BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//EWA3NK BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-8FJMMB@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T173000 DTEND;TZID=GMT:20230612T174500 DESCRIPTION:The damages generated by fire events on vegetation structure an d its evolution and the economic impacts on human activity\, life and infr astructures have led the scientific interest to develop tools and algorith ms able to support the detection and monitoring of burned areas (BA). \nTh e possibility of monitoring the fire evolution and mapping the BA has been strongly supported in last decades by the opportunity to use a significan t quantity of satellite observations. The freely and timely availability of remote sensing data has grown so faster in the last years as well as a higher spatial resolution that makes the earth observation derived data th e key component in supporting both government agencies and local decision- makers in monitoring natural disasters such as wildfire or floods. \nThe C opernicus Sentinel-2 with 20-m spatial resolution and a 5-day return perio d is a great candidate for near real-time (NRT) applications of change det ection based on spectral indices. An automatic near-real time (NRT) burned area (BA) mapping approach designed to map BA using Sentinel-2 (S2) data was proposed in [1] and recently updated in [2]. The AUTOmatic Burned Area s Mapper (AUTOBAM) tool was originally designed to respond the need of the Italian Department of Civil Protection in monitoring spatial distribution and numerousness of BA during the fire season (June- September) over the Italian territory. The atmospherically corrected Level-2A(L2A) surface ref lectance products from S2 are used: the automatic chain downloads and proc esses the most updated L2A products available on Copernicus Open Access Hu b over the studied area. At the three spectral indices estimated (Normaliz ed Burn Ratio\, the Normalized Burned Ratio 2\, and the Mid-Infrared Burne d Index) a change detection approach is applied. AUTOBAM compares the valu es of these indices acquired at current time with the values derived from the most recent cloud-free S2 data. The procedure for BA mapping is based on different sequential image processing techniques such as clustering\, a utomatic thresholding\, region growing that conduce to a final BAs map wit h grid pixel size of 20m. Finally\, a quality flag is included for each AU TOMAB BAs to certify a temporal and spatial correspondence with ancillary data\, such as derived active fire detections from MODIS\, VIIRS and natio nal fire notifications.\nThe daily run of AUTOBAM allowed us to produce a burned area database for Italy. To evaluate the quality of the database\, the AUTOBAM-derived BAs have been compared with the burn perimeters compil ed by Carabinieri Command of Units for Forestry\, Environmental and Agri-f ood protection. These perimeters represent the official burned area data f or Italy. A validation procedure based of both a pixel-based confusion mat rix and a set object-based accuracy metrics has been set up considering th e whole Italian territory and years 2019-2021. Good results have been obta ined by AUTOBAM in terms of detection capability (the Correctness paramete r) and overlap factor (both larger than 60%). However\, quite high values of the commission error were obtained\, especially in 2019. Through a per land cover analysis\, it was found that this error mostly occurred in cult ivated land. Excluding the latter target\, the commission error was always less than 35%\, the omission error was less than 27% and the Dice Coeffic ient was larger than 69%. Moreover\, starting from 2021\, the Lazio region is providing AUTOBAM with accurate fire notifications derived from its SO UP (Italian acronym of Permanent Unified Operations Room). An experimental activity has been performed to verify whether these notifications can be used as trigger for the burned area mapping algorithm to reduce the number of false positives.\n\n\nReferences:\n\n[1] L. Pulvirenti et al.\, “An automatic processing chain for near real-time mapping of burned forest are as using sentinel-2 data\,” Remote Sens.\, vol. 12\, p. 674\, 2020.\n[2] L. Pulvirenti\, G. Squicciarino\, E. Fiori\, D. Negro\, A. Gollini\, and S. Puca\, “Near real-time generation of a country-level burned area data base for Italy from Sentinel-2 data and active fire detections\,” Remote Sens. Appl. Soc. Environ.