spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

spatio - temporal analysis of the dead sea area using remote sensing and GIS-based model : markov - cellular automata

spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

 

 

 

spatio - temporal analysis of the dead sea area using remote sensing and GIS-based model : markov - cellular automata
spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

 

تأليف: maher el-hallaq(مشرف):: mohammed habboub(اعداد)
اللغة: إنجليزي
النشر: gaza strip:islamic university of gaza 2013
المكان: خانيونس-مكتبة الجنوب المركزية-مجموعات خاصة/خـانيونس
النوع: رسائل جامعية
عدد الصفحات: 69
المواضيع: engineering and allied operations:: الهندسة
رقم التصنيف: 620
الرقم العام: 1342012

العنوان : spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

ABSTRACT

 

Understanding spatio-temporal changes is essential to many aspects of engineering, geographic and planning researches.
The area of the Dead Sea surface – at the end of the fifties – was about 1000 km2. The altitude of the surface was around 350 m below sea level. Since 1978, the Dead Sea has retreated, and the sea body turned into two basins: the principal northern one that was about 631.27 km2 with a water level of about -430 m MSL (in 2010), and the shallow southern one with the Lisan Peninsula and the Lynch Straits in between, which has a sill elevation of about 400 m below the sea level.

In this research fourteen satellite imageries are collected from 1972 to 2010 in addition to 2011-ASTGTM-DEM. Satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume.

Dead Sea shrinks by 2.5 km2/yr while the water level decreases by 0.7 m/yr. Consequently, the volume changes by – 0.33 km3/yr.
The use of Dead Sea water for the industry by both Israel and Jordan is one of the essential factors that affect area of the Dead Sea.

The intensive human water consumption from the Jordan and Yarmouk Rivers for other usage are the main reasons of that shrinkage in area as well.

The direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to slopes of bathymetry.
No shrinkage is considered from the east direction due to the same reason since the bathymetric slope is so sharp. In order to make a rational prediction of the Dead Sea shape, data is prepared for suitability map creation using Markov chain analysis and Multicriteria Evaluation (MCE).

Then, Markov Cellular Automata model and spatial statistics are used in prediction and validation processes. The validation process shows a standard Kappa index of 0.9545 which means a strong relation between the model and reality.

The predicted shapes of 2020, 2030 and 2040 follow the same conditions from 1984 to 2010. The predicted areas of 2020, 2030 and 2040 are 610, 591 and 574 km2 which are considered a logical extension of the trend from 1984 till 2010. So, Markov – Cellular Automata model can be used to predict closed seas as the Dead Sea.

spatio - temporal analysis of the dead sea area using remote sensing and GIS-based model : markov - cellular automata
spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

 

 

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تحميل

 

spatio - temporal analysis of the dead sea area using remote sensing and GIS-based model : markov - cellular automata
spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata
spatio - temporal analysis of the dead sea area using remote sensing and GIS-based model : markov - cellular automata
spatio – temporal analysis of the dead sea area using remote sensing and GIS-based model : markov – cellular automata

طالع أيضا

المصدر 

 

 

الاقتباس

Habboub, Mohammed (2013). Spatio -Temporal Analysis of the Dead Sea Area Using Remote Sensing and GIS-Based Model: Markov – Cellular Automata. الجامعة الإسلامية – غزة. http://hdl.handle.net/20.500.12358/18790

Spatio -Temporal Analysis of the Dead Sea Area Using Remote Sensing and GIS-Based Model: Markov – Cellular Automata

 

مؤسسة المجلة العربية للعلوم و نشر الأبحاث أنشئت عام 1436هـ 2015م، وهي متخصصة في مجال النشر العلمي ومن خلال سنوات عملها، أثبتت المؤسسة لها مكاناً في مجال التحكيم والنشر العلمي في العالم العربي من خلال مجموعة من المجلات العلميّة المحكّمة والمتخصصة والمعتمدة دوليا من الكثير من الجامعات و الكليات و المفهرسة في عدد من قواعد البيانات العالمية.

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