This paper is focused on investigating the capabilities of SAR S-1 sensors for burned area mapping. To this aim, we analyzed S-1 data focusing on a fire that occurred on August 10th, 2017, in a protected natural site. An unsupervised classification, using a k-mean machine learning algorithm, was carried out, and the choice of an adequate number of clusters was guided by the calculation of the silhouette score. The ΔNBR index calculated from optical S-2 based images was used to evaluate the burned area delimitation accuracy. The fire covered around 38.51 km2 and also affected areas outside the boundaries of the reserve. S-1 based outputs successfully matched the S-2 burnt mapping.

Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm

De Luca G.;Modica G.
;
2020-01-01

Abstract

This paper is focused on investigating the capabilities of SAR S-1 sensors for burned area mapping. To this aim, we analyzed S-1 data focusing on a fire that occurred on August 10th, 2017, in a protected natural site. An unsupervised classification, using a k-mean machine learning algorithm, was carried out, and the choice of an adequate number of clusters was guided by the calculation of the silhouette score. The ΔNBR index calculated from optical S-2 based images was used to evaluate the burned area delimitation accuracy. The fire covered around 38.51 km2 and also affected areas outside the boundaries of the reserve. S-1 based outputs successfully matched the S-2 burnt mapping.
2020
978-3-030-58813-7
978-3-030-58814-4
Burned area detection
Forest fire
K-mean clustering
Machine learning
Normalized burn ratio (NBR)
PCA
Protected natural site
Radar burn difference (RBD)
Radar burn ratio (RBR)
SAR
Sentinel-1
Silhouette score
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/66254
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