assessment of the spatio-temporal … · proceedings, 6th international conference on cartography...

Proceedings, 6th International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria

ISSN: 1314-0604, Eds: Bandrova T., Konecny M.

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ASSESSMENT OF THE SPATIO-TEMPORAL EVOLUTION OF

AGUELMAM SIDI ALI LAKE USING MULTITEMPORAL

LANDSAT IMAGERY (MIDDLE ATLAS - MOROCCO)

Faiza Menjour, Fouad Amraoui, Toufik Remmal

Laboratoire de Géosciences Appliquées à l'Ingénierie de l'Aménagement, Faculté des Sciences,

Université Hassan II-Casablanca. [email protected]

Abstract

Aguelmam Sidi Ali Lake is an endorheic depression located in the volcano-karst environment of the Moroccan Middle

Atlas. this permanent depression of about 44m depth is mostly fed by karst springs and meteoric runoff waters. The

regular observations from the lake’s banks has found that a very sensitive withdrawal of their level of submersion

during the dry years. The purpose of this project is to present the spatiotemporal changes model of Aguelmam Sidi Ali

Lake during the period 1985-2015 through the application of the Modified Normalized Difference Water Index

(MNDWI) and Water Ratio Index (WRI) using the Landsat 5-TM and 8-OLI images. The Overall Accuracy and Kappa

Coefficient were calculated to assess the accuracy of the results. The results indicate an intense decreasing trend in the

Lake in the dry period 1985-2007. During the wet period 2007-2015, the results show a progressive increasing trend of

the level of reference covering about one third of its surface.

Keywords: Aguelmam Sidi Ali Lake, Middle Atlas, landsat Imagery, water Index, surface water, spatiotemporal

changes.

INTRODUCTION

The Middle Atlas is not only the "Morocco's water tower" (Hardy and Celerier, 1927), the plateau which extends from

Azrou to Tamayoust form even the hydrographic node of the entire country (Blache, 1919); the mountain supplies the

most important rivers (Oum-er-Rbia and Sebou) or provides them the tributaries the most numerous interspersed by

lakes and lakebed whose grandeur varies by location. The most extended, the best known and the most visited of the

same district is undoubtedly the Aguelman Sidi Ali Lake.

Located in the eastern of the central Middle Atlas, the lake Sidi Ali, whose first bibliographic citation returns to J.

Blache (1919), is one of the most important natural lakes of Morocco, and represents a rare ecosystem in North Africa

(Chillasse and al., 2001).

In June 1975, after the measures carried out by the Public Laboratory of tests and studies (LPEE-Casablanca), the lake

was full and ranged in depth to more than 44 m, the water area represented 337 ha for the volume stored 35 million m3

(ABHS, Unpublished document). Into account the rainfall deficits observed in Morocco between 1980 and 2008 (EL

jihad, 2003; Sayad, 2005; El Bouqdaoui and al., 2006; Stour and Agoumi, 2008), the lake Sidi Ali, as other lakes (EL

Jamal, 2006; Ladouki et al., 2008), experienced a strong lowering water levels (Amhaouch, 1995; ABHS, 2007, 2009).

The daily measures effectuated by an observer of the Hydraulic Basin of Sebou Agency (ABHS) offer a chronic data

relatively long. But following a hardware problem (absence of measurement slide), the observations have been

interrupted in December 2009 and only resumed in 2011. To make up for this gap of field data, hydrological modeling

of the lake has been used.

