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

نوع مقاله : علمی -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]

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. 

کلیدواژه‌ها [English]

  • Shannon’s entropy
  • Degree-of-freedom
  • Degree-of-goodness
  • Impervious surface
  • CA- Markov model
-Almeida, C.M.D., Monteiro, A.M.V., Camara, G., Soares‐Filho, B.S., Cerqueira, G.C., Pennachin, C.L. and Batty, M., 2005. Gis And Remote Sensing As Tools For The Simulation Of Urban Land‐Use Change. International Journal Of Remote Sensing, v. 26, p. 759-774.
-Bek, M.A., Azmy, N. and Elkafrawy, S., 2018. The Effect Of Unplanned Growth Of Urban Areas On Heat Island Phenomena. Ain Shams Engineering Journal, v. 9(4), p. 3169-3177.
-Belal, A.A. and Moghanm, F.S., 2011. Detecting Urban Growth Using Remote Sensing And Gis Techniques In Al Gharbiya Governorate, Egypt, The Egyptian Journal Of Remote Sensing And Space Science, v. 14, p. 73-79.
-Bhatta, B., 2009. Modelling Of Urban Growth Boundary Using Geoinformatics. International Journal Of Digital Earth, v. 2, p. 359-381.
-Bhatta, B., Saraswati, S. and Bandyopadhyay, D., 2010. Quantifying The Degree-Of-Freedom, Degree-Of-Sprawl, And Degree-Of-Goodness Of Urban Growth From Remote Sensing Data, Applied Geography, v. 30, p. 96-111.
-Blake, R., Grimm, A., Ichinose, T., Horton, R., Gaffin, S., Jiong, S., Bader, D. and Cecil, L., 2011. Urban Climate: Processes, Trends, And Projections. Climate Change And Cities: First Assessment Report Of The Urban Climate Change Research Network, p. 43-81.
-Chakraborty, A. and Mcmillan, A., 2018. 2.17 - Gis And Scenario Analysis: Tools For Better Urban Planning. In: Huang, B. (Ed.) Comprehensive Geographic Information Systems. Oxford: Elsevier, 385 p.
-Chander, G. and Markham, B., 2003. Revised Landsat-5 Tm Radiometric Calibration Procedures And Postcalibration Dynamic Ranges. Ieee Transactions On Geoscience And Remote Sensing, v. 41, p. 2674-2677.
-Chander, G., Markham, B.L. and Helder, D.L., 2009. Summary Of Current Radiometric Calibration Coefficients For Landsat Mss, Tm, Etm+, And Eo-1 Ali Sensors, Remote Sensing Of Environment, v. 113, p. 893-903.
-Congalton, R.G., 1991. A Review Of Assessing The Accuracy Of Classifications Of Remotely Sensed Data. Remote Sensing Of Environment, v. 37, p. 35-46.
-Dadras, M., Shafri, H.Z.M., Ahmad, N., Pradhan, B. and Safarpour, S., 2015. Spatio-Temporal Analysis Of Urban Growth From Remote Sensing Data In Bandar Abbas City, Iran. The Egyptian Journal Of Remote Sensing And Space Science, v. 18, p. 35-52.
-Deep, S. and Saklani, A., 2014. Urban Sprawl Modeling Using Cellular Automata. The Egyptian Journal Of Remote Sensing And Space Science, v. 17, p. 179-187.
-Du, Z., Li, W., Zhou, D., Tian, L., Ling, F., Wang, H., Gui, Y. and Sun, B., 2014. Analysis Of Landsat-8 Oli Imagery For Land Surface Water Mapping. Remote Sensing Letters, v. 5, p. 672-681.
-Duncan, B., Sabagh, G. and Van Arsdol, M.D., 1962. Patterns Of City Growth, American Journal Of Sociology, v. 67, p. 418-429.
-ENVI, 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide, Accessed 19 December 2014.
-Estoque, R.C. and Murayama, Y., 2013. Landscape Pattern And Ecosystem Service Value Changes: Implications For Environmental Sustainability Planning For The Rapidly Urbanizing Summer Capital Of The Philippines, Landscape And Urban Planning, v. 116, p. 60-72.
-Estoque, R.C. and Murayama, Y., 2015. Classification And Change Detection Of Built-Up Lands From Landsat-7 Etm+ And Landsat-8 Oli/Tirs Imageries: A Comparative Assessment Of Various Spectral Indices. Ecological Indicators, v. 56, p. 205-217.
