پیش‌بینی تغییرات اراضی ساخته ‌شده و رشد شهری با استفاده از داده‌های سنجش ‌از دور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران

2 گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، ایران

چکیده

رشد پراکنده شهری منشأ بسیاری از مشکلات شهرهای جهان و در نتیجه عدم برنامه‌ریزی و مدیریت مناسب است. این مطالعه به تحلیل فضایی و زمانی الگوی رشد شهری و پیش‌بینی آن در شهر رشت با هدف برنامه‌ریزی برای آینده پرداخته است. داده‌های مورد استفاده در این تحقیق شامل تصاویر لندست 5، 7 و 8 برای بازه زمانی 1987 تا 2017 است. روش انجام این تحقیق بدین ‌صورت است که با استفاده از شاخص NDISI اقدام به استخراج سطوح نفوذناپذیر شهری شده است سپس با استفاده از مدل سلول‌های خودکار- مارکوف اقدام به پیش‌بینی سطوح نفوذناپذیر شهری برای سال 2032 شده است. عملکرد این روش‌ها با استفاده از 300 نمونه که به‌صورت تصادفی انتخاب‌ شده است مورد ارزیابی قرار گرفته است. نتایج این مطالعه حاکی از دقت بالای شاخص NDISI برای استخراج سطوح نفوذناپذیر (12/86 تا 78/89 درصد)  بوده است. علاوه بر این، دقت مدل CA-Markov  برای پیش‌بینی سطح غیرقابل نفوذ در سال 2018 حدود 21/83 درصد بود. نتایج الگوی رشد شهری مشاهده ‌شده و مورد انتظار با یکدیگر تطابق نداشته و دارای اختلاف بوده‌اند. نتایج کلی تحلیل درجه آزادی (96/2 =) و آنتروپی شانون (08/3H= ) حاکی از الگوی رشد پراکنده بوده‌اند. سپس H و  برای محاسبه درجه خوب بودن شهری (12/1G= - ) مورد استفاده قرار گرفتند و این پارامتر نشان داد که الگوی رشد رشت نامناسب و بد است. این مشکل را می‌توان با برنامه‌ریزی شهری حل کرد.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of built-up changes and urban growth using remotely sensed data

نویسندگان [English]

  • Keyvan Ezimand 1
  • Hossein Aghighi 1
  • Yasaman Asadi 2
  • Mohamad Javanbakht 2
1 Department of Remote Sensing and GIS, Faculty of Earth Science, University of Shahid Beheshti,Tehran, Iran
2 Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
چکیده [English]

Introduction
The world is rapidly moving towards urbanization, and with large populations living in cities, and ever increasing
population in urban areas, urban sprawl has occurred in many cities around the world. Lack of urban planning and
management regarding the development of urban sprawl has been known as the source of many problems in cities
around the world. Urban sprawl negatively impacts the environment, quality of life, social equity, climate change,
air pollution and LST. Therefore, one of the goals of this study is to show the pattern of urban growth from the
past to present, and predict the future of urban growth that may occur. It can be considered as an innovation of
this study, which was less prominent in previous studies.
Materials and Methods
The study area is Rasht, where its natural attractions and tourism characteristics have increased its population and
physical development. This research utilized Landsat 5 (TM), 7 (ETM+), and 8 (OLI/TIRS) images. In the first
step, the pre-processing operations including geometric correction, atmospheric correction, and radiometric
correction were performed on the remote sensing images. In the next step, Normalized Difference Impervious
Index (NDISI) values were computed and employed to extract the impervious surface information in urban area.
Then a hybrid cellular automaton–Markov (CA-Markov) model was used to predict both the quantity and spatial
distribution of impervious surfaces in the city of Rasht. The performances of these methods were evaluated using
300 randomly selected samples. Finally, statistical analysis has been used to show the growth pattern of Rasht
from its past to present and also to predict the future.
Results and Discussion
The results of this study indicated that the impervious surface of this city can be extracted with high accuracy
(from 86.12 percent to 89.88 percent) using NDISI computed from Landsat images. Moreover, the accuracy of
the CA-Markov model through prediction of impervious surface for the year 2018 was about 83.21 percent. With
regard to the results of this work, the observed and expected urban growth results are not consistent with each
other. The analysis of degree-of-freedom ( ) and Shannon's entropy ( ) are reflecting the
urban sprawl pattern. Then, H and were used to compute the degree of goodness ( ). This
parameter demonstrated that the growth pattern of Rasht was inappropriate.
Conclusion
This study shows that the sprawl of the city of Rasht can be characterized by a scattered growth that
is expected to be worse in future, if management measures are not taken by governmental authorities.
The results of this study can be useful for future urban planning and decision making such as preventing
vertical land use changes.

کلیدواژه‌ها [English]

  • Shannon’s entropy
  • Degree-of-freedom
  • Degree-of-goodness
  • Impervious surface
  • CA- Markov model

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