طبقه‌بندی و پیش‌بینی تغییرات مکانی-زمانی سطوح نفوذ ناپذیر شهری و اثرات آن بر شدت جزیره حرارتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سنجش از دور و سیستم اطلاعات جغرافیایی، مرکز سنجش از دور و GIS

2 استادیار سنجش از دور و سیستم اطلاعات جغرافیایی، مرکز سنجش از دور و GIS، دانشگاه شهید بهشتی

3 استاد ، سنجش از دور و سیستم اطلاعات جغرافیایی، مرکز سنجش از دور و GIS، دانشگاه شهید بهشتی

چکیده

پدیده جزایر حرارتی به‌عنوان یکی از مخاطرات، فعالیت‌ها و زندگی انسان در محیط‌های شهری را تحت تأثیر قرار می‌دهد. سطوح نفوذناپذیر شهری یکی از عوامل مهم در تغییرات جزیره حرارتی است. تصاویر سنجش‌ازدور روشی ارزان، کارآمد و سریع  در بررسی شدت جزایر حرارتی و تغییرات سطوح نفوذناپذیر در محیط‌های شهری  محسوب می­شود. لذا هدف از این تحقیق بررسی و ارتباط بین سطوح نفوذناپذیر وتغییرات شدت جزایر حرارتی است. منطقه مورد مطالعه در این پژوهش شهر رشت است و  از سری زمانی تصاویر لندست مربوط به  سال 1989 تا سال 2018 استفاده شده است. روش پژوهش بدین صورت است که  ابتدا پیش‌پردازش اولیه بر روی تصاویر انجام‌گرفته و سپس با استفاده از شاخص NDISI به طبقه‌بندی سطوح نفوذناپذیر شهری پرداخته‌ شده است. برای تعیین حد آستانه تفکیک سطوح نفوذناپذیر (اراضی ساخته‌شده) از سطوح نفوذپذیر (اراضی ساخته نشده)، از روش آستانه گذاری Otsuاستفاده‌ شده است. دقت طبقه‌بندی با استفاده از نقاطی که به‌صورت تصادفی انتخاب‌ شده بود،  مورد ارزیابی قرار گرفت. در این تحقیق از مدل CA- Markov برای پیش‌بینی تغییرات آتی سطوح نفوذناپذیر شهری استفاده‌شده است و درنهایت ارتباط بین سطوح نفوذناپذیر شهری و تغییرات شدت جزیره حرارتی موردبررسی قرارگرفته است. نتایج این پژوهش حاکی از دقت کلی 5/84 تا 90 درصد برای روش NDISI بوده است. اختلاف نقشه پیش‌بینی CA- Markov با نقشه واقعیت کمتر از 8 درصد بوده و نشان از قابل‌اعتماد بودن این مدل است. ارتباط بین سطوح نفوذناپذیر و جزایر حرارتی حاکی از همبستگی مثبت و قوی بین 69/0 تا 89/0 برای سال‌های مختلف بوده است. جهت تغییرات سطوح نفوذناپذیر شهری و تغییرات شدت جزیره حرارتی با یکدیگر منطبق بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Classification and prediction of spatio-temporal Change of impervious urban surfaces and its impacts on urban heat intensity

نویسندگان [English]

  • Keyvan Azimand 1
  • Hossein Aghighi 2
  • Ali Akbar Matkan 3
1
2 Shahid Beheshti University
3
چکیده [English]

Introduction
The urban heat islands are hazardous to the health of urban residence, their activities, lifestyle and the quality of their life. This phenomenon occurs, in particular, as a result of the urbanization process, land use/cover changes and the rate of impervious surface coverage. Since early 1970s, the urban heat islands have been studied using remotely sensed data; because this approach is cheaper, more efficient and faster than traditional techniques to detect the heat islands as well as to examine the severity of them. However, less attention has been paid on the relationship between urban heat island (UHI) and impervious surface patterns. Therefore, this work aims to study UHI based on the analysis of land-surface temperature (LST) and impervious surface patterns (ISP) retrieved from remote sensing data covering a 29-year period.

