Assessing the extent of vegetation loss and mapping burn severity for the 2015 fire event in Sebangau National Park, Indonesia Abstract This study aimed to quantify the area of peat-swamp forest lost in Sebangau National Park to the 2015 fires, and demonstrate the severity of the fires, using remote sensing methods. Normalized burn ratio (NBR) images were created to discriminate between burned and non-burned forest areas, and a bi-temporal differenced NBR image was created. Fire severity within the burned areas was mapped using this image. It was found that over 10% of forest cover within the national park was lost, and that most of the burned areas are considered to be moderately-highly to highly severely burned, therefore making it more likely that these areas will burn again. Aims The aims of this investigation were to quantify to spatial extent of vegetation loss which occurred during the 2015 fire event in Sebangau National Park, and to attempt to map the severity of the fires by examining their burn scars.

Introduction Each year Indonesia experiences fires to some extent. These fires have complex causes, both natural and anthropogenic, and often lead to massive environmental, social and economic problems (Page et al, 2002). These fires usually occur in the dry season, generally between June and October, with the wet season usually occurring between November and March (Naylor et al, 2001). Although there are fires every year, major fire events occur every few years, and have been linked to droughts caused by the El Nino phase of the El Nino/Southern Oscillation (ENSO) (Bowen et al, 2000). This is a cycle of colder and warmer sea-surface temperatures in the central and eastern Pacific Ocean, which has been shown to impact weather atmospheric pressures, precipitation and winds globally (Zhai, 2017). The exact causes of ENSO are still unclear (Cobb et al, 2003), however Spessa et al (2015) have demonstrated that the severity of fire seasons in Indonesia can generally be predicted in advance, with ENSO playing a significant role in the predictions. Slash and burn agriculture is thought to play a large role in the ignition and spread of these fires (Varma, 2003), where land-owners (both commercial companies and small-scale indigenous land-rights) chop down and burn areas of forest to prepare the ground for agriculture. Tropical peat swamp forests are serve as significant stores of carbon, both above-ground in tree biomass and below ground in the peat. Page et al (2010) estimated that tropical peatlands contain up to 88.6 gigatonnes (Gt) carbon, with those in Indonesia containing approximately 57.367Gt (the largest stock in the world). Tropical peatlands are therefore of critical importance in the context of climate change. During the 1997 fire event, an estimated 13-40% of mean annual carbon emissions from fossil fuels were released from burning peat and above-ground biomass in Indonesia (Page et al, 2002). The normalized burn ratio (NBR) is an index similar to the normalized difference vegetation index (NDVI), but using the short-wave infrared band instead of the red band (Escuin et al, 2006). It is particularly good for discriminating recently burned areas because healthy vegetation reflects strongly in the near-infrared portion of the electromagnetic spectrum, but exhibits low reflectance in the shortwave infrared, and the opposite is true for recently burned areas (fig.1). Because NDVI uses the red portion of the spectrum, in which both healthy vegetation and burned areas exhibit low reflectance, it is not as useful for this purpose(Chen and Zhu, 2007).

Figure 1 - The difference in reflectance between burned and vegetated areas in the NIR and SWIR. Image credit: US

Forest Service

Methods and materials Study area This investigation was carried out in Sebangau National Park, near the city of Palangaraya, in Central Kalimantan, Indonesian Borneo (fig. 1). It is composed of peat-swamp forest, growing on top of a large peat dome. The national park was declared in 2006 (Page et al, 2008), and between 1976 and 1997 was divided into logging concessions. From 1998 until the national park was declared (and to a lesser extent until today), the areas has been subject to illegal logging. Much of the park is intersected by canals dug by logger to transport timber out of the forest. These canals have had a significant impact on the hydrology of the forest, as previously the forest would have been waterlogged year round. Now however, it is not uncommon for the water level to drop below ground level in the dry season. The national park is of particular importance because it is home to up to 37% of the critically endangered Bornean orangutan population left in the wild (Morrogh-Bernard et al, 2003).

