunisia) during the period 1975-2000. Use was made of both supervised and non-supervisedclassification, vegetation indices, image differentiation, change detection, and pattern metrics ofLandsat MSS, TM and ETM+ time series images. While the physical framework (rainfall andwind erosion) emerges as the dominant causative factor for the degradation, there are evidencesof human-induced changes (dry-farming and livestock) coupled to climate trends. Although
the comparative study has not resulted in a consensus on either the direction of changes or its
underlying causes, the results indicate that the proposed method is applicable to identify thedynamic nature of the local ecosystems and its susceptibility to changes and, therefore, has
good potential for monitoring desertification processes.
Keywords: Change detection, Desertification, Rangelands, Remote sensing, Watering points

1. Introduction
Desertification and land degradation encompasses a wide range of physical and biological processes:
decline of harvest, plant cover deterioration, exacerbation of soil physical mechanisms, qualitative andquantitative deterioration of water resources, and air pollution (Mainguet, 1998). Such quantitative and/orqualitative degradation may be accompanied by irreversible or reversible changes (Tarhouni et al., 2006)
which might affect the entire ecosystem components, and consequently, the human well beings. Studying
the erosion risks through tracking the distribution of ecological indicators is a key issue for rangeland
ecosystem management. Among varied criteria proposed to assess desertification severity, vegetal coverseems to be the most relevant indicator, particularly in case when results are obtained from remote sensing
images (Huang and Siegert, 2006). With lack of precise and adequate tools to monitor desertification,
several studies have emphasized remote sensing possibilities in the Tunisian arid regions (Le Floc’h et al.,1978). Satellite data, jointly analyzed with other data (field work, socio-economy, etc.) in order to extract
understandable information that can be integrated in a GIS offer an undeniable potential for knowledge andmodeling environmental phenomena. In this context, this study aims at analyzing degradation anddesertification risks around watering points in the rangelands of the dry areas of southern Tunisia throughthe use of multi-temporal satellite data.
2. Materials and Methods
The study zone encompasses four watering points in the El Ouara plain (Bir Ouled Hamed and BirJaouacha) and in the Dahar plateau (Bir Mijna and Bir Angoud) located in the southern arid region ofTunisia (Tataouine). A multi-sources cartography was attempted to bring analysis elements about soil
degradation risks through detecting temporal and spatial changes of biomass and sand dunes and,
consequently determine high-risk zones in the area of watering points from 1975 to 2000. Change
detection of time-series Landsat (MSS, TM, and ETM+) images was used to map vegetation (natural orcultivated) and sand dunes evolution around the studied watering points. Figure 2 summarizes different
levels of preprocessing, processing, and post-processing of remote sensing multi-temporal data, run mainly
in IDRISI software.

1

Dépt de Géographie, Fac. de Manouba, Univ. de Manouba, 2010 Manouba, TUNISIA;
Institut des Régions Arides (IRA), 4119, Médenine, TUNISIA, Fax: +216-75633006;* Corresponding author: Med.Ouessar@ira.agrinet.tn

2320Radiometric & Geometric Data Preprocessing

Image Enhancement:
Photo-Interpretation

Classification:
Supervised: Maxlike
Non Supervised: Isoclust

Ancillary Data/Fieldwork

Accuracy Assessment

Transformation:
CC – FCCSAVI – PCA – TASS CAP

Change Detection Maps

Land Cover Maps

Results & Discussion

Fig. 1. Flow chart of the study methodology.
Table 1. Land cover classification scheme.
LAND COVER CLASSES - DAHARLAND COVER CLASSES - EL
CHANGE DETECTIONOUARA
CLASSES
Active Steppic Vegetation/Cropland
Active Steppic Vegetation
BV: Bright Vegetation
Sand Dunes
Sand Dunes/Psammophitic
BBS: Bright Bare Soil
Vegetation
Sparse Steppic Vegetation/Bare Soil
Bare Soil/Sparse Vegetation
OV/BS: OtherVegetation/Bare Soil

In El Ouara, characterized by very heterogeneous land cover, an Isoclust (Iterative Self Organizing
Cluster Analysis) non-supervised classification approach was applied whereas in Dahar, the training sites
with a large size relative to the different land cover categories were appropriately delineated, Maximum
Likelihood (Maxlike) supervised classifier was used (Eastman and Thiam, 2001). Although not entirely
adopted, the classification scheme (Table 1) has had the advantage of simultaneously coupling information
about vegetation and soil (Crews-Meyer and Adamo, 2006).
3. Results and Discussion
The resultsA), shown in El Ouara, are closely linked to satellite snapshots timing at the end of croplandseason (December). Therefore, these parcels, generally associated to soil degradation, were integrated in
the photosynthetically active vegetation class (Figs. 2 and 3). Though methodologically necessary, itcannot be interpreted as a sign of plant cover recovery. In fact, signs of vegetation decline aroundwatering points are the most expressed in the combination: bare soil/less chlorophyllian shrubs. This
class has been the most often observed in the region of Bir Ouled Hamed with about 61 % in 1975 and42 % in 1999, which means that water deficits is the normal situation in general. Similarly, these trends
are encountered in Bir Jaouacha: sand dunes have decreased from about 35 % in 1975 to 23 % in 1990.

