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

POUYA S., Aghlmand M., KARSLI F.

GEOGRAFIE, cilt.127, sa.3, ss.219-240, 2022 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 127 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.37040/geografie.2022.008
  • Dergi Adı: GEOGRAFIE
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Geobase
  • Sayfa Sayıları: ss.219-240
  • Anahtar Kelimeler: Google Earth Engine, Sentinel 1, Sentinel 2, PlanetScope, Green spaces factor, flash floods, green roofs, Ankara/Mamak district, URBAN, CLASSIFICATION, PERFORMANCE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This research aimed to increase the green space factor to mitigate flash flood effects on urban storm water runoff in the Ankara Mamak region and to minimize the damages by flash floods. The land use/cover map was first obtained by using the images of Sentinel-1, Sentinel-2, and PlanetScope satellites with the LIBSVM algorithm on the Google Earth Engine. The GSF value was then calculated and it was low (0.26) compared to world standards. This study was proposed as a solution for the flood disaster, using the extensive green roof scenario. After green roof conversion scenarios, the GSF value was recalculated. It was found to be above the minimum of green infrastructure that human settlements should achieve, regardless of density or land use (0.43). Offering high resolution images and the possibility of processing them via different algorithms of machine learning has revolutionized the environmental and urban-related studies as they help urban managers and planners to make decisions accurately and quickly.