Introduction
The impact of vegetation fires on the balance of the global ecosystem is generally recognized. 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).

In Indonesia burning occurs annually but a number of unusually large fire events have occurred in recent times. Several studies have documented the increased fire activity occurred in Indonesia during El Niño years, particularly the devastating fire activity of the extreme 1982-83 and 1997-98 ENSO periods, (Malingreau et al., 1985; Wooster et al., 1998; Legg and Laumonier, 1999; Wooster and Strub, 2002).Previous studies have shown a close relationship among fire activity and ENSO in various parts of the world (Simard et al., 1985; Swetnam and Betancourt, 1990; Kitzberger, 2002). The aim of this project is to investigate the relationship between fire activity and El-Nino-Southern Oscillation (ENSO) events in South East Asia during the 1982-1998 period using Borneo, Indonesia as a case-study.

Data, Methods & Results
NOAA AVHRR Global Area Coverage (GAC) satellite data were used to investigate the occurrence of active fires during the El-Nino episodes of the last 20 years. All the available GAC images that included the entire case study region of Borneo for the five study periods over ten years were downloaded from the SAA archive. AVHRR images, in both LAC and GAC format, are distributed freely online by NOAA, through the web-based Satellite Active Archive (SAA) server. Although GAC data are available since July of 1981, LAC data occur only sporadically in this archive and not for all of the years. This is mainly due to storage limitations on board the NOAA Polar Orbiting Environmental Satellites (POES) (Belward and Lambin, 1990).

GAC images were collected be in agreement with the following standards:

1. Borneo should be observed close to the sensor’s nadir viewing angle to prevent use of data with a long atmospheric path and coarse spatial resolution, which may introduce a bias in the derived fire number. A cut off threshold of Borneo being between 65 and 85 pixels from the borders of the 409-pixel wide GAC image was used (Rauste et al., 1997).
2. The images must be nighttime data to preclude false fire alarms by highly reflective surfaces (e.g. cloud edges) and generally to make active fire detection less sensitive to error (Lee and Tag, 1990; Cahoon, 1990; Langaas, 1993a; Langaas, 1993b).
3. Borneo must be at least 40-50% cloud free to ensure a sufficient representation of the study area.

The rationale in terms of fire detection was to use the lowest possible active fire detection threshold in order to reduce errors of omission and to minimise the along scan averaging effect of the GAC data production. Therefore, fire detection and false detection identification were performed using a multispectral approach, rather than by applying a single threshold value (Kaufman et al., 1990). Thresholds were reduced progressively until contamination of resultant fire counts by the broad background environment became visually apparent. Threshold values were defined first by examining the histograms of AVHRR channel 3, 4, 5 and channel 3 minus channel 4 (T3-T4) and second by calculating basics statistics of minimum, maximum, mean, variance and standard deviation values for fire affected pixels and main background features such as cloud free vegetated land, clouds, mainland waterbodies and sea.

The developed pixel-by-pixel multi-channel fixed threshold method was applied to the AVHRR data. According to this method a series of criteria must be fulfilled by a pixel in order to be classified as a fire. These criteria have the following form:

Test (1)	T3 > 305°K	to detect features with high channel 3 brightness temperature i.e. likely (potential) fires
Test (2)	T3-T4 > 6°K	to avoid warm surfaces without fires
Test (3)	T4 or T5 > 275°K	to exclude clouds and sensor noise (damaged pixels)
Test (4)	p1, p2 > 5-6 %	to prevent false detections by highly reflective surfaces

Where T₃, T₄ and T₅ represent the top of atmosphere (ToA) brightness temperatures (K) for channel 3, 4 and 5 respectively, p₁, p₂ the % ToA reflectance in channel 1 and 2 respectively while a_i is the brightness temperature threshold for test i = 1, 2, 3, 4 and 5.

However, the GAC data are produced from the original Local Area Coverage (LAC) data onboard by averaging four out of every five samples (pixels) along the scan line, skipping the fifth, and by processing data from only every third scan line, thus skipping the other two scans. This degradation of the full resolution data which is performed onboard the satellite before their transmittance to the receiving station, reduces the number of pixels from 2048 to 409 within the retained scan line and the data volume by an order of 15 for the whole image. The spatial resolution of GAC data is effectively 4.4 km x 1.1 km with a 2.2 km gap between each scan line (Belward and Lambin, 1990). Therefore, initially, a comparison of the detection capabilities of low spatial resolution NOAA-AVHRR GAC and higher, resolution 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. Thirteen pairs of LAC, and the corresponding GAC data from the same orbit, were collected for the 1997-98 El Niño related year. Since the data volume of the original LAC images is 15 times larger than the contemporaneous GAC product, in order to create an analogous GAC fire count comparable to the LAC fire product, the GAC derived fire counts were multiplied by a factor of 15.

Results showed that the adjusted GAC fire numbers were very well related to the LAC fire counts of the coincident imagery (r² = 0.99, n = 13, p < 0.0001) (Figure 1). The mean percentage difference between the two fire count datasets was –1.6% with a standard deviation of 13.9%. Taking into account the substantial degradation of LAC data during the GAC production, these variations are minor, indicating the efficacy of GAC data for providing quantitative fire information in Borneo during El Niño periods when the fire occurrence is high.

Figure 1. Active fire counts as derived from 13 LAC and their contemporaneous GAC images from the 1997-98 El Niño period have shown a very good agreement.
Then the fire counts detected in each GAC image were adjusted for different cloud coverage and observation time of each GAC image. 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). However, the spatial distribution of clouds varies significantly according to regional and local climate characteristics, the general atmospheric circulation over that region, and the topographic structure of Borneo. Consequently, some regions of Borneo are more frequently covered by clouds than others. Empirically, it was observed that the most cloud affected regions correspond to the Malaysian provinces Sarawak and Sabah, the independent sultanate of Brunei, and the Indonesian province of west Kalimantan, while the less affected regions correspond to East- (6x5°) South- (3x2°) and Central (4x4°) Kalimantan.