Bir Jaouacha

668000667000668000Ô BBS: 19 ha/year – 35% to 23%
Change Detection-Sanddunes (1975-1990)

6660003574000

6640003574000

6670003575000

6660003575000

6650003576000

6640003576000

665000g

Ò BV: 27 ha/year
Change Detection-Vegetation (1975-1990)

Fig. 2. Change detection maps of sand dunes and vegetation between 1975 and 1999 around Bir Jaouacha.

321g3608000

3608000

3607000

3607000

3606000

3606000

3605000

Ô BBS: 3 ha/year – 25% to 23%
Change detection-Sand dunes (1975-1990)

Bir Ouled Hamed

6700006690006680006670006700006690006680006670003605000

Ò BV: 25 ha/year – 14% to 33%
Change detection-Vegetation (1975-1990)g

3607000

3607000

3606000

3606000

3605000Ô BBS: 7 ha/year – 25% to 20%
Change detection-Sand dunes (1990-1999)

Bir Ouled Hamed

6700006690006680006670006660006700006690006680006670006660003605000

Ò BV: 11 ha/year – 14% to 38%
Change detection-Vegetation (1975-1990)

Fig. 3. Change detection maps of sand dunes and vegetation between 1975 and 1999 around Bir Ouled Hamed.

According to the FAO methodology (Mtimet et al., 1983), the two watering points are classified in theclass of strong gravity for a percentage of sand dunes area between 15 and 30 % (Bir Jaouacha: 23 % in 1990,and Bir Ouled Hamed: 20 % in 1999). Besides, with a heavy population pressure on agricultural area, theerosion risk is strong to very strong. It seems that overgrazing, fuel-wood collection, and cropland expansion
through rangeland clearing are the major degradation causes around Bir Ouled Hamed and Bir Jaouacha.
In addition, livestock pressure is involved in the two sites, considered as crossing points for transhumant
flocks (Bir Ouled Hamed) or element of hydraulic network (Bir Jaouacha), and therefore there is higher water
demand. On the other hand, sand dunes can be explained through a redistribution realized by the erosion agents(active winds higher than 3 m/s). The situation is growing worse, after plowing: pulverized soils having noprotection against aeolian erosion which removes soil surface layer, and creates dune fields; especially in Bir
Jaouacha where the S-SE side evolves at present in dunes. The land cover change analysis in El Ouara is a
critical question for the interpretation of ecosystem condition because it indicates a probable recovery but it
greatly depends on climatic constraints and mapping scale. Nevertheless, it’s obvious that satellite images, eachone representing a fixed point in time, can record just a stage of an ongoing continuous process.
Visual inspection of the change detection maps gives an idea about spatial distribution of changes. In theDahar (Bir Mijna and Bir El Angoud) (Fig. 4), the results indicate that two sites can be qualified as moderately
sensitive to desertification with more accentuated degree in Bir Mijna: 6.71% of the studied zone was sandencroached in 2000. According to the FAO criteria (Mtimet et al., 1983), it corresponds to the class ofmoderate gravity. Furthermore, the annual rate of vegetal cover decrease between 1987 and 2000 is about
1.75%; which corresponds to the mean class of speed of desertification processes. In order to explain theseresults, it’s useful to mention that the plateau of Dahar is dominated by rocky outcropping and limestone crust;sands essentially accumulated in spreading zones (wadis). Considering the meteorological data of Remada
station as reference for the study zone, we can deduce that the region is particularly windy (January-May with
13-21 km/h), and consequently higher speed of sand redistribution. On the other hand, the degradation,
particularly in Bir Mijna, is due to flocks repetitive trampling and absence of croplands in the vicinity of thestudied sites. However, the observed vegetation cover reduction is spectacular following long years of drought.

322
585000590000595000585000590000595000NN

Bir Mijna

Bir Mijna#3645000

3640000

3645000

3645000

3640000

3640000#3645000

3640000

Bir Angoud
Bir Angoud##585000

590000585000595000590000595000Ô BV: 436 ha/year – 31% to 14%

Ò BV: 73 ha/year – 14% to 17%

CHANGE DETECTION MAP - VEGETATION (1975-1987)

CHANGE DETECTION MAP - VEGETATION (1987-2000)

585000590000595000585000590000595000N

N

Bir Mijna

Bir Mijna#3645000

3640000

3645000

3645000

3640000

3640000#

3640000

Bir Angoud

Bir Angoud##

5850003645000

5900005850005950000
Ô BBS: 62 ha/year – 6%
to 3%

5 Kl

CHANGE DETECTION MAP – SAND DUNES (1975-1987)

5900005950000

Ò BBS: 77 ha/year – 3% to 7%

5 KlCHANGE DETECTION MAP – SAND DUNES (1987-2000)

Fig. 4. Change detection maps of vegetation and sand dunes between 1975 and 2000 in Dahar.

Precipitations, often localized, don’t allow to steppic species to recover.
Annotation
A) The percentages are expressed here in relation to the extent of the studied area which differs in each site.
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