Figure 2 - Location of the study site. The red outline indicates the border of the national park within Central Kalimantan provice. Landsat image from 19/08/2015. Map credit: Wikitravel

Images used in analysis The images used for this investigation were from the Operational Land Imager (OLI) instrument, aboard the Landsat 8 satellite, and were obtained from the USGS EarthExplorer service. Landsat 8 images are composed of 11 bands, all of which have a pixel size of 30m (apart from the panchromatic band, which has a pixel size of 15m). Bands 5 and 7 (near-infrared and shortwave infrared-2, respectively) were used to generate the index used in this study. The study area was contained within one path/row

Sebangau National Park, Central Kalimantan, Indonesia

(118/62, respectively). Level-1 images were used. When selecting suitable images for analysis, those with the least amount of cloud cover were chosen. The most cloud free image for 2015 (before the fires) was unfortunately taken after small amounts of fire had already occurred. Anniversary date images were selected to minimise the effects of illumination conditions and vegetation phenological conditions on pixel values. The first image was acquired on 19/08/2015, and the second on 21/08/2016. Spatial subsets of the input images were created using a shapefile of the national park. Pre-processing Both images were calibrated to top of atmosphere (TOA) reflectance using the metadata files provided, using the radiometric calibration tool available in ENVI. Cloud masking was attempted but, due to the widespread coverage of thin cloud over even the most cloud-free images, it was not possible to mask the clouds and still perform analysis of the images. In both images used, the worst of the cloud cover was to the far north and south of the study area, where preliminary investigation has shown to have been less affected by fires. It was therefore decided to do without masking. Indices A normalised burn ratio (NBR) image was created from the pre-fire and post-fire images using the following formula:

𝑁𝐵𝑅 = 𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅

where NIR is reflectance in the near infrared (0.85-0.88µm) and SWIR is reflectance in the shortwave infrared (2.11-2.29µm) portions of the electromagnetic spectrum. A differenced normalized burn ratio (ΔNBR) image was then created as follows:

𝛥𝑁𝐵𝑅 = 𝑝𝑟𝑒𝑓𝑖𝑟𝑒𝑁𝐵𝑅 − 𝑝𝑜𝑠𝑡𝑓𝑖𝑟𝑒𝑁𝐵𝑅 Vegetation loss The areal extent of vegetation loss was calculated by classifying the pre- and post-fire NBR images into burned and unburned classes, using an ISODATA unsupervised classification algorithm. Statistics for the classification images were then generated, and area calculated by multiplying the pixel numbers by 0.09, to convert to hectares. Burn severity The thematic burn severity map was created by defining the pixel values of regions of interest using the thresholds shown in tab.1, and then applying the regions of interest to the ΔNBR image.

Table 1 – Differenced normalized burn index values and corresponding burn severity/vegetation regrowth categories – adapted from USGS Firemon program (Firemon BR cheatsheet,, 2004).

𝛥𝑁𝐵𝑅 value Burn severity < -0.25 High post-fire regrowth -0.25 to -0.1 Moderate post-fire regrowth -0.1 to 0.1 Unburned 0.1 to 0.27 Low severity burned 0.27 to 0.44 Moderate-low severity burned 0.44-0.66 Moderate-high severity burned >0.66 High severity burned

Values for the ∆NBR can potentially range from -2 to 2, but typically fall between -0.5 and 1.2. Areas of no change usually have a value near zero, and burned areas over 0.1 (Key and Benson, 2006).

Results Vegetation loss Vegetation loss due to the 2015 fire was mainly concentrated into three areas (fig.3). The

(b)

area to the east of the national park is centered on an area at the confluence of several rivers. The second large area of loss was to the north-east of this, where fires have occurred several times in the past, and the third located to the north of these areas. Other burned areas were detected around the edges of the national park. The two areas circled in the pre-fire image were already showing signs of regrowth in the post-fire image.

