Remotely-Sensed Active Fire Data for Protected AreaManagement: Eight-Year Patterns in the ManasNational Park, India

Chihiro Takahata • Rajan Amin • Pranjit Sarma •

Gitanjali Banerjee • William Oliver • John E. Fa

Received: 7 January 2009 / Accepted: 22 November 2009 / Published online: 17 December 2009

� Springer Science+Business Media, LLC 2009

Abstract The Terai-Duar savanna and grasslands, which

once extended along most of the Himalayan foothills, now

only remain in a number of protected areas. Within these

localities, grassland burning is a major issue, but data on

frequency and distribution of fires are limited. Here, we

analysed the incidence of active fires, which only occur

during the dry season (Nov.–Mar.), within a significant

area of Terai grasslands: the Manas National Park (MNP),

India. We obtained locations of 781 fires during the 2000–

2008 dry seasons, from the Fire Information for Resource

Management System (FIRMS) that delivers global MODIS

hotspot/fire locations using remote sensing and GIS tech-

nologies. Annual number of fires rose significantly from

around 20 at the start of the study period to over 90 after

2002, with most (85%) detected between December and

January. Over half of the fires occurred in tall grasslands,

but fire density was highest in wetland and riverine

vegetation, dry at the time. Most burning took place near

rivers, roads and the park boundary, suggesting anthropo-

genic origins. A kernel density map of all recorded fires

indicated three heavily burnt areas in the MNP, all within

the tall grasslands. Our study demonstrates, despite some

technical caveats linked to fire detection technology, which

is improving, that remote fire data can be a practical tool in

understanding fire concentration and burning temporal

patterns in highly vulnerable habitats, useful in guiding

management.

Keywords Grasslands � Protected areas � Terai �Fires � FIRMS � Remote sensing

Introduction

The Terai ecoregion is a narrow belt of marshy grasslands,

savannas, open woodlands and forests stretching south of the

Himalayan foothills in India, Nepal, and Bhutan, from the

Yamuna River in the west to the Brahmaputra River in the

east. The tall grasslands within the Terai are among the

tallest in the world (3–4 m), made up primarily of Saccha-

rum spp. grasses. According to Peet and others (1997), Terai

grasslands represent at least nine distinct assemblages and

eight successional phases. With changes in river courses and

flooded areas, plant communities are occasionally disrupted,

often resulting in different assemblages depending on suc-

cessional stages (Peet and others 1999). These highly pro-

ductive grasslands are important for many animal species

including nationally or globally threatened species such as

tiger (Panthera tigris), Asian elephant (Elephas maximus),

greater one-horned rhinoceros (Rhinoceros unicornis), and

swamp deer (Cervus duvauceli). Endangered medium-sized

mammals, the hispid hare (Caprolagus hispidus) and pygmy

C. Takahata � J. E. Fa

Conservation Science Group, Imperial College London, Silwood

Park, Buckhurst Road, Ascot, Berks SL5 7PY, UK

R. Amin � G. Banerjee

Conservation Programmes, Zoological Society of London,

Regent’s Park, London NW1 4RY, UK

P. Sarma

Aaranyak, 50, Samanwoy Path Survey, P.O. Beltola,

Guwahati, Assam 781028, India

W. Oliver

IUCN/SSC Wild Pigs Specialist Group, 11 Graham House,

Birdcage Walk, Newmarket CB1 0NE, UK

J. E. Fa (&)

Durrell Wildlife Conservation Trust, Les Augres Manor,

Trinity, Jersey JE3 5BP, UK

e-mail: jfa@durrell.org

123

Environmental Management (2010) 45:414–423

DOI 10.1007/s00267-009-9411-8

hog (Porcula salvania) are also restricted to tall grasslands

(Oliver 1977, 1980, 1981, 1989; Oliver and Deb Roy 1993;

Bell and Oliver 1992; Narayan and others 2008).

Despite their uniqueness and biological importance,

Terai grasslands are highly threatened. Most grasslands

have been converted to agriculture leading to the disap-

pearance of much of the biodiversity linked to them.

Currently, only about 2% of the original habitat remains,

often severely fragmented, and largely confined to pro-

tected areas. The underlying driver of such widespread

habitat loss and fragmentation has been the unprecedented

rapid human population growth throughout southern Nepal

and northern India.

