Evaluation of the effect of spatial and temporal resolutions for digital change detection: case of forest fire

Balsak A., San B. T.

NATURAL HAZARDS, cilt.1, sa.1, ss.1-20, 2023 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11069-023-06199-0
  • Dergi Adı: NATURAL HAZARDS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, PAIS International, Pollution Abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-20
  • Akdeniz Üniversitesi Adresli: Evet

Özet

One of the most important subjects in remote sensing is digital change detection which

identiies the diferences between the before and after an event in spatial context. Even

though lots of new satellite images have been launched to use with improvement of their

resolutions, there needs to utilize at least two satellite images one of which must be acquired

before the event happened. It is rather diicult to always ind out proper spatial resolutions

for change detection. The aim of this study is to evaluate and investigate the efect of

spatial and temporal resolution on change detection. In this respect, the forest ire case

was chosen as an event with ive diferent satellite images (i.e. IKONOS, WorldView-2,

ASTER, Landsat7 and Landsat8) having diferent acquisition time which is almost 10-year

range. In this study, diferent spatial resolutions vs temporal resolutions were examined

on diferent change detection algorithms which are image diferencing, image rationing,

NDVI diferences, principle component analyses and minimum noise fractions (MNF).

These data sets were tested on Adrasan area (Antalya, Turkiye) for change detection of

forest ire. The obtained results has been shown that change detection using proposed MNF

method for Landsat data sets have the highest accuracy with the value of 95.65%. Then

the other high accuracy was obtained as 92.89% in MNF method for ASTER data sets. In

addition, the other method (i.e. image ratio) is another high accuracy as 92.82% obtained

for IKONOS and WorldViev-2 images. Finally, the relation between temporal resolution

and spatial resolution has been generated as a graphical representation with spatial kernel

size/filtering.