-Ezimand, K., Kakroodi, A.A. and Kiavarz, M., 2018. The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature. International journal of remote sensing, v. 39(23), p. 8428-8449.
-Fan, F., Wang, Y.. and Wang, Z., 2008. Temporal And Spatial Change Detecting (1998–2003) And Predicting Of Land Use And Land Cover In Core Corridor Of Pearl River Delta (China) By Using Tm And Etm+ Images. Environmental Monitoring And Assessment, v. 137, p. 127-147.
-Feng, Y., Liu, Y., Tong, X., Liu, M. and Deng, S., 2011. Modeling Dynamic Urban Growth Using Cellular Automata And Particle Swarm Optimization Rules, Landscape And Urban Planning, v. 102, p. 188-196.
-Firozjaei, M.K., Kiavarz, M., Alavipanah, S.K., Lakes, T. and Qureshi, S., 2018. Monitoring And Forecasting Heat Island Intensity Through Multi-Temporal Image Analysis And Cellular Automata-Markov Chain Modelling: A Case Of Babol City, Iran, Ecological Indicators, v. 91, p. 155-170.
-Foody, G.M., 2002. Status Of Land Cover Classification Accuracy Assessment, Remote Sensing Of Environment, v. 80, p. 185-201.
-Gamba, P. and Herold, M., 2009. Global Mapping Of Human Settlement: Experiences, Datasets, And Prospects, Crc Press, 285 p.
-Gidey, E., Dikinya, O., Sebego, R., Segosebe, E. and Zenebe, A., 2017. Cellular Automata And Markov Chain (Ca_Markov) Model-Based Predictions Of Future Land Use And Land Cover Scenarios (2015–2033) In Raya, Northern Ethiopia, Modeling Earth Systems And Environment, v. 3, p. 1245-1262.
-Goward, S.N., Davis, P.E., Fleming, D., Miller, L. and Townshend, J.R., 2003. Empirical Comparison Of Landsat 7 And Ikonos Multispectral Measurements For Selected Earth Observation System (Eos) Validation Sites, Remote Sensing Of Environment, v. 88, p. 80-99.
-Jaeger, J.A. and Schwick, C., 2014. Improving The Measurement Of Urban Sprawl: Weighted Urban Proliferation (Wup) And Its Application To Switzerland, Ecological Indicators, v. 38, p. 294-308.
-Jat, M.K., Garg, P.K. and Khare, D., 2008. Monitoring And Modelling Of Urban Sprawl Using Remote Sensing And Gis Techniques. International Journal Of Applied Earth Observation And Geoinformation, v. 10, p. 26-43.
-Jhawar, M., Tyagi, N. and Dasgupta, V., 2013. Urban Planning Using Remote Sensing. International Journal Of Innovative Research In Science, Engineering And Technology, v. 1, p. 42-57.
-Jiao, L., Mao, L. and Liu, Y., 2015. Multi-Order Landscape Expansion Index: Characterizing Urban Expansion Dynamics. Landscape And Urban Planning, v. 137, p. 30-39.
-Kohler, M., Tannier, C., Blond, N., Aguejdad, R. and Clappier, A., 2017. Impacts Of Several Urban-Sprawl Countermeasures On Building (Space Heating) Energy Demands And Urban Heat Island Intensities. A Case Study. Urban Climate, v. 19, p. 92-121.
-Kompil, M., Aurambout, J., Ribeiro Barranco, R., Jacobs-Crisioni, C., Pisoni, E. and Zulian, G., 2013. European Cities: Territorial Analysis Of Characteristics And Trends-An Application Of The Luisa Modelling Platform (Eu Reference Scenario 2013-Updated Configuration 2014). Eur.
-Kumar, J.A.V., Pathan, S. and Bhanderi, R., 2007. Spatio-Temporal Analysis For Monitoring Urban Growth–A Case Study Of Indore City. Journal Of The Indian Society Of Remote Sensing, v. 35, p. 11-20.
-Li, T. and Meng, Q., 2018. A Mixture Emissivity Analysis Method For Urban Land Surface Temperature Retrieval From Landsat 8 Data. Landscape And Urban Planning, v. 179, p. 63-71, p. 63-71.
-Li, X. and Gong, P., 2016. Urban Growth Models: Progress And Perspective, Science Bulletin, v. 61, p. 1637-1650.
