پيش‌بيني تغييرات اراضي ساخته ‌شده و رشد شهري با استفاده از داده‌هاي سنجش ‌از دور

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

ﺷﺪ ﭘﺮاﮐﻨﺪه ﺷﻬﺮي ﻣﻨﺸﺄ ﺑﺴﯿﺎري از ﻣﺸﮑﻼت ﺷﻬﺮﻫﺎي ﺟﻬﺎن و در ﻧﺘﯿﺠﻪ ﻋﺪم ﺑﺮﻧﺎﻣﻪرﯾﺰي و ﻣﺪﯾﺮﯾﺖ ﻣﻨﺎﺳﺐ اﺳﺖ. اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﻪ ﺗﺤﻠﯿﻞ ﻓﻀﺎﯾﯽ و زﻣـﺎﻧﯽ اﻟﮕـﻮي رﺷـﺪ ﺷـﻬﺮي و ﭘﯿﺶﺑﯿﻨـﯽ آن در ﺷـﻬﺮ رﺷـﺖ ﺑـﺎ ﻫـﺪف ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﺮاي آﯾﻨﺪه ﭘﺮداﺧﺘﻪ اﺳﺖ. دادهﻫﺎي ﻣﻮرد اﺳﺘﻔﺎده در اﯾﻦ ﺗﺤﻘﯿﻖ ﺷﺎﻣﻞ ﺗﺼـﺎوﯾﺮ ﻟﻨﺪﺳـﺖ 7 ،5 و 8 ﺑﺮاي ﺑﺎزه زﻣﺎﻧﯽ 1987 ﺗﺎ 2017 اﺳﺖ. روش اﻧﺠﺎم اﯾﻦ ﺗﺤﻘﯿﻖ ﺑﺪﯾﻦ ﺻﻮرت اﺳـﺖ ﮐـﻪ ﺑـﺎ اﺳـﺘﻔﺎده از ﺷـﺎﺧﺺ NDISI اﻗﺪام ﺑﻪ اﺳﺘﺨﺮاج ﺳﻄﻮح ﻧﻔﻮذﻧﺎﭘﺬﯾﺮ ﺷﻬﺮي ﺷﺪه اﺳﺖ ﺳﭙﺲ ﺑﺎ اﺳﺘﻔﺎده از ﻣـﺪل ﺳـﻠﻮلﻫﺎي ﺧﻮدﮐـﺎر- ﻣﺎرﮐﻮف اﻗﺪام ﺑﻪ ﭘﯿﺶﺑﯿﻨﯽ ﺳﻄﻮح ﻧﻔﻮذﻧﺎﭘﺬﯾﺮ ﺷﻬﺮي ﺑﺮاي ﺳﺎل 2032 ﺷـﺪه اﺳـﺖ. ﻋﻤﻠﮑـﺮد اﯾـﻦ روشﻫـﺎ ﺑـﺎ اﺳﺘﻔﺎده از 300 ﻧﻤﻮﻧﻪ ﮐﻪ ﺑﻪﺻﻮرت ﺗﺼﺎدﻓﯽ اﻧﺘﺨﺎب ﺷﺪه اﺳﺖ ﻣﻮرد ارزﯾﺎﺑﯽ ﻗﺮار ﮔﺮﻓﺘﻪ اﺳﺖ. ﻧﺘﺎﯾﺞ اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺣﺎﮐﯽ از دﻗﺖ ﺑﺎﻻي ﺷﺎﺧﺺ NDISI ﺑﺮاي اﺳﺘﺨﺮاج ﺳﻄﻮح ﻧﻔﻮذﻧﺎﭘﺬﯾﺮ )86/12 ﺗﺎ 89/78 درﺻﺪ( ﺑـﻮده اﺳـﺖ. ﻋﻼوه ﺑﺮ اﯾﻦ، دﻗﺖ ﻣﺪل CA-Markov ﺑﺮاي ﭘﯿﺶﺑﯿﻨﯽ ﺳـﻄﺢ ﻏﯿﺮﻗﺎﺑـﻞ ﻧﻔـﻮذ در ﺳـﺎل 2018 ﺣـﺪود 83/21 درﺻﺪ ﺑﻮد. ﻧﺘﺎﯾﺞ اﻟﮕﻮي رﺷﺪ ﺷﻬﺮي ﻣﺸﺎﻫﺪه ﺷﺪه و ﻣﻮرد اﻧﺘﻈﺎر ﺑـﺎ ﯾﮑـﺪﯾﮕﺮ ﺗﻄـﺎﺑﻖ ﻧﺪاﺷـﺘﻪ و داراي اﺧـﺘﻼف ﺑﻮدهاﻧﺪ. ﻧﺘﺎﯾﺞ ﮐﻠﯽ ﺗﺤﻠﯿﻞ درﺟـﻪ آزادي )𝜒2=2/96( و آﻧﺘﺮوﭘـﯽ ﺷـﺎﻧﻮن )3/08 =H( ﺣـﺎﮐﯽ از اﻟﮕـﻮي رﺷـﺪ ﭘﺮاﮐﻨﺪه ﺑﻮدهاﻧﺪ. ﺳﭙﺲ H و 𝜒2 ﺑﺮاي ﻣﺤﺎﺳﺒﻪ درﺟﻪ ﺧـﻮب ﺑـﻮدن ﺷـﻬﺮي )1/12 - =G( ﻣـﻮرد اﺳـﺘﻔﺎده ﻗـﺮار ﮔﺮﻓﺘﻨﺪ و اﯾﻦ ﭘﺎراﻣﺘﺮ ﻧﺸﺎن داد ﮐﻪ اﻟﮕﻮي رﺷﺪ رﺷﺖ ﻧﺎﻣﻨﺎﺳﺐ و ﺑﺪ اﺳﺖ. اﯾﻦ ﻣﺸﮑﻞ را ﻣﯽﺗﻮان ﺑﺎ ﺑﺮﻧﺎﻣـﻪرﯾﺰي ﺷﻬﺮي ﺣﻞ ﮐﺮد.

IntroductionThe 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 methodsThe 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 discussionThe 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. ConclusionThis 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.