The tracked changes using remote sensing is widely used in different applications, such as the hydrogeology (Dronova

and al., 2011; Zhu and al., 2011), the changes of occupation of the soil (Salmon and al., 2013; Demir and al., 2013) and

of the forest (Kaliraj and al., 2012; Markogianni, 2013), the monitoring of natural disasters (Volpi and al., 2013; Brisco

and al., 2013) The urban growth (Bagan and al., 2012; Raja and al., 2013) etc. It provides reliable information about the

spatial distribution of open surface water in different scientific disciplines, in wetland monitoring (Desmet and al.,

1996; Davranche and al., 2010; Behera and al., 2012; Sun and Al., 2012), flood monitoring and disaster assessment

(Schumann and al., 2011; Zhang et al., 2002; Qi and Altinakar, 2011; Kuenzer and al., 2013) surface water area

estimation (Du and al., 2012; Muster and al., 2013) and water resources management (Ding and Li, 2011; Van Dijk and

Renzullo, 2011). Remote sensing provide an enormous amount of data that have become primary sources, widely used

for detecting and extracting surface water and its changes in recent decades from satellite data (Nath and Deb, 2010 ;

McFeeters, 2013).

mailto:[email protected]

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Following the increase and the availability of satellite images, the development of tools for geographic data analysis

(Geographical Information Systems “GIS”) and image processing techniques, numerous scientific studies have

attempted to extract and delineate the masses of water from these images (Smith, 1997). The detection of water is not

difficult; the challenge is to delimit exactly the shoreline water (true land–water interface) which is unclear in most

cases.

This study aimed to model the spatiotemporal changes of Lake Sidi Ali in the period 1985-2015 using the multi-

temporal data of Landsat TM-5 (Thematic Mapper) and OLI-8 (Operational Land Imager) by putting them with the

changing climate conditions. In each temporal image of the Landsat TM 1985, TM 1995, TM 2007 and OLI 2015, the

surface area of the lake has been extracted individually, two spectral water index methods, such as the modification of

Normalized Difference Water Index; NDWI of Xu or MNDWI (Xu, 2006) and Water Ratio Index (WRI) (Shen and Li,

2010) have been examined for extracting and mapping the open water.

MATERIALS AND METHODS

The general methodology followed for this research is presented in the flow chart in figure 1, it shows the main tasks:

description of the study area, downloading data, pretreatment of images, application of the different spectral indices,

extraction of the open water in each image, modeling and calculates of efficacy.

Figure 1. Flow chart of suggested methodology in the present study

Study area

The Aguelmane Sidi Ali (ASA) depression, located 55 kilometers south of Azrou, belongs to the folded Middle Atlas

(33° 03' N, 5° 00' W) for 2070 to 2080 meters of altitude. It presents an elongated form of direction NE-SW with

dimensions can exceed 3 km in length and 1 km in width when the water level is high (Sayad and al., 2011). In a

mountainous environment, the lake is surrounded to the east by the Jbel Sidi Ali (2338 m), to the west and the south-

west by the basaltic flow which separates it from the plain of Taanzoulte and to the north by the volcano of Sidi Ali

(figure 2).

The apparent watershed is approximately 15.6 km2 and it’s composed of lower Liasic dolomite and limestone, upper

Bajocian limestone and Plio-Quaternary basalts (Martin, 1981; Baali, 1998). The lake is bordered by two major NE-SW

trending faults (including South Middle Atlas Fault) associated with a broad network of submeridian trending fractures

(Baali, 1998; Hinaje, 2004).

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This permanent lake is mostly fed by karst springs and meteoric runoff waters received directly in the depression. Some

intermittent streams are located in the west and resurgences found in the east of area (Aghbalou Oumançour and

Aghbalou Taddat). A supply by springs located at the bottom of the lake itself was highlighted in a physico-chemical

study of its deep waters (Amhaouch, 1995).

Lake ASA has a multi-lobed outline, and the lake floor, up to 44m deep, has semi-circular depressions that are probably

karstic dolines (figure 3), so this Lake ASA could be called an uvala (coalesced collection of karstic collapses). Such

funnel-like coalesced dolines are abundant in the Middle Atlas append in all Liasic formation areas including the lower

Liasic dolomites (Martin 1981).

The region is dominated by a humid climate with an average temperature of 10.4°C, the Rainfall patterns is

Mediterranean; the wet period lasts from October to May, while the dry period and temperate goes from June to

September.

Figure 2. Structural map of the Middle Atlas

(Zizi, 2002; Lakroud, 2015), and the location of

the Aglmam Sidi Ali lake.