-Li, Z., Goldstein, R.H. and Franseen, E.K., 2017. Meteoric Calcite Cementation: Diagenetic Response To Relative Fall In Sea-Level And Effect On Porosity And Permeability, Las Negras Area, Southeastern Spain, Sedimentary Geology, v. 348, p. 1-18.
-Lillesand, T., Kiefer, R.W. and Chipman, J., 2014. Remote Sensing And Image Interpretation, John Wiley & Sons, 420 p.
-Lima, G.N.D. and Magaña Rueda, V.O., 2018. The Urban Growth Of The Metropolitan Area Of Sao Paulo And Its Impact On The Climate. Weather And Climate Extremes, v. 21, p. 17-26.
-Liu, W., Zhan, J., Zhao, F., Yan, H., Zhang, F. and Wei, X., 2019. Impacts Of Urbanization-Induced Land-Use Changes On Ecosystem Services: A Case Study Of The Pearl River Delta Metropolitan Region, China. Ecological Indicators, v. 98, p. 228-238.
-Liu, X., Li, X., Chen, Y., Tan, Z., Li, S. and Ai, B., 2010. A New Landscape Index For Quantifying Urban Expansion Using Multi-Temporal Remotely Sensed Data, Landscape Ecology, v. 25, p. 671-682.
-Lu, Q., Chang, N.B., Joyce, J., Chen, A.S., Savic, D.A., Djordjevic, S. and Fu, G., 2018. Exploring The Potential Climate Change Impact On Urban Growth In London By A Cellular Automata-Based Markov Chain Model, Computers, Environment And Urban Systems, v. 68, p. 121-132.
-Markham, B.L. and Helder, D.L., 2012. Forty-Year Calibrated Record Of Earth-Reflected Radiance From Landsat: A Review. Remote Sensing Of Environment, v. 122, p. 30-40.
-Mertes, C.M., Schneider, A., Sulla-Menashe, D., Tatem, A. and Tan, B., 2015. Detecting Change In Urban Areas At Continental Scales With Modis Data, Remote Sensing Of Environment, v. 158, p. 331-347.
-Mitsova, D., Shuster, W. and Wang, X., 2011. A Cellular Automata Model Of Land Cover Change To Integrate Urban Growth With Open Space Conservation, Landscape And Urban Planning, v. 99, p. 141-153.
-Mohamed, A. and Worku, H., 2018. Quantification Of The Land Use/Land Cover Dynamics And The Degree Of Urban Growth Goodness For Sustainable Urban Land Use Planning In Addis Ababa And The Surrounding Oromia Special Zone, Journal Of Urban Management, v. 7(2), p. 129-143.
-Mou, Y., Song, Y., Xu, Q., He, Q. and Hu, A., 2018. Influence Of Urban-Growth Pattern On Air Quality In China: A Study Of 338 Cities, International Journal Of Environmental Research And Public Health, v. 15, p. 1805-1821.
-Nations, U., 2015. World Population Prospects: The 2015 Revision, United Nations Econ Soc Aff, v. 33, p. 1-66.
-Nielsen, M.M., 2015. Remote Sensing For Urban Planning And Management: The Use Of Window-Independent Context Segmentation To Extract Urban Features In Stockholm. Computers, Environment And Urban Systems, v. 52, p. 1-9.
-Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, v. 9, p. 62-66.
-Patino, J.E. and Duque, J.C., 2013. A Review Of Regional Science Applications Of Satellite Remote Sensing In Urban Settings. Computers, Environment And Urban Systems, v. 37, p. 1-17.
-Patra, S., Sahoo, S., Mishra, P. and Mahapatra, S.C., 2018. Impacts Of Urbanization On Land Use /Cover Changes And Its Probable Implications On Local Climate And Groundwater Level. Journal Of Urban Management, v. 7, p. 70-84.
-Pontius, R.G., 2000. Quantification Error Versus Location Error In Comparison Of Categorical Maps. Photogrammetric Engineering And Remote Sensing, v. 66, p. 1011-1016.
-Potere, D. and Schneider, A., 2007. A Critical Look At Representations Of Urban Areas In Global Maps. Geojournal, v. 69, p. 55-80.
-Pourahmad, A., Bagh, V.A., Zangenehe, S.S. and Givehchi, S., 2007. The Impact Of Urban Sprawl Up On Air Pollution, v. 1(3), p. 347-353.
-Rajitha, K., Mukherjee, C., Vinu Chandran, R. and Prakash Mohan, M., 2010. Land-Cover Change Dynamics And Coastal Aquaculture Development: A Case Study In The East Godavari Delta, Andhra Pradesh, India Using Multi-Temporal Satellite Data. International Journal Of Remote Sensing, v. 31, p. 4423-4442.