Frequent burning, heavy grazing, grass harvesting and

modification of river channels continue to affect tall

grassland areas (Mathur 1999). Above all, anthropogenic

fires, even within protected areas, cause major alterations

to the ecosystem. Burning has taken place since earliest

human settlement, but in the past remote areas have

remained unaffected, thus providing refuge sites for wild-

life. Currently, tall grasslands are subject to regular (i.e.

annual or greater) and widespread (often 100%) burning

during the dry season. Regular burning is justified as a

means of reducing the risk of accidental or uncontrolled

fires later in the dry season, potentially far more destruc-

tive, and to maintain grasslands by preventing natural

reforestation. However, frequent and widespread burning

deprives grassland-dependant species of cover and other

resources for longer periods, inevitably forcing these spe-

cies to aggregate in remaining unburnt patches, or seek

alternative cover elsewhere (if suitable habitat exists).

Excessive burning also encourages artificial fire-climax

communities dominated by a few fire-resistant species.

A better understanding of the role of fire within this

ecosystem is essential, but, basic data of the occurrence and

distribution of fires within grassland habitats, especially in

protected areas, can be used for improved fire and habitat

management (Ghosh 1997). Current developments in

remote sensing technology and geographic information

systems (GIS) permit more sophisticated investigations of

fire frequency and distribution through the use of satellite

fire detection figures (Eva and Lambin 2000; Franca and

Setzer 2001; Laris 2002). Through these data, it is possible

to examine, and even model probability of fire occurrence at

a landscape level (Arroyo and others 2008). Potential ‘hot-

spots’ of fire prevalence can also be identified whereupon

fire protection measures can be implemented. Such data are

also invaluable for documenting changes in the ecosystem as

a result of fire (Herrera and others 2005; Vadrevu and others

2006).

In the Indian sub-continent, relatively few studies (but see

Kodandapani and others 2004, 2008; Prasad and others

2008) have assessed fires at the landscape level using

satellite imagery. In this study, we used information on

active fires from daily satellite observations to analyse spa-

tial and temporal patterns of fires within the Manas National

Park (MNP), Assam, India. Here, we aimed to understand the

frequency of fires occurring between 2000 and 2008 in the

MNP, determine which habitats within the park were most

frequently burnt, and indicate zones within the protected area

most vulnerable to fires. Given the paucity of information of

fire occurrence in the MNP (and possibly other protected

areas in India), our study is a first step towards determining

the regularity of fires, and for now, allude to their impacts on

habitats and wildlife. We argue that the combination of

freely-accessible fire data, like the ones used in this study,

alongside field verification of origin and intensity, can be a

potent tool in managing fire in Terai grasslands.

Study Area

MNP is recognized as one of the richest wildlife reserves in

India, with over 450 bird species, 55 species of mammals,

50 reptiles and 3 amphibians recorded. The park also

supports 33 threatened wildlife species, including the

largest population of tigers in India, the largest remaining

pure-bred population of wild buffalo (Bubalus bubalis) and

the only remaining population of pygmy hogs. After des-

ignation as a World Heritage site in 1985, the MNP

experienced tribal and civil unrest accompanying illegal

logging and poaching of wild animals (Vigne and Martin

1998), until signing of an agreement between indigenous

groups and the Indian government in 2003. Before this

date, the park was off limits to protection staff and resource

managers. Nowadays, the major threats to biodiversity are

human encroachment, illegal extraction of natural resour-

ces and uncontrolled burning. Frequent grassland fires have

had substantial impacts on the last remaining wild popu-

lation of pygmy hog and other grassland dependent, fire-

vulnerable species (Narayan and others 2008).

The MNP (26�350N to 91�150E) has an area of about

528.8 km2, with an elevation ranging from 50 m to 250 m

above sea level, along a gradual slope from the northern

hills down to the southern boundary. The Manas River runs

southwards through the park into the River Brahmaputra

50 km south (Fig. 1). These main rivers along with other

small rivers deposit huge amounts of silt and rocks from

the Himalaya that make up the fertile alluvial lands.