Materials and methods

In this research, the city of Rasht as the center of Gilan province, Iran, is taken as the study area. Rasht is the largest city in the South Cost of Caspian Sea. In order to study the relationship between UHI and both LST and ISP, the time series of Landsat-5 / Thematic Mapper (TM) sensor, Landsat-7 / Enhanced Thematic Mapper Plus (ETM+) sensor and Landsat-8 / Operational Land Imager (OLI) sensor as well as Thermal Infrared Sensors (TIRS) of Landsat-8 from 1989 to 2018 have been utilized. Then preprocessing of satellite images including geometric correction and image referencing, radiometric corrections, and atmospheric corrections were applied on the images prior to other image processing steps. Then, by using the Normalized Difference Impervious Surface Index (NDISI), the impervious urban surfaces classified. The Otsu thresholding method was employed to determine a threshold value for the separation between impenetrable surfaces (constructed) and permeable surfaces (not constructed) in each utilized image. The classification accuracy was evaluated considering 300 randomly selected points. After mapping land use change over the years from 1989 to 2018, the future land use changes in the impenetrable urban areas were simulated to the year 2036 using CA-Markov model. Finally, the relationship between urban impermeable coverage and thermal island intensity changes were studied.

Results and discussion
The results of this study showed an overall accuracy of 84.5 percent to 90 percent for impervious surface classification using the NDISI method and the Otsu threshold. The results of the CA-Markov's model also indicate overall accuracy of 83.6 percent for impervious surface prediction. The difference in CA-Markov's prediction map with a reality map was less than 8 percent; hence, CA-Markov can be considered as reliable method in predicting land-use change in Rasht. Moreover, the obvious peaks and valleys values can be seen in the histogram of NDISI index; therefore, the determined threshold has well been able to classify the impervious surface.
The spatio-temporal change of impervious surface showed an increasing trend, more than double over the city, from 1989 to 2018. Moreover, the prediction results of CA-Markov model indicates that the impervious area would double again within the next 18 years. The highest levels of urban impervious are located at an average distance of 5,000 meters from the city center, which has had an important impact on the thermal island's severity. The relationship between impervious surface and thermal islands showed a positive and strong correlation coefficient between 0.69 and 0.89 for various years. Furthermore, the pattern of urban impervious surface growth and thermal island intensity changes coincided with each other.
The spatio-temporal change of UHI showed that the spatial extent of heat islands in Rasht was increasing with time and the temporal trend of UHI was also increased. Moreover, the trend of heat island changes illustrated that area of regions with very low and low temperature were decreased. On the other hand, the coverage of regions with medium, high and very high temperature were increased.

Conclusion
The time series of Landsat images along with spectral indices are the convenient dataset to classify the impervious surface of the city with proper accuracy. The produced land cover map can also be employed as a proper input data for prediction models. The spatio-temporal analysis of urban heat island in Rasht illustrated that the urban heat intensity was increased. This trend was because of increasing rate of impervious urban surface. Ultimately, in order to control the heat island, it is required to prevent the unplanned urban construction and developments.

کلیدواژه‌ها [English]

  • Remote sensing
  • Heat island intensity
  • change detection
  • urban impervious surface
  • CA-Markov model

مراجع

  1.  

