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طبقه‌بندی و شناسایی تغییرات اراضی ساخته‌شده با استفاده از تصاویر سنجش‌ازدور | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 6، دوره 5، شماره 3، مهر 1396، صفحه 445-468 اصل مقاله (1.34 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2018.229640.687 | ||
| نویسندگان | ||
| کیوان عزی مند1؛ عطاءاله عبدالهی کاکرودی* 2؛ مجید کیاورز مقدم2 | ||
| 1دانشجوی کارشناسی ارشد سنجش ازدور و سیستم اطلاعات جغرافیایی دانشگاه تهران | ||
| 2استادیار گروه سنجش ازدور و سیستم اطلاعات جغرافیایی دانشکدة جغرافیای دانشگاه تهران | ||
| چکیده | ||
| در پی شهرنشینی بی‌سابقه در دهه‌های گذشته و افزایش جمعیت شهرها، چشم‌اندازهای طبیعی در حال تبدیل‌شدن به چشم‌اندازهای انسانی است و فضاهای باز شهری به اراضی ساخته‌شده مبدل شده است. در این بین، تغییرات کاربری اراضی مدیران شهری را مجاب می‌کند که همواره اطلاعات به‌روزی از این تغییرات داشته باشند تا بتوانند دربارة مدیریت شهری سریع‌تر تصمیم‌گیری کنند. هدف از انجام این مطالعه طبقه‌بندی اراضی ساخته‌شده و شناسایی میزان تغییرات این اراضی در شهر تهران است. همچنین این مطالعه به بررسی و عملکرد هفت شاخص طیفی به‌منظور طبقه‌بندی و تشخیص تغییر اراضی ساخته‌شده با استفاده از تصاویر ماهوارة لندست 7 سنجندة ETM + و تصاویر ماهوارة لندست 8 سنجندة OLI / TIRS می‌پردازد. محدودة مورد مطالعه در این تحقیق شهر تهران با وسعت 68995 هکتار است. روش انجام این تحقیق نیز بدین گونه است که ابتدا برای جداسازی سطوح دارای آب از سطوح بدون آب بر روی تصاویر، از شاخصMNDWI و روش آستانه‌گذاری اتسو استفاده شده است. پس از آن به‌منظور توجه مطلق بر مناطق بدون آب، یک ماسک آب تولید، و برای پوشاندن آب در تمام تصاویر به‌کار رفته است. درنهایت با استفاده از روش اتسو برای تمامی شاخص‌ها اراضی ساخته‌شده و ساخته‌نشده از یکدیگر جدا و طبقه‌بندی شده‌اند. دقت طبقه‌بندی نیز با استفاده از 3500 نقطة مرجع برای هر تصویر بررسی شده است. نتایج نشان می‌دهد شاخص VbSWIR1-BI با دقت کلی 88/92 درصد (لندست 7) و 68/92 درصد (لندست 8)، دقت کلی بیشتری دارد. همچنین نتایج تغییرات اراضی ساخته‌شدة شهر تهران براساس شاخص VbSWIR1-BI در بازة زمانی 2001 تا 2015 به میزان 38/6 درصد بوده است. گفتنی است بیشترین تغییرات مکانی اراضی ساخته‌شده در بخش‌های غربی و جنوب غربی شهر تهران دیده می‌شود. | ||
| کلیدواژه‌ها | ||
| تصاویر لندست 7 و لندست 8؛ شاخص‌های طیفی؛ شناسایی تغییرات؛ طبقه‌بندی؛ گسترش شهری | ||
| عنوان مقاله [English] | ||
| Classification and Change Detection of Urban Built-up Lands Using Remote Sensing Images | ||
| نویسندگان [English] | ||
| Keyvan Ezimand1؛ Ataallah Abdollahi Kakroodi2؛ Majid Kiavarz Moghaddam2 | ||
| 1MSc Student in Remote Sensing and GIS, University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Iran | ||
| 2Assistant Professor of Remote Sensing and GIS, University of Tehran, Faculty of Geography, Department of Remote Sensing and GIS, Iran | ||
| چکیده [English] | ||
| Introduction Urbanization and use of urban lands is the result of social and economic development. Urbanization is a major concern in many parts of the world. By 2050, the world's urban population is expected to double from about 3.3 billion in 2007 to 6.4 billion in 2050. Today, changes in land use occur without clear planning and little attention to their environmental impacts. At present, the built-up lands cover 400,000 square kilometers of the Earth's surface and it is expected to increase to 120,000 square kilometers by 2030. Recently, urban studies, classification of built-up lands and land-use change detection in urban areas using remote sensing data have been highlighted on an unprecedented manner. Various spectral indices have been proposed for rapid detection and accurate classification of built-up lands using satellite images. The purpose of this study is to compare the performance of the indices and the introduction of a new index for classification of the built-lands using satellite images to determine spatial and temporal differences of land-use in the city of Tehran. Methodology The data used in this study is Landsat 7 ETM + and Landsat 8 OLI / TIRS satellite images for Tehran. In this research, we have initially used the MNDWI index and the Otsu thresholding method to separate water surfaces