7719619_dissertation

THE UNIVERSITY OF MANCHESTER
Evaluating the impact of fire on the dry forest flora in the
Mahamavo region, western Madagascar: vegetation metrics
and remote sensing as tools for analysis
Student ID: 7719619
Degree Program: BSc Geography
Supervisor: Dr Timothy Allott
Year: 2014
Word Count: 11,000

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School of Environment and Development - Geography
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Evaluating the impact of fire on the dry forest flora
in the Mahamavo region, western Madagascar:
Vegetation metrics and remote sensing as tools for
analysis
Abstract
The existing critical state of global biodiversity is epitomised in the dry tropical
forests of Madagascar. The Mahamavo region of Madagascar exhibits a
complex fire history, in which traditional slash and burn agriculture (tavy) and
charcoal collection is affecting the health and composition of the forest. A study
site located in the Mahamavo region, near the town of Mariarano, is divided
into 885 MODIS pixels; each with a MODIS generated burn ratio value (ΣdNBR).
A probability sampling strategy is utilised to select 49 pixels for data collection,
stratified according to burn ratio and representative of the range of values at
the site. Three forest plots are randomly placed within each pixel and three
vegetation metrics; above ground biomass (AGB), species richness and species
diversity are measured in each plot. The Pearson product-moment test for
correlation and a linear regression analysis for association are employed and
indicate significant negative associations between ΣdNBR and the vegetation
metrics (AGB: R2
=0.12, P<0.02; richness: R2
=0.19, P<0.01; diversity: R2
=0.10,
P<0.03). This suggests that fire is a significant contributor to the degradation
of the forest area surveyed, and that it should be regarded as such when
considering biodiversity conservation in this ecosystem and in others like it.
However, the data are noisy, and reflect the heterogeneous nature of the
landscape. As such, conclusions are determined with due caution.

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Table of Contents
Plagiarism Form i
Abstract ii
List of Tables v
List of Figures vi
Acknowledgements vii
1. Introduction 1
1.1 Aims 2
1.2 Hypotheses 2
1.3 Wider Contribution 2
2. Academic context 3
2.1 Forests 3
2.2 Fire 4
2.3 Intermediate Disturbance Hypothesis 7
2.4 Madagascar 8
2.5 Mahamavo Region 8
2.6 Remote Sensing 9
3. Methodology 12
3.1 Site Description 12
3.2 Data Collection 14
3.2.1 Burn History 14
3.2.2 Forest Plots 18
3.3 Data Processing 19
3.3.1 Species Richness and Diversity 19
3.3.2 Above Ground Biomass 20
3.4 Analytical methods 21
4. Results and Analysis 22

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4.1 Reconnaissance 22
4.2 Descriptive Statistics 22
4.3 Tests for Difference 26
4.4 Tests for Correlation 28
4.5 Regression Analysis 29
5. Discussion 34
5.1 Summary of Results 34
5.2 Tree Abundance 34
5.3 Species Richness and Forest Composition 35
5.4 Species Diversity 36
5.5 Above Ground Biomass 37
5.6 Intermediate Disturbance Hypothesis 40
5.7 Limitations 41
5.7.1 Forest Health Metrics 41
5.7.2 Remote Sensing and ΣdNBR 42
5.8 Confounding Variables 43
5.9 Forest or Savannah? 44
5.10 Fire, the Forest and its Future 45
6. Conclusion 48
6.1 Key Findings 48
6.2 Conclusion 48
Glossary of Terms/Acronyms 50
Bibliography 51
Appendices 62

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List of Tables
Table 2.1 – Key characteristics of SDTF and the study site 4
Table 4.1 – Descriptive statistics summary (pixels) 24
Table 4.2 – Descriptive statistics summary (deciles) 25
Table 4.3 – Descriptive statistics summary (high and low pixels) 27
Table 4.4 – Linear regression statistics (pixels) 31
Table 4.5 – Linear regression statistics (deciles) 31

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List of Figures
Figure 2.1 – Fire controls at different scales 6
Figure 2.2 – IDH hump-shaped curve 7
Figure 2.3 – Landsat 742 composite satellite image of the Mahamavo region 10
Figure 2.4 – Photograph of the landscape mosaic 10
Figure 3.1 – Map of the study site 13
Figure 3.2 – Photograph of a charcoal pit in situ 14
Figure 3.3 – Map of ΣdNBR values in Mahamavo region 15
Figure 3.4 – Burn history scenario graphs 16
Figure 3.5 – Maps of pixel locations and ΣdNBR distribution 17
Figure 4.1 – Distribution histograms (pixels) 24
Figure 4.2 – Distribution histograms (deciles) 25
Figure 4.3 – Scattergraphs (pixels) 29
Figure 4.4 – Scattergraphs (deciles) 30
Figure 4.5 – Tree abundance bar chart 33
Figure 4.6 – Common species abundances 36
Figure 5.1 – Burn ratio scaling issues 38
Figure 5.2 – Scattergraph of AGB against ΣdNBR without neutral values 39
Figure 5.3 – Scattergraphs of richness and diversity with IDH curves 41

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Acknowledgements
There are many who I would like to thank for their advice and support throughout
the planning, fieldwork and writing of this dissertation. Firstly, my supervisor Prof.
Tim Allott, whose wisdom and guidance, presented always with great enthusiasm,
was a mainstay throughout the process.
This research could not have been possible without the backing and support of the
team in Madagascar; and it is my pleasure to acknowledge and endorse the efforts
of Operation Wallacea and DBCAM, who are uncovering an ecological treasure trove
in the Mahamavo region that deserves serious attention from the scientific
community and conservation efforts alike.
My deepest thanks must go to my colleagues and friends who I worked alongside
collecting data in the field. Michael Chatting and Paviter Dhillion helped collect a vast
bank of profitable data, without them this dissertation would have faltered at the
first hurdle. I would also like to thank Harison Andriambelo, our specialist, guide and
companion in the field.
Finally, my sincere thanks goes to Dr Peter Long, who helped me develop my research
from day one and offered his time, efforts and expertise up until completion. I am
incredibly grateful for his unwavering support throughout the process.
Informal thanks go to those closest to me, my parents Simon and Jen, and all my
friends that gave words of advice, and were there to relieve the strain in times of
despondency. To Adnan, Becky, David and Oscar especially, thank you.

2. Academic Context
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1. Introduction
Biodiversity is “the variability among living organisms… and the ecological complexes
of which they are part; this includes diversity within species, between species and of
ecosystems” (United Nations, 1993). Although it can be measured at a range of scales
(Myers et al., 2000), from the genetic level to the ecosystem, it is generally measured
at the species level as species richness and species diversity (Sutherland, 2008).
Biodiversity plays a key role in human survival and activity, through the plethora of
ecosystem services it provides and maintains (Costanza et al., 1998; Washington,
2011).
The world currently faces an impending extinction crisis on a global scale (May, 2010),
with the tropics at the greatest risk of catastrophic biodiversity loss (Baillie et al.,
2004). Tropical forests represent the world’s most degraded forests (Gaston, 2000;
Sánchez-Azofeifa and Portillo-Quintero, 2011), in particular the dry tropical forests
which are highly fragmented and continue to be damaged and removed at an
alarming rate (Maass et al., 2005). Fire is considered to be a major contributor to
vegetation cover loss and floral biodiversity decline in tropical forests (Jacquin et al.,
2010). This is an ecosystem and ecological process that require the focus of scientific
research, and the efforts of conservation and protection. Particularly in biodiversity
hotspot locations, one of which is Madagascar – home to the most fragmented
tropical dry forest in the world, a ecosystem that has been studied surprisingly little
(Elmqvist et al., 2007; Holsinger and Gottlieb, 1991; Miles et al., 2006).
This study uses remote sensing to assess the fire history of a small section of this
ecosystem and compares it to plant biodiversity metrics and above ground biomass
to evaluate the relationship between fire and degradation.

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1.1 Aims
 Evaluate the relationship between species richness and Sigma delta-
Normalised Burn Ratio (ΣdNBR)
 Evaluate the relationship between species diversity (Simpson 1/D) and
ΣdNBR
 Evaluate the relationship between above ground biomass (AGB) and ΣdNBR
1.2 Hypotheses
Null: There is no discernable relationship between burning (ΣdNBR) and forest
composition (richness, diversity and AGB).
Alternative: There is a significant relationship between burning (ΣdNBR) and forest
composition (richness, diversity and AGB).
1.3 Wider Contribution
Data presented here will also contribute to a biodiversity status report for the region,
to be presented by Operation Wallacea in partnership with Development and
Biodiversity Conservation Action for Madagascar (DBCAM) and the University of
Oxford. This information may contribute to future conservation and development
planning in the region, particularly with regard to the potential impact of
uncontrolled burning and habitat degradation.

