bmnp fire study

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Factors Influencing Fire Extentand Frequency in the Bale

Mountains National Park

By Kasahun Abera

With Financial Support from Frankfurt Zoological Society (FZS),

European Union (EU) and British Embassy in Addis Ababa.


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Factors Influencing Fire Extent and Frequency

Published 2009

This publication was made possible by the support of the Frankfurt Zoological Society,the European Commission and the British Embassy

Compiled by: Kasahun Abera, and Dr. Anouska Kinahan, Frankfurt Zoological Society,Bale Mountains Conservation Project, Bale Mountains National Park, Ethiopia

http://www.fzs.orghttp://www.balemountains.org

Disclaimer: This document has been produced with the financial assistance of the EuropeanUnion. The contents of this document are the sole responsibility of the Frankfurt ZoologicalSociety and can under no circumstances be regarded as reflecting the position of the EuropeanUnion.


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TABLE OF CONTENTS

1. INTRODUCTION .............................................................................................................4

2. MATERIALS AND METHODS ..........................................................................................6

2.1THE STUDY AREA ..................................................................................................................... 62.2DATA SOURCES ....................................................................................................................... 72.3IMAGE PREPARATION ............................................................................................................... 92.4.DATA ANALYSIS ...................................................................................................................... 9

2.4.1 Fire Frequency and Extent .......................................................................................... 12

3. RESULTS .....................................................................................................................13

3.1FACTORS AFFECTING FIRE FREQUENCY AND EXTENT.............................................................. 153.1.1 Vegetation.................................................................................................................... 153.1.2 Soil Type ...................................................................................................................... 193.3.3 Altitudinal Belts ............................................................................................................ 213.1.4 Distance to roads......................................................................................................... 233.1.4 Distance to settlements ............................................................................................... 25

4. DISCUSSION ................................................................................................................ 27

5. REFERENCES ..............................................................................................................30


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1. Introduction

As defined in the Global Fire Monitoring Centre (GMFC) wild land fire management

terminology document, fire is a simultaneous release of heat, light and flame generated

by the combustion of flammable materials. Fires have both advantages and

disadvantages, if managed, a fire can help improve ecosystem functioning; conversely

uncontrolled fires can devastate, degrade and reduce the availability of natural

resources (Giri and Shrestha 1999). A fire occurring in any ecosystem has the potential

to cause disastrous social, ecological, and economic impacts resulting in the loss or

transformation of habitat; which in turn affects biodiversity and triggers carbon dioxide

release and global warming (Lymberopoulos et. al.,1996). Most of the present day forest

loss is attributed to uncontrolled burning practices (IUCN, 2000).

Ethiopia, whose forest resource was estimated to be 40% of the total land cover a

century ago, is now left with only 2.5% forest cover (MOA, 2000). Forest is disappearing

at an alarming rate. The increase in population growth has lead to increased land

fragmentation which is posing a pressure on the remaining forest patches of the country.

Unwise forest resource uses such as timber extraction, fuel wood and charcoal

production, wild fires and expansion of agricultural fields are the causes for forest

destructions in Ethiopia. Wild fire and agriculture are however some of the major causes

(MOA, 2000). It is human induced fires which are usually set for the preparation of new

agricultural plots and collection of wild honey that are the predominant causes of fire.

According to the GFMC the number of fire occurrences in Ethiopia has increased from 4

to 20 between the years 1990 and 1993, pulling up the total area of burnt forest from

1,072 to 3,159 ha. After seven years, in 2000, the loss of natural forests due to fire is

recorded to be more than 95,000 ha (Table 1). The 2000 fire incidence in the Bale eco-

region is one of the worst fires in Ethiopia with extreme fires occurring also in 2007/2008

dry season. In 2008, a total of 12, 825 ha of land were burnt in the Bale Eco-region; from

which the land burnt in BMNP account for 10,747 ha (Belayneh et. al, 2008).


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The Bale Mountains National Park (BMNP) which is one of the 34 Conservation

International biodiversity hot spots has been encountering both natural and man made

fires through out history. However, in recent times the influence of man made fires has

posed a serious threat to the parks ecosystem particularly to the Erica forest and shrub

land. Forest fires which are set by people to collect wild honey and preparing land for

agriculture are also creating damage to the Harenna forest of BMNP (GMP, 2007). As a

result developing a fire management plan for the park has been identified as a priority

activity in the GMP. In order to be able to do this a detailed fire assessment examining

fire extent and frequency as well as factors which may influence the occurrence of fire

needs to be investigated. In this study we used remote sensing and GIS technologies in

particular Moderate Resolution Imaging Spectrometer (MODIS) to map the extent and

frequency of fire in the BMNP. Specifically we examined if vegetation and soil type,

month, altitudinal belt, distance to roads and distance to settlements influence the

occurrence and area affected by fire. It is aimed that these findings will facilitate the

development of a fire management plan for the park by identifying fire hot spots and their

key factors, thereby enabling mitigation measures to be developed.

