沙漠研究 19-1, 313- 317 (2009 )Journal of Arid Land Studies

The Role of Remote Sensing in the Monitoring of Arid Ecosystems Dynamics-The Case of Bouhedma National ParkB. ATTOUI1, A. OULED BELGACEM2
Abstract: The exceeded and traditional techniques of the monitoring of arid ecosystems become
futile. The great evolution of remote sensing with higher resolution images and its large scaleutilization in natural resources monitoring and assessment has shown good results all over theworld. It can be therefore considered as an efficient technique for ecosystems dynamics
monitoring through a diachronic analysis of data satellites (Landsat images, Radar, etc.).
The aim of this work is to monitor the ecosystem dynamics in Bouhedma national park, abiosphere reserve considered as the most important protected area in southern Tunisia.
To meet this objective, two radiometric indicators, the soil adjusted vegetation index(SAVI) and the soil brightness index (SBI) were determined from three Landsat images (1985,1995 and 2003).
The characterisation of environment degradation is necessarily based on diachronic studies
so that physical and biological changes affecting the components of ecosystems can be detected.
The Multitemporal analysis in Bouhedma National Park showed that the SAVI is mostly
influenced by both grazing with introduced fauna and precipitation whereas the SBI is influenced
by the soil moisture. Therefore, the ecosystems of study area alternated between regression andregeneration cycles.
Keywords: Dynamics, Ecosystems, Remote sensing, SAVI, SBI

1. Introduction
Remote sensing techniques have long been applied for the quantitative and qualitative evaluation of theecosystems dynamics in the southern of Tunisia. Many studies have demonstrated the ecologic importance of
the “protected areas” in reserving biosphere, by remote sensing techniques and, visible and Near Infrared (NIR)
multispectral images, following their very useful data to examine vegetations patterns and corresponding
ecological process at regional and global scales.
Radiometric indicators, derived from remotely sensed data have frequently been proposed as a method forpredicting ecosystems situations, and they become efficient because of the large-scale surveys required tomonitor the ecosystems dynamics, and for an economically and logistically reasons. However, no attempts
have been made to use these indicators to monitor ecosystems in the study area (Bouhedma national park). To
achieve this objective, we should choose some indices, that they can study all components of an ecosystem(principally soil and vegetation), and in the same time they could show the relation between these components.
There are many techniques of remote sensing which can show the response of an ecological system live has
screw the changes related to different parameters of the environment, but many of these techniques can’t respondto the fixed objectives, especially when we are looking for monitoring the evolution of arid ecosystems, andthat’s correlated with many conditions and obstacles dependent on the image processing and the necessary
corrections to extract wished information, as well as the difficulties of access to the ground, if one has a methodof follow-up by inventory of ground and mapping. In this study, we apply and evaluate a vegetation index (theSAVI: Soil Adjusted Vegetation Index), in order to estimate the vegetation dynamics between 1985 to 1995 and1995 to 2003. This radiometric indicator represents the second generation of indices, it’s much evolved when
we talk about compensation of the effect of the soil. In addition to SAVI, we try to apply the Soil Brightness
Index (SBI) in the aim to show the evolution of the second component of the ecosystems: the soil. The
importance of this index appears principally in its potential to show the different situations of soils, byrepresenting their different reflectance and brightness, and the principal changes.

1

Laboratoire d'Erémologie et Lutte Contre la Désertification, Institut des Régions Arides, Route de Jorf Km 22.5,Médenine 4119, Tunisie ; E-mail : attoui24@yahoo.fr
2Laboratoire d’écologie pastorale, Institut des Régions Arides, Route de Jorf Km 22.5, Médenine 4119, Tunisie.
E-mail : azaiez.ouledbelgacem@ira.rnrt.tn

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Fig. 2. Georeferenced satellite
images of the park.

Fig. 1. Maps of Bouhedma national park’s location in Tunisia

2. Materials and Methods
2.1. Study Area
The study area was Bouhedma National Park, a biosphere reserve considered as the most important
protected area in Tunisia. This park is located in a zone called Bled Talah, because of the specifity of this zoneby the presence of the Acacia tortilis. This area was considered, along the time as the most successful reserve
for rehabilitation and reintroduction of some species in danger. The Park is located in central Tunisia, in thenatural region of eastern plain low Bled Talah and Seguis (Le Houerou, 1959). The geographical coordinates of
the Park are: 34˚15’ to 34˚30’North and 9˚15’ to 9˚40’Est. The territory of the observatory is part of the coreprotected area of Bouhedma national Park which covers an area of 16488 ha, of which 3660 ha are fully
protected (protected zone) and 11625 ha have the status of biosphere reserve (Mab Unesco) since 1977.
2.2. Satellite Data Processing
We used 3 Landsat 7 Thematic Mapper (TM) images of the study region with a spatial resolution of30
m. The satellite images represent the end of rainy season (March-April) of the years 1985, 1995 and 2003. The
pre-processing of the Landsat TM data in this study includes several correction algorithms carried out in several
steps, including radiometric and geometric corrections of the Landsat TM data.
2.2.1. Radiometric correction
This is the conversion of the TM 8-bit digital numbers into physical quantities of radiance and reflectance(Markham and Barker 1986 and Thome et al. 1997). Digital Numbers (DN) are converted to spectral calibrated
radiances (L) using the following formula:
L = Gain ∗ DN + Offset
The lower and upper radiance limits are related to their correspondent digital number, respectively thelower digital number (Qmin) and the upper digital number (Qmax) that are respectively equal to 0 and 255. In ourcase, Lmin and Lmax are determinated from the sensor in the moment of tacking image by satellite.
Table 1. Main characteristics of studied images.
ImagesSensor

