Geografie 2022, 127, 219-240

https://doi.org/10.37040/geografie.2022.008

Looking into the green roof scenario to mitigate flash flood effects in Mamak, Turkey, via classifying images of Sentinel-1, 2, and PlanetScope satellites with LibSVM algorithm in Google Earth Engine cloud platform

Sima Pouya1ID, Majid Aghlmand2ID, Fevzi Karsli3ID

1Inonu University, Faculty of Fine Arts and Design, Department of Landscape Architecture, Malatya, Turkey
2Eskisehir Technical University, Civil Engineering Department, Eskisehir, Turkey
3Karadeniz Technical University, Department of Geomatics, Trabzon, Turkey

Received November 2021
Accepted May 2022

References

1. ATİK, A., ASLAN, F., YILMAZ, B., ATEŞ, O. (2013): Modelling purchasing demand of urban people for ornamental plants using logistic regression analysis: sample of Malatya City. Journal of Animal and Veterinary Advances, 12, 16, 1317–1324.
2. BARNES, K.B., MORGAN, J., ROBERGE, M. (2001): Impervious surfaces and the quality of natural and built environments. In Baltimore, MD: The Department of Geography and Environmental Planning, Towson University.
3. BARR, C.M., GALLAGHER, P.M., WADZUK, B.M., WELKER, A.L. (2017): Water Quality Impacts of Green Roofs Compared with Other Vegetated Sites. Journal of Sustainable Water in the Built Environment, 3, 3, 4017007. <https://doi.org/10.1061/JSWBAY.0000825>
4. BECKER, W.R., LÓ, T.B., JOHANN, J.A., MERCANTE, E. (2021): Statistical features for land use and land cover classification in Google Earth Engine. Remote Sensing Applications: Society and Environment, 21, 100459. <https://doi.org/10.1016/j.rsase.2020.100459>
5. BENGTSSON, L., GRAHN, L., OLSSON, J. (2005): Hydrological function of a thin extensive green roof in southern Sweden. Hydrology Research, 36, 3, 259–268. <https://doi.org/10.2166/nh.2005.0019>
6. BERNDTSSON, J.C. (2010): Green roof performance towards management of runoff water quantity and quality: A review. Ecological Engineering, 36, 4, 351–360. <https://doi.org/10.1016/j.ecoleng.2009.12.014>
7. BEYHAN, F., ERBAS, M. (2013): A Study on Green Roofs with the Examples from the World and Turkey. Gazi University Journal of Science, 26, 2, 303–318.
8. BRUDERMANN, T., SANGKAKOOL, T. (2017): Green roofs in temperate climate cities in Europe–An analysis of key decision factors. Urban Forestry & Urban Greening, 21, 224–234. <https://doi.org/10.1016/j.ufug.2016.12.008>
9. CARTER, T.L., RASMUSSEN, T.C. (2006): Hydrologic behavior of vegetated roofs 1. JAWRA Journal of the American Water Resources Association, 42, 5, 1261–1274. <https://doi.org/10.1111/j.1752-1688.2006.tb05611.x>
10. CHANG, C.-C. (2011): LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27, 1-27. <https://doi.org/10.1145/1961189.1961199>
11. CONNELLY, M., LIU, K. (2005): Green roof research in British Columbia: An overview. Proc. of 3rd North American Green Roof Conference: Greening Rooftops for Sustainable Communities, Washington, DC, 4–6.
12. DUNNETT, N., KINGSBURY, N. (2008): Planting Green Roofs and Living Walls. Portland, London: Timber Press Inc.
13. DÜZENLİ, T. (2018): Kentsel Dönüşüme Natif Bir Yöntem: Yeşil Çati Tasarimi. International Journal of Social Humanities Sciences Research (JSHSR), 5, 20, 745–752. <https://doi.org/10.26450/jshsr.412>
14. ESRINGÜ, A., TOY, S. (2021). Kent İklimine Çatı ve Cephe Bahçelerinin Etkisi. Climate and Health Journal, 1, 2, 101–107.
15. FARR, T.G., ROSEN, P.A., CARO, E., CRIPPEN, R., DUREN, R., HENSLEY, S., KOBRICK, M., PALLER, M., RODRIGUEZ, E., ROTH, L. (2007): The shuttle radar topography mission. Reviews of Geophysics, 45, 2. <https://doi.org/10.1029/2005RG000183>
16. FASSMAN-BECK, E., VOYDE, E., SIMCOCK, R., HONG, Y.S. (2013): 4 Living roofs in 3 locations: Does configuration affect runoff mitigation? Journal of Hydrology, 490, 11–20. <https://doi.org/10.1016/j.jhydrol.2013.03.004>
17. FIORETTI, R., PALLA, A., LANZA, L.G., PRINCIPI, P. (2010): Green roof energy and water related performance in the Mediterranean climate. Building and Environment, 45, 8, 1890–1904. <https://doi.org/10.1016/j.buildenv.2010.03.001>
18. FOODY, G.M., MATHUR, A. (2004): Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93, 1–2, 107–117. <https://doi.org/10.1016/j.rse.2004.06.017>
