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UID:pretalx-foss4g-it-2023-8FJMMB@talks.osgeo.org
DTSTART;TZID=GMT:20230612T173000
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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:20260422T232241Z
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.osgeo.org/foss4g-it-2023/talk/8FJMMB/
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