SMAF: South Middle Atlas Fault;

AOF: Ait Oufella Fault; NMAF:

North Middle Atlas Fault; TTF: Tizi

n’Tretten Fault. Jurassic, cretaceous

and paleogene synclines:

1:Bekrite; 2:Timahdite; 3:Bou-

Anguer; 4:Aïn-Nokra; 5:Oudiskou ;

6:Tirhboula; 7:Ait Oufella;

8:El-Mers; 9:Guigou.



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Figure 3. Bathymetric map of Lake Aguelmane Sidi Ali.

Public Laboratory of tests and studies (LPEE-Casablanca) surveys, 1975; modified)

Satellite data

Three scenes of Landsat-5 TM (Thematic Mapper) acquired in May 1985, April 1995 and April 2007 and one scene of

Landsat-8 OLI (Operational Land Imager) acquired in May 2015 were obtained from (U.S. Geological Survey website)

in GeoTiff format. The idea behind the selection of the months April and May was based on a minimum of cloud cover

(preset to less than 10%) and the availability of images. The main characteristics of these satellites imagery are

illustrated in Table 1.

Table 1. Specifications of Landsat TM and OLI data.

Satellite Sensor Date Bande Name Bandwidth (µm)

Landsat-5 TM

May 1995

April 1995

April 2007

Band 1 Blue

Band 2 Green

Band 3 Red

Band 4 NIR

Band 5 SWIR 1 / MIR

Band 7 SWIR 2

0.45 – 0.52

0.52 – 0.60

0.63 – 0.69

0.75 – 0.90

1.55 – 1.75

2.08 – 2.35

Landsat-8 OLI May 2015

Band 1 Coastal

Band 2 Blue

Band 3 Green

Band 4 Red

Band 5 NIR

Band 6 SWIR 1

Band 7 SWIR 2

Band 9 Cirrus

0.43 – 0.45

0.45 – 0.51

0.53 – 0.59

0.63 – 0.67

0.85 – 0.87

1.56 – 1.65

2.10 – 2.29

1.36 – 1.38

Data Pre-Processing

The primary data of the sensors do not represent with precision the soil targets which have been photographed, because

these data are sometimes distorted during the scan process and digital data conversion. A series of atmospheric and

radiometric corrections were applied to all images in order to correct this distortion related to the sensor, satellite and

Earth. Subsequently, they were georeferenced using the Lambert Conformal Conic (LCC) projection zone1 and finally,

the extraction of the study area is helpful to reduce the size of image file to include only the area of interest (AOI). This

not only eliminates the extraneous data in the file, but it speeds up processing due to the smaller amount of data

process. The software ENVI 5 and ArcGIS 10 were used to treat and view satellite data in the present study.



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Spectral Water Index Methods

In order to detect the spatiotemporal changes of Lake ASA in the period 1985-2015, the water surface of the lake in

each temporal image of the Landsat TM 1985, TM 1995, TM 2007 and OLI 2015 was extracted individually.

By doing so, the performance of two indices satellite-derived including Modified Normalized Difference Water Index

(MNDWI) (Xu, 2006) and Water Ratio Index (WRI) (Shen and Li, 2010) were examined for the extraction surface

water (Table 2).

Table 2. Satellite-derived indexes used for water features extraction

Index Equation Reference Normalized Difference Water Index NDWI = (Green – NIR) / ( Green + NIR) McFeeters, 1996

Modified Normalized Difference Water Index MNDWI = (Green – MIR) / ( Green + MIR) Xu, 2006

Water Ratio Index WRI = (Green – Red) / ( NIR + MIR) Shen et Li, 2010

The Normalized Difference Water Index (NDWI) ''NDWI'' was introduced by McFeeters (1996), to delimit the water

surface in function the relation between the reflectance of the green and the near infrared (NIR). Xu (2006) was

replaced the reflectance of the near infrared by the reflectance of the middle infrared (MIR) and suggested a change