19. FRAMPTON, W.J., DASH, J., WATMOUGH, G., MILTON, E.J. (2013): Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83–92. <https://doi.org/10.1016/j.isprsjprs.2013.04.007>
20. GEE (2021): Earth Engine Data Catalog. Google Earth Engine. https://developers.google.com/earth-engine/datasets (16.09.2021).
21. GORELICK, N., HANCHER, M., DIXON, M., ILYUSHCHENKO, S., THAU, D., MOORE, R. (2017): Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. <https://doi.org/10.1016/j.rse.2017.06.031>
22. GRANT, G. (2017): Greater London Authority Urban Greening Factor for London. https://www.london.gov.uk/sites/default/files/urban_greening_factor_for_london_final_report.pdf (16.09.2021).
23. HU, T., YANG, J., LI, X., GONG, P. (2016): Mapping urban land use by using landsat images and open social data. Remote Sensing, 8, 2, 151. <https://doi.org/10.3390/rs8020151>
24. IENCO, D., INTERDONATO, R., GAETANO, R., MINH, D.H.T. (2019): Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 11–22. <https://doi.org/10.1016/j.isprsjprs.2019.09.016>
25. JAFARI, N., UTABERTA, N., YUNOS, M.Y.M., ISMAIL, N.A., ISMAIL, S., ARIFFIN, N.F.M., JAFARI, N., VALIKHANI, M. (2015): Benefits of roof garden in order to usage of urban agriculture at roof garden in high-rise building in Malaysia. Advances in Environmental Biology, 9, 24, 86–92.
26. JANSSON, M. (2014): Green space in compact cities: the benefits and values of urban ecosystem services in planning. NA, 26, 2.
27. JENSEN, J.R., LULLA, K. (1987): Introductory digital image processing: A remote sensing perspective. Geocarto International, 2, 1, 65. <https://doi.org/10.1080/10106048709354084>
28. JULIE, K., KRAGH, K. (2017): GIS and the Green Space Factor. Using GIS to create a baseline for the Green Space Factor in Copenhagen Municipality: Using GIS to create a baseline for the Green Space Factor in Copenhagen Municipality. Aalborg University.
29. KAYMAZ, I. (2019): The Lost Streams of Ankara: A Case Study of Bentderesi. IOP Conference Series: Materials Science and Engineering, 603, 5, 52040. <https://doi.org/10.1088/1757-899X/603/5/052040>
30. KOÇ, G., NATHO, S., THIEKEN, A.H. (2021): Estimating direct economic impacts of severe flood events in Turkey (2015–2020). International Journal of Disaster Risk Reduction, 58, 102222. <https://doi.org/10.1016/j.ijdrr.2021.102222>
31. KÖHLER, M., SCHMIDT, M., GRIMME, F.W., LAAR, M., DE ASSUNÇÃO PAIVA, V.L., TAVARES, S. (2002): Green roofs in temperate climates and in the hot‐humid tropics – far beyond the aesthetics. Environmental Management and Health.
32. LEE, J.Y., MOON, H.J., KIM, T.I., KIM, H.W., HAN, M.Y. (2013): Quantitative analysis on the urban flood mitigation effect by the extensive green roof system. Environmental Pollution, 181, 257–261. <https://doi.org/10.1016/j.envpol.2013.06.039>
33. LI, W.C., YEUNG, K.K.A. (2014): A comprehensive study of green roof performance from environmental perspective. International Journal of Sustainable Built Environment, 3, 1, 127–134. <https://doi.org/10.1016/j.ijsbe.2014.05.001>
34. LI, W., DONG, R., FU, H., WANG, J., YU, L., GONG, P. (2020): Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping. Remote Sensing of Environment, 237, 111563. <https://doi.org/10.1016/j.rse.2019.111563>
35. MANTERO, P., MOSER, G., SERPICO, S.B. (2005): Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing, 43, 3, 559–570. <https://doi.org/10.1109/TGRS.2004.842022>
36. MOUNTRAKIS, G., IM, J., OGOLE, C. (2011): Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 3, 247–259. <https://doi.org/10.1016/j.isprsjprs.2010.11.001>
37. PAL, M., MATHER, P.M. (2005): Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 5, 1007–1011. <https://doi.org/10.1080/01431160512331314083>
38. PARK, S., IM, J., PARK, S., YOO, C., HAN, H., RHEE, J. (2018): Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sensing, 10, 3, 447. <https://doi.org/10.3390/rs10030447>
39. Planet Team (2018): Planet Application Program Interface: Space for Life on Earth, https://api.planet.com (16.09.2021).