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2. Academic Context
2.1 Dry Tropical Forest
Although less studied, and often overshadowed by the wet tropical forests,
seasonally dry tropical forests (SDTF) represent 42% of all tropical forests (Kauffman
et al., 2003; Lugo and Murphy, 1986; Murphy et al., 1995; Vargas et al., 2008), with a
coverage of approximately 1,048,700km2
(Miles et al., 2006). Some of the largest and
most recognisable SDTFs are those of the Mexican and Bolivian lowlands (Dirzo et al.,
2011; Gentry et al., 1995; Parker et al., 1993), but others include the Southeastern
Indochina dry evergreen forests (Corbet and Hill, 1992; Stewart-Cox, 1995), the
Maputal and-Pondoland bushland and thickets (Cowling and Hilton-Taylor, 1994),
and the dry forests of New Caledonia (Wikramanayake, 2002). Key features of such
environments are outlined in table 2.1, alongside the study site. Characterised by a
pronounced seasonality, with a long, hot, dry season, SDTF is considered to be the
most threatened tropical forest type by human activity (Dirzo et al., 2011; Janzen,
1988; Vargas et al., 2008). Comparatively, wet and moist tropical forests are much
less affected by anthropogenic activity (Jaramillo et al., 2003; Maass et al., 1995), and
yet the effects of such activity are studied far more in these forests than in dry ones
(Dirzo et al., 2011; Masera et al., 1997).
Table 2.1 – Key characteristics of seasonally dry tropical forests (SDTF) and the study site.
Data from Moat and Smith (2007) and Climatemps.com (2014).
Typical seasonally dry tropical
forest (SDTF)
Study site (15°28'47S, 46°41'41E)
Mean annual temperature >17°C 26.3°C
Annual precipitation 250-2000mm 600-1500mm
Ratio of potential evapotranspiration
to precipitation
<1.0 n/a
Months with <100mm precipitation 4-6 6
Coverage
1,048,700 km2
(42% of tropical
ecosystems globally)
60 km2
forested area
Biological diversity High High
Degradation 48% converted, highly fragmented 40% since the 1970's
Net Primary Productivity ~12 Mg ha-1
n/a
Above Ground Biomass 35-140 Mg ha-1
77 Mg ha-1
(Hawton, 2013)

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Both natural and anthropogenic fires are primary threats to SDTF, particularly
throughout the dry season (Chazdon, 2003; Kauffman et al., 2003; Nepstad et al.,
2001; Vargas et al., 2008). The effects of such fires is documented to directly reduce
biomass in these forests (Kauffman et al., 2003; Vargas et al., 2008), but the effects
of fire on species richness, vegetation density and cover is not well known. Fox and
Fox (1987) suggest that even subtle changes in fire regime can have devastating
impacts.
This study uses the term dry tropical forest interchangeably with SDTF.
2.2 Fire
Today, and historically, fire has shaped the landscapes of the tropics (Cahoon et al.,
1992; Cochrane and Ryan, 2009; Cochrane, 2009; Kull and Laris, 2009; Pyne, 1995).
For the people who live in such locations; fire is a key determining factor in their way
of life (Kull and Laris, 2009; Whelan, 1995). Fire is not only an environmental issue,
but is also a social, political and economic concern at a range of scales. As an agent
of change, fire is highly influential; altering biological, chemical, atmospheric and
hydrological processes both locally and globally (Csiszar et al., 2004). Smoke
generated from fires impacts atmospheric fluxes and can influence evaporation
processes, cloud formation and precipitation (Menon et al., 2002; Reid et al., 2009;
Vargas et al., 2008). Although a natural process, fire regimes across the globe are
being altered by humans and the effects of unnatural fire can be substantial. Linked
to climate change, soil damage, aerosols, nitrogen flux change, forest degradation
and loss - the list of potential impacts of anthropogenic fire is interminable (Cavelier
et al., 1998; DeBano et al., 1998). In Madagascar, loose estimates suggest that up to
a third of the island is burnt annually, equating to about 200,000km2
(Kull, 2004). 25-
50% of Madagascan grassland is burnt annually (Kull and Laris, 2009), but more
worryingly tavy (forest slash and burn agriculture) is estimated to remove from
between 2000 and 7000km2
of forest each year (Kull, 2004). Considering the
biological importance of Madagascar (Harrison and Pearce, 2000; Jacquin et al.,
2010), it is surprising how little research on the impacts of fire has been conducted
on a local scale and it seems a pertinent issue to address before policy can be

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implemented effectively. Such policy may introduce and encourage effective land
management, pastoralism, protected areas, sustainable development and so forth.
Investigating fires is problematic, due to its dynamic nature, and the heterogeneity
of environmental responses to it. Fires are highly variable, and influenced by a huge
assortment of different factors. Whitlock et al. (2010) summarise the main fire
controls at a range of spatial and temporal scales from the microsite to the continent
and from seconds to millennia (figure 2.1). Fires will burn at one or more of three
levels; ground fires smoulder through soils and organic matter in the earth; surface
fires spread through fuels lying close to the ground, grasses being a notable example;
while crown fires are associated with forest environments where the canopy is
aflame (Bond and Keeley, 2005). Fires can vary in intensity, severity and frequency;
all of which impact the environment in different ways. The intensity of a fire refers to
energy released by it, or its physical size, speed and behaviour. Intensity can be
measured as the total energy released per metre (kW m-1
) (Byram, 1959). The
severity of a fire is the extent of its impact on an ecosystem, and various metrics can
be applied in attempting to measure such effects. Frequency can be difficult to
measure considering the dynamic nature of fires and the varying scales at which they
act, but is often measured as an expression of fire occurrence or rate; the time
interval between successive fires or the number of fires within a given period for
example (Keeley, 2009).
Plants have adapted to fire in a number of ingenious ways. Some exhibit pyriscence,
whereby seed maturation and release is initiated, directly or indirectly, by fire. Fire
tolerant species can endure and sometimes thrive despite fire damage (Clarke et al.,
2005). Fire resistant trees suffer little to no damage during typical fires, due to their
clever evolutionary traits; thick or flaking bark, low specific leaf areas, fire resistant
seeds etc. Ecosystems made up of fire-adapted species have been shaped by regular
burning, and will often rely upon it to maintain a stable ecology (Bond and Keeley,
2005). Such communities include savannah (Lamotte, 1975; van Langevelde et al.,

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2014), chaparral (Haidinger and Keeley, 1993; Halsey, 2005) and prairies (Collins and
Wallace, 1990; Collins, 1992; Old, 1969).
Dirzo et al. state that while exposed to infrequent occurrences of fire, a regular fire
regime like that of the above environments is not a natural part of dry tropical forests
(2011). Fire in SDTF is an anthropogenic process, initiated primarily by farmers
through traditional slash-and-burn (tavy) techniques (Dirzo et al., 2011). Knowledge
of the short and long-term impacts of such fires on SDTF is limited in current literature
(Otterstrom et al., 2006). The general school of thought is that fire is a destructive
force and a major threat to the longevity of dry tropical forests, and studies like this
will improve understanding.
Figure 2.1 – Fire controls conceptualised as triangles (modified from Whitlock et al., 2010).
Sides of triangles indicate dominant controls at different spatial and temporal scales. The
blue arrows demonstrate possible feedback systems, and the triangle overlapillustrates the
nested nature of fire systems.

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2.3 Intermediate Disturbance Hypothesis
There is evidence to suggest (Horn et al., 1975) that forests fires may contribute to
the Intermediate Disturbance Hypothesis (IDH) phenomenon, where by species
diversity and forest health is maximised through frequent, low level, ecological
disturbance (Connell, 1978). The theory draws credence from the idea that a healthy
and diverse ecosystem should contain both competitive K-selected species, alongside
more opportunistic r-selected species (Catford et al., 2012; Wilkinson, 1999). IDH
proposes that the relationship between disturbance and species richness and
diversity is hump-shaped (figure 2.2), with moderate levels of disturbance
maintaining the highest ecological diversity (Connell, 1978; Horn et al., 1975). Too
little disturbance will encourage competitive exclusion which inhibits the growth of
r-selected species, while too much disturbance will kill K-selected species that are
unable to recolonize without extended periods of stability. Although fire is clearly an
actor of disturbance broadly speaking, its contribution to the IDH is contested.
Schwilk et al. found that in South Africa, the relationship between species diversity
and disturbance frequency was “opposite that predicted by the intermediate
disturbance hypothesis” (1997, p. 77) and that it was the least frequently burnt sites
that exhibited the highest diversity, a phenomenon corroborated by Beckage and
Stout (2000).
Figure 2.2 – A generalised illustration of the IDH hump-shaped curve. Adapted from Grime
(1973) and Connell (1978).

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2.4 Madagascar
Situated 500 km off the east coast of South Africa, Madagascar is the fourth largest
island on the planet and due to its historical isolation is home to a vast number of
endemic flora and fauna. Over 88% of the known species in Madagascar are endemics
(Harrison and Pearce, 2000); eight plant families are unique to the island, and 280
plant species are listed as threatened (Baillie et al., 2004). The recent colonisation of
the island by humans and its subsequent population boom, which continues to rise
at a rate of 3% per year (Harrison and Pearce, 2000), poses numerous threats, much
of which have already devastated much of the environment. Tavy, logging, invasive
species and collection are a few of the direct pressures on the biodiversity across the
island. There are several distinct habitat types in Madagascar; ranging from the wet
tropical forests in the east; the dry savannah plains across much of the centre of the
island – surrounding the islands capital, Antananarivo (Tana); to the dry, seasonal,
forests in the west.
Fire in Madagascar is a highly charged political issue, as well as an environmental one
(Kull, 2004). Attention from outside the country, off academics, NPOs and
environmentalists, has put pressure on the national government to impose anti-fire
laws, despite much of the population’s dependence on tavy for subsistence survival
(Kull and Laris, 2009; Kull, 2004, 2000). Kull argues that this is unreasonable and that
the consequences of a heavy-handed top down approach to fire management will be
resistance from tavy-reliant communities, who have for centuries used fire in a
sustainable way (2004, 2002). However, the limited forest size is unlikely to support
the country’s rapidly growing population and degradation and loss of forests is
inevitable while carefully managed policy is absent (d’ Oliveira et al., 2011; Kull, 2004;
Rakotomanana, 1989; Ramangalahy and Talata, 1995).
2.5 Mahamavo
The Mahamavo watershed lies to the north west of Tana, with Mahajamba bay in the
north, and Bombetoka bay in the south. The area is governed by a small rural
commune in the centrally located town of Mariarano, and the vast majority of the