34Image 1985
Lmin
Lmax
-122043-15
2062

Image 1995
Lmin
Lmax
-122043-15
2062

Image 2003
Lmin
Lmax
-50
2344
-51
2411

2.2.2. Geometric corrections
Geometric corrections were done so that the Landsat TM data had the same geometrical information as theground observations (Jensen 1986). These errors were correcting using ground control points (GCPs) on thesurface of the earth where both image coordinates could be identified with acceptable accuracy.
2.2.3. Satellite Data Analysis
Many different methods were developed for monitoring arid ecosystems dynamics, but in our case weused 2 indices, from 2 groups that characterize the vegetation cover and soil condition. The first index
belongs to the group of vegetation indices (VIs) based on the Red/Near-infrared (R/NIR) slope. This

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group of VIs is used when there are strong influencesof soil (background) brightness on vegetation indices,and special VIs must be applied in order to reduce thebackground noises (Huete, 1986; Huete and Jackson,1987). From this group we selected Soil Adjusted
Vegetation Index (SAVI: Huete, 1988) is defined byan additional soil adjustment factor (L). In our case
L=0.5, because of the sparse vegetation cover.
⎛ NIR − R ⎞SAVI = ⎜⎟ × (1 + L )⎝ NIR + R + L ⎠The second index represents the first axis ofmodel “Tasseled Cap” (Jackson, 1983). Indeed, thisauthor defined this index as the first axis of the model,whish represents the right soil owner to the soil pixels
on the satellite image.
SBI = G 2 + NIR 2 + R 2