40. POURSANIDIS, D., CHRYSOULAKIS, N., MITRAKA, Z. (2015): Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping. International Journal of Applied Earth Observation and Geoinformation, 35, 259–269. <https://doi.org/10.1016/j.jag.2014.09.010>
41. PRATICÒ, S., SOLANO, F., DI FAZIO, S., MODICA, G. (2021): Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sensing, 13, 4, 586. <https://doi.org/10.3390/rs13040586>
42. QU, L., CHEN, Z., LI, M., ZHI, J., WANG, H. (2021): Accuracy improvements to pixel-based and object-based lulc classification with auxiliary datasets from Google Earth engine. Remote Sensing, 13, 3, 453. <https://doi.org/10.3390/rs13030453>
43. REINWALD, F., RING, Z., KRAUS, F., KAINZ, A., TÖTZER, T., DAMYANOVIC, D. (2019): Green Resilient City – A framework to integrate the Green and Open Space Factor and climate simulations into everyday planning to support a green and climate-sensitive landscape and urban development. IOP Conference Series: Earth and Environmental Science, 323, 1, 12082. <https://doi.org/10.1088/1755-1315/323/1/012082>
44. SADEH, Y., ZHU, X., DUNKERLEY, D., WALKER, J.P., ZHANG, Y., ROZENSTEIN, O., MANIVASAGAM, V.S., CHENU, K. (2021): Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring. International Journal of Applied Earth Observation and Geoinformation, 96, 102260. <https://doi.org/10.1016/j.jag.2020.102260>
45. Sentinel-1 (n.d.). European Space Agency. Retrieved September 14, 2020, from https://sentinel.esa.int/web/sentinel/missions/sentinel-1 (16.09.2021).
46. SHAFIQUE, M., KIM, R., KYUNG-HO, K. (2018): Green roof for stormwater management in a highly urbanized area: the case of Seoul, Korea. Sustainability, 10, 3, 584. <https://doi.org/10.3390/su10030584>
47. SHUSTER, W.D., BONTA, J., THURSTON, H., WARNEMUENDE, E., SMITH, D.R. (2005): Impacts of impervious surface on watershed hydrology: A review. Urban Water Journal, 2, 4, 263–275. <https://doi.org/10.1080/15730620500386529>
48. SOZER, B., KOCAMAN, S., NEFESLİOGLU, H.A., FİRAT, O., GOKCEOGLU, C. (2018): Preliminary investigations on flood susceptibility mapping in Ankara (Turkey) using modified analytical hierarchy process (M-AHP).
49. SWAN, A. (2010): How increased urbanisation has induced flooding problems in the UK: A lesson for African cities? Physics and Chemistry of the Earth, Parts A/B/C, 35, 13–14, 643–647. <https://doi.org/10.1016/j.pce.2010.07.007>
50. TAVUS, B., KOCAMAN, S., GOKCEOGLU, C., NEFESLİOGLU, H.A. (2018): Considerations on the use of Sentinel-1 data ın flood mapping in urban areas: Ankara (Turkey) 2018 floods. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
51. Turkish State Meteorological Service (2022): Extreme Maximum, Minimum and Average Temperatures Measured in Long Period, https://www.mgm.gov.tr/eng/forecast-cities.aspx (16.09.2021).
52. VAN WOERT, N.D., ROWE, D.B., ANDRESEN, J.A., RUGH, C.L., FERNANDEZ, R.T., XIAO, L. (2005): Green roof stormwater retention: effects of roof surface, slope, and media depth. Journal of Environmental Quality, 34, 3, 1036–1044. <https://doi.org/10.2134/jeq2004.0364>
53. VARTHOLOMAIOS, A., KALOGIROU, N., ATHANASSIOU, E., PAPADOPOULOU, M. (2013): The green space factor as a tool for regulating the urban microclimate in vegetation-deprived Greek cities. International Conference on “Changing Cities”: Spatial, Morphological, Formal & Socio-Economic Dimensions, Skiathos Island.
54. VILLARREAL, E.L., BENGTSSON, L. (2005): Response of a Sedum green-roof to individual rain events. Ecological Engineering, 25, 1, 1–7. <https://doi.org/10.1016/j.ecoleng.2004.11.008>
55. WANG, L., DIAO, C., XIAN, G., YIN, D., LU, Y., ZOU, S., ERICKSON, T.A. (2020): A summary of the special issue on remote sensing of land change science with Google earth engine.
56. WANG, Y., LI, Z., ZENG, C., XIA, G.-S., SHEN, H. (2020): An urban water extraction method combining deep learning and Google Earth engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 768–781.
57. WENG, Q., QUATTROCHI, D.A. (2006): Urban Remote Sensing. CRC Press. Elsevier.
58. YANAR, T., KOCAMAN, S., GOKCEOGLU, C. (2020): Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS International Journal of Geo-Information, 9, 2, 114. <https://doi.org/10.3390/ijgi9020114>
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