Figure 1: Fire in Goba Woreda near to the North east boundary of the Park. Source:Anteneh Belayeneh and Temesgen Yohanis (2008)


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2. Materials and Methods

2.1 The study area

The Bale Mountains National Park is found in the Oromia regional state of Ethiopia. It

lies in 3928 to 3957 longitude and 629 to 710 latitude. The park which covers

2,200 km2

was established in 1971 by the then Ethiopian Wildlife Conservation

Organization. The Bale Mountains, from which the park got its name, are part of the 34

International Conservation Biodiversity Hotspots and is on the tentative list for world

heritage site listing.

The Park with its large altitudinal range (1500m to 4377asl) has the largest piece of Afro

alpine habitat in Africa and holds the second largest moist tropical forest in Ethiopia. The

afro alpine ecosystem of the park is a source for more than 40 streams and seven major

rivers which support about 12 million people living in the lowlands from Ethiopia to

Somalia and Kenya. It is also known by its rich flora and fauna resources. BMNP has

1600 plants from which 160(10%) are endemic to the country; it has also 78 mammal

and 282 bird species from which 31(58.4%) & 16(48.7%) respectively are endemic to

Ethiopia. The park also holds 40% of Ethiopian medicinal plants. It plays a vital role in

carbon storage with 45.8 million ton carbon stored in the Harenna forest park (Watson et

al. 2008).


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Figure 2: Location of Bale Mountains National Park

2.2 Data Sources

Moderate Resolution Imaging Spectrometer (MODIS) level 3 burned area products and

a 2.5 m resolution SPOT Image acquired May 14th of 2008 were used for this study. In

addition ground truthing fire data collected in the park was used to verify and calibrate

the MODIS images. The MODIS MCD45A1 product was downloaded from NASA -

MODIS Fire and Thermal Anomalies Project /University of Maryland/ website

(http://modis-fire.gsfc.nasa.gov). The SPOT image was provided by Planet Action. Nine

years of MODIS data (2000- 2008) was used for this study as this was as far back as the

appropriate images went for this area.

2.2.1 MODIS Scanners and MCD45A1Product Description

MODIS is a 36 band instrument which has two sensors, Terra (Launched in 18

December 1999) and Aqua (launched in 4 May 2002). The 36 spectral bands of MODIS

fall under three spatial resolution classes, two bands (band 1& 2) have 250m resolution,

five bands (bands 3- 7) have 500m resolution and the rest of the 29 bands (bands 8-36)

have a 1km spatial resolution. This study used MODIS Level 3 Monthly Tiled Burned

Area Products which are identified as MCD45A1. This product has a 500m spatial

resolution (Laboda, et. al, 2006) It is produced in the standard MODIS land tile format in

Sinusoidal projection. Each tile has a fixed earth location, covering an area of

approximately 1200 X 1200 km (10 X 10 degree at the equator). The product defines for

each 500m pixel the approximate day of burning. It is a monthly product which is

obtained by processing combined MODIS Terra and MODIS Aqua 500m (from 2002)

land surface reflectance data.

Each product tile contains the following components:

Per-pixel burning information

The approximate day of burning (1-366) or 0 (no burning detected)


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Codes to indicate no decision due to persistent missing, bad quality or cloudy

data.

Quality Assurance (QA) information.

Mandatory and product-specific metadata

This product is known to have a better spatial (500m) and spectral accuracy for mapping

the spatial extent of burnt areas, than AVHRR which has 1.1Km of spatial resolution

(Laboda et al, 2006).

The MCD45A1 product is produced based on abi-directional reflectance (BRDF)

algorithm model. The MODIS algorithm is defined to map burned areas has been

developed and demonstrated in southern Africa, Australia, Siberia and South America

(Roy et al. 2002, Roy 2003). The algorithm developed for the product is characterized

through the use abi-directional reflectance (BRDF) model based change detection

approach which detects the approximate date of burning by locating the occurrence or

rapid changes in daily MODIS reflectance time series. The algorithm maps the spatial

extent of recent fires (last 90 days) and not of fires that occurred in previous season or

year. Because of the BRDF model incorporated in the algorithm, the production of one

month of MCD45A1 requires the availability of 90 days of daily MODIS data (i.e. that is

including both the previous and the following month) (NASA MODIS Fire and Thermal

Anomaly Website).

