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Algorithm Performance Evaluation Burnt surface area statistics compared to inventories/fire surveys

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Presentation on theme: "Algorithm Performance Evaluation Burnt surface area statistics compared to inventories/fire surveys"— Presentation transcript:

1 Algorithm Performance Evaluation Burnt surface area statistics compared to inventories/fire surveys (JRC) Where the GBA2000 algorithms are applied The months in the year when the algorithms are applied A global map of burnt vegetation at 1km resolution for the year 2000 derived from SPOT VGT data Kevin Tansey 1, Jean-Marie Grégoire 1, Ilaria Marengo 1, Luigi Boschetti 1, Alessandro Brivio 2, Dmitry Ershov 3, Robert Fraser 4, Dean Graetz 5, Marta Maggi 1, Pascal Peduzzi 6, Jose Pereira 7, João Silva 8, Adélia Sousa 9 and Daniela Stroppiana 10 1: Joint Research Centre, Italy. 2: Ist. per il Rilevamento Elettromagnetico dell’Ambiente, Italy. 3: International Forest Ins., Russia. 4: Canada Centre for Remote Sensing. 5: CSIRO: EOC, Australia. 6: UN Environmental Programme, Switzerland. 7: Tropical Research Ins., Portugal. 8: Uni. Técnica de Lisboa, Portugal. 9: Uni. de Évora, Portugal. 10: Ist. Agronomico per l’Oltremare, Italy The Burnt Area AlgorithmsReferencesInput Data and Product Definition Brivio et al. (2002), Exploiting spatial and temporal information for extracting burned areas from time series of SPOT VGT data. In Analysis of Multi-temporal Remote Sensing Images, edited by L. Bruzzone and P. Smith (World Scientific Publishing, Singapore), pp Ershov and Novik (2001), Mapping burned areas in Russia with SPOT4 VEGETATION (S1 product) imagery. Final Report for the Joint Research Centre of the European Commission (Contract Number: F1EI ISP RU). Fraser et al. (2002), Multi-temporal mapping of burned forest over Canada using satellite-based change metrics. Geocarto International, submitted. Silva et al. (2001), Burned area mapping in Southeastern Africa using SPOT VEGETATION: Methods and validation. GOFC Fire Satellite Product Validation Workshop, 9-11 July 2001, Lisbon, Portugal (http://www.isa.utl.pt/cef/eventos/gofc/index.html). Stoppiana et al. (2002), Using temporal change of the land cover spectral signal to improve burnt area mapping. In Analysis of Multi-temporal Remote Sensing Images, edited by L. Bruzzone and P. Smith (World Scientific Publishing, Singapore), pp Boschetti et al. (2002), A multitemporal change-detection algorithm for the monitoring of burnt areas with SPOT-VEGETATION data. In Analysis of Multi-temporal Remote Sensing Images, edited by L. Bruzzone and P. Smith (Singapore: World Scientific), pp Grégoire and Tansey (in press), The GBA2000 initiative: Developing a global burned area database from SPOT-VEGETATION imagery. International Journal of Remote Sensing. SPOT4 VEGETATION S1 daily, global imagery for the year 2000 GBA2000 image and algorithm processing chain (JRC) Monthly and annual binary burnt area (BA) products Text file listing lat./lon. coordinates of the centre of each burnt pixel Algorithm performance evaluations made using Landsat TM data Estimates of the global map’s regional accuracy using independent TM data Information & data access portals: JRC GBA2000 and UNEP Websites Surface area accuracy of burn scars compared to Landsat TM data Sampling grid of 15x15 km, regression line, R 2, small low resolution bias UTL Europe R 2 = 0.71 UTL AfricaR 2 = 0.4 – 0.99 CNRR 2 = 0.6 Per pixel confusion matrix using Landsat TM data CNROA = 82.3% – 87% Overall (OA), omission (OM) and commission (COM) map accuracies determined IFIR 2 = 0.89* JRC (Stroppiana)COM = 36% * errors in burnt area mapping reported to be less than 15% CCRSOM = 15.5% (2000)** ** OM in Canada in 1998 = 23%, 1999 = 11%. OM in the USA in 2000 = 6.6% Work in progress: UOE/UTL and JRC (Boschetti) (UNEP) Download BA maps View examples of BA statistics Obtain reference information Global Burnt Areas in the Year 2000 For display purposes, the original map has been re-sampled (x16) to indicate the number of burnt pixels (n) in each moving window and kept if n  16. Hence, the burnt areas appear greatly exaggerated. Areas burnt in Jan. to March may burn again in Oct. to Dec. For clarity, they are indicated as burnt in Jan. to March. Projection: Plate Carrée. Ellipsoid: WGS84. Pixel size: . Per pixel confusion matrix using a visual classification of 1km data


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