\, vol. 29\, 2023. DTSTAMP:20260516T204646Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:A burned area database for Italy from Sentinel-2 images and ancilla ry data - Luca Pulvirenti\, Giuseppe Squicciarino\, Dario Negro\, Silvia P uca URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/8FJMMB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-DT7LLU@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T170000 DTEND;TZID=GMT:20230613T171500 DESCRIPTION:In the past\, the scarcity of hyperspectral Earth Observation ( EO) data hindered the development of operational applications based on suc h 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 im portant to evaluate the potential retained by hyperspectral remote sensing for EO applications that could provide operational services in the next f ew years. Within this context\, this work was conceived to perform a preli minary investigation of the capabilities of the PRISMA hyperspectral senso r for burned area (BA) mapping in an operational context (e.g.\, civil pro tection applications).\n \nOne of the most common approaches used for BA m apping via EO data is based on the Differenced Normalized Burn Ratio (dNBR ) index\, which detects the fire-induces alterations to vegetation and soi ls by taking advantage of the spectral information acquired in the Near In fraRed (NIR: 0.7-1.2 µm) and Short-Wave InfraRed (SWIR: 1.2-2.5 µm) band s of two images: one acquired before the fire event\, one after [1]. Multi spectral imagery commonly used for performing BA mapping for operational a pplications (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 SW IR spectral regions\, thereby generating numerous (and\, in some cases\, s lightly) different definitions of dNBR maps. Amongst these bands’ combin ations\, the more reliable ones shall be identified (i.e.\, the ones capab le of producing BA maps more accurate). At the same time – since the dNB R is also sensible to non-fire induced spectral alterations [1] – the le ss reliable ones shall be avoided.\n\nThe aim of this study was to set up an experiment in which it was prototyped an automatic methodology of opera tional 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 thi s event\, there were available two PRISMA images acquired on 06/08/2022 (p re-event) and 16/07/2022 (post-event). An ancillary shapefile produced by the Copernicus Emergency Management Service (EMS) and representing the ext ent of the BA on 19/08/2022 (ca. 28 ha) was used as a reference layer to v alidate the analysis results. \n\nThe methodology that was set up – conc eptually similar to the one developed by [2] – produced more than 7600 d NBR maps (obtained from the combinations of the PRISMA NIR and SWIR spectr al bands)\, from which the pixels corresponding to the BA were mapped by u sing the Otsu approach for automatic threshold selection. The analysis was carried out over the whole Pantelleria Island territory\, where water bod ies\, clouds and clouds’ shadows were masked out (as well as poor qualit y PRISMA bands). Then\, the accuracy of the classification was quantified (as a percentage) by means of the Dice Coefficient (DC) [3]\, which was ca lculated 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%.\n\nIn an operational contex t\, ancillary information (i.e.\, BA reference layers) are often not avail able to identify the most reliable bands for BA mapping. Therefore\, an im age-based selection criterion useful to achieve this objective shall be us ed. Indeed\, for every NIR/SWIR bands combination used during the analysis \, the spectral separability [3] of the pixels classified as BA – from t he neighbouring ones classified as not BA – was computed. Then\, the ban ds combination characterized by the highest separability value was used fo r identifying the best dNBR map to use for BA mapping. For this specific e xercise\, this combination corresponds to the 1.038 µm (NIR) and 2.245 µ m (SWIR) wavelengths. The DC associated with this BA map was 88.8%. This v alue is very similar to the one identified via the ancillary reference BA layer.\n\nThe details of the methodology will be presented at the conferen ce\, where the analysis results will be also thoroughly discussed.\n\nRefe rences:\n\n[1] van Gerrevink M.J. & Veraverbeke S. (2021). Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Ass essing Fire Severity. Remote Sensing. 13(22):4611.\n\n[2] Pulvirenti L. et al. (2023). Near real-time generation of a country-level burned area data base for Italy from Sentinel-2 data and active fire detections. Remote Sen sing Applications: Society and Environment. 29.\n\n[3] Roteta E. et al. (2 019). Development of a Sentinel‐2 burned area algorithm: Generation of a small fire database for sub‐Saharan Africa. Remote Sensing of Environme nt. 222\, 1–17. DTSTAMP:20260516T204646Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:A Preliminary Investigation of the PRISMA Hyperspectral Sensor Pote ntial for Burned Area Mapping in an Operational Context - Luca Cenci\, Luc a Pulvirenti\, Giuseppe Squicciarino URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/DT7LLU/ END:VEVENT END:VCALENDAR