Geodaysit 2023

A Preliminary Investigation of the PRISMA Hyperspectral Sensor Potential for Burned Area Mapping in an Operational Context
06-13, 17:00–17:15 (Europe/London), Sala Videoconferenza @ PoliBa

In the past, the scarcity of hyperspectral Earth Observation (EO) data hindered the development of operational applications based on such technology. Considering the current increasing availability of this kind of data (e.g., PRISMA, EnMap), that it is expected to further grow in the future (e.g., Copernicus CHIME, PRISMA Second Generation), it is important to evaluate the potential retained by hyperspectral remote sensing for EO applications that could provide operational services in the next few years. Within this context, this work was conceived to perform a preliminary investigation of the capabilities of the PRISMA hyperspectral sensor for burned area (BA) mapping in an operational context (e.g., civil protection applications).

One of the most common approaches used for BA mapping via EO data is based on the Differenced Normalized Burn Ratio (dNBR) index, which detects the fire-induces alterations to vegetation and soils by taking advantage of the spectral information acquired in the Near InfraRed (NIR: 0.7-1.2 µm) and Short-Wave InfraRed (SWIR: 1.2-2.5 µm) bands of two images: one acquired before the fire event, one after [1]. Multispectral imagery commonly used for performing BA mapping for operational applications (e.g., Sentinel 2, Landsat) have specific NIR and SWIR bands that can be used for dNBR computation [2]. Hyperspectral images, instead, allow for several bands combinations of data acquired in the NIR and SWIR spectral regions, thereby generating numerous (and, in some cases, slightly) different definitions of dNBR maps. Amongst these bands’ combinations, the more reliable ones shall be identified (i.e., the ones capable of producing BA maps more accurate). At the same time – since the dNBR is also sensible to non-fire induced spectral alterations [1] – the less reliable ones shall be avoided.

The aim of this study was to set up an experiment in which it was prototyped an automatic methodology of operational BA mapping based on PRISMA Level2D products (i.e., orthorectified, surface reflectance imagery; GSD: 30 m). The wildfire that occurred in Pantelleria Island (Italy) on 17/08/2022 was used as a case study. For this event, there were available two PRISMA images acquired on 06/08/2022 (pre-event) and 16/07/2022 (post-event). An ancillary shapefile produced by the Copernicus Emergency Management Service (EMS) and representing the extent of the BA on 19/08/2022 (ca. 28 ha) was used as a reference layer to validate the analysis results.

The methodology that was set up – conceptually similar to the one developed by [2] – produced more than 7600 dNBR maps (obtained from the combinations of the PRISMA NIR and SWIR spectral bands), from which the pixels corresponding to the BA were mapped by using the Otsu approach for automatic threshold selection. The analysis was carried out over the whole Pantelleria Island territory, where water bodies, clouds and clouds’ shadows were masked out (as well as poor quality PRISMA bands). Then, the accuracy of the classification was quantified (as a percentage) by means of the Dice Coefficient (DC) [3], which was calculated by using the Copernicus EMS reference BA layer. According to the DC, the best bands combination for mapping the BA of the Pantelleria 2022 wildfire corresponds to the 0.903 (NIR) and 2.253 µm (SWIR) wavelengths. The DC associated with this BA map was 89.4%.

In an operational context, ancillary information (i.e., BA reference layers) are often not available to identify the most reliable bands for BA mapping. Therefore, an image-based selection criterion useful to achieve this objective shall be used. Indeed, for every NIR/SWIR bands combination used during the analysis, the spectral separability [3] of the pixels classified as BA – from the neighbouring ones classified as not BA – was computed. Then, the bands combination characterized by the highest separability value was used for identifying the best dNBR map to use for BA mapping. For this specific exercise, this combination corresponds to the 1.038 µm (NIR) and 2.245 µm (SWIR) wavelengths. The DC associated with this BA map was 88.8%. This value is very similar to the one identified via the ancillary reference BA layer.

The details of the methodology will be presented at the conference, where the analysis results will be also thoroughly discussed.


[1] van Gerrevink M.J. & Veraverbeke S. (2021). Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity. Remote Sensing. 13(22):4611.

[2] Pulvirenti L. et al. (2023). Near real-time generation of a country-level burned area database for Italy from Sentinel-2 data and active fire detections. Remote Sensing Applications: Society and Environment. 29.

[3] Roteta E. et al. (2019). Development of a Sentinel‐2 burned area algorithm: Generation of a small fire database for sub‐Saharan Africa. Remote Sensing of Environment. 222, 1–17.

Luca Cenci received the BSc degree (cum laude) in “Coordination of Civil Protection Activities” from the University of Perugia (Italy), in 2010; the MSc degree (cum laude) in “Geosciences and Geotechnologies” from the University of Siena (Italy), in 2013; the PhD degree (grade: Excellent) in “Understanding and Managing Extremes” (Curriculum: “Weather-Related Risk”) from the University School for Advanced Studies IUSS Pavia (Italy), in 2016.
In 2009, he was a visiting student with the University of Malta and a trainee in the Malta Civil Protection Department (Malta). In 2012, he was a visiting student with the Department of Environment and Planning, University of Aveiro (Portugal). In 2013, he was a trainee with the Geomatics Laboratory of the Centre for GeoTechnologies (CGT) - University of Siena (Italy). In 2016, he was a visiting researcher with the Luxembourg Institute of Science and Technology - LIST (Luxembourg). From 2014 to 2019, he was a doctoral and postdoctoral Researcher with CIMA Research Foundation (Italy). From 2017 to 2019, he was a Postdoctoral Researcher with the Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome (Italy). From 2019 to 2022, he worked with Serco Italia SpA (Italy), for the European Space Agency (ESA), as Senior Earth Observation (EO) - Geomatic Engineer in the Copernicus Coordinated data Quality Control (CQC) service. Since 2022, he has been working as researcher for CIMA Research Foundation (Italy) in the EO Department.
Throughout his academic and professional experiences, he gained advanced theoretical knowledge and practical skills in the fields of satellite remote sensing data processing, analysis and quality assessment (e.g., optical, SAR, GNSS-R, DEM). His main research interests are mostly focused on the integration of EO & GIS -based technologies for land applications, with an emphasis on geosciences (mainly, hydrology & geology), natural hazards (mainly, floods & coastal erosion; lately, also wildfires & drought) and disaster risk management. Within this context, Luca worked in the development of innovative EO-based solutions for both research purposes (e.g., EU, ESA, ASI -funded scientific projects) and operational/downstream services (e.g., Copernicus services), in both academic and industrial contexts.
In 2013, Luca received the “Premio Nazionale di Laurea Eugenio Zilioli” for the best Italian Master's Thesis on remote sensing by the National Research Council (CNR) of Italy (IREA institute) and the ASITA federation.