The Indian monsoon rapidly advances in June and

engulfs the entire country by mid-September. It brings

extremely heavy rainfall to the study region, reaching up to

3,300 mm annually. Temperature varies between a mean

maximum of 37�C in summer, to a mean minimum of 5�C

in winter. In a normal year, the winter dry season starts in

November and continues until reoccurrence of the pre-

Environmental Management (2010) 45:414–423 415

123

monsoon, usually commencing towards the end of March

or early April.

The climatic conditions prevalent in the region, charac-

terized by the Indian monsoon, influence vegetation com-

munities in MNP. Four main vegetation types are present in

the park: semi-evergreen forest, mixed-moist deciduous

forest, dry grassland and swampy grassland (Lahkar and

others 2007; Sarma and others 2008). Grasslands occupy

the southern part of the park (one third of MNP), typically

dominated by the grasses, Imperata cylindrica, Narenga

porphyrocoma, Phragmites karka and Saccharum proce-

rum. Woodlands are found mostly in the northern areas,

continuous with the international border with Bhutan, and

the south-west area close to the Manas River.

Fire Data

In MNP, burning only occurs during the dry season

between November and April. This study employed active

fire data derived from MODIS (Moderate Resolution

Imaging Spectroradiometer) satellite images (Justice and

others 2002; Davies and others 2009). MODIS is a key

instrument aboard the Terra (EOS AM) and Aqua (EOS

PM) satellites. Terra’s orbit around the Earth is timed so

that it passes from north to south across the equator in the

morning, while Aqua passes south to north over the equator

in the afternoon. Terra MODIS and Aqua MODIS orbit the

entire Earth’s surface every one to two days, acquiring data

in 36 spectral bands, or groups of wavelengths. Fire on the

Earth’s surface can be detected by MODIS as the location

of a thermal anomaly using data from the middle infrared

and thermal infrared bands. In most cases, this heat

irregularity is a fire, but volcanic eruptions or the flare from

a gas well can also be registered as an anomaly. None of

the latter was found in our study area.

Active fire locations are processed by the MODIS Rapid

Response System using the standard MODIS MOD14 Fire

and Thermal Anomalies Product employing the algorithm

developed by Roy and others (1999). The algorithm

examines each pixel of the MODIS swath, and ultimately

assigns to each one of the following classes: missing data,

cloud, water, non-fire, fire, or unknown. Each active fire

location represents the centre of a 1 km2 pixel that is

flagged by the algorithm as containing a fire within the

pixel. The spatial resolution is 100 m2 at 50% probability

in usual conditions, or 50 m2 of fire flaming detectable at

nearly 100% probability under ideal conditions, such as

being free of clouds, heavy smoke and sun glint. Although

MODIS can only supply information on the location of

fires, access to such satellite remote sensing data allows

managers to understand overall conditions in inaccessible

areas and permits observation without direct disturbances

on a target site (Longley and others 2005).

MODIS active fire data from December 2000 to May

2008 were available for analyses for the present study

(Giglio 2007) via the University of Maryland and NASA’s

automated Fire Information for Resource Management

System (FIRMS) (FIRMS 2008). FIRMS is a global fire

monitoring and alert system for protected areas which

delivers MODIS active fire data and imagery to natural

resource managers. The system, which sends subscribers

regular email alerts on newly-detected burning, will

eventually be expanded to include fire risk.

Each fire point contained information on the exact time

and day of detection, a global georeference system location

Fig. 1 Location of the Manas

National Park, Assam, India

416 Environmental Management (2010) 45:414–423

123

(longitude, latitude), the brightness of the fire and classified

confidence level that indicate the degree of missing data

with clouds, heavy smoke and sun glint.

Methods

In this study, GIS layers resulting from analyses of satellite

data and ground surveys by Lahkar and others (2007), were

used to delimit the main vegetation types and landscape

features found within the MNP; woodland, grassland,

wetland, swampy land, river channels, river sand and

agricultural land. Due to insufficient data for wetland and

swampy land (combined into ‘wetland’), and vegetation

within river channels and river sand (combined into ‘river

channel’), the seven land-cover categories were collapsed

into four main types: woodland (256.9 km-2, 48.6% of

total area) grassland (168.2 km-2, 31.8%), wetland

(33.7 km-2, 6.4%), and river channel (70 km-2, 13.2%).

Agricultural land was excluded.

