Abstract
The impact of vegetation fires on the balance of the global ecosystem is generally recognised. Biomass burning emissions of CO₂, trace gases such as CH₄, NH₃, NOx, SOx, CO, hydrocarbons, and of particulates play a significant role in global climate change. It is estimated for example that 5%-20% of the total atmospheric CO₂ is produced by tropical rain forest destruction, including that by burning. Large scale vegetation fires can lead to ecosystem degradation by changing the water balance, reducing evapotranspiration and increasing soil erosion. Moreover they can be a threat to the biodiversity of flora and fauna as well as to settlements and even human life. Therefore the importance of improved monitoring and management of large-scale vegetation fires is essential (Andrea, 1991).

The aim of this project is to investigate the relationship between fire activity and El-Nino-Southern Oscillation (ENSO) events in South East Asia using Borneo, Indonesia as a case-study. In this region burning occurs annually but a number of unusually large fire events have occurred in recent times (Woooster et al., 1998).

Low spatial resolution NOAA AVHRR GAC satellite data were used to investigate the occurrence of active fires during the El-Nino episodes of the last 20 years.

A comparison of the detection capabilities of low spatial resolution NOAA-AVHRR Global Area Coverage (GAC) and higher resolution Local Area Coverage (LAC) data has been carried out in order to investigate the potential for the long-term archive of the latter to be used in the time-series analysis of active fires in Borneo, Indonesia during El Niño conditions. Results showed that the adjusted GAC fire count numbers were very well related to the LAC fire counts of the coincident imagery (R2 = 0.99, n = 13), indicating the efficacy of GAC data for providing quantitative fire information during El Niño periods and specifically the1997-98 event on Borneo.

However, the AVHRR is a passive sensor, which cannot penetrate clouds. Clouds might obscure fire events and since each GAC image usually presents different cloud coverage, a bias in the derived fire number is likely to occur (Kaufman et al., 1990). Therefore, the number of detected active fires for different regions of Borneo was corrected according to the proportion of that region which was covered by clouds. Then the total all-Borneo fire number was derived by aggregating the cloud corrected fire counts occurred in all regions. However, the overall cloud corrected fire pattern remained similar to non-corrected fire signal, in terms of the time period and peak of the major fire occurrence depicting equivalent trends in fire activity in Borneo during El Niño events.

Fires usually exhibits a strong diurnal cycle suggesting that satellite observations at different local times may substantially bias the detected active fire number (Eva and Lambin, 1998). This prevents the valid temporal comparison of the AVHRR GAC derived fire product since different NOAA platforms with various observation times operated during the 1982-present studied period. Significant discrepancies of the detected fire number were observed during different NOAA satellite times indicating the maximum diurnal fire activity to occur at late afternoon local time.

The distribution of fires in time was analyzed and compared to the strength of the El Nino-Southern Oscillation (ENSO) as measured by the sea surface temperature anomaly (SSTA) in the Niño 3 region of the Pacific Ocean. The relationship between fire and Niño 3 anomaly variation was explored in three different ways. First, by plotting the cloud and time corrected AVHRR GAC derived fire counts together with the Niño 3 anomaly in order to observe the general signal of the association. Secondly, a cross-correlation analysis between the monthly fire counts and the Niño 3 anomaly was applied to identify the existence and the best range of possible lag time, in which some ENSO-fire relation occur. And finally, a regression analysis was conducted between different time composites of fire counts and the Niño 3 anomaly to accurately identify and quantitatively measure the strongest possible ENSO-fire relation.

Although the Niño 3 anomaly presented different evaluation in time among the five studied El Niño events (1982-83, 1986-87, 1991-92, 94 and 1997-98), the fire activity occurred constantly during August-October and February-April of the El Niño Year 0 and Year 1 respectively. However, the overall strength of each El Niño event related closely with the corresponding observed fire magnitude.

The strongest ENSO-fire association was observed when the total 16-months sum of ENSO index (Niño 3 anomaly), from January of Year 0 to April of Year 1, compared against the total fire activity of the same time period. The stepwise linear regression for the five ENSO-fire pairs resulted a R-square equal to 0.97 highly significant within 99% confidence level. Consequently, the 97 per cent of the 16-months fire activity in the whole Borneo during the five studied El Niño periods could be explained by the 16-months ENSO strength as measured by the SSTA in the Niño 3 region of the Pacific ocean. However, results revealed that these first sixteen months of each 24-months studied fire event included the majority of the entire detected fire events, representing the 80.61 per cent of the total 10-years fire activity in Borneo. Therefore, if this studied ENSO-fire relation remained consistent in the future El -Niño events, it is possible to predict in advance the all-Borneo fire activity based on predictions of Niño 3 anomaly. Then the accuracy of the derived fire activity would depend primarily on the forecast precision of the Niño 3 anomaly by statistical and/or dynamical coupled models.