The algorithm developed works in such detail process that; the product is generated

from time series of daily 500 m MODIS land surface reflectance data. Measurements in

the seven MODIS land surface reflectance bands (bands 1-7) are corrected for

atmospheric effects, including aerosols (Vermont et al. 2002). These data are processed

into daily geolocated files (Wolfe et al. 1998) and all high view zenith (>65), high solar

zenith (>65), bad quality, high aerosol, snow, cloudy, and non-land, MODIS

observations labeled in land surface reflectance product are rejected. These data

provide good quality observations of the land surface, although shadow contaminated

observations and a minority of cloud, snow, and water observations may remain. This

gives a maximum of one observation per geolocated pixel per day. MODIS bands that

are sensitive and insensitive to biomass burning are used to detect changes due to fire

and to differentiate them from other types of change respectively. The near infrared and


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longer wavelength 500 m MODIS reflectance bands are used because they are

generally insensitive to smoke aerosols emitted from vegetation fires (Kaufman and

Remer 1994, Miura et al. 1998). An analysis of the ability of the MODIS land surface

reflectance bands to discriminate between recently burned and unburned vegetation

(Roy et al 2002, 2005a) has shown that MODIS bands 5 [1230-1250 nm] and 2 [841-876

nm] provide the highest burned unburned discrimination and MODIS band 7 [2105-2155

nm] provides little discrimination. Bands 5, 2 and 6 [1628-1652 nm] reflectance

decreases immediately, and for many days, after burning, and band 7 reflectance

changes relatively less (with both positive or negative changes observed). Some surface

changes not associated with biomass burning may exhibit similar spectral changes as

those caused by fire. This condition might cause false detections. Those ambiguous

detections are further tested using the BA pixel QA (burnt area pixel quality assurance)

testing index; the result is a confident value of fire pixel detection. Ranging from 1 (most

confident) and 4(least confident) of detection. Generally this product show as the spatial

extent of fire for the year we are concerned on. Indirectly the areas that have been

entertaining burning for the days indicated on the product are identified.

2.3 Image Preparation

A mosaic of the four scenes comprising the park in the SPOT image was created to form

one image. This image was geometrically and radio metrically corrected to remove

topographic and atmospheric influences. The part of the image covering the park was

extracted by masking the boundary of the park. Erdas Imagine 9.1 and ArcGIS 9.2

softwares were used to undertake this data preparation process.

The MODIS MCD45A1 products came in Hierarchical data (.hdf) file formats and

Sinusoidal projection, this file format is not suitable to work on ArcGIS and Erdas

Imagine softwares. The Projection is not also compatible for our database projection.

Hence the .hdf file was converted to geotiff (.tiff) file formats and the projection was

reprojected to World Geological Survey 1984 (WGS 84) datum and UTM Zone 37N

projection status using the MODIS reprojection tool. Then the subset for the area of the

park was extracted from the MODIS image as we did for SPOT image.

2.4. Data Analysis


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Monthly data collected from MODIS were merged to create each fire season so that they

could be analyzed independently. A fire season was defined as October-December in

year t, and January-May in year t+1. In this study therefore we had a total of nine fire

seasons- 1999/2000 (incorporating Jan-May 2000 only), 2000/2001, 2001/2002 etc. up

to 2007/2008. In order to validate MODIS images, images from 2008 were used as well

as the SPOT image and field data collected in 2008. A total of 3097 GPS points of burnt

areas in the park were taken from March-April 2008. The GPS points were taken

following the perimeter of a burnt area. A polygon of the burnt areas from these GPS

points was then generated using XTools Pro (vector data management extension to

ArcGIS). Using these polygons as signatures the Spot image was then classified into

burnt and non burnt areas. Corresponding MODIS images were then overlaid on the

classified 2008 image and visually assessed to ensure they overlapped as well as using

the MODIS quality assurance data to ensure reliability of fire detection (see Figure 3a, b,

and c).


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a. b. c

Figure 3: Figure showing burned area polygons generated from field observations (a), burned areas fro

Image (b) and overlaying of MODIS images onto Classified SPOT image and field polygons (c)


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2.4.1 Fire Frequency and Extent

The total number and extent of fires were calculated by counting the number of fire

polygons in each of the MODIS fire seasons and determining the total area of each

polygon. Each fire season was then overlaid on different maps classifying vegetation

and soil type, altitudinal belt and distance buffers to roads and settlements and

frequency and extent were calculated as described above. For vegetation, a number of

different vegetation types could occur in one polygon, if this was the case one fire would

be considered occurring in each of the vegetation types, consequently each of the

polygons therefore would also have a specific area burnt for each of those vegetation

types occurring in that polygon. Unlike vegetation, since the boundaries of other classes

were generally easier to define, the dominant soil, altitudinal belt and buffer were used.

