Algorithm Performance Evaluation

Burnt surface area statistics compared to inventories/fire surveys

http://www.gvm.ies.jrc.it/fire/gba2000_website/index.htm (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 Tansey1, Jean-Marie Grégoire1, Ilaria Marengo1, Luigi Boschetti1, Alessandro Brivio2, Dmitry Ershov3, Robert Fraser4, Dean Graetz5, Marta Maggi1, Pascal Peduzzi6, Jose Pereira7, João Silva8, Adélia Sousa9 and

Daniela Stroppiana10 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 Algorithms ReferencesInput 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. 133-139.

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: 18176-2001-07-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. 209-216.

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. 75-82.

Algorithm Input data Compositing criteria

Classification approach

Boschetti et al. (2002) Single date images n.a. Temporal change in the NIR & SWIR after BRDF correction

Brivio et al. (2002) Single date images n.a. Multi-Layer Perceptron (MLP) classifier & contextual analysis

Ershov and Novik (2001) Single date images n.a. Temporal change in the NIR & SWIR

Fraser et al. (2002) 10-day composites Maximum NDVI Multiple logistic regression model

Sousa et al. Monthly composites 3rd Min. NIR Classification trees & temporal change

Silva et al. (2001) Monthly composites Minimum NIR Linear Discriminant Analysis of temporal change of six variables

Stroppiana et al. (2002) 10-day composites Minimum NIR Classification trees & temporal change in the NIR and SWIR

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 dataSampling grid of 15x15 km, regression line, R2, small low resolution bias

UTL Europe R2 = 0.71UTL Africa R2 = 0.4 – 0.99CNR R2 = 0.6

Per pixel confusion matrix using Landsat TM data

CNR OA = 82.3% – 87%

Overall (OA), omission (OM) and commission (COM) map accuracies determined

IFI R2 = 0.89*

JRC (Stroppiana) COM = 36%

* errors in burnt area mapping reported to be less than 15%

CCRS OM = 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)

http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/ (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: 0.1428571.

Per pixel confusion matrix using a visual classification of 1km data