Where: “G” is the green slope.
2.3. Undirected classification
2.3.1. Ecosystems mapping
Referring to studies of Tarhouni et al. (2003), toplan of Bouhedma national park March 2007 and to
the truth of land, it was able to identify the different
vegetal groups that are in the protected area. This Fig. 3. Vegetation and soil maps of the protected areaof the national park of Bouhedma.
map will serve as the support to determinate statistic
values of indices. It will also serve, as thetechnical support to apply the different process of the VI; The SAVI. With
regard to the study of the SBI, we will refer to the soil map.
2.3.2. Choice of drives pieces
In this part of the study, we are interested to delimit a whole of the pieces,which appear most homogeneous on the various images. The choice of
these areas is not arbitrary, since, starting from the soil map and that of thevegetation communities, we could identify that each group is a piece of driveon which we will represent ROIs (Region Of Interest).
After identifying the pieces of the drives, we will represent, in each ofFig. 4. Representation of thethem, 3 polygons. These polygons take the shape of rectangle, and by
Regions Of Interest.
identifying their statistics, we take the weakest standard deviation likeparameter of homogeneity. In fact, the choice of the weakest standard
deviation is not arbitrary because it seems necessary to highlight the homogeneity of such or such grouping, andmoreover, the choice of 3 polygons in each piece has as a utility to show the SAVI and the SBI variations even
within only one grouping.
3. Results and Discussion
3.1. Results of SAVI processing
After different analysis of the vegetable index, it seems so important to classify images in order ofpanchromatic appearance. In fact, the procedure is so usually, we have only to show the statistics of the index after
calculations of the VIs. This statistics will help us to identify different histograms, in the raison to show how
dynamics of ecosystems alter between progressive and regressive.
Since the average recovery of the dynamics of vegetation taken in this study is the SAVI index, it seems
important to represent the results of the classification, in other words the results of the variation of this index, in theraison to assess changes in this area through a survey Diachronic (variation of such a test on a given area for aperiod of time or slightly less extent). The average reflectance weaker, developed by the statistics of ROIs in316classification will be taken as a reference for thetreatment of the change in SAVI.
3.2. Multitemporal analysis of SAVI
3.2.1. Regression between 1985 and 1995
This phenomenon of regression is also
explained by the high grazing pressure exerted
Fig. 5. Delimination of homogeneous units on the basis ofat the plant groups, mostly perennial grasses.
SAVI index.
Indeed, grazing pressure of introduced fauna tothe park, can lead to regression of species withhigh pastoral value. We can validate the rate variations Table 2. Annual rainfall of the studied region
between 1985 and 1995.
affected during the period between 1985 and 1995, throughyear
rainfallRemarkthe histograms analysis, in order to have an argument about
1985
169
Normal year
the regressive dynamic between these two dates. Indeed,
Deficit year
1990
96,2
the regression histogram shows that there are significant
variations in plant groupings in the period under review,
1995
84
Deficit year
Source: Metrological station of “Mezzouna”
but what is more important is that these variations have anegative effect on vegetation in the study area. That’s
why the percentages of changes in ecosystems seem Table 3. Annual rainfall of the studied region
between 1995 and 2003.
negative figure below.
YearRainfall
Remark
3.2.2. Progression between 1995 and 2003
199584
Deficit year
After analyzing the values of SAVI, it was able to2000
110
Deficit year
clarify ideas well suited to the dynamics of vegetation
2003
158
Normal year
between these two dates. In fact this increase is mainly
Source: Metrological station of “Mezzouna”
due to significant changes in weather conditions, especiallybetween these two dates, as the Table 3 shows excellent
rainfall precipitation values that promote the developmentof different plant species groups registered in this area.
3.3. Results of SBI processing
The results of calculations of SBI from the images arepresented in Figure 6. As an indicator used to evaluatechanges in ecosystems in the region of Bouhedma, mainly
Fig. 6. Test of homogeneous units on the basis
the protected area, it seems very important to analyze theof SBI index.
data and values acquired by SBI. Therefore, it seems thata diachronic study is needed to clarify the ideas for changes
affected on this area and identify the causes of those changes. The soil brightness index on his part reflects
changes in shades of bare soil and rocks. The passage of dark-tinted shades is accompanied by a simultaneous
increase in radiometric values in both channels. The index also varies inversely proportional manner withmoisture and soil roughness. The value of soil brightness index varies mainly between 0 and 1.
3.4. Multitemporal analysis:
3.4.1. Regression between 1985 and 1995
This increase of SBI is explained by the fact that soil moisture fell between these two dates, since theseparameters are inversely proportional. In fact, Table 2 shows that during the period 1985-1995 there was adecrease in rainfall, which justifies the increase in values of this index. Based on the rising values of the SBI,
which was explained by the fact degradation of ecosystems of this area, we can deduce such a relationship between
this index and vegetation index (SAVI). Because as it was explained that such elevated values of SBI reflects anincrease in the percentage of bare soil compared to covered soil, which leads us to confirm the results presented by
the SAVI, as regards the degradation of vegetation cover.
3.4.2. Progression between 1995 and 2003
The analysis of the progression histogram shows that the values of SBI decreased during the period from

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1995 to 2003, which leads us to affirm the progressive dynamics between these two dates. In fact, the analysis ofthese values shows that the space occupied by the bare soil in this area, declined during the period between 1995
and 2003, and it reflects the presence of plants that cover these soils. This has already been proven by analysis
SAVI during this period, which shows that there is a regeneration of different plant groups with very important
chlorophyll activities because of the occurrence of rainfall. The diminution of SBI values is also explained by anincrease in the humidity of different classes of land, as Table presenting data rainfall between 1995 and 2003
shows that during this period the study area has received significant rainfall, which may have a direct effect onsoil moisture. The histogram below represents the rate of change (%) of soil types recorded between 1995 and2003.
4. Conclusions
This study was conducted in the Protected area of Bouhedma National Park aiming at quantifying
ecosystem dynamics during the years 1985, 1995 and 2003 on the basis of two space parameters namely Soil
Adjusted Vegetation Index (SAVI) and the Soil Brightness Index (SBI). The main findings of this study are asfollows.
The multitemporal studies related to SAVI results show that changing values of this index is linked torainfall and grazing by introduced wildlife while neglecting the effect of human intervention since the study isdone within the protected zone of the park. Analysis shows that the increase in SAVI values indicates that there
is a progressing dynamic and there is a regeneration level of plant groups and a very important chlorophyllactivity. This is due to good rains and a moderate grazing pressure and the absence of human intervention.
This phenomenon described has already been identified for the time period going from 1995 to 2003. On theother hand, lower values of SAVI in a few plant groups shows the degradation of vegetation cover.
Regarding SBI, diachronic studies shows that this index is inversely proportional to soil moisture, and forthis reason that in the period between 1985 and 1995 has identified a dynamic regressive because of drought
unlike the period between 1995 and 2003, relatively humid, where there was a progressing dynamic. At thisstage, it was noted that these two indices are complementary, in other words we can combine the results of thisindices to study the dynamics of arid ecosystems, to have a clear idea about changes affecting two componentsof an ecosystem (soil, vegetation), which are both in direct relation (sol = support; vegetation = supported).
Finally, the results obtained in this study show the importance of these indices in assessing the dynamics of
dry land ecosystems. Further work on the relevance of these indices as indicators of environmental monitoring
space seem necessary.
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