When data was normally distributed a repeated measures ANOVA was used to

determine differences between each of the classes in either frequency or extent. If data

was not normally distributed a Freidmans repeated measure analysis was carried out.

A Bonferonis confidence interval procedure (Neu et al., 1974) was used to see if the

frequency of fires occurring were in proportion to the area available. This gives an

indication if vegetation or soil types etc. were burnt more, less or as expected given their

respective areas available. We then assumed that those that were burnt more than

expected were brunt preferentially over other vegetation/soil types.


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3. Results

A total of 142 fire incidents were identified by MODIS Images between 1999/2000 and

2007/2008 fire seasons, burning accumulative total of 38,150 hectares (ha) of land in the

park. The highest number of fires occurred in 2000/2001 where 6,615 ha of park landwere burned followed by 2007/2008 with 21 fires but covering only 9,309 ha of land

(Table 1). A similar phenomenon occurred in 2002/2003 and 2003/2004, although the

numbers of fires were the same the extent of fire was almost doubled in 2003/2004

compared to 2002/2003; 6,129 and 3,913 ha was burnt respectively. Despite this,

typically the extent of burnt area is positively correlated to the number of fires (r= 0.83,

N=9.9; P


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05

1015

2025

3035

1999

_200

0

2000

-200

1

2001

-200

2

2002

-200

3

2003

-200

4

2004

-2005

2005

-200

6

2006

-2007

2007

-200

8

Years

NumberofFires

Fire Frequency

Figure 4: Graph showing number of fire incidences between the years 1999/2000 to2007/2008

Although March appears to be the month in which the largest numbers of fires occur and

the biggest total area burned (Table 2), figure 5 shows that this can be largely attributed

to an anomaly occurring in 2000/2001 where a huge number of fires occurred in March.

January, the middle of the dry season is the second most common month for fire

incidences (Figure 5).

Table 2: Total number of fires and their extent in each month of the fire season

Month

Number

of Fires

Area

Burnt(Ha)

January 25 6325

February 12 3010

March 53 15683

April 10 1304

May 4 805

October 16 4053

November 11 4269

December 11 2701


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0

5

10

15

20

25

30

2000 2001 2002 2003 2004 2005 2006 2007 2008

Years

NumberofFires

January

February

March

April

May

October

November

December

Figure 5: Graph showing the number of fires occurring in each month for each fireseason

3.1 Factors Affecting Fire Frequency and Extent

3.1.1 Vegetation

Woodland (N=92), Montane forest (N=63), Erica shrub (N=54) and Shrub land (N=40)

are the main vegetation types that were burnt the most frequently over the last 9 years

(table 3 and figure 6). However these differences in fire frequency are not significantly

different between the vegetation types, except for woodland (F=33.76, N=8, P


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Table 3: Frequency of fires in dominant vegetation types through out the fire season

YEAR ESH MF WL EF GLA HEL SHL GL Total2000 1 4 6 2 1 0 0 0 14

2001 8 23 27 0 0 0 0 0 58

2002 0 4 2 0 2 0 0 0 82003 6 8 15 0 0 5 5 1 40

2004 16 5 12 0 0 0 8 0 41

2005 1 0 4 0 0 2 3 4 14

2006 3 8 8 0 0 5 8 0 32

2007 5 7 8 0 0 4 5 5 34

2008 14 4 10 0 0 0 11 3 42

Tolal 54 63 92 2 3 16 40 13 283

0

5

10

15

20

25

30

1999/2

000

2000/2

001

2001/2

002

2002/2

003

2003/2

004

2004/2

005

2005/2

006

2006/2

007

2007/2

008

Years

NumberofFires

ESH

MF

WL

EF

GLA

HEL

SHL

GL

Figure 6: The number of fires in each vegetation types through out the fire season

Bonferonis analysis shows that Erica Shrub was the only vegetation type to be burnt

more then expected given its availability in the park and this was in 2004 and 2008, only.

Generally, the other vegetation types were burnt less than expected with the exceptionof woodland which was burnt as frequently as expected given its total available area in

the park (Table 4).


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Table 4: Bonferonis analysis result for fire in vegetation

Veg 1999-2000 2000-2001 2001-2002 2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 Total

EF < < < < < < < > >

GL < < < <

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