from the waterless surfaces. Then, for the purpose of masking the water in the image, water mask was created. Finally, using indices such as Urban Index (UI), Normalized Difference Built-up Index (NDBI), Index-based Built-up Index (IBI), Normalized Difference Impervious Surface Index (NDISI), visible red/green-based built-up indices (VrNIR-BI and VgNIR-BI), visible blue based built-up index (VbSWIR1-BI) and Otsu , the built-up lands are separated and classified. The accuracy of the classification was examined using 3500 reference points for each image. Results and discussion The histogram of the spectral indices of two satellite images and the Otsu method has showed that for the ETM + sensor, all indices except NDBI and VrNIR-BI show double distribution signs. For the OLI / TIRS sensor, only the IBI, VgNIR-BI and VbSWIR1-BI indices show signs of a dual distribution. The classification accuracy results show that the VbSWIR1-BI index has the highest overall accuracy and the NDISI index has the lowest overall accuracy for both Landsat 7 and Landsat 8 images. The temporal and spatial variations of the built-up lands indicate that the highest increase of built-up lands can be found geographically in the western and southwestern part of Tehran. According to the results of the VbSWIR-BI index, built-up lands in the studied area between 2001 and 2015 increased to 6.38%. Conclusion The rapid development of geography and remote sensing technology has led to creation of different spectral indexes for classification. A review of studies on spectral indices indicates that the blue band coupled with the near infrared band, has not been used for classification of built-up and non-built-up lands and the results of this study have shown that this index is good and has been able to classify the built-up lands and increased classification accuracy. This index also enables the determination of changes in spatial and temporal built-up lands in Tehran accurately. | ||
| کلیدواژه‌ها [English] | ||
| : Landsat 7 and Landsat 8 images, Classification, spectral indices, Change detection, Urban Growth | ||
| مراجع | ||
|
حجازی‌زاده، زهرا و نادر پروین (1388) «بررسی تغییرات دما و بارش تهران طی نیم قرن اخیر»، فصلنامة جغرافیا (برنامه‌ریزی منطقه‌ای)، پیش‌شماره، شمارۀ صفر، صص 43-56. مرکز آمار ایران، 1390، نتایج تفصیلی سرشماری عمومی و نفوس و مسکن. Angel, S. and Blei, A. M. (2016), The spatial structure of American cities: The great majority of workplaces are no longer in CBDs, employment sub-centers, or live-work communities. Cities, No. 51: 21–35. https://doi.org/10.1016/j.cities.2015.11.031
Angel, S. and Sheppard, S. (2005), The dynamics of global urban expansion. Transport and Urban …, 1–207. https://doi.org/10.1038/nature09440
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, Vol. 113, NO. 5: 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Chen, X. L. Zhao, H. M. Li, P. X. and Yin, Z. Y. (2006), Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote Sensing of Environment,Vol.104, No.2:133–146. https://doi.org/10.1016/j.rse.2005.11.016
Cibula, W. G. Zetka, E. F. and Rickman, D. L. (1992), Response of thematic mapper bands to plant water stress, International Journal of Remote Sensing, Vol. 13, No. 10: 1869–1880.
https://doi.org/10.1080/01431169208904236
Coisnon, T. Oueslati, W. and Salanié, J. (2014), Urban sprawl occurrence under spatially varying agricultural amenities, Regional Science and Urban Economics, Vol. 44, No. 1: 38–49.
https://doi.org/10.1016/j.regsciurbeco.2013.11.001
Congalton, R. G. (1991), A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment,Vol. 37, No. 1: 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Du, Z. Li, W. Zhou, D. Tian, L. Ling, F. Wang, H. Sun, B. (2014), Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sensing Letters, Vol.5, No. 7: 672–681.
https://doi.org/10.1080/2150704X.2014.960606
ENVI, 2009. Atmospheric correction module: QUAC and FLAASH User’s Guide, Available online: _www.exelisvis.com/portals/0/pdfs/envi/Flaash Module.pdf_(accessed 19 December 2014).