The MODIS active fire dataset was clipped with the MNP

boundary shape file using the same regional coordinate

system. The total incidence of fires during the study period

was determined within the different land-cover types, and

fire density calculated as the number of fires per km-2. The

relationship between fires and land-cover type was investi-

gated using a chi-squared test. The response variable (count)

was fire density, rather than total fire frequency, because the

difference in area among the land-cover types may have

affected the total incidence of fire. Density values were

multiplied by 100 to obtain integers required for the con-

tingency table. The categorical variables were two factors:

land cover with 4 levels (woodland, grassland, wetland and

rivers) and dry season with 3 levels (early, mid and late).

To map distribution and concentration of fires within the

MNP, to indicate areas that had a higher frequency (and

possibly at greater risk) of fires, we combined all active fire

data into one GIS layer for density analysis. A kernel

density estimation method (Duda and Hart 1973) was used.

The spatial distribution of all fire points were modelled as

density ‘‘kernel’’ functions which weights frequency of

location based on a 2-dimensional Gaussian distribution,

with density represented as contour plots.

Park boundaries, roads and rivers, as surrogates of

accessibility to the park (and therefore probability of

ignitions), were used to correlate with fire distribution. The

boundary along the international border with Bhutan

(northern park margin) was not included because there is

none or little access. Using these data represented as vector

layers, the minimum distance from any recorded fire point

to the nearest point on the park boundary line, road or river

was calculated using the GIS software, ArcMap. To

investigate the relationship between fire occurrence and

distance from these geographical features, distance values

were first partitioned into 100 m intervals and fire fre-

quency counted for each interval to obtain a set of response

and explanatory variables; fire frequency and distance,

respectively. The dataset was then analyzed using a Gen-

eralized Linear Model (GLM) with a log link and Poisson

errors. The statistical software R was used for the analysis.

We were only able to obtain precipitation data for the

period December 2000–May 2004, from the Fatemabad

Tea Estate, Bansbari (south-western MNP). We used these

data to investigate the relationship between average rainfall

and monthly fires for the same period, using a Pearson’s

correlation test.

Results

Temporal Patterns

A total of 781 active fires were detected in MNP during the

2000–2008 dry seasons (Fig. 2). No fire was detected

during wet seasons. Annual fires recorded increased sig-

nificantly from about 20 at the start of the study period

(2000–2001, 2001–2002) to over 100 after 2002–2003,

peaking at 157 in the 2005–2006 dry season.

Most fires (85%) occurred between December and

March each year, with fewer fires recorded during the first

and last two months of the dry season (Fig. 3). Overall,

fires increased from an average low of 9.5 ± 3.6 in

November, peaking at 25.5 ± 6.3 in December and Janu-

ary (25.5 ± 5.7), thereafter declining from February

(22.39 ± 6.638) to May (0.6 ± 0.4).

There was a significant inter-annual variation in monthly

patterns of fires, with the 2003–2004 season deviating most

from other years with most fires in February (Fig. 3). The

2005–2006 season was exceptional since most fires

(43.9%) occurred in the early part of the dry season.

Relation Between Fires and Land-Cover Type

Over 416 fires were detected in grassland; more than half of

total fire records (53.3%) (Table 1). Highest density (number

per km-2) of fires was detected within wetland areas, fol-

lowed by grassland, with the lowest incidence in woodland.

A chi-square test indicated that temporal changes in the

number of fires per month differed significantly by land-

cover type (v2 = 77.03; P \ 0.0001). Average number of

wetland and river channel fires peaked in December whilst

fires in the grassland reached the highest average in January,

and woodlands a month later in February (Fig. 4). Overall

occurrence of average monthly fires (n = 6 months) for the

period December 2000–May 2004, was negatively corre-

lated with monthly rainfall (R2 = 0.6237; d.f. = 5).