    1. Alavi Moghadam, M. R., Mokhtarani, N.  Mokhtarani, B., (2009), Municipal Solid Waste Management In Rasht City, Iran. Waste Management, 29, 485-489.
    2. Amiri, R., Weng, Q., Alimohammadi, A.  Alavipanah, S. K., (2009), Spatial–Temporal Dynamics Of Land Surface Temperature In Relation To Fractional Vegetation Cover And Land Use/Cover In The Tabriz Urban Area, Iran. Remote Sensing Of Environment, 113, 2606-2617.
    3. Arnold Jr, C. L.  Gibbons, C. J., (1996), Impervious Surface Coverage: The Emergence Of A Key Environmental Indicator. Journal Of The American Planning Association, 62, 243-258.
    4. Barata, M., Ligeti, E., De Simone, G., Dickinson, T., Jack, D., Penney, J., Rahman, M.  Zimmerman, R., (2011), Climate Change And Human Health In Cities. Climate Change And Cities: First Assessment Report Of The Urban Climate Change Research Network, 179-213.
    5. Blake, R., Grimm, A., Ichinose, T., Horton, R., Gaffin, S., Jiong, S., Bader, D.  Cecil, L., (2011), Urban Climate: Processes, Trends, And Projections. Climate Change And Cities: First Assessment Report Of The Urban Climate Change Research Network, 43-81.
    6. Braun, M.  Herold, M., (2004), Mapping Imperviousness Using Ndvi And Linear Spectral Unmixing Of Aster Data In The Cologne-Bonn Region (Germany).  Remote Sensing For Environmental Monitoring, Gis Applications, And Geology Iii, International Society For Optics And Photonics, 274-285.
    7. Chakraborty, S. D., Kant, Y.  Mitra, D., (2015), Assessment Of Land Surface Temperature And Heat Fluxes Over Delhi Using Remote Sensing Data. Journal Of Environmental Management, 148, 143-152.
    8. Chander, G., Markham, B. L.  Helder, D. L., (2009), Summary Of Current Radiometric Calibration Coefficients For Landsat Mss, Tm, Etm+, And Eo-1 Ali Sensors. Remote Sensing Of Environment, 113, 893-903.
    9. Congalton, R. G., (1991), A Review Of Assessing The Accuracy Of Classifications Of Remotely Sensed Data. Remote Sensing Of Environment, 37, 35-46.
    10. Du, Z., Li, W., Zhou, D., Tian, L., Ling, F., Wang, H., Gui, Y.  Sun, B., (2014), Analysis Of Landsat-8 Oli Imagery For Land Surface Water Mapping. Remote Sensing Letters, 5, 672-681.
    11. Estoque, R. C.  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, 116, 60-72.
    12. Estoque, R. C.  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, 56, 205-217.
    13. Ezimand, K., Chahardoli, M., Azadbakht, M., & Matkan, A. A. (2021) Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques. Sustainable Cities and Society, 64, 102508.
    14. Ezimand, K., Kakroodi, A. A.  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, 1-22. https://doi.org/10.1080/01431161.2018.1488282
    15. Fan, F., Wang, Y.  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, 137, 127-147.
    16. Firozjaei, M. K., Kiavarz, M., Alavipanah, S. K., Lakes, T.  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, 91, 155-170.
    17. Goward, S. N., Davis, P. E., Fleming, D., Miller, L.  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, 88, 80-99.
    18. Haase, D.  Nuissl, H., (2007), Does Urban Sprawl Drive Changes In The Water Balance And Policy?: The Case Of Leipzig (Germany) 1870–2003. Landscape And Urban Planning, 80, 1-13.
    19. Haashemi, S., Weng, Q., Darvishi, A.  Alavipanah, S. K., (2016), Seasonal Variations Of The Surface Urban Heat Island In A Semi-Arid City. Remote Sensing, 8, 352-367.
    20. Han-Qiu, X.  Ben-Qing, C., (2004), Remote Sensing Of The Urban Heat Island And Its Changes In Xiamen City Of Se China. Journal Of Environmental Sciences, 16, 276-281.
    21. Jaeger, J. A.  Schwick, C., (2014), Improving The Measurement Of Urban Sprawl: Weighted Urban Proliferation (Wup) And Its Application To Switzerland. Ecological Indicators, 38, 294-308.
    22. Jensen, J. R., (2009), Remote Sensing Of The Environment: An Earth Resource Perspective 2/E, Pearson Education India.
    23. Jimenez‐Muñoz, J. C.  Sobrino, J. A., (2003), A Generalized Single‐Channel Method For Retrieving Land Surface Temperature From Remote Sensing Data. Journal Of Geophysical Research: Atmospheres, 108.