Estoque, R. C. Estoque, R. S, and Murayama, Y. (2012), Prioritizing Areas for Rehabilitation by Monitoring Change in Barangay-Based Vegetation Cover. ISPRS International Journal of Geo-Information, Vol. 1, No.1: 46–68. https://doi.org/10.3390/ijgi1010046
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, No. 116: 60–72. https://doi.org/10.1016/j.landurbplan.2013.04.008
Estoque, R, and Murayama, Y. (2014), A geospatial approach for detecting and characterizing non-stationarity of land- change patterns and its potential effect on modeling accuracy. GIScience & Remote Sensing, No. 51(June 2014): 239–252. https://doi.org/10.1080/15481603.2014.908582
Foody, G. M. (2002), Status of land cover classification accuracy assessment, Remote Sensing of Environment, Vol. 80, No.1:185–201.
https://doi.org/10.1016/S0034-4257(01)00295-4
Ganeshkumar.B, and Mohan.M, (2014),Urban Sprawl Spatial Modelingusing SLEUTH Model,International Journal of Geospatial Engineering and Technology Vol.1,No.1:22 – 28.
Gao, B. C. (1996), NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, Vol. 58, No. 3: 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Ginkel,V.H.2010,Sustainable Urban Futures:Challenges And Opportunities. Paper Presented at School of Humanities, Universiti Sains Malaysia, Penang.May 17, 2010.
Han, J. Hayashi, Y. Cao, X. and Imura, H. (2009), Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China, Landscape and Urban Planning,Vol. 91, No. 3: 133–141. https://doi.org/10.1016/j.landurbplan.2008.12.002
Jaeger, J. A. G. and Schwick, C. (2014), Improving the measurement of urban sprawl: Weighted Urban Proliferation (WUP) and its application to Switzerland. Ecological Indicators, No. 38: 294–308. https://doi.org/10.1016/j.ecolind.2013.11.022
Jiang, G. Ma, W. Qu, Y. Zhang, R. and Zhou, D. (2016), How does sprawl differ across urban built-up land types in China? A spatial-temporal analysis of the Beijing metropolitan area using granted land parcel data. Cities, No. 58: 1–9. https://doi.org/10.1016/j.cities.2016.04.012
Kawamura, M. Jayamanna, S. and Tsujiko, Y. (1996), Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. International Archives of Photogrammetry and Remote Sensing.
Kong, F. Yin, H. Nakagoshi, N. and James, P. (2012), Simulating urban growth processes incorporating a potential model with spatial metrics. Ecological Indicators, No. 20: 82–91. https://doi.org/10.1016/j.ecolind.2012.02.003
Laben, C.A. and Brower, B.V. (2000), Process for enhancing the spatial resolution of mul-tispectral imagery using pan-sharpening. US Patent 6011875, Eastman KodakCompany, Rochester, N.Y.
Li, W. Bai, Y. Chen, Q. He, K. Ji, X. and Han, C. (2014), Discrepant impacts of land use and land cover on urban heat islands: A case study of Shanghai, China. Ecological Indicators, No. 47: 171–178. https://doi.org/10.1016/j.ecolind.2014.08.015
Li, W. Du, Z. Ling, F. Zhou, D. Wang, H. Gui, Y. Zhang, X. (2013), A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI, Remote Sensing, Vol. 5, No.11, 5530–5549. https://doi.org/10.3390/rs5115530
Longley, P. (2002), Geographical Information Systems: will developments in urban remote sensing and GIS lead to “better” urban geography? Progress in Human Geography,Vol. 26, No. 2:231–239. https://doi.org/10.1191/0309132502ph366pr
Matthias, B. Martin, H. (2003), Mapping imperviousness using NDVI and linearspectral unmixing of ASTER data in the Cologne-Bonn region (Germany). In: Proceedings of the SPIE 10th International Symposium on Remote Sensing, 8–12September, Vol. 5239: 274–284.