NDWI called Modified Normalized Difference Water Index (MNDWI) and he observed that the MNDWI is

more precise in the delineation of wetlands than the NDWI, it could eliminate the pixels not characteristics of the water

such as vegetation, soil, urban…

Generally, the near infrared (NIR) and the middle infrared (MIR) bands are of high potential for detecting wetlands

(Lillesand and Kiefer, 1994). Therefore, the near infrared band (NIR: 4th band in Landsat 5 and 5th band for Landsat 8)

is generally preferable, because the NIR is strongly absorbed by water, but, in the same extent reflected by terrestrial

vegetation and/or dried soil (Sun, 2012). In this regard, these indices were calculated, analyzed and classified

independently from the Landsat images to evaluate their performances for extraction of surface water.

A threshold land-water suitable for each index was applied using the Otsu method to classify images in two classes;

suppose the pixels of an image range from [a, ⋅⋅⋅, b], where - 1 ≤ a < b ≤ 1. Using this method, the pixels can be divided

into two classes: a non-class water ranging from [a, ⋅⋅⋅, t] and a water class ranging from [t ⋅⋅⋅, b] where t is the

threshold value, this method allows you to separate the water bodies from the background feature and on different

luminous tones adjusted by using a simple editing tool (Wenbo and al., 2013).

The threshold values were set to zero (Mcfeeters, 1996; Xu, 2006), but threshold adjustment in individual situations can

achieve a more accurate delineation of water bodies (Ji and al., 2009).

RESULTS

Once the required satellite data were obtained and the image pre-treatment techniques were realized, the indices

MNDWI and WRI were applied and examined for the extraction of surface water from the Landsat TM and OLI data.

The numbers of pixels above the threshold value were together counted to represent the lake outline recoded in white

color (figure 4).



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Figure 4. Illustrates the resulting MNDWI and WRI images after application of various thresholds for Lake

ASA between 1985 and 2015. White color refers to water and black color refers to no water.

The Overall Accuracy and Kappa Coefficient were calculated to support the accuracy of the results and to evaluate the

efficiency of the methods proposed for detecting changes of the lake. The water areas in 1985 and 2015 estimated and

the assessment analyses are presented in table 3.

Table 3. Performance evaluation of the satellite-derived indexes and accuracy assessment analyses.

Sensor Acquisition

Date index

Land-Water

Threshold Lake Area (ha) Overall

Accuracy (%) Kappa

Coefficient

TM May 1985 MNDWI -0,13 224 95,81 0,91

WRI 0,78 223 93,64 0,87

TM April 1995 MNDWI -0,19 170 93,52 0,88

WRI 0,81 166 90,98 0,85

TM April 2007 MNDWI -0,054 155 98,67 0,97

WRI 0,8 156 94,13 0,94

OLI May 2015 MNDWI 0,43 231 92,89 0,85

WRI -0,045 225 86,25 0,72

The results show that the MNDWI provides a highest result than the WRI for the extraction of water surface; it seems

the errors in the results are due mainly to the omission (deficit error) and/or the commission (surplus error) effects of

water pixels around the edges of the lake. In this study, the MNDWI was used to model the spatiotemporal changes of

Lake Aguelmane Sidi Ali in the period 1985-2015. For this purpose, the lake surface area in each year was extracted

through the classification of the MNDWI images using image-specific thresholds referred in the table. Finally, the

multi-temporal lake surface area maps were overlaid to produce the lake surface area changes map in the period 1985-

2015 (figure 5).



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Figure 5. Lake ASA surface area changes map in the period 1985 – 2015.

DISCUSSION AND CONCLUSION

This present work aims to model the spatiotemporal changes of Lake ASA during the period 1985-2015 from the

Landsat images 5-TM and Landsat 8-Oli. Through a comparative analysis, the index MNDWI was selected and

employed in this study to extract the extended water surface of the lake.

The bathymetric map of Sidi Ali (hydrometric surveys - LPEE, 1975), has allowed to have a reference situation of the

lake, which extended over 307 ha and accumulated a water volume of 35 mm3 (Figure 3).

