The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

Creative Commons License

ASLAN N., Koc-San D.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, cilt.10, sa.6, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 6
  • Basım Tarihi: 2021
  • Doi Numarası: 10.3390/ijgi10060416
  • Dergi Adı: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: surface urban heat island (SUHI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI), index-based built-up index (IBI), Landsat, Otsu thresholding, SPLIT-WINDOW ALGORITHM, LOCAL CLIMATE ZONES, TEMPERATURE RETRIEVAL, SEASONAL VARIABILITY, VEGETATION INDEX, SATELLITE DATA, CITY, URBANIZATION, AREA, MITIGATION
  • Akdeniz Üniversitesi Adresli: Evet

Özet

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.