Masek, J. G. Lindsay, F. E. and Goward, S. N. (2000), Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations, International Journal of Remote Sensing,Vol. 21, No. 18: 3473–3486. https://doi.org/10.1080/014311600750037507
McFeeters, S. K. (1996), The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, Vol. 17, No. 7: 1425–1432.
https://doi.org/10.1080/01431169608948714
Otsu, N. (1979), A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics,Vol. 9, No.1: 62–66.
https://doi.org/10.1109/TSMC.1979.4310076
Polydoros, A. and Cartalis, C. (2015). Use of Earth Observation based indices for the monitoring of built-up area features and dynamics in support of urban energy studies. Energy {and} Buildings, No. 98: 92–99. https://doi.org/10.1016/j.enbuild.2014.09.060
Rouse, J.W. Haas, R.H. Schell, J.A. Deering, D.W. (1973), Monitoring vegetation sys-tems in the Great Plains with ERTS. In: Third ERTS Symposium, NASA SP-351 I,pp. 309–317.
Seto, K. C. Güneralp, 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 of the United States of America, Vol. 109, No. 40: 16083–16088. https://doi.org/10.1073/pnas.1211658109
Thanapura, P. Helder, D. L. Burckhard, S. Warmath, E. O’Neill, M. and Galster, D. (2006), Mapping urban land cover using QuickBird NDVI image and GIS spatial modeling for runoff coefficient determination. Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration, ASPRS 2006,VoL. 3, No. 1: 1421–1432. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84869030547&partnerID=40&md5=91481ffad2567b9cef1320833023f2c6
Thapa, R. B. & Murayama, Y. (2011), Urban growth modeling of Kathmandu metropolitan region, Nepal. Computers, Environment and Urban Systems, Vol. 35, No. 1: 25–34. https://doi.org/10.1016/j.compenvurbsys.2010.07.005
USGS, (2006), Multi-resolution Land Characteristics 2001 (MRLC2001) ImageProcessing Procedure, Available online: _http://landcover.usgs.gov/pdf/image preprocessing.pdf_ (accessed: 19 December 2014).
USGS, (2013a), Using the USGS Landsat 8 Product, Available online:_http://landsat.usgs.gov/Landsat8 Using Product.php_ (accessed: 19 December2014).
USGS, (2013b), September 27, 2013—Landsat 7 Thermal Band CalibrationUpdate, Available online: _http://landsat.usgs.gov/science L7 Cal Notices.php_(accessed: 19 December 2014).
United Nations. (2007), World Urbanization Prospects The 2007 Revision Highlights. Desa, ESA/P/WP/2(4), 883. https://doi.org/10.2307/2808041
Weng, Q. (2012), Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment, No. 117: 34–49. https://doi.org/10.1016/j.rse.2011.02.030
Weng, Q. Lu, D. and Schubring, J. (2004), Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment,Vol. 89, No. 4: 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Xu, H. (2006), Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, Vol. 27, No. 14: 3025–3033. https://doi.org/10.1080/01431160600589179
Xu, H. (2008), A new index for delineating built‐up land features in satellite imagery. International Journal of Remote Sensing, Vol. 29, No. 14: 4269–4276. https://doi.org/10.1080/01431160802039957
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, Vol. 76, No. 5: 557–565. https://doi.org/10.14358/PERS.76.5.557
Xu, H. Huang, S. and Zhang, T. (2013), Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China. Advances in Space Research, Vol. 52, No.8: 1437–1449. https://doi.org/10.1016/j.asr.2013.07.026
Zeng, C. Liu, Y. Stein, A. and Jiao, L. (2015), International Journal of Applied Earth Observation and Geoinformation Characterization and Spatial Modeling of Urban Sprawl in the Wuhan Metropolitan Area, China. International Journal of Applied Earth Observations and Geoinformation, No.34: 10–24. https://doi.org/10.1016/j.jag.2014.06.012
Zha, Y. Gao, J. and Ni, S. (2003), Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, Vol. 24, NO. 3: 583–594. https://doi.org/10.1080/01431160304987
Zhang, Y. Odeh, I. O. A. and Han, C. (2009), Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, Vol. 11, No. 4: 256–264. https://doi.org/10.1016/j.jag.2009.03.001 | ||
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