Environmental Management (2010) 45:414–423 417

123

Distances of Fires from Park Boundary,

Roads and Rivers

Distances from all recorded fire points to the park boundary,

roads and rivers are shown in Fig. 5. The largest mean dis-

tance recorded was 3.7 ± 0.09 km (range 0.014–11.3 km)

from fires to the boundary. Mean distance of fires to roads

was less than to the park boundary (1.9 ± 0.06 km, range

0.045–7.4 km), but distances to rivers were on average

shorter than for roads and boundary (0.9 ± 0.04 km, range

0.003–11.2 km). There were statistically significant

(P \ 0.001) correlations between fires and distances to

Fig. 2 Map showing all recorded fire points (according to three-

month blocks from October to May corresponding to early, mid or

late dry season), in relation to main vegetation and landscape features

used in this study. The projected coordinate system of this map is

Everest_Bangladesh_Polyconic

0

10

20

30

40

50

60

70

D J F M A M O N D J F M A M O N D J F M A M O N D J F M A M O N D J F M A M O N D J F M A M O N D J F M A M O N D J F M

2000 2001 2001 2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008

Nu

mb

er o

f F

ires

Grassland River Channel Woodland Wetland

23 18

97

110

124

153127

129

Fig. 3 Monthly changes in number of fires recorded during the 2000–2001 to 2007–2008 dry seasons in the MNP, according to land-cover type.

Total number of fires recorded per year is shown above each the year’s columns

418 Environmental Management (2010) 45:414–423

123

rivers, roads and park boundary. The closest fit between the

observed and predicted fire frequencies was found for dis-

tance to rivers (Fig. 5).

Spatial Aggregation of Fires

The fire kernel density map generated from all fire points

(for the period 2000–2008) indicated that fires were not

evenly or randomly distributed throughout MNP; clear

intensively burnt and unburnt areas are revealed (Fig. 6).

There were three distinct areas of high fire density identi-

fied; the largest located along the furthest western part of

the park (longitude 90�50 E), around 104 km-2. Another

heavily burnt area was found along the southern part of the

central portion of the MNP (around 117 km-2), with major

fire concentrations in grassland and wetland. This fre-

quently burnt area stretches northwards along the grassland

patches, but the main concentration is close to the southern

boundary of the park. A third fire aggregation (around

80 km-2), overlapped with the central south-eastern

grasslands, but also extended towards the north and east of

the park.

There were also some areas which had no recorded fires,

essentially a 2-3 km fringe along the Manas River, but

virtually no sizeable unburnt areas in grasslands.

Characteristically, woodlands in northern part of the

MNP experienced fewer fires except along the small rivers

intersecting this habitat.

Discussion

Tropical grasslands are linked to areas where rainfall is

concentrated in six or eight months of the year, followed by

a long period of drought. An annual period of productivity

during the wet season inevitably produces growth which

dries out and becomes flammable during the dry season.

Burning of these grasslands either deliberate by humans or

through the natural incidence of lightning has dominated

these landscapes for over 40,000 years (Lacey and others

Table 1 Summary of fire distribution in land-cover types in the MNP during dry seasons (October to May), from 2000 to 2008

Land-cover type Total fires % Fires Fire density

(no/km-2)

Monthly Fire Frequency

(mean ± SE)

Highest

(month)

Lowest

(month)

Woodland 189 24.2 0.736 23.5 ± 17.28 58 (Feb) 1 (Oct, May)

Grassland 416 53.3 2.473 52.12 ± 40.01 127 (Jan) 1 (May)

Wetland 122 15.6 3.62 15.25 ± 12.73 46 (Dec) 0 (Apr, May)

River 54 6.9 0.771 6.75 ± 4.04 16 (Dec) 0 (Oct)

Note: Fire data only available from December to May for the 2000–2001 dry season

Month

Nu

mb

er o

f F

ires

24

18

12

6

0

MayAprMarFebJanDecNovOct

24

18

12

6

0

MayAprMarFebJanDecNovOct

(a) (b)

(c) (d)

Fig. 4 Changes in the mean

(95% CI) number of fires

recorded by land-cover types

during the dry season months,

for the entire study period

Environmental Management (2010) 45:414–423 419

123

1982). However, natural fires from lightning strikes are

infrequent, often affecting only a relatively small area. By

contrast, anthropogenic fires are common, usually profli-

gate, and invariably set deliberately in areas otherwise

unlikely to be burnt.

In our study, most detected fires within the MNP occurred

in tall grasslands, although fire incidence may have been

related to vegetation height or species composition. The

limited rainfall data available indicated, as expected, that

average number of monthly fires was related to moisture

levels; more fires occurring during the drier months. More-

over, because most burning was detected close to roads,

rivers and the park boundary, structure of the vegetation may

have been less important than anthropogenic activity in

explaining fire occurrence. Whether fires started outside the

boundary could have contributed to burning detected near

the park boundary is currently not known.

