A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems. Issue 4 (19th May 2021)

• Record Type: Journal Article
• Authors: De Luca, Giandomenico; Silva, João M.N.; Modica, Giuseppe
• Is Part Of: GIScience & remote sensing. Volume 58:Issue 4(2021)
• Page Start: 516
• Page End: 541
• Publication Date: 2021-05-19
• Subjects: SNAP-python (snappy) interface -- k-means clustering -- scikit-learn libraries -- radar vegetation index (RVI) -- dual-polarization sar vegetation index (DPSVI); Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2021.1907896
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: This paper investigates the capability of the free synthetic aperture radar (SAR) Sentinel-1 (S-1) C-band data for burned area mapping through unsupervised machine learning open-source processing solutions in the Mediterranean forest ecosystems. The study was carried out in two Mediterranean sites located in Portugal (PO) and Italy (IT). The entire processing workflow was developed in Python-based scripts. We analyzed two time-series covering about one month before and after the fire events and using both VH and VV polarizations for each study site. The speckle noise effects were reduced by performing a multitemporal filter and the backscatter time averages of pre- and post-fire datasets. The spectral contrast between changed and unchanged areas was enhanced by calculating two single-polarization radar indices: the radar burn difference (RBD) and the logarithmic radar burn ratio (LogRBR); and two temporal differences of dual-polarimetric indices: the delta modified radar vegetation index (ΔRVI) and the delta dual-polarization SAR vegetation index (ΔDPSVI), all exhibiting greater sensitivity to the backscatter changes. The scene's contrast was enhanced by extracting the Gray Level Co-occurrence Matrix (GLCM) textures (dissimilarity, entropy, correlation, mean, and variance). A principal component analysis (PCA) was applied for reducing the number of the GLCM image layers. The burned area was delineated through unsupervised classification using the k -means clustering algorithm. A suitable number of clusters ( k value) were set using a silhouette score analysis. To assess the accuracy of the resulting detected burned areas, an official burned area map based on multispectral Sentinel-2 (S-2) was used for PO, while for IT, a reference map was produced from S-2 data, based on the normalized burned ratio difference (ΔNBR) index. Recall ( r ), precision ( p ) and the F-score accuracy metrics were calculated. Our approach reached the values of 0.805 ( p ), 0.801 ( r ) and 0.803 ( F-score ) for PO, and 0.851 ( p ), 0.856 ( r ) and 0.853 ( F-score ) for IT. These results confirm the suitability of our approach, based on SAR S-1 data, for burned area mapping in heterogeneous Mediterranean ecosystems. Moreover, the implemented workflow, completely based on free and open-source software and data, offers high adaptation flexibility, repeatability, and custom improvement.