Based on the areas deducted from the treatment of the Landsat images to the four dates, the water volumes of the Lake

correspondents were estimated by comparison to the reference situation of 1975, assuming that the surface area is

proportional to the volume. Table 4 summarizes these data:

Table 4. Statistics of the lake surface area and volume changes (1975 – 2015)

Year Lake Surface Area (ha) Lake volume (Mm3) Lake volume change (Mm3)

1975 307 35 1985 224 25.5 - 9.5 1995 170 19.4 - 6.1 - 8.7/40 years 2007 155 17.7 - 1.7 2015 231 26.3 + 8.6

This information reflects the different situations of the lake on forty years. The water area is reduced in the dry period to

155 ha in 2007; with a stored volume less than 18 Mm3. In wet period, this area had reached more than 300 ha in 1975

with volume of water stored of 35 Mm3.

Between 1975 and 1985, the surface area of the lake is reduced by 27% with a destocking of approximately 9.5

Mm3. This is linked to the recurrent drought that began in 1980.

Between 1985 and 1995, the downward trend is continued; the lake has lost again one quarter of its area with

another destocking of approximately 6 Mm3.

Between 1995 and 2007, the decline continued but with a cadence more low. In effect, the lake is reduced by

approximately 9 % with a loss of 1.7 Mm3.

Between 2007 and 2015, the trend is reversed through a series of rainy years. The surface area of the lake has

extended to more than 50%, with an additional volume stored of some 8.6 Mm3.



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It is therefore fair to say that the extended of the lake and, in consequence, the volume stored can fluctuate in time

according to the meteoric contributions.

To better understand these multiannual fluctuations of the lake, these results are coupled with the rainfall data of the

meteorological station of Sidi El Makhfi, followed by the ABHS, and located at 10 km SW of Azrou. Figure 6

represents deviations in percentage from average of the annual rainfall (531 mm). This graph shows the irregularity of

the precipitations during 35 years, with a lot of dry years, except for the year 1996 and the cycle 2009-2011 which were

exceptionally watered.

Figure 6. Deviations in percentage from average of the annual rainfall (Sidi El Makhfi, ABHS, 1980 – 2015)

Between 1985 and 1995, there is a cumulative deficit of rain of 86%, which has led to a reduction of the lake reserve of

6.1 Mm3. This allows estimating by deduction, the contribution equivalent to an annual average of the rain to 7 Mm

3.

The same reasoning applied to the period 1995-2007 gives the following results: shrinking of Lake of 1.7 Mm3, while

the accumulation of deviations from average recorded a surplus of 16%. In minimizing arbitrarily of half the effect of

the wet year 1996, this accumulation becomes deficit of 30%. The average annual rainfall of 5.7 Mm3 is deduced

from this. The period 2007-2015 is characterized by the significant rainfalls with an accumulation of deviations from

average largely in surplus of 178%, which has led to an increase in the lake reserves of 8.6 Mm3. Reduced to the year,

the average annual rainfall would be 4.8 Mm3.

The results of these calculations prove that the average annual precipitation at Lake ranges from 4.8 to 7 Mm3,

representing an average of 5.8 Mm3. The gap between the extreme values can be explained by a number of factors: the

importance of the snowfall and hence of the runoff deferred, the initial situation of the system, the piezometric level of

the aquifer, the variations in temperature, etc...

This study may be useful in order to prevent the water state of the lake in the future. Indeed, it could be projecting on

favorable or unfavorable scenarios and quantify the consequences in terms of areas and volumes of the lake. Thus, at

maturity of 2035 (20 years), and in context of aridification with a rainfall annual deficit of 10%, the equivalent of two

rainy years will be lost, i.e. a volume of 11.6 Mm3, corresponding to a withdrawal of water approximately 102 ha.

Compared to 2015, the surface area of the lake would increase from 231 to 129 ha. In the favorable case (10% in

addition annually) and for the same term 2035, the lake would gain a volume of 11.6 Mm3 and would be spread out on

102 ha. Compared to 2015, the lake area would increase from 231 to 333 ha.

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