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region’s population are scattered in small rural villages. The population consists of
eleven fokotany, which locally administer the inhabitants of a few small settlements.
The area, like much of Madagascar, is ethnically diverse and individuals from the
Tsimihety, Sihanaka, Merina, Sakalava, Antandroy, Antaisaka, and Betsileo groups
make up the population. Most belong to the Tsimihety and Sihanaka groups.
Historically, the region was dominated by dry tropical forests but aforementioned
pressures have removed much of the forest, in favour of agricultural land and the
production of charcoal (Ferguson, 2009). Today, the land cover consists of a complex
mosaic of forest, wooded grassland and bush land, with mangrove communities
along the coast and river systems. Figure 2.3 illustrates the heterogeneity of the
region with a Landsat 742 composite of land cover type. Figure 2.4 shows how this
mosaic translates to the field, with expanses of primary and secondary forest, burnt
areas, water bodies and savannah areas. The largest area of forest is located near
Mariarano, but even here there is clear evidence of burning and selective logging.
Areas of savannah are frequently burned, and the causes are both natural and
anthropogenic. A scientific research group, Operation Wallacea, operates alongside
DBCAM (Development Biodiversity Conservation Management) in the region, with
scientists and locals working together to assess biodiversity in the area.
2.6 Remote Sensing
Mapping environmental change has become substantially more accurate thanks to
recent developments in remote sensing systems. Satellite based imaging devices such
as the Moderate Resolution Imaging Spectoradiometer (MODIS) and Landsat provide
up to date and accurate images that can be modified and analysed to assess a wide
range of environmental conditions and dynamics. Regular updates to the system and
its modelling capabilities have led to the development of MODIS and Landsat fire
products, which can be employed as a means of assessing fire dynamics and impacts
(Franklin, 2009; Lehsten et al., 2010; Wimberly and Reilly, 2007).

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Figure 2.3 – A Landsat 742 composite satellite image acquired in
2010 of the Mahamavo region (USGS, 2014). Green indicates
vegetation, blue open water, and red/pink bare soil or degraded
habitats.
Figure 2.4 – Photograph showing the characteristic appearance of the
landscape studied, the heterogeneous nature of the environment can
be observed; areas of primary and secondary forest, scrubland, recently
burnt areas, savannah and bodies of water (Taken by author in 2013).

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Such techniques have been utilised in studies by Lehsten et al. (2010), van Leeuwen
(2008), Wimberly (2007) and Lentile et al. (2007). Though few have employed remote
sensing to investigate the effects of fire on dry tropical forests, and none have done
so to examine the impacts of fire severity in the Mahamavo region. A study by Palfrey
(2013) concluded that fire frequency had little impact on forest composition in the
region, despite observing large scale forest degradation associated with burnt areas.
Such findings prompt the exploration into fire severity as being of greater influence,
a hypothesis presented by Scharenbroch et al. (2012).

3. Methodology
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3. Methodology
3.1 Site Description
The spatial focus of the study is a small area of dry tropical forest (15°28'47S,
46°41'41E) located in the Mahamavo watershed, 50km north-east of Mahajanga –
the largest city in the Mahajanga region of Madagascar (figure 3.1). Key
characteristics are summarised in table 2.1. Climatically, the area is sub-humid to dry
with precipitation ranging from 600-1500mm per annum (Moat and Smith, 2007), the
majority of the rain falls over the wet summer months, following a long dry winter
period. A relatively flat area, the Mahamavo watershed has a maximum altitude of
600m above sea level with a geology consisting mainly of well-drained soils and
unconsolidated sands (Moat and Smith, 2007).
The floristic physiognomy of the region can be broadly split into six classifications
according to Moat and Smith’s Atlas of the Vegetation of Madagascar (2007). More
than half of the region is a plateau grassland-wooded grassland mosaic, with vast
areas of grassland and savannah that is maintained primarily by fire and grazing
(White, 1986). The second most widespread ecosystem is wooded grassland-
bushland mosaic, similar to the plateau grassland-wooded grassland mosaic, but with
a larger wooded element with expanses of low forest thicket and scrub heavily
influenced by anthropogenic fire and domestic grazing (Faramalala, 1995). The
western dry forest, studied in this paper, makes up a relatively small proportion of
the landscape than was likely before human colonisation. The forest has an incredibly
diverse physiognomy, depending on variations in rainfall and substrate and range
from largely deciduous forest and impenetrable thicket, to low, spiny, scrubland and
bushland (Moat and Smith, 2007). The ecosystem consists of many spiny and small
leaved floras with canopies of around 10 metres and a high degree of endemism
(Faramalala, 1995; Koechlin et al., 1974). Wetlands and mangroves make up areas
alongside rivers and lakes and are often exploited for salt production, riziculture, and
crab and shrimp farming (Rasolofo, 1993; Spalding et al., 1997). Lastly, much of the
landscape has been converted for the cultivation of rice, cassava, sweet potatoes,
sisal, sugar cane, coffee and vanilla (Faramalala, 1995).

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The watershed is sparsely populated and remote, with small villages few and far
between, generally located along the coastline, rivers or profitable areas of dry
tropical forests. Agricultural subsistence living is common, alongside traditional
practices, paddy cultivation and zebu (cattle) grazing. Selective logging of desirable
wood puts further pressure on the remaining pockets of natural forest (Ganzhorn et
10km
Figure 3.1 – The
geographical location
of the Mahamavo
region and the
maximum extent of
the dry tropical forest
area studied (in red
on lower left map).
Modified from
Landsat and Google
Earth (2014; 2014).

3. Methodology
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al., 1990). A growing charcoal (Montagne et al., 2010) trade has led to the selective
burning of the forest, and charcoal pits were observed frequently during the 6 weeks
field study (figure 3.2 shows a fire pit in situ).
The maximum extent of the study site was planned around pre-existing routes
through the forest, set up by Operation Wallacea and the local organisation,
Development Biodiversity Conservation Management (DBCAM) in 2010 to study
various elements of the forest. For practical reasons, it was decided that no forest
plots should be more than 1km from the routes, as much of the forest is hard to
navigate. The total study area was then divided according to the MODIS generated
grid resolution into 885 square pixels, 240m in length and breadth.
3.2 Data Collection
3.2.1 Burn History
This study employs a space for time probability sampling strategy, and samples were
spatially stratified according to their fire history. Fire history was determined by use
of a modified version of the MODIS fire product MOD14 which provides information
on the location of a fire, its emitted energy, the flaming and smouldering ratio and
an estimate of area burned (Justice et al., 2006). Data are captured in 36 spectral
Figure 3.2 – Photograph of a fire pit in the Mahamavo study area
(taken on site by study author in July, 2013).

3. Methodology
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wavelength bands and two of these bands, 2 and 7, are analysed to determine recent
burning, based on the daily surface reflectance dynamics recorded. The normalised
ratio between these near-infrared (NIR) and mid-infrared (MIR) nadir surface
reflectance bands (b2-b7/b2+b7) was calculated every month between February
2000 and May 2013 (NBR), and the monthly delta-Normalised Burn Ratio (dNBR) is
the difference between month n and month n-1. The net change in burn severity
between 2000 and 2013 is calculated using Sigma (Σ) dNBR, the sum of dNBR in all
transitions between months in the period between February 2000 and May 2013.
High ΣdNBR indicates degradation by burning in the period, while a low ΣdNBR would
indicate recovery from burning over the same period. Figure 3.3 shows the ΣdNBR
values calculated in the Mahamavo region via the MODIS fire product with the study
site highlighted.
Figure 3.3 – Map showing the ΣdNBR values calculated via the MODIS fire product in the
Mahamavo region. Data obtained from the MODIS MOD09A1 satellite (USGS, 2014).

3. Methodology
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Although the ratio does well to describe areas that have experienced significant
burning or substantial recovery, there are issues when it comes to the intermediate
ΣdNBR values. There are various scenarios that may explain a neutral ΣdNBR value,
and deducing the indicative meaning of such a value is problematic. Figure 3.4
demonstrates how three very different burn histories may produce a ΣdNBR value of
close to zero: (a) exemplifies a history lacking in burning altogether, and therefore
producing a value of 0 with no degradation and no recovery; (b) shows how a series
of low intensity fires and recovery periods may produce the same ΣdNBR value; while
(c) illustrates a burn history where one high intensity fire that causes considerable
degradation, followed by a long, nourishing period of recovery may also give a neutral
ΣdNBR value. This paper assumes that values above 0.1 and below -0.1 represent
either; greater degradation caused by burning than recovery
Figure 3.4 – Graphs
showing time against burn
history. Demonstrating
how three very different
burn history scenarios
may all give a neutral
ΣdNBR value of 0.