We showed that the average number of fires detected

was highest during the drier months in the dry season, at

least for the 2000–2004 period for which rainfall data was

available. Importantly also, number of fires during the dry

season increased after 2002–2003. Although we could not

distinguish between controlled and indiscriminate fires, it is

possible that the increase in fires after 2002 reflects the use

of prescribed burning by park authorities and the penetra-

tion by the surrounding communities. Before this period

(since 1989), the park was closed to local peoples and park

staff due to political insurgency (Vigne and Martin 1998).

This is the most likely explanation for fire increases, since

rainfall patterns were not significantly different during the

period 2000–2004 for which precipitation data were

available. But, because no information on time and location

of fires initiated by park staff was available, we were

unable to distinguish level of incidence of illegal burning.

Future analyses of fires in the MNP should encourage the

recording of prescribed burning by park staff, to distinguish

occurrence of illegal fires and areas at greater risk.

Wildlife managers in Indian and Nepalese protected areas

with tall grasslands believe annual burning is necessary for

thatch grass production and/or maintaining ostensible graz-

ing for wild (e.g. rhino, buffalo, swamp deer) and domestic

ungulates. Cutting and burning of grasslands during the early

dry season can prevent more serious consequences by

wildfires, but as shown in our study, most recorded fires in the

MNP occurred during mid to late dry season (Dec.–Mar.).

This is disquieting since frequent, widespread burning

affects survival of grassland-dependant species, as vast areas

of grasslands are burnt indiscriminately without the safety of

fire lines to halt wildfires (Bell and Oliver 1992). Late season

burning is particularly devastating to slower-moving

Fig. 5 Correlations between distances from recorded fires to: a park

boundary. Slope = -2.006 e-04, SE = 1.228 e-05, z value = -

16.34, P \ 2 e-16; b roads. Slope = -4.996 e-04, SE = 1.886 e-05,

z value = -26.33, P \ 2 e-16; c rivers. Slope = -1.152 e-03,

SE = 4.126 e-05, z value = -27.93, P \ 2 e-16. The fitted line on

each graph shows the predicted fire frequency according to distance

420 Environmental Management (2010) 45:414–423

123

species, or those dependant on dense cover (such as the

threatened pygmy hog and hispid hare) as such fires are at

their most severe, though early season burning when the

water table or precipitation are low can also preclude vege-

tation re-growth (Peet and others 1999). Studies have shown

that late dry season burning has more significant impacts on

vegetation cover than burning in the early season (Bucini and

Lambin 2002). For example, in the Kruger National Park,

South Africa, fire severity was greatest at the end of the dry

season due to higher fuel loads (Govender and others 2006).

The significant factor is always fuel moisture content, which

is generally lower by the end of the dry season, therefore

making vegetation more flammable (van Wilgen and others

2004).

Limitations of the Study and Future Work

FIRMS is an easily accessible almost real-time system that

provides for efficient fire monitoring for field managers.

Although effective monitoring no doubt depends on scale

considerations and thus other sensors may be more

appropriate than MODIS, the MODIS sensor was designed

to include characteristics specifically for fire detection and

provides a unique capability over existing sensors in terms

of fire monitoring. An additional strength of MODIS is the

synoptic source of information derived from regular

observation in a precise time frame from satellite (Reeves

and others 2006).

Oversight of some proportion of fires is likely if the

duration of a fire was shorter than six hours, since fire

observations are made two times a day from the Terra AM

and PM satellites (Eva and Lambin 2000; Jin and others

2003). Furthermore, fire detection is affected by scan angle,

size of fire temperature, cloud cover, heavy smoke and sun

glint (Giglio 2007), and so the proportion of fires missed is

unknown. However, if we assume the level of error is

comparable between years (acknowledging that fire detec-

tion probability possibly differs between months) the fire

trends observed in MNP are realistic. Given that all fires

occur during the dry season, cloud cover is likely to be low,

but information on this is lacking. Nonetheless, determining

the number of fires missed can only be achieved by exam-

ining the satellite images to assess actual cloud cover.