    24. Khakpoor, B.A.,Rastgar, M., Vaisi, R., Mirjafari,R.,&Ahmadi,  S.(2016).Spatial analysis of factors affecting the unbalanced physical growth of Rasht city using geographic information system (GIS). Journal of Geography and Urban Space Development,3 (1), 1-16.
    25. Kloog, I., Chudnovsky, A., Koutrakis, P.  Schwartz, J., (2012), Temporal And Spatial Assessments Of Minimum Air Temperature Using Satellite Surface Temperature Measurements In Massachusetts, Usa. Science Of The Total Environment, 432, 85-92.
    26. Liao, P.-S., Chen, T.-S.  Chung, P.-C., (2001), A Fast Algorithm For Multilevel Thresholding. J. Inf. Sci. Eng., 17, 713-727.
    27. Lillesand, T., Kiefer, R. W.  Chipman, J., (2014), Remote Sensing And Image Interpretation, John Wiley  Sons.
    28. Liu, L.  Zhang, Y., (2011), Urban Heat Island Analysis Using The Landsat Tm Data And Aster Data: A Case Study In Hong Kong. Remote Sensing, 3, 1535-1552.
    29. Markham, B. L.  Helder, D. L., (2012), Forty-Year Calibrated Record Of Earth-Reflected Radiance From Landsat: A Review. Remote Sensing Of Environment,122, 30-40.
    30. Mather, P. M.  Koch, M., (2011), Computer Processing Of Remotely-Sensed Images: An Introduction, John Wiley  Sons.
    31. Matkan, A., Nohegar, A., Mirbagheri, B., Torkchin, N. (2014). Assessment relations of land use in heat islands using time series ASTER sensor data (Case study: Bandar Abbas city). Journal of RS and GIS for Natural Resources, 5(4), 1-14.
    32. Mccoy, R. M., (2005), Field Methods In Remote Sensing, Guilford Press.
    33. Mchugh, M. L., (2012), Interrater Reliability: The Kappa Statistic. Biochemia Medica: Biochemia Medica, 22, 276-282.
    34. Mitsova, D., Shuster, W.  Wang, X., (2011), A Cellular Automata Model Of Land Cover Change To Integrate Urban Growth With Open Space Conservation. Landscape And Urban Planning, 99, 141-153.
    35. Morabito, M., Crisci, A., Gioli, B., Gualtieri, G., Toscano, P., Di Stefano, V., Orlandini, S.  Gensini, G. F., (2015), Urban-Hazard Risk Analysis: Mapping Of Heat-Related Risks In The Elderly In Major Italian Cities. Plos One, 10, E0127277.
    36. Morabito, M., Crisci, A., Messeri, A., Orlandini, S., Raschi, A., Maracchi, G.  Munafò, M., (2016), The Impact Of Built-Up Surfaces On Land Surface Temperatures In Italian Urban Areas. Science Of The Total Environment, 551, 317-326.
    37. Mushore, T. D., Odindi, J., Dube, T., Matongera, T. N.  Mutanga, O., (2017), Remote Sensing Applications In Monitoring Urban Growth Impacts On In-And-Out Door Thermal Conditions: A Review. Remote Sensing Applications: Society And Environment, 8, 83-93.
    38. Poelmans, L.  Van Rompaey, A., (2009), Detecting And Modelling Spatial Patterns Of Urban Sprawl In Highly Fragmented Areas: A Case Study In The Flanders–Brussels Region. Landscape And Urban Planning, 93, 10-19.
    39. Pontius, R. G., (2000), Quantification Error Versus Location Error In Comparison Of Categorical Maps. Photogrammetric Engineering And Remote Sensing, 66, 1011-1016.
    40. Qin, Z., Karnieli, A.  Berliner, P., (2001), A Mono-Window Algorithm For Retrieving Land Surface Temperature From Landsat Tm Data And Its Application To The Israel-Egypt Border Region. International Journal Of Remote Sensing, 22, 3719-3746.
    41. Rajitha, K., Mukherjee, C., Vinu Chandran, R.  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, 31, 4423-4442.
    42. Rashmi, M.  Lele, N., (2010), Spatial Modeling And Validation Of Forest Cover Change In Kanakapura Region Using Geomod. Journal Of The Indian Society Of Remote Sensing, 38, 45-54.
    43. Rosenzweig, C., Solecki, W. D., Hammer, S. A.  Mehrotra, S., (2011), Climate Change And Cities: First Assessment Report Of The Urban Climate Change Research Network, Cambridge University Press.
    44. SadeghiniaA., AlijaniB., & ZeaieanfirouzabadiP. (2013). Analysis of Spatial - Temporal Structure of the Urban Heat Island in Tehran through Remote Sensing and Geographical Information System. Geography and environmental hazards, 1 (4), 1-17.