3. Methodology
17
Figure 3.5 – Maps showing (a) the positions of the 49 stratified 240x240m pixels and (b)
the variation in ΣdNBR across the pixel distribution (modified from USGS, 2014).
(a)
(b)

3. Methodology
18
after burning, or greater recovery and growth after burning than damage caused by
burning. Neutral, intermediate, data are included; however results and analysis
should be regarded with these issues in mind.
Using the ΣdNBR data for the study site, which ranged from -0.465 to 0.531, the 885
pixels in the study area were stratified into 10 deciles indicating the degradation or
recovery levels for each pixel. Decile 1 covers the lowest ΣdNBR values and decile 10
the highest. Pixels were selected such that all pixels had a non-zero probability of
being sampled, 5 from each decile; excluding decile 10, which covered only 4 pixels,
all of which were selected. After initial reconnaissance of the forest, it was
determined that these 49 pixels were a representative sample of the forest and the
varying influence of fire upon it. Figure 3.5 (a) shows the distribution of the 49 pixels
and (b) the spread of ΣdNBR values across this area.
3.2.2 Forest Plots
The size of a quadrat should depend on the size and form of the vegetation being
sampled and should always be significantly larger than the largest growth forms
(Kent, 2012). Keeping in mind the process of scaling up to the size of the MODIS pixels
used for ΣdNBR (240x240m), 20 by 20 metre plots were chosen as both an
appropriate and manageable quadrat size. This is also the minimum plot size
recommended for woodland canopies by Kent (2012). Three of these plots were
randomly placed (occasionally impenetrable terrain necessitated the adjustment of
coordinates) within each of the 49 stratified pixels to give 147 plots, that offered a
representative sample of each decile.
A number of observations were made within each plot in order to understand the
nature of the forest within them. For the purpose of this study, Schatz’s (2001)
definition of a tree: “a woody plant at least (4-) 5m tall and or with at least one vertical
stem attaining 5cm in diameter at breast height”, was used. However, often height
was disregarded as a defining factor, as the deciduous thicket forests found across
much of the study area contains many trees reaching only 3-4m tall with both single
and multi-trunked individuals. In this study, all trees with a chest height
circumference of 1500mm or more were counted, measured with a tape measure
and identified according to native vernacular with the help of a local botanist.

3. Methodology
19
Voucher specimens were taken to allow identifications to be confirmed in the PBZT
(Parc Botanique et Zoologique de Tsimbazaza) herbarium later with the help of
specialists (Andraimbelo, 2013, pers. comm.). The height of the trees were estimated
in meters, 10 canopy cover readings were taken at random locations within the plot
as a crude measure of leaf area index (LAI), and saplings (shoots with a circumference
smaller than 50mm) were counted in a small 2x2m plot within each larger forest
sample plot. Four photos were taken from the centre of the plot toward each edge.
3.3 Data Processing
Field data collected on paper data sheets were entered into a custom relational
database which summarised the data using queries appropriate for further analysis.
In order to evaluate forest health, which is a notoriously difficult concept to
determine, let alone measure, three of the above measures were selected as metrics.
Ideally, many physiological and ecological aspects of the forest would be measured
and used to give a comprehensive evaluation of forest health. As a practical and
effective compromise, species richness, species diversity and above ground biomass
(AGB) were chosen as appropriate proxies in this study. Appendix A presents the pixel
dataset, with ΣdBNR, decile allocation, AGB, richness and diversity values. Appendix
B presents the decile dataset, with AGB, richness and diversity values.
3.3.1 Species Richness and Diversity
Counting and identifying the number of species and individuals within each plot
enabled calculations of species richness and diversity. Species diversity was
determined using the Simpson Diversity Index and was calculated for each pixel, and
each decile. Scaling up to the pixel size required the combining of data within the
three forest plots to give data for a 20x60m area; and assuming that all the forest
within the pixel is identical to this area, the data were multiplied by 48 to give the
total dataset for a single pixel. Datasets for deciles are the combination of pixel data
for those within the ΣdNBR value range for that decile. The index is expressed in the
following formula:

3. Methodology
20
𝐷 = ∑ 𝑝𝑖
2
where pi
2
is the abundance of each species as a proportion of the total population.
The formula below is used to calculate the index (D):
𝐷 = ∑ (
𝑛𝑖[𝑛𝑖 − 1]
𝑁[𝑁 − 1]
)
where n is the number of individuals of the ith species, and N is the total number of
individuals within the area. This study presents the index as the reciprocal; 1/D.
Diversity ranges from 0 to the total number of species in a plot, with diversity
increasing as the value increases.
3.3.2 Above Ground Biomass (AGB)
The above ground biomass (AGB) of a tree is typically assumed to be equal to the
diameter (D) of the tree, proportional to the product of its wood density (ρ, dry wood
over green volume), times its trunk basal area (BA = π D2
/4), times its height (H).
Accordingly, the below equation should represent AGB across forests:
𝐴𝐺𝐵 = 𝜌 × (
𝜋𝐷2
4
) × 𝐻
Though this simple equation cannot represent the various differences between
individuals, therefore changes to the equation, tested by Chave (2005), have been
made to suit different forest types. He concluded that for dry tropical forest stands
the following equation was most accurate:
〈 𝐴𝐺𝐵〉 𝑒𝑠𝑡 = exp(−2.187 + 0.916 × ln( 𝜌𝐷2
𝐻))
≡ 0.112 × (𝜌𝐷^2 𝐻)0.916
Wood density (ρ) is measured in grams per centimetre cubed, trunk diameter (D) in
centimetres and height (H) in metres. The symbol ‘≡’ indicates a mathematical
identity: formulas can be used interchangeably for the biomass estimation

3. Methodology
21
procedure. Wood density was available from the World Agroforestry wood density
database (Harja et al., 2014), the African Wood Density Database (Carsan et al., 2012)
and IPCC (2006) for 28 of the 98 tree species acknowledged. Individuals of these
species accounted for 41% of the total individuals identified. A weighted mean was
used to calculate the average wood density value at 0.653 g m-3
. The total sum of AGB
per plot was calculated and scaled up to megagrams of biomass per hectare for each
pixel and decile.
3.4 Analytical Methods
Excel was used to organise and present data before SPSS predictive analytics software
was applied for analysing the key metrics and testing the hypothesis. Initial tests
described the data in terms of frequencies and descriptives. A one-way ANOVA,
Mann-Whitney and a two sample Kolmogorov-Smirnov test for difference was used
to compare AGB, diversity and richness in two burn groups; one comprising pixels
with a ΣdNBR value of below -0.1 and the other pixels with a ΣdNBR value above 0.1.
After differences between the groups became evident, further analysis was utilised
to investigate whether ΣdNBR had anything to do with the variance. The Kolmogorov-
Smirnov test determined whether the datasets had normal distributions and the
suitable test for correlation, the Pearson product-moment, was used to assess
possible relationships between the metrics and burn history. If appropriate, a linear
regression was applied to the data alongside a one-way ANOVA and test for
coefficients to investigate a possible cause and effect relationship.

4. Results and Analysis
22
4.1 Reconnaissance
Spending time in and around the forest at the site provided the opportunity not only
for the collection of hard, quantitative, data; but also for general observation and
assessment of the forest. The fact that the forest at the site was highly degraded was
clear without the need for statistical analysis. Burnt areas were obvious, and were
evident in and among areas of primary and secondary forest. Recovering secondary
forest was distinguishable from old-growth primary forest in terms of height, density,
canopy cover, floral composition and faunal assemblages. Aside from burning, there
was abundant evidence of cutting, largely for the production of charcoal, verified by
the presence of several charcoal pits (figure 3.2) found in and around the forest.
Through correspondence with locals and guides, assumptions about shifts from
forest to savannah environments following burning were confirmed. After severe,
stand clearing fires, forests were fragmented and burnt areas far from forest edges
often became grassland or savannah environments. Much of these areas were
subsequently used as land for grazing zebu. The composition of the landscape at the
site was extremely heterogeneous; with areas of primary and secondary dry tropical
forest, spiny forest, scrubland, bushland, wooded grassland and grassland in the drier
areas; while wetlands and mangroves border rivers and lakes. There were also large
areas of cultivated land, where forests had been cleared.
4.2 Descriptive Statistics
The above observations are an important precursor to the more detailed analysis of
the forest composition and its complex relationship with fire. One could be forgiven
for comparing this forest to any other dry tropical forest, and drawing conclusions
based the study site having a complete cover of forest. However, all the analysis
should be regarded with the diverse and heterogeneous nature of the landscape in
mind. Descriptive statistics are used to quantify some of the initial observations made
above, and are applied at the pixel level. These are presented in table 4.1, which
shows the mean, mean standard error, median, standard deviation, skewness and its

23
standard error, kurtosis and its associated standard error, and whether the
distribution is significantly different from a normal distribution; for species richness,
species diversity (1/D) and AGB (Mg ha-1
).
The forest at the Mahamavo site can be summarised as follows. The average, or
weighted mean wood density of the observed species based on the IPCC (2006),
World Agroforestry wood density database (Harja et al., 2014) and African Wood
Density Database (Carsan et al., 2012) listing is 0.65. The mean above ground biomass
of the forest was 23.82 megagrams per hectare (Mg ha-1
) with a standard error of
3.34 Mg ha-
1, while the median was 17.23 Mg ha-1
. AGB had a standard deviation of
23.37 Mg ha-1
.Although exhibiting a normal distribution, confirmed by its asymptotic
significance of 0.20 when tested using the Kolmogorov-Smirnov test, the AGB data
were right skewed with a value of 0.71 and a standard error of 0.34. The distribution
was also platykurtic with a value of -0.62 and a standard error of 0.67. The mean and
median diversity (Simpson 1/D) of the forest, based on the plots investigated was
8.36 and 9.17 respectively. The standard error of the mean diversity was 0.95. A
standard deviation of 6.66 was determined for diversity. The Kolmogorov-Smirnov
test gave an asymptotic significance of 0.08 and therefore revealed that pixel
diversity had a distribution not significantly different from normal. Its distribution
was however slightly right skewed with a value of 0.15 with a standard error of 0.34;
it was also highly platykurtic with a value of -0.97 and a standard error of 0.67. The
mean species richness was 16 with a standard error of 1.78. The median richness was
19; and the standard deviation was 12.47. The Kolmogorov-Smirnov test determined
that species richness was normally distributed but only once scaled up from plots to
pixels. The distribution was not significantly different from normal with an asymptotic
significance of 0.09. It was also very slightly left skewed with a value of -0.10 and a
standard error of 0.34, and extremely platykurtic with a value of -1.39 and a standard
error of 0.67.

