Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire
Recently, increasing wildfires have caused severe damage to vegetation and many living creatures. Remote sensing technologies and various algorithms are used to determine and analyze the burned forest areas. Different remotely sensed images such as Sentinel-2 MSI, Landsat, MODIS, SPOT were used to d...
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Artvin Coruh University
2022-01-01
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| Series: | Doğal Afetler ve Çevre Dergisi |
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| Online Access: | http://dacd.artvin.edu.tr/tr/pub/issue/68003/941456 |
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| author | Beyza Yılmaz Mehveş Demirel Filiz Bektaş Balçık |
| author_facet | Beyza Yılmaz Mehveş Demirel Filiz Bektaş Balçık |
| author_sort | Beyza Yılmaz |
| collection | DOAJ |
| description | Recently, increasing wildfires have caused severe damage to vegetation and many living creatures. Remote sensing technologies and various algorithms are used to determine and analyze the burned forest areas. Different remotely sensed images such as Sentinel-2 MSI, Landsat, MODIS, SPOT were used to determine forest fire damage and to produce maps for burned areas. In this study, the 6 July 2020 dated wildfire that occurred in the Gallipoli district of Çanakkale province has been analyzed by using Sentinel-2 MSI and Landsat-8 OLI satellite images. Burned Area Index (BAI), Normalized Moisture Index (NDMI), Normalized Burn Ratio (NBR), and Normalized Difference Vegetation Index (NDVI) were calculated with the pre and post-fire satellite images of the study area. The differences of the pre and post-fire indices were calculated to determine the burned forest area. Error matrix was produced for accuracy assessment. Overall accuracy, user accuracy, producer accuracy, and Kappa statistics were calculated, and performances were evaluated for different sensors and different indices by comparing the accuracy assessment results. The highest accuracy results were achieved with Differenced Normalized Difference Vegetation Index (dNDVI) for both Landsat-8 OLI and Sentinel-2 MSI images, and Kappa statistic results were obtained as 0.94 and 0.95, respectively. |
| format | Article |
| id | doaj-art-5c37c60ca2f1401ba0c860dca8e17267 |
| institution | Kabale University |
| issn | 2528-9640 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Artvin Coruh University |
| record_format | Article |
| series | Doğal Afetler ve Çevre Dergisi |
| spelling | doaj-art-5c37c60ca2f1401ba0c860dca8e172672025-02-02T16:24:18ZengArtvin Coruh UniversityDoğal Afetler ve Çevre Dergisi2528-96402022-01-01817686https://doi.org/10.21324/dacd.941456Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest FireBeyza Yılmaz0https://orcid.org/0000-0002-5058-4521Mehveş Demirel1https://orcid.org/0000-0002-7611-3496 Filiz Bektaş Balçık2https://orcid.org/0000-0003-3039-6846İstanbul Teknik Üniversitesi, İnşaat Fakültesi, Geomatik Mühendisliği Bölümü, 34469, İstanbul.İstanbul Teknik Üniversitesi, İnşaat Fakültesi, Geomatik Mühendisliği Bölümü, 34469, İstanbul.İstanbul Teknik Üniversitesi, İnşaat Fakültesi, Geomatik Mühendisliği Bölümü, 34469, İstanbul.Recently, increasing wildfires have caused severe damage to vegetation and many living creatures. Remote sensing technologies and various algorithms are used to determine and analyze the burned forest areas. Different remotely sensed images such as Sentinel-2 MSI, Landsat, MODIS, SPOT were used to determine forest fire damage and to produce maps for burned areas. In this study, the 6 July 2020 dated wildfire that occurred in the Gallipoli district of Çanakkale province has been analyzed by using Sentinel-2 MSI and Landsat-8 OLI satellite images. Burned Area Index (BAI), Normalized Moisture Index (NDMI), Normalized Burn Ratio (NBR), and Normalized Difference Vegetation Index (NDVI) were calculated with the pre and post-fire satellite images of the study area. The differences of the pre and post-fire indices were calculated to determine the burned forest area. Error matrix was produced for accuracy assessment. Overall accuracy, user accuracy, producer accuracy, and Kappa statistics were calculated, and performances were evaluated for different sensors and different indices by comparing the accuracy assessment results. The highest accuracy results were achieved with Differenced Normalized Difference Vegetation Index (dNDVI) for both Landsat-8 OLI and Sentinel-2 MSI images, and Kappa statistic results were obtained as 0.94 and 0.95, respectively.http://dacd.artvin.edu.tr/tr/pub/issue/68003/941456burned forest areasentinel-2 msilandsat-8 oliremote sensing indicesaccuracy assessment |
| spellingShingle | Beyza Yılmaz Mehveş Demirel Filiz Bektaş Balçık Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire Doğal Afetler ve Çevre Dergisi burned forest area sentinel-2 msi landsat-8 oli remote sensing indices accuracy assessment |
| title | Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire |
| title_full | Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire |
| title_fullStr | Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire |
| title_full_unstemmed | Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire |
| title_short | Detection and Analysis of Burned Areas with Sentinel-2 MSI and Landsat-8 OLI: Çanakkale / Gelibolu Forest Fire |
| title_sort | detection and analysis of burned areas with sentinel 2 msi and landsat 8 oli canakkale gelibolu forest fire |
| topic | burned forest area sentinel-2 msi landsat-8 oli remote sensing indices accuracy assessment |
| url | http://dacd.artvin.edu.tr/tr/pub/issue/68003/941456 |
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