Figure 3 - Pre-fire (a) and post-fire (b) classification images. Red indicates burned and blue unburned. Burned areas in the pre-fire image are present because the only available moderately cloud-free image was taken after some fires had occurred. The green circles in (a) are areas which were already burning but when view in the white circles in (b), are not.

N N

Burned Unburned (a)

Table 2 – Areal extent of unburned and burned classes before and after the fires.

Over the course of the 2015 fires, approximately 54366 hectares of vegetation were lost (tab. 2), with 36440 hectares already burned before the first image was taken. This amounts to a loss of over 10% of forest cover within the national park. Burn severity The thematic map produced for this study is shown in fig. 4. Most pixels where fire occurred were classified as either ‘moderate-high severity’ or ‘high severity’. Several cloud patches to the north and south of the national park were wrongly classified as ‘low-severity’ burned areas, and patches of burned vegetation already present when the first image was taken have been classified as regrowth, which may not be the case. The three areas circled in fig. 3 all have a patch of pixels classified as ‘moderate post-fire regrowth’, and there are several patches of ‘high post-fire regrowth’. Large proportions of the burned areas were classified as ‘moderate-high severity burned’ or ‘high severity burned’.

Pre-fire

Class Pixelcount HectaresUnburned 5608945 504805.05Burned 404896 36440.64

Post-fireClass Pixelcount Hectares

Unburned 5409769 486879.21Burned 604072 54366.48

Discussion and conclusions As the results of this investigation show, over 10% of forest cover in the national park was lost during the period of study. On a local level, this is particularly important as there are many species already threatened, such as orangutans and white faced gibbons, which rely on the forest. It is also very concerning how much peat may have been burned, and therefore how much carbon released into the atmosphere, not only damaging the planet, but also the millions of people who have to live in the haze for months of most years. Further investigation using the results of this study could attempt to quantify the volume of carbon lost. However, field data would be necessary to produce accurate results. The NBR proved to be a useful and simple method of discriminating between unburned and burned forest. However, accuracy assessments were not carried out due to time constraints and the lack of field data, and so any results must be viewed with caution. It is encouraging that several patches of forest identified as burned in the pre-fire image were already classified as moderate or high regrowth areas. However, it is unlikely

High severity burned

Moderate-high severity burned

Moderate-low severity burned

Low-severity burned

Unburned

Moderate post-fire regrowth

High post-fire regrowth

Figure 4 - Thematic burn severity map. For the three largest burn areas (circled), the most severe levels of burning occur towards the origin of the fires, easing to less severe towards the edges. Orange areas (should be 'low-severity burned') to the northern and southern limits of the national park are cloud cover wrongly classified.

N

that these areas will return to pre-fire conditions anytime soon. Indeed, it is likely that, if the draining of the forest is not halted, and global temperatures continue to rise, these areas will provide kindling for more fires every dry season. The study has demonstrated the need to understand more fully the mechanisms behind ENSO, so that more accurate predictions can be made of when these event will happen, and more effective countermeasures can be put in place.

One limitation of this study was that, when calculating burn severity levels, the thresholds used were not specific to the study area. In order to generate a more accurate classification, ground studies should be carried out in the aftermath of the fire to determine how severely the vegetation has actually been affected, and to create ground truth data, as outlined in by Key and Benson (2006). Because cloud removal was not performed, a number of pixels where clouds present were classified as burned areas, which may not have been the case. If the investigation were to be taken further, results could be improved by creating image mosaics from temporally similar images, to ensure that as many pixels as possible were cloud free. However, as Landsat 8 OLI data has a relatively low temporal resolution (16 days) and the study site is located in the tropics, it may still prove difficult to create an image in which every pixel is clear. In conclusion, the 2015 fires in Sebangau national park had a large scale impact on vegetation cover. This will have impacted not only the wildlife within the park but also local people and the world at large.

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