24
RichnessAGBDiversity1/D
Mean16.00000023.820372Mgha-1
8.364320
Standarderror1.7816623.338230Mgha
-1
0.952007
19.00000017.226133Mgha-1
9.168330
12.47163423.367610Mgha-1
6.664052
Skewness-0.0960.7130.151
Standarderror0.3400.3400.340
Kurtosis-1.386-0.615-0.965
Standarderror0.6680.6680.668
DistributionKolmogorov-SmirnovtestAsymptoticsignificance0.0899220.1954740.080660
Descriptives
Mean
Median
Standarddeviation
Skewness
Kurtosis
Figure 4.1 – Histograms of the distribution of
AGB, species richness and species diversity in
the 49 pixels.
Table4.1–Summaryofdescriptiveanddistributionstatisticsforspeciesrichness,AGBandspeciesdiversityacrossthe49
pixels.

25
RichnessAGBDiversity1/D
Mean41.40000023.49327Mgha-1
17.534020
Standarderror4.9602875.83771Mgha
-1
2.435217
43.00000018.80246Mgha-1
17.410690
15.68580418.46046Mgha-1
7.700831
Skewness-0.3440340.8110000.274000
Standarderror0.6870430.6870000.687000
Kurtosis-1.345179-0.468000-0.606000
Standarderror1.3342491.3340001.334000
DistributionKolmogorov-SmirnovtestAsymptoticsignificance0.8639010.9120000.971781
Descriptives
Mean
Standarddeviation
Skewness
Kurtosis
Median
Table4.2–Summaryofdescriptiveanddistributionstatisticsforspeciesrichness,AGBandspeciesdiversityacrossthe10deciles.
Figure 4.2 – Histograms of the distribution of
AGB, species richness and species diversity in
the 10 deciles.

26
The low AGB averages partially reflect the aforementioned heterogeneity of the
landscape, containing savannah, bushland, scrubland and grassland as well as forest.
The right skewness is produced by the numerous treeless plots, giving AGB and
diversity values of 0 and skewing the data. Treeless plots can also partially explain the
platykurtic nature of the distribution of the metrics, with 0 values giving the
distribution a flattened shape. Figure 4.1 shows distribution histograms of the
metrics.
Scaling up to deciles from the pixel level increases the mean and median of the
diversity within the plots to 17.53 and 17.41, although does little to change the
average AGB. Scaling up also makes the distribution of AGB, richness and diversity
substantially more normal with asymptotic significances of 0.91 and 0.97
respectively. This increases the validity and strength of parametric tests for
correlation and linear regression analysis. A summary of the decile descriptive
statistics and accompanying histograms are shown in table 4.2 and figure 4.2.
4.3 Tests for Difference
The data were split up into two groups with distinct burn histories according to the
ΣdNBR. To assess the possibility of a relationship between this ratio and the
vegetation metrics, the two groups were compared and analysed for a statistical
difference. Group one contained pixels with a ΣdNBR below -0.1 and group two
contained those with a ΣdNBR above 0.1. An analysis of variance showed that AGB,
richness and diversity of the two groups were significantly different (AGB: F1,33=18.21,
P<0.01; richness: F1,33=15.88, P<0.01; diversity: F1,33=5.74, P=0.02). A two sample
Kolmogorov-Smirnov test determined that the distributions of the two groups were
significantly different for AGB, richness and diversity (AGB: Asymp. Sig.<0.01;
richness: Asymp. Sig.<0.01; Diversity: Asymp. Sig.=0.01). Table 4.3 summarises the
descriptive statistics for the two groups.

27
MeanStandarderrorSkewnessStandarderrorKurtosisStandarderror
AGB26.557Mgha-1
4.22Mgha-1
25.067Mgha-1
16.877Mgha-1
0.1690.5640.5781.091
Diversity1/D10.6041.50610.2186.0250.0160.5640.2671.091
Richness20.8132.43523.5009.738-0.8850.5641.0541.091
AGB7.357Mgha-1
2.115Mgha-1
0.787Mgha-1
9.218Mgha-1
1.0580.5240.5141.014
Diversity1/D5.3441.5652.5006.8221.1860.5240.4721.014
Richness7.6842.2222.0009.6840.8450.524-0.9771.014
<-0.1
>0.1
Descriptives
Mean
Median
Standard
deviation
SkewnessKurtosis
Table4.3–Summaryofdescriptiveanddistributionstatisticsforspeciesrichness,AGBandspeciesdiversityacrossthepixelswithdNBRvaluesbelow
-0.1andabove0.1.

28
4.4 Tests for Correlation
After establishing that the two burn groups were significantly different in terms of
their AGB, species richness and species diversity, statistical tests for relationships
were employed to determine whether ΣdNBR correlated and if so, the nature and
significance of the correlation. Having confirmed that the distribution of the metrics
across both deciles and pixels was normal (the ΣdNBR distribution was also normal,
as determined by a one sample Kolmogorov-Smirnov test against a normal
distribution (pixel ΣdNBR: Asymp. Sig.=0.84; decile: Asymp. Sig.=1.00), the
appropriate parametric test for correlation, the Pearson product-moment, was
applied to the metric datasets at the pixel and decile level.
Figure 4.3 shows scattergraphs of the three metrics against ΣdNBR. A Pearson
product-moment correlation indicates a significant negative association between
ΣdNBR and AGB at the pixel level (r=-0.35, d.f.=49, P=0.01). Similar negative
associations of statistical significance were found between ΣdNBR and the remaining
metrics, species richness and species diversity, at the pixel level (richness: r=-0.43,
d.f.=49, P=0.02; diversity: r=-0.32, d.f.=49, P=0.03). Once scaled up to deciles, the
association found between decile and richness using the Pearson product-moment
correlation was found to be much stronger, while remaining statistically significant
(r=-0.71, d.f.=10, P=0.02). There was no significant correlation found between the
remaining metrics and decile (AGB: r=-0.50, d.f.=10, P=0.14; diversity: r=-0.60,
d.f.=10, P=0.06), although for diversity, the P-value is sufficiently close to 0.05 to
warrant further investigation. These relationships are shown in figure 4.4 as
scattergraphs of the metrics against burn deciles.
4.5 Regression Analysis
The strongest significant correlations were those between ΣdNBR and species
richness. At the pixel scale, all three metrics exhibited significant negative
correlations with ΣdNBR and warranted the application of a linear regression to
determine whether a cause and effect relationship is acting upon the data. Table 4.4
summarises the statistics for pixel data.

29
Figure 4.3 – Scattergraphs showing the pixel variance of AGB,
species richness and species diversity in relation to dNBR.

30
Figure 4.4 – Scattergraphs showing the variance of AGB, species
richness and species diversity in relation to burn deciles.

31
A linear regression analysis between ΣdNBR and AGB indicated that there is very little
doubt that a significant amount of the variation (12%) in pixel AGB is explained by
ΣdNBR (R2
=0.12, P<0.02). The relationship was a significantly negative one
(F1,47=6.416, P<0.02), with an estimated decrease of 28.64 Mg ha-1 with every
increase of 1.0 in ΣdNBR (b=-28.64, a=24.64). A linear regression analysis between
ΣdNBR and richness indicated that there is very little doubt that a significant amount
of the variation (19%) in pixel richness is explained by ΣdNBR (R2
=0.19, P<0.01). The
relationship was a significantly negative one (F1,47=10.65, P<0.01), with an estimated
decrease of 19 species with every increase of 1.0 in ΣdNBR (b=-18.96, a=16.46). A
linear regression analysis between ΣdNBR and diversity (1/D) indicated that there is
little doubt that a significant amount of the variation (10%) in pixel diversity is
explained by ΣdNBR (R2
=0.10, P<0.03). The relationship was a significantly negative
one (F1,47=5.39, P<0.03), with an estimated decrease of 7.56 with every increase of
1.0 in ΣdNBR (b=-7.56, a=8.55).
Richness AGB Diversity 1/D
R 0.429782 0.346570 0.320712
R2
0.184713 0.120111 0.102856
Adjusted R2
0.167366 0.101390 0.083768
Standard error 11.380214 24.950170 6.378831
F-ratio 10.648 6.416 5.388
Degrees of freedom 1,47 1,47 1,47
p-value 0.002 0.015 0.025
Slope (b ) -18.955 -28.640 -7.558
Intercept (a ) 16.455 24.640 8.546
Regression
Linear
ANOVA
Coefficients
Table 4.4 – A summary of linear regression analysis for tests of association between dNBR
and the vegetation metrics; species richness, AGB and species diversity.
Richness Diversity 1/D
R 0.713584 0.604363
R2
0.509201 0.365255
Adjusted R2
0.447852 0.285912
Standard error 11.655601 6.507489
F-ratio 8.300 4.603
Degrees of freedom 1,8 1,8
p-value 0.020 0.064
Slope (b ) -3.697 -1.537
Intercept (a ) 61.733 25.989
Regression
Linear
ANOVA
Coefficients
Table 4.5 – A summary of linear regression analysis for tests of association between burn
deciles and vegetation metrics; species richness and species diversity.

