Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

Liu, S.; Zheng, Y.; Dalponte, M.; Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. EUROPEAN JOURNAL OF REMOTE SENSING, 53 (1): 104-112. doi: 10.1080/22797254.2020.1738900 handle: http://hdl.handle.net/10449/60108

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

Dalponte, M.;
2020-01-01

Abstract

Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.
Change detection
Burned area
Spectral index
NBRSWIR
Separation index
Landsat-8 OLI
Settore AGR/05 - ASSESTAMENTO FORESTALE E SELVICOLTURA
2020
Liu, S.; Zheng, Y.; Dalponte, M.; Tong, X. (2020). A novel fire index-based burned area change detection approach using Landsat-8 OLI data. EUROPEAN JOURNAL OF REMOTE SENSING, 53 (1): 104-112. doi: 10.1080/22797254.2020.1738900 handle: http://hdl.handle.net/10449/60108
File in questo prodotto:
File Dimensione Formato  
2020 IJRS Dalponte.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 1.51 MB
Formato Adobe PDF
1.51 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/60108
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 52
  • ???jsp.display-item.citation.isi??? 41
social impact