The major limitation in this study was the lack of

information on the size of fire-affected areas essential to

estimate fire impact on vegetation or landscape changes.

Many users are ultimately interested in the area of land that

is burned, and some supplemental research has been

undertaken through the detection of burn scars using higher

resolution radiometers, such as AVHRR and ASTER (Eva

and Lambin 2000). Currently, MODIS offers unique spatial

and radiometric capabilities for burn scar detection. An

automatic procedure for burn scar detection has been

developed and now implemented in the MODIS production

stream (Roy and others 2005). The products will be

available at full resolution and as spatial summaries and

temporal composites. In this study, MODIS active fire data

(at 1 km2 pixel resolution) can be used to calculate the

maximum size of the burned areas, but this is coarse. Given

the refinement and improvement in detectability of burned

areas being developed by MODIS (provisional MODIS

burned area products are now available http://modis-fire.

umd.edu/MCD45A1.asp#3) more refined analyses of fire

convergence and patterns in the MNP will be possible in

the near future. Despite this representing a shortcoming for

this study, MODIS active fire data has been most useful in

identifying temporal patterns of burning in the MNP, for

detecting seasonal and yearly variation in the number of

fires, and as a means for understanding distribution and

Fig. 6 Density of fire

occurrences in the Manas

National Park. Graduation of

colours indicates the degree of

fire intensity

Environmental Management (2010) 45:414–423 421

123

convergence of fires by generating a map of fire-presence.

Such a product, as developed by Roy and others (2005) and

Trigg and Roy (2007) for southern Africa have been used

by resource managers to determine localities or vegetation

types more vulnerable to fires.

Data on the effect of burning in these areas is not

available, but information gathered from other studies

indicates that such frequent more intense burning will

inevitably cause major ecological impacts on grasslands.

For example, savanna tree mortality was higher in more

intensively burnt areas, because longer post-fire periods

allow trees to grow tall enough to survive during sequential

burning. Furthermore, early successional species were

more prevalent in frequently burned areas while more

midstorey plants and log cover were found in low fre-

quency areas (Spencer and Baxter 2006).

In early successional stages, grasslands in the Terai are

assemblages composed of short grass species, Imperata

cylindrica, in relatively drier lands, often preferred by

grazers (Sah 1999). If there are no disturbances such as fire,

floods or grazing, the short grass community will become

taller grassland dominated by Saccharum spontanuem.

Eventually, Saccharum grassland will convert into thatch-

scrub savanna, suitable for cover-dependent species; some

species such as pygmy hog, hispid hare are now critically

endangered (Oliver 1981; Peet and others 1999; Yadava

1990). Such differences in plant composition and structure

are apparent when areas of high and low fire frequency are

compared (Spencer and Baxter 2006). Our fire convergence

map clearly points to three main areas with significant

concentration of fires; these areas being burnt almost every

year. More importantly, our map shows that unburnt

grasslands, which are important refugia for grassland-

dependant species are virtually non-existent. This is

extremely worrying since such concentrated burning can

precipitate the extinction of the pygmy hog from the wild,

as documented for other areas in Assam (Oliver 1980,

1981; Oliver and Deb Roy 1993).

Although active fire data from FIRMS is a ‘‘snap-shot’’

measurement of fire activity within a protected area, it

provides park managers with real-time information on fire

locations. Historical data, like those employed in this study,

can also be used to determine habitats or areas at greater

risk of burning. Equally, long-term monitoring of active

fires can inform management as well as track climate

change impacts. However, presently, remote sensing of

active fires can support the application of a controlled

burning strategy to ensure the survival of large as well as

smaller species, and habitats. As a follow-up study, fire

monitoring beyond the park boundaries could be poten-

tially instructive in establishing whether fires close to

perimeter of the park are extensions of those started from

outside.

Acknowledgments We are most grateful to Goutam Narayan, Parag

Deka, Bibhuti Lakhar and other colleagues engaged in the Pygmy Hog

Conservation Programme for their support and insights on the study

area. MODIS active fire products were supplied by Fire Information

for Resource Management System FIRMS (NASA Information for

Research Management System) at the University of Maryland. This is

a publication resulting from Darwin Initiative Project # 162/15/017.

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