32
Linear regression was applied to species richness and species diversity against burn
deciles, as the Pearson product-moment analysis found there to be a significant, or
sufficiently close to significant, negative correlation between these metrics and
deciles. The regression statistics are summarised in table 4.5.
Linear regression analysis between burn decile and richness indicated that there is
very little doubt that a significant amount of the variation (51%) in richness is
explained by the decile (R2
=0.51, P=0.02). The relationship was a significantly
negative one (F1,8=8.30, P=0.02), with an estimated decrease of 4 species per decile
(b=-3.70, a=61.73). The Pearson product-moment analysis indicated that there is
significant doubt as to whether variation in the diversity is explained by the deciles
(R2
=0.37, P>0.05).

33
Aside from the analysis of the three metrics, it was clear that the plots with higher
burn deciles had less total individuals and overall canopy cover. Figure 4.5 shows
tree abundance against decile. The combined plots in decile 10 had 91 individuals,
those in deciles 7 and 9 had 90 and 66 trees respectively, decile 8 had 229
individuals, while the plots in decile 6 had 389. Plots in decile 5 had the most
individuals with 803, followed by those in decile 4, 2, 1 and 3 with 768, 656, 560
and 499 respectively. Degradation through burning can explain the low number of
trees in plots with higher burn deciles, and it is reasonable to assume that a
recovering plot would contain a larger number of individuals. The intermediate
deciles 4, 5 and 6 contained a large number of individuals, something that could be
explained by intermediate disturbance theory, where many low intensity fires
maintains a high abundance of trees. It is also possible that, due to the
aforementioned nature of the burn ratio, plots with intermediary burn deciles have
experienced little to no burning during the measured period, which would also
explain the dense and richly populated forest observed.
Figure 4.5 – Bar chart showing the number of individual trees counted within each decile.
While most of the deciles consisted of 5 pixels, decile 10 comprises only 4, and this should
be considered when drawing conclusions from the patterns seen in tree abundance.
0
100
200
300
400
500
600
700
800
900
1 2 3 4 5 6 7 8 9 10
Numberofindividuals
Decile
Tree abundance across burn deciles

5. Discussion
34
5. Discussion
5.1 Summary of Key Results
The clear trend in terms of the number of trees and forest density is that tree
abundance falls as the ΣdNBR rises. Fewer individuals were found at higher ΣdNBR
values. Key significant differences in species richness, species diversity and AGB were
found between forest with low ΣdNBR values and forest with high ΣdNBR values.
Further investigation found significant correlations between all of the metrics and
ΣdNBR at the pixel level. Linear regression analysis found that ΣdNBR can explain at
least some of the variation seen in the metrics across the pixels. These associations
were all negative ones; as ΣdNBR values increase, richness, diversity and AGB fall.
Only richness was found to have a significant correlation with the burn deciles, and
linear regression analysis indicated that the relationship was a significant negative
one; over half of the variation in richness can be explained by the burn deciles.
5.2 Tree Abundance
The clearest pattern with regards to tree count is that beyond a certain burn
‘threshold’ at around a ΣdNBR value of 0.14 or at some point around decile 6 or 7,
the number of individuals drops considerably to below 250, including a large number
of treeless plots. Below this threshold, the forest is significant more populated, with
the number of individuals extending from between 350 and 800. The most obvious
explanation for such a dramatic drop in trees is simply that recent fires - often stand
replacing, have killed a large proportion of the forest. Although, many species in dry
tropical forests are adapted to fire, unusually high fire frequency or severity can
devastate forest populations through forced changes in sexual reproduction,
vegetative reproduction, survival and growth (Hoffmann, 1999). It is also important
to note that there were many species at the study location adapted to post fire
conditions, resprouters that disperse and colonise recently burnt areas such as the
varied species in the Grewia genus (Andriambelo, 2013, pers. comm.) Individuals of
such species would not be measured and included in this study when smaller than
5cm in diameter, therefore only after sufficient growing time after burning would this

5. Discussion
35
recovery be quantified (Clarke et al., 2005). The ΣdNBR would then quantify such
regrowth as negative or low values, producing the patterns observed in this study.
5.3 Species Richness and Forest Composition
Patterns in species richness were the easiest to discern, with strong significant
correlations and associations found between the burn metrics and species richness.
Similarly to the patterns seen in tree abundance, there seems to be somewhat of a
threshold at a ΣdNBR value of ~0.10. The presence of a statistical relationship
between ΣdNBR points towards the fact that fire, is at least partially responsible for
the variance in species richness, and that richness drops considerably following fire
events. This tendency is related to individual species response to fire, and suggests
that burning does not kill trees uniformly. Instead, certain species are completely
eliminated from the forest, while others persist and occasionally thrive. Not only does
fire reduce the density of forests, removing up to around 90% of individuals, it also
changes the composition of the forest. The findings of this study are substantiated by
the documented response of species richness to fires, Bond and Keeley (2005)
suggest that in tropical forests, a single fire can reduce woody plant richness by up to
two thirds depending on the severity of the fire. Similar fallout in richness is described
studies by Cochrane (2003) and Clarke et al. (2005).
It is perhaps more interesting to investigate the relative abundances of different
species, rather than to simply count the number of species present in areas of forest
with different burn histories. Figure 4.6 presents a graph of the most abundant four
species for each decile, as a percentage of the total number of individuals. The
composition of the forest differed along the burn scale. Certain species were found
commonly across all deciles; Trilepsium madagascariense being a notable example.
Others were common, dominant even, in plots within a more narrow range of deciles.
Albizia sp was found only at the lowest deciles and in abundance, Dalbergia mollis
was found commonly toward the lower deciles, while the mid deciles were favoured
by Hyperacanthus perrieri and Homalium sp. Strychnos madagascariensis was
common in the mid to higher deciles, while the higher deciles were dominated by
fewer, more prominent, species. These included Commiphoria coleopsis and Bridelia

5. Discussion
36
pervilleana. Memecylon bakerianum, a recognised pyrophyte (Andriambelo, 2013,
pers. comm.), was also found, unsurprisingly, at the high end of the burn scale.
5.4 Species Diversity
The patterns observable in forest diversity are somewhat similar to those seen in
richness. Pixels with a ΣdNBR value of -0.1 or below, diversity ranges from around 5
to up and around 20. Above 0.1, there are many plots that are treeless, and are
therefore have a diversity of 0; however, plots that contain trees are diverse, with
values similar to those with ΣdNBRs below -0.1. This may be explained in a number
of ways; pixels may have an abundance of fire resistant or tolerant flora, they may be
rich in mature trees undamaged by low intensity fires, or they may be estimated using
plots unrepresented by the pixel ΣdNBR value.
Fire frequency and severity is a documented predictor of forest diversity (Pickett,
1985; Swaine, 1992), and in particular dry tropical forests (Fensham, 1995; Gillespie
et al., 2000; Janzen, 1988; Otterstrom et al., 2006; Sabogal, 1992; Swaine, 1992).
Figure 4.6 – The abundances of the four most common species present in each decile, given
as a percentage of the total individuals.

5. Discussion
37
Buring kills and removes species susceptible to fire, while selecting for resistant
species or post fire colonisers (Dirzo et al., 2011; Gentry, 1982; Swaine et al., 1990).
In this way, the diversity of the forest is limited to the number of fire-adapted species
within it. The findings of this study were similar to those of Gilliespie (2000), who
found that areas with a more active fire history had significantly lower species
diversity than those that had active suppression programs in place.
5.5 Above Ground Biomass
The patterns in AGB across the site were somewhat more difficult to decipher than
those of species richness and diversity. Although correlations with ΣdNBR and burn
deciles were found, and linear regression analysis indicated that ΣdNBR could explain
around 12% of the AGB variance in the pixels; upon inspection of the datasets it
became clear that the relationship was not a simple linear one, but was more
complex in nature.
There was huge variation in AGB across all of the pixels, even without including
treeless plots. AGB ranged from 0 up to over 80 Mg ha-1
. This spread can be found
across all burn values, although the distribution across this range varies. Pixels with a
low ΣdNBR (<-0.1) generally had an AGB of between 10 and 60, with a small number
of anomalous pixels that can be explained by the presence of few mature, thick
trunked (>5cm diameter), individuals or agricultural land. The majority of pixels with
a high ΣdNBR (>0.1) have a very low total AGB or a lack of biomass altogether due to
the presence of savannah, grassland or a recent stand clearing fire. Some pixels with
a high ΣdNBR contained a larger amount of AGB, up to 25 Mg ha-1
, and there are a
number of factors that could explain this phenomenon; firstly, the flora of these
pixels may be well adapted to tolerate fire, a notable example being the
aforementioned Memecylon bakerianum; secondly, there were a number of much
larger, fire resistant, mature trees undamaged by low intensity fires that may kill
smaller trees found in other pixels; it is also possible that errors in the sampling
strategy meant that the three plots within a pixel were not representative of that
pixel’s burn history. Figure 5.1 illustrates how in a pixel with a very high ΣdNBR, three
randomly placed plots may give a highly unrepresentative sample of the forest within
the pixel. Another strategy could have utilised higher resolution data from Landsat

5. Discussion
38
and selected only for homogenous pixels, therefore eliminating the errors discussed
above. A larger sample size, or larger individual plots, would also reduce the
probability of pixel error. With more time and man power, this would be an
endeavour worth exercising.
Intermediate pixels, with ΣdNBR values between -0.1 and 0.1 are difficult to interpret
in terms of AGB, due to the complications outlined above (pg. 16). Indeed, when
running statistical tests on the data when the pixels with neutral ΣdNBR values are
removed, the correlation between ΣdNBR and AGB becomes markedly stronger and
more significant. A Pearson product-moment correlation indicates a strong
significant association between the two variables (r=-0.51, d.f.=35, P<0.01). A linear
regression analysis between ΣdNBR and AGB, with neutral pixels omitted, indicated
that there is very little doubt that a significant amount of the variation (24%) in pixel
AGB can be explained by ΣdNBR (R2
=0.26, P<0.01). The relationship was a significantly
negative one (F1,33=11.604, P<0.01), with an estimated decrease of 24.97 Mg ha-1
with
every decrease of 1.0 in ΣdNBR (b=-24.97, a=17.09). These results demonstrate that
the neutral ΣdNBR values are potentially skewing the output, reducing both the
strength and significance of the linear association between burning and AGB. Figure
5.2 shows a scattergraph of ΣdNBR against AGB with neutral pixels omitted, included
is the linear regression trendline with the R2
value of 0.26.
Figure 5.1 - Illustrates the
erroneous nature of scaling up to
240x240m MODIS pixels from
three 20x20m forest plots
(numbered 1 to 3). Red indicates
areas with a very high dNBR,
orange indicates a high, yellow an
intermediate, and green a low
dNBR. All forest data from within
this pixel will be described as
having a high dNBR due to the
averaging and scaling out of minor
variations for the MODIS burn
ratio. However, in reality the plot
data all have low dNBR values.