-Ramachandra, T., Bharath, H. and Sowmyashree, M., 2014. Urban Footprint Of Mumbai-The Commercial Capital Of India. Journal Of Urban And Regional Analysis, v. 6, p. 71-89.
-Rashmi, M. and Lele, N., 2010. Spatial Modeling And Validation Of Forest Cover Change In Kanakapura Region Using Geomod, Journal Of The Indian Society Of Remote Sensing, v. 38, p. 45-54.
-Roy, D.P., Wulder, M.A., Loveland, T.R., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., Mccorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H. and Zhu, Z., 2014. Landsat-8: Science And Product Vision For Terrestrial Global Change Research. Remote Sensing Of Environment, v. 145, p. 154-172.
-Sapena, M. and Ruiz, L.Á., 2019. Analysis Of Land Use/Land Cover Spatio-Temporal Metrics And Population Dynamics For Urban Growth Characterization, Computers, Environment And Urban Systems, v. 73, p. 27-39.
-Seto, K.C., Guneralp, B. and Hutyra, L.R., 2012. Global Forecasts Of Urban Expansion To 2030 And Direct Impacts On Biodiversity And Carbon Pools, Proceedings Of The National Academy Of Sciences, v. 109, p. 16083-16088.
-Sheykhi, H., Parizadi, T., Rezaei, M.. and Sajadi, M., 2012. Determining The Physical Form Of Isfahan Using Gary And Moran Model, v. 3(9), p. 119-136.
-Shi, Y., Sun, X., Zhu, X., Li, Y. and Mei, L., 2012. Characterizing Growth Types And Analyzing Growth Density Distribution In Response To Urban Growth Patterns In Peri-Urban Areas Of Lianyungang City. Landscape And Urban Planning, v. 105, p. 425-433.
-Sun, C., Wu, Z.F., Lv, Z.Q., Yao, N. and Wei, J.B., 2013. Quantifying Different Types Of Urban Growth And The Change Dynamic In Guangzhou Using Multi-Temporal Remote Sensing Data. International Journal Of Applied Earth Observation And Geoinformation, v. 21, p. 409-417.
-Un, 2014. World Urbanization Prospects: The 2014 Revision-Highlights, Un.
-Wang, Z.H. and Upreti, R., 2019. A Scenario Analysis Of Thermal Environmental Changes Induced By Urban Growth In Colorado River Basin, Usa. Landscape And Urban Planning, v. 181, p. 125-138.
-Weng, Q., 2012. Remote Sensing Of Impervious Surfaces In The Urban Areas: Requirements, Methods, And Trends. Remote Sensing Of Environment, v. 117, p. 34-49.
-Winsborough, H.H., 1962. City Growth And City Structure. Journal Of Regional Science, v. 4, p. 35-49.
-Wondrade, N., Dick, B. and Tveite, H., 2014. Landscape Mapping To Quantify Degree-Of-Freedom, Degree-Of-Sprawl, And Degree-Of-Goodness Of Urban Growth In Hawassa, Ethiopia. Environment And Natural Resources Research, v. 4, p. 223-241.
-Xu, H., 2010. Analysis Of Impervious Surface And Its Impact On Urban Heat Environment Using The Normalized Difference Impervious Surface Index (Ndisi), Photogrammetric Engineering & Remote Sensing, v. 76, p. 557-565.
-Yang, X., Zheng, X.Q. and Lv, L.N., 2012. A Spatiotemporal Model Of Land Use Change Based On Ant Colony Optimization, Markov Chain And Cellular Automata. Ecological Modelling, v. 233, p. 11-19.
-Zhang, L., Zhang, M. and Yao, Y., 2018. Mapping Seasonal Impervious Surface Dynamics In Wuhan Urban Agglomeration, China From 2000 To 2016. International Journal Of Applied Earth Observation And Geoinformation, v. 70, p. 51-61.
-Zhou, W., Jiao, M., Yu, W. and Wang, J., 2017. Urban Sprawl In A Megaregion: A Multiple Spatial And Temporal Perspective, Ecological Indicators, DOI: 10.1016/j.ecolind.2017.10.035.
-Zhou, X. and Chen, H., 2018. Impact Of Urbanization-Related Land Use Land Cover Changes And Urban Morphology Changes On The Urban Heat Island Phenomenon, Science Of The Total Environment, v. 635, p. 1467-1476.