    45. Santamouris, M., (2013), Using Cool Pavements As A Mitigation Strategy To Fight Urban Heat Island—A Review Of The Actual Developments. Renewable And Sustainable Energy Reviews, 26, 224-240.
    46. Seto, K. C., Guneralp, B.  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, 109, 16083-16088.
    47. Sezgin, M.  Sankur, B., (2004), Survey Over Image Thresholding Techniques And Quantitative Performance Evaluation. Journal Of Electronic Imaging, 13, 146-166.
    48. Shafizadeh-Moghadam, H., Tayyebi, A., Ahmadlou, M., Delavar, M. R.  Hasanlou, M., (2017), Integration Of Genetic Algorithm And Multiple Kernel Support Vector Regression For Modeling Urban Growth. Computers, Environment And Urban Systems, 65, 28-40.
    49. Shakiba, A., Firoozabadi, P., Ashourloo, D.  Namdari, S., (2009), Analysis Of Relationship Between Land Use/Cover And Urban Heat Island, Using ETM+, Iranian Journal of Remote Sensing & GIS, 1(1), 39-56.
    50. Shen, H., Huang, L., Zhang, L., Wu, P.  Zeng, C., (2016), Long-Term And Fine-Scale Satellite Monitoring Of The Urban Heat Island Effect By The Fusion Of Multi-Temporal And Multi-Sensor Remote Sensed Data: A 26-Year Case Study Of The City Of Wuhan In China. Remote Sensing Of Environment, 172, 109-125.
    51. Sobrino, J. A., Jimenez-Munoz, J. C.  Paolini, L., (2004), Land Surface Temperature Retrieval From Landsat Tm 5. Remote Sensing Of Environment, 90, 434-440.
    52. Sutton, P. C., Anderson, S. J., Elvidge, C. D., Tuttle, B. T.  Ghosh, T., (2009), Paving The Planet: Impervious Surface As Proxy Measure Of The Human Ecological Footprint. Progress In Physical Geography, 33, 510-527.
    53. Thanapura, P., Helder, D. L., Burckhard, S., Warmath, E., O’neill, M.  Galster, D., (2007), Mapping Urban Land Cover Using Quickbird Ndvi And Gis Spatial Modeling For Runoff Coefficient Determination. Photogrammetric Engineering  Remote Sensing, 73, pp.57-65.
    54. Un, (2014). World Urbanization Prospects: The 2014 Revision-Highlights, Un.
    55. Walawender, J. P., Szymanowski, M., Hajto, M. J.  Bokwa, A., (2014), Land Surface Temperature Patterns In The Urban Agglomeration Of Krakow (Poland) Derived From Landsat-7/Etm+ Data. Pure And Applied Geophysics, 171, 913-940.
    56. Weng, Q., (2012), Remote Sensing Of Impervious Surfaces In The Urban Areas: Requirements, Methods, And Trends. Remote Sensing Of Environment, 117, 34-49.
    57. Wu, Q., Li, H.-Q., Wang, R.-S., Paulussen, J., He, Y., Wang, M., Wang, B.-H.  Wang, Z., (2006), Monitoring And Predicting Land Use Change In Beijing Using Remote Sensing And Gis. Landscape And Urban Planning, 78, 322-333.
    58. 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, 76, 557-565.
    59. Xu, H., Chen, Y., Dan, S.  Qiu, W., (2011), Spatial And Temporal Analysis Of Urban Heat Island Effects In Chengdu City By Remote Sensing.  Geoinformatics, 2011 19th International Conference On, 1-5.
    60. Xu, H., Huang, S.  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, 52, 1437-1449.
    61. Xunqiang, M., Chen, C., Fuqun, Z.  Hongyuan, L. Study On Temporal And Spatial Variation Of The Urban Heat Island Based On Landsat Tm/Etm+ In Central City And Binhai New Area Of Tianjin.  Multimedia Technology (Icmt), (2011) International Conference On, 2011. Ieee, 4616-4622.
    62. Yang, X., Zheng, X.-Q.  Lv, L.-N., (2012), A Spatiotemporal Model Of Land Use Change Based On Ant Colony Optimization, Markov Chain And Cellular Automata. Ecological Modelling, 233, 11-19.
    63. Yu, B. Y., Elbuken, C., Ren, C. L.  Huissoon, J. P., (2011), Image Processing And Classification Algorithm For Yeast Cell Morphology In A Microfluidic Chip. Journal Of Biomedical Optics, 16,  066008.
    64. Yue, W.  Xu, L., (2013), Thermal Environment Effect Of Urban Water Landscape. Shengtai Xuebao/Acta Ecologica Sinica, 33, 1852-1859.
    65. Zullo, F., Fazio, G., Romano, B., Marucci, A. Fiorini, L., (2019), Effects Of Urban Growth Spatial Pattern (Ugsp) On The Land Surface Temperature (Lst): A Study In The Po Valley (Italy). Science Of The Total Environment, 650, 1740-1751.
پژوهش های اقلیم شناسی
دوره 1399، شماره 41
دوره یازدهم- شماره 41- بهار 1399
اردیبهشت 1399
صفحه 15-34

فایل ها

هم رسانی

ارجاع به این مقاله

آمار