5. Discussion
39
The most obvious transitional response observed in the forest as ΣdNBR values
change from negatives to positives is the number of treeless pixels with positive
ΣdNBR values. These treeless plots bring the average AGB of pixels with a >0.1 ΣdNBR
value down to 7.4 Mg ha-1
, while the average AGB of pixels with a <-0.1 ΣdNBR value
is 26.6 Mg ha-1
. It must be noted that while the average AGB of both sets is relatively
low in comparison to expected AGB in healthy dry tropical forests, the average
includes plots in areas of agriculture, savannah and highly degraded forests. A study
by Hawton (2013), which employed similar methods to those used in this study,
found an average AGB of 77.0 Mg ha-1 in plots randomly placed within forested areas
at the Mahamavo site. Many plots in this study have AGB at and above Ross’s
average, falling well within the expected 35-140 Mg ha-1
for forests of this type (Dirzo
et al., 2011), and these plots should be considered ‘healthy’ with regards to AGB.
Using this range of AGB as a standard for healthy forest; 4 out of 16, or 25% of, pixels
with a <-0.1 ΣdNBR value can be considered healthy; while none of the 19 pixels with
a >0.1 ΣdNBR value can be considered healthy.
Removing neutral deciles, 5 and 6, from the Pearson product-moment correlation
analysis increases the significance of a possible relationship between burn deciles and
Figure 5.2 – Scattergraph showing ΣdNBR against AGB, with the neutral ΣdNBR
data omitted. A linear trendline is shown with an associated R2
value.

5. Discussion
40
AGB (r=-0.63, d.f.=8, P<0.10), though the correlation significance value remains
below the critical 0.05 level. Only deciles 2 and 4 are associated with ‘healthy’ forest,
according to Dirzo’s (2011) AGB range, and the average AGB of the four highest
deciles is 7.4 Mg ha-1
, far below the healthy range.
Assuming that the Sigma delta-Normalised Burn Ratio is a fair measurement of
burning and that the chosen vegetation metrics give a fair representation of forest
health, the main finding of this study is that fire and burning negatively impacts the
forest. Broadly speaking, that conclusion is a reasonable one; the data generally
matches observations made on site. Highly degraded forest, with visible evidence of
burning, typically has high ΣdNBR values. While dense, outwardly healthy forest has
low ΣdNBR values.
5.6 Intermediate Disturbance Hypothesis
Despite previous research suggesting that it is unlikely that fire contributes to an
intermediate disturbance non-equilibrium (Beckage and Stout, 2000; Schwilk et al.,
1997), this paper evaluates the possibility that the Mahamavo forest site is an
exception. The vast residual variation seen in the pixel data makes finding and
confirming an IDH hump-shaped curve problematic, and any conclusions drawn from
the data would be tenuous. However, fitting an IDH curve to the distribution of
richness and diversity in the 10 deciles reveals a potentially significant relationship.
Figure 5.3 demonstrates how a slightly modified IDH curve might fit with the species
data. Both species richness and species diversity appear to fit the model, with low
residual variation. It must be noted that the effect of averaging pixels has removed
most of the noise from the data, which has critically altered the pattern observed.
For this reason, one should be cautious of drawing conclusions as to whether IDH is
at play in the Mahamavo dry forest. Additional research, with a stricter regulation of
confounding variables alongside an in depth analysis of the intermediate disturbance
hypothesis is recommended, with the pattern in decile variation found here
advocating such research.

5. Discussion
41
5.7 Limitations
5.7.1 Forest Health Metrics
No model of a natural environment can be perfectly accurate or representative of the
complexities and dynamics of an ecosystem such as the dry tropical forest in the
Mahamavo region. The whole concept of forest health is a highly contested one, and
many, like Wicklum and Davies (1995) argue that the notion of ecosystem health is
ecologically inappropriate. The idea of health is used by scientists because of its
familiarity; it applies well to humans and animals that depend on a homeostatic
Figure 5.3 – Scattergraphs showing species richness and
diversity against deciles. Marginally adjusted IDH hump-
shaped curves are fit to the data to illustrate a possible
intermediate disturbance relationship.

5. Discussion
42
balance for survival. Things that upset this balance are potential causes for a decline
in health, foreign pathogens or extreme changes in temperature in animals more
than often the cause for deteriorations in health. This concept may not be entirely
applicable to ecosystems in that they are, while being relatively stable, wholly
dynamic and heterogeneous in their nature (Dahms and Geils, 1997). Despite being
a difficult concept to define and quantify, proxies for ecosystem health are widely
used as a means of assessing the condition of an ecosystem. With regards to forest
health, a reasonable proposition for the concept comes from Twery and Gottschalk
(1996), who state that forest health is a condition wherein a forest has the capacity
across the landscape for renewal, for recovery from a wide range of disturbances,
and for retention of its ecological resiliency, while meeting current and future needs
of people for desired levels of values, uses, products, and services.
As a compromise between practically measurable metrics, and those that effectively
represent the condition of the forest; species richness, species diversity and above
ground biomass were chosen as appropriate metrics. While AGB is often used as an
indicator of forest health, or more appropriately of the condition of the forest (Basuki
et al., 2009; Chave et al., 2005; Deo, 2008; Dirzo et al., 2011; Foody et al., 2001;
Kauffman et al., 2003; Vargas et al., 2008), species richness and diversity are more
useful for assessing the state of a forest relative to itself. In other words, how does
the species richness and diversity of burnt areas compare to areas of forest that have
not experienced burning or degradation? In this way, a comparison of forest with low
ΣdNBR values to forest with high ΣdNBR can be made to test the hypothesis. A more
accurate model may use additional metrics for the measurement of forest health,
such as leaf area index (LAI) or indices obtained through remote sensing; the
Normalised Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI)
being prime examples (Cabacinha and de Castro, 2009; Huete et al., 1997).
5.7.2 Remote Sensing and ΣdNBR
As well as the potential limitations of the selected vegetation metrics, the assumed
accuracy of the burn ratio should be questioned. The ΣdNBR is produced through a
highly utilised technique in the field of ecosystem change detection called univariate
differencing (Coppin et al., 2004; Miller and Thode, 2007). Differencing results in a

5. Discussion
43
measurement of absolute change using before and after images, and the method has
been commonly used in assessing fire and fire severity (Brewer et al., 2005; Cocke et
al., 2005; Das and Singh, 2012; Epting et al., 2005; Key and Benson, 2005; Miller and
Yool, 2002). dNBR is adapted from the more basic NBR and is generally thought to be
more effective at measuring fire and its severity. Although Hudak et al. (2004) found
that the degree in which it was more effective depended upon the length of time
elapsed since the fire that the post-fire image was captured. When the image was
taken immediately after a fire, the efficacy of the dNBR decreased. In 2006, Hudak et
al. concluded that NBR and dNBR are the most accurate measurements of fire when
compared to 11 other burn severity indices, and advised RSAC and EROS to use these
indices when producing maps as indications of burn severity after wildfire events.
Careful observations in the field helped further validate the choice of burn index. As
previously discussed there are, at least for this particular ecosystem, issues with the
neutral ΣdNBR values, though these are difficult to address given the alternatives.
5.8 Confounding variables
Linear regression analysis indicated that, across the pixels, 18% of the variation in
richness, 12% of the variation in AGB and 10% of the variation in diversity can all be
explained by ΣdNBR. Although this suggests that fire is an influential feature of the
environment that deserves further investigation, it also implies that the majority of
variance in the three metrics is explained by features other than fire. A number of
covariates are potentially influencing the vegetation at the site; biotic and abiotic,
natural and anthropogenic. Topographic wetness, elevation, slope, distance from
forest edge, distance from villages, and distance from pathways are some of the
variables that could be tested for influence over forest composition and condition.
Modelling these covariates in future research might help isolate fire in terms of its
overall contribution to variance in any chosen vegetation metrics.
Perhaps more interesting are those variables that are causing similar degrading
effects to that of fire. Sánchez-Azofeifa and Portillo-Quintero in Dirzo et al. (2011)
suggest, based on studies in Neotropical SDTF; that selective logging, invasion of
exotics, agriculture, cattle grazing, hunting and collection are all contributing to the
overall degradation of Latin American SDTF. Much of these disturbance factors are

