Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

Abstract

The purpose of this paper is to identify the burned areas that occurred in Italy during the summer of 2017 using change detection analysis techniques. This task is possible thanks to continuous, free and open availability of the multispectral images obtained by Sentinel-2 satellites. Indeed, comparing the satellite images of the same scene recorded at different times, it was possible to evaluate the landscape change. In this paper, the Direct Comparison change detection technique was applied to the analysis and identifi cation of burned area using several Remote Sensing indexes. In particular, in order to achieve this aim, NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) were used. By case study in South Italy region (Sorrento peninsula), using images derived from Sentinel-2A imagery, it was possible to identify the burned areas in a specifi c period and evaluate the performance of the two indexes. In fact, after having constructed the confusion matrix for the two tested indexes, through the use of methods that indicate the quality of a thematic map (User's Accuracy, Producer's Accuracy, Overall Accuracy and Kappa coeffi cient), the percentage values for each remote sensing index analyzed were compared. The analysis of the different methods revealed, from one side the high quality of the results achievable by NBR index, on the other side, it was shown how, in some areas, the NDVI was inadequate for the recognition of burned areas.

Environmental Aftermath of the 2019 Stromboli Eruption

“…The Normalized Burn Ratio (NBR) index has been used to map the areas covered by fire, which has permitted to easily identify the areas covered by the fire and the degree of severity of a fire [10][11][12][13][14][15][16]. This index has been calculated on two Sentinel-2 images acquired on different dates before and after the wildfire (after a not excessively high number of days, especially if the area affected by the fire consists mainly of pasture or low bush).…”

“…Multi-temporal comparison of LC and LU has been based on the digitalization of images The RBR values have been used to derive the map of the areas impacted by the wildfire produced by the 2019 paroxysmal explosions. The USGS FireMon program, a National Burn Severity Mapping Project of the U.S. Geological Survey, indicates severity layer variable [16]. The calculated values usually vary because of the different characteristics of the image and the acquisition conditions; in the case of Stromboli, all areas with an RBR value greater than 0.270 have been considered burnt areas.…”

Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

“…However, in the case of small fires and fragmented ecosystems, such as in the Mediterranean area, low spatial resolution satellite imagery can significantly undersize the burned area, risking the loss of useful information for post-fire damage assessment. Therefore, medium or high-resolution satellite imagery as Landsat [ 116 , 117 ] or Sentinel-2 (S2) [ 118 ] become useful and strategical for BA analysis in a complex and fragmented ecosystem. The systematic acquisition every 5-days over the same area and the free availability of S2 data are fundamental for setting a systematic monitoring system.…”

On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity

“…The remote sensing of burned areas has traditionally been based on optical data and carried out using vegetation indices (see for example [4][5][6][7][8][9][10][11][12][13][14][15][16][17]) that are a spectral combination of diverse bands, devised to emphasize the spectral changes caused by fire on vegetation in the short-and long-term. The vegetation indices operate by contrasting intense chlorophyll pigment absorption in the red (RED in Equation (1)) against the high reflectance of leaf mesophyll in the most commonly used spectral channels for vegetation monitoring at near infrared (NIR in Equation (1)) and shortwave (SWIR in Equation 2).…”

“…Therefore, in this study, the ∆NBR was used as it is expected to perform better than NBR in capturing the spatial complexity of fire-induced features and patterns (see [11]). It is expected that the ∆NBR maps should highlight burnt areas, characterize burn severity levels, and provide indications of the changes induced by fire in terms of biomass loss, carbon release, and smoke production.…”