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Land Use/Land Cover Classification Using Remote Sensing Techniques . A Case Study AL Mazraa Sub-District .

تصنيف استخدامات الأراضي و توزع الغطاء النباتي باستخدام تقنيات الاستشعار عن بعد ( مركز ناحية المزرعة أنموذجاً )

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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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Studying of land use changing Detection needs to the speed of implementation to convoy the changes on the ground. the traditional ways in the analysis and visual interpretation of the images and field studying need a lot of time and effort. So The objective of this search is to classify group images (Landsat TM , ETM+) taken in different dates automatically, and then to calculate the area of each land use/land cover, during the years studied (1990 -2000- 2010) and comparison areas to identify the most important changes occurring during that period.


Artificial intelligence review:
Research summary
تناولت الدراسة التي أعدتها الدكتورة صفية عيد مناع أحمد عيسى استخدام تقنيات الاستشعار عن بعد لتصنيف استخدامات الأراضي وتوزيع الغطاء النباتي في منطقة مركز ناحية المزرعة بمحافظة السويداء في سوريا. استخدمت الدراسة مرئيات فضائية من التابع الأمريكي لاندسات الملتقطة في أعوام مختلفة (1990، 2000، 2010) لتصنيف استخدامات الأراضي آلياً وحساب مساحات كل نوع من الغطاء الأرضي. بلغت دقة التصنيف المراقب 79%، وهي دقة جيدة يمكن الاعتماد عليها في الدراسات العامة. أظهرت النتائج زيادة في مساحة الأراضي الزراعية المروية والأراضي الزراعية بشكل عام على حساب تراجع مساحة التكشفات الصخرية من البازلت والبازلت المختلط بالتربة، كما زادت مساحة الاستعمالات العمرانية بشكل واضح خلال الفترة المدروسة. توصلت الدراسة إلى أن النطاقين الخامس والسابع من معطيات لاندسات يمكن استخدامهما بنجاح في الفصل بين التكشفات الصخرية والاستعمالات العمرانية، بينما يمكن استخدام النطاق الرابع في فصل الزراعات المروية بدقة كبيرة. أوصت الدراسة بالتوسع في استخدام الطرق الآلية في تصنيف المرئيات وزيادة دقة التصنيف باستخدام مرئيات ذات دقة مكانية أكبر.
Critical review
دراسة نقدية: تعتبر الدراسة خطوة مهمة في استخدام تقنيات الاستشعار عن بعد لتصنيف استخدامات الأراضي، ولكن هناك بعض النقاط التي يمكن تحسينها. أولاً، على الرغم من أن دقة التصنيف بلغت 79%، إلا أن هناك مجالاً لتحسين هذه الدقة باستخدام مرئيات ذات دقة مكانية وطيفية أعلى. ثانياً، كان من الممكن أن تكون الدراسة أكثر شمولية إذا تضمنت تحليلاً أعمق للعوامل المؤثرة على تغير استخدامات الأراضي، مثل العوامل الاجتماعية والاقتصادية. ثالثاً، يمكن أن تكون الدراسة أكثر فائدة إذا تم تطبيقها على مناطق أخرى لمقارنة النتائج وتعميم الاستنتاجات. وأخيراً، كان من الممكن تحسين الدراسة بإجراء مزيد من الدراسات الميدانية للتحقق من نتائج التصنيف الرقمي.
Questions related to the research
  1. ما هي الأهداف الرئيسية للدراسة؟

    الأهداف الرئيسية للدراسة هي تصنيف استخدامات الأراضي وتوزيع الغطاء النباتي باستخدام تقنيات الاستشعار عن بعد، وتحليل قيم الانعكاسية الطيفية للأهداف المختلفة، وتحديد الأطوال الموجية الأفضل في الفصل بين الأهداف، ورصد تغيرات استخدامات الأراضي في منطقة الدراسة خلال الفترة من 1990 إلى 2010.

  2. ما هي دقة التصنيف التي تم تحقيقها في الدراسة؟

    بلغت دقة التصنيف المراقب 79%، وهي دقة جيدة يمكن الاعتماد عليها في الدراسات العامة التي لا تحتاج إلى معلومات تفصيلية.

  3. ما هي الأطوال الموجية التي تم استخدامها بنجاح في الفصل بين الأهداف المختلفة؟

    تم استخدام النطاقين الخامس والسابع من معطيات لاندسات بنجاح في الفصل بين التكشفات الصخرية (البازلت) والبازلت المختلط بالتربة والاستعمالات العمرانية، بينما تم استخدام النطاق الرابع في فصل الزراعات المروية بدقة كبيرة.

  4. ما هي التغيرات الرئيسية في استخدامات الأراضي التي تم رصدها خلال الفترة المدروسة؟

    أظهرت الدراسة زيادة في مساحة الأراضي الزراعية المروية والأراضي الزراعية بشكل عام على حساب تراجع مساحة التكشفات الصخرية من البازلت والبازلت المختلط بالتربة، كما زادت مساحة الاستعمالات العمرانية بشكل واضح خلال الفترة المدروسة.


References used
GOTEZ , S.J; Prince; S.P; THAWLEY, M.M ;SMIT , A.J.Applications of multitemporal land cover information in the Mid-Atlantic Region.2000
DMITRY, L.V; Wright, R . K; Goetz, S. J. Advances in land cover classification for applicationsresearch. a case study from the mid-Atlantic(RESAC) Department of GeographyUniversity of Maryland . 2006
HUI, Y. R .Development and evaluation of advanced classification systems using remotely sensed data for accurate land-use \land cover mapping . north Carolina state university 2002
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