5. Discussion
44
also playing a role in and around the forest in the Mahamavo region. Many species in
the Dalbergia genus are present in the Mahamavo forest and are selectively logged
and collected for their valuable, rose coloured, hard wood (palissandre or rosewood)
(Schatz, 2001). There were numerous remnants of cut and logged Dalbergia trees at
the site. Other genera selectively logged for their use in construction include
Homalium and Commiphora (Schatz, 2001), both of which were found in relative
abundance in the forest at the site. One invasive species stood out in profusion in the
studied forest, Albizia lebbeck, which may threaten the ecological balance of the
environment. The forest had also been cleared in areas for the use of riziculture or
for zebu grazing. Although poorly studied in dry tropical forests, hunting has been
found to negatively disrupt plant-animal interactions such as seed dispersal and
cause subsequent degradation in moist tropical forests (Nunez-Iturri et al., 2008;
Wright et al., 2007). Hunting is still a widely employed method of sustenance in
Madagascar, and locals employ traditional techniques in the studied forest in
Mahamavo which merit further investigation. Furthermore, drought observed
between 2009 and 2013 will have reduced water availability for plants (Andriambelo,
2013, pers. comm.), and interesting future research might hypothesise on the
impacts of water shortage on forest composition.
5.9 Forest or Savannah?
The obvious adaptations to fire found in savannah plants; high wood densities, thick
bark, basal meristems, protected buds and so forth; demonstrate the historical
presence of fire in these ecosystems (Pennington et al., 2009). Most of these
characteristics are diminutive or completely lacking in dry tropical forest plants,
suggesting that if there were an historical incidence of fire, it wasn’t sufficient to force
evolutionary changes in the vegetation comparable to those found in savannah
systems. The flora of dry tropical forests are not necessarily equipped for the
changing fire regime brought about by human intervention, as evidenced by the
negative impacts on the vegetation metrics tested in this paper.
Those plants that did persist, even in the forest with very high ΣdNBR values, had
certain attributes suited for survival, regrowth, resprouting or dispersal following fire.
As the plots chosen for investigation were not entirely forest, it could be argued that

5. Discussion
45
many of these species are not ‘forest’ plants, but are rather savannah and forest edge
plants. In this case, it may be reasoned that burning causes a transition to these
ecosystems rather than a restructuring of the forest composition.
One well documented characteristic of woody savannah plants, and of fire adapted
plants in general, is thick bark (Hoffmann et al., 2003; Otterstrom et al., 2006). Pinard
et al. (1999) suggest that amongst dry forest species, it is those with insufficient bark
thickness that suffer fatal cambial injury. When comparing dry tropical forest species
to their savannah counterparts, bark in savannah trees is over twice as thick
(Hoffmann et al., 2003). Van Mantgem and Schwarts (2003) found that when dry
tropical forest species exhibited this trait, often associated with savannah trees, they
were significantly more likely to withstand damage by fire. Future study in the
Mahamavo region or in the PBZT herbarium may endeavour to measure the bark
thickness of species to assess the influence on survivorship during fire, and possibly
to compare the bark thickness of savannah and forest species. Daubenmire (1972)
suggests that many trees that persist in fire rich environments, including those in
savannahs, also have a higher specific wood density to those which are susceptible
to burning. Unfortunately, wood density values are unavailable or unknown for many
of the species observed in this study. Interestingly, the wood density of Memecylon
bakerianum was available and is 0.8 g m-3
, 0.15 above the 0.65 average for this forest,
possibly concurrent with Daubenmire’s (1972) hypothesis. Density values for more of
the observed species are needed before conclusions can be made.
5.10 Fire, the Forest and its Future
“A fire regime includes the patterns of frequency, season, type, severity and
extent of fires in a landscape. Vegetation consumed and patterns of fire spread
vary across landscapes, and different fire regimes produce different landscape
patterning and select for different plant attributes. It follows that changes in fire
regimes, within a given landscape, should have major ecosystem consequences.”
- Bond and Keeley (2005, p. 389)
It is clear that the landscape in the Mahamavo region is one that has been subject to
burning historically, with a heterogeneous mix of savannah species, edge species and

5. Discussion
46
forest species with varying fire tolerances and adaptations. However, the evidence of
burning seen at this site suggests that a regime different from that which is natural is
acting upon the environment. Personal communication with locals and longstanding
camp residents offered insights into the causes for some of the fires evidence in this
data. During the summer previous to that in which fieldwork was conducted, fires
were lit in order to clear the landscape for grazing, and so that burnt wood could be
collected for the production of charcoal. As charcoal is the primary fuel source for
much of Madagascar, ‘foraging’ the forest for charcoal in this fashion is a profitable
source of income for locals (Montagne et al., 2010). Although illegal, barges carrying
cargos of charcoal frequently come and go from the port in Mahajanga. Until such
exploitation becomes unprofitable, it is likely that similar burning of dry forests will
continue despite increasingly strict laws and regulations (Kull, 2004). And so,
anthropogenic fire supplements an already complex fire regime, upsetting the
balance of an ecosystem produced over many millennia. Bond and Keeley suggest
that such changes to fire regimes are bound to have major ecological consequences
(2005), some of which have been highlighted in this study – a significant decrease in
species richness, species diversity and AGB.
With increasing intensification of animal grazing, global warming, cutting, spread of
invasive species (Albizia lebbeck) and population increase alongside ever more
frequent and severe fires, changes to the forest composition can be expected with
increasing rapidity. These changes will, in turn, affect populations of dependent
fauna, as habitats change and are lost. This poses an enormous and growing
challenge to conservation efforts as a result. With this in mind, the continuation and
improvement of remote sensing for the mapping of fires is indispensable (Pereira,
2003). This study has highlighted the fact that there are fundamental flaws in current
fire mapping techniques that rely upon remote sensing, and that a refinement of the
MODIS fire products, or the development of a new tool altogether, is required in
order for a better model of fire severity and distribution. Once this science is
perfected, a greater understanding of the biological and ecological effects of fire on
forest communities can be attained. Scientific understanding is the first and most
essential step towards effective conservation of any ecosystem. The western dry
forests of the Mahamavo region and in Madagascar in general are a highly threatened

5. Discussion
47
ecosystem, and the alteration of natural fire regimes by anthropogenic stimuli is
possibly its greatest threat. Studies like the one presented here, contribute to the
scientific understanding vital to its protection and longevity.

6. Conclusion
48
6. Conclusion
6.1 Key Findings
 Species richness showed the strongest negative association with ΣdNBR,
implying that burning reduces the species richness of a dry tropical forest
ecosystem.
 Species diversity also exhibited a negative association with ΣdNBR, likewise
indicating that fire reduces biodiversity in dry tropical forests.
 Above ground biomass showed a negative association with ΣdNBR, although
weaker and less significant to those of the biodiversity metrics.
6.2 Conclusion
These results indicate that the null hypothesis should be rejected, in favour of the
alternative; that there is a significant relationship between burning (ΣdNBR) and
forest composition (richness, diversity and AGB). Furthermore, the nature of said
relationship has been found to be a negative one, with fire actively contributing to
the degradation of the Mahamavo forest. However, the high noise and residual
variation found in the pixel data implies that there are other complex elements acting
upon the environment. It should be noted that while the association between
burning and the vegetation is generally a negative one, patterns in the decile data
alluded to the possibility of an intermediate disturbance nonequilibrium state in the
forest, where moderate fire disturbance favours high biodiversity (Wilkinson, 1999).
This is a subject area that justifies further study, and should not be regarded as
superfluous in this conclusion.
Areas of future research have been highlighted in the above discussion, and this
paper, alongside preceding and prospective studies, should be considered as
supplementary to local knowledge and historical understanding. Aforementioned
developments and improvements to the current system of fire distribution, severity
and frequency modelling, are elements essential for the better understanding of
forest-fire dynamics. With this, and the continued research by organisations like
Operation Wallacea and DBCAM, conservation efforts can endeavour to recognise

6. Conclusion
49
hard scientific fact, leading to the subsequent effective conservation of unique
ecosystems like that of the Mahamavo dry tropical forest.

50
Glossary of Terms and Acronyms
AGB Above ground biomass
ANOVA Analysis of variance
DBCAM Development Biodiversity Conservation Management
ΣdNBR Sigma delta-Normalised Burn Ratio
EROS The USGS (United States Geological Survey) Centre for Earth
Resources Observation and Science
EVI Enhanced Vegetation Index. An improved version of NDVI with an
additional coefficient group to avoid the vegetation signal to
saturation problems. It was developed based on SAVI (soil effect)
and ARVI (atmospheric effect) (Cabacinha and de Castro, 2009).
Fokotany An elder political unit; the smallest community unit under
communes and districts.
IDH Intermediate Disturbance Hypothesis
IPCC Intergovernmental Panel on Climate Change
LAI Leaf area index
MIR Mid-infrared
MODIS The Moderate-resolution Imaging Spectroradiometer (MODIS) is
an imaging payload device orbiting the Earth aboard the Terra and
Aqua satellites.
NDVI Normalised Difference Vegetation Index. Obtained from
normalized difference between the red and near infrared bands.
NIR Near-infrared
PBZT The Parc Botanique et Zoologique de Tsimbazaza at the
Madagascar Biodiversity Center in Antananarivo.
RSAC The USFS (United States Forest Service) Remote Sensing
Applications Centre
SDTF Seasonally dry tropical forest
Tavy Slash and burn agriculture in Madagascar
Zebu Madagascan domestic cattle, originating from South Asia (Porter,
2007)

51
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