analysing recent land use land cover change.pdf

Analysing Recent Land Use/ Land Cover Change

in the Mangabe Reserve Area of Madagascar

Using Remote Sensing Techniques

Natalie L. Bakker (1332760)

2014

This dissertation is submitted as part of a MSc degree in Global Environmental Change at

Kings College London

KINGS COLLEGE LONDON

UNIVERSITY OF LONDON

DEPARTMENT OF GEOGRAPHY

MA/MSc DISSERTATION

I, Natalie L. Bakker, hereby declare (a) that this Dissertation is

my own original work and that all source material used is

acknowledged therein; (b) that it has been specially prepared for a

degree of the University of London; and (c) that it does not contain

any material that has been or will be submitted to the Examiners of

this or any other university, or any material that has been or will

be submitted for any other examination.

This Dissertation is 10,535 words.

Signed: ....

Date: ....

Abstract

In this study, supervised classification maps derived from Landsat imagery were used to

analyse LULCC trends for the Mangabe Reserve area in Madagascar between 1976 and 2014.

Through remote sensing techniques, large-scale deforestation patterns were detected in this

area between 1976 and 2008. After the implementation of a temporary protected status in

2008, a minor increase of forest extent was observed, whereas deforestation continued to

occur outside the boundaries of the Mangabe Reserve. Moreover, vegetation density changes

were studied using NDVI and LAI data. The precise effect of LULCC on vegetation density

was difficult to estimate due to the large effect of precipitation on NDVI trends. From a

hypothetical scenario in which precipitation rates were equal over time to 2014 precipitation

rates, it could be calculated that mean LAI, and thus vegetation density, likely had decreased

by around 4.78% as a result of LULCC in the Mangabe Reserve.

Table of Contents

1. Introduction ......................................................................................................................... 1

2. Literature Review ................................................................................................................ 2

2.1 Introduction to Land Use/Land Cover Change ................................................................. 2

2.2 Methodologies for measuring LULCC ............................................................................. 2

2.3 Global LULCC datasets .................................................................................................... 3

2.4 LULCC in Madagascar and Mangabe .............................................................................. 6

2.4.1 History of LULCC ..................................................................................................... 6

2.4.2 Recent trends .............................................................................................................. 6

2.4.3 Drivers of LULCC ..................................................................................................... 8

2.4.4 Effects of LULCC .................................................................................................... 10

3. Research Aim and Objective............................................................................................. 11

4. Study Area ........................................................................................................................ 12

5. Methodology ..................................................................................................................... 15

5.1 Data ................................................................................................................................. 15

5.2 Pre-processing ................................................................................................................ 16

5.2.1 Pre-processing by USGS .......................................................................................... 16

5.2.2 Scan line gap filling ................................................................................................. 16

5.2.3 Digital numbers to top of the atmosphere reflectance values .................................. 17

5.2.4 Atmospheric correction ............................................................................................ 18

5.2.5 Geometric correction................................................................................................ 18

5.2.6 Mosaicking the 1976 images.................................................................................... 18

5.3 LULCC detection ........................................................................................................... 19

5.3.1 Determining land use classes and regions of interest .............................................. 19

5.3.2 Supervised classification .......................................................................................... 19

5.4 Accuracy assessment ...................................................................................................... 20

5.5 NDVI .............................................................................................................................. 20

5.6 LAI .................................................................................................................................. 21

5.6.1 Inclined point quadrat .............................................................................................. 21

5.6.2 Hemispherical photography ..................................................................................... 22

5.7 Characterising land use classes ....................................................................................... 22

6. Results ............................................................................................................................... 23

6.1 LULCC detection (1976-2014) from supervised imagery ............................................. 23

6.2 Accuracy assessment ...................................................................................................... 26

6.3 NDVI change detection .................................................................................................. 29

6.4 LAI data and correlation with NDVI .............................................................................. 30

6.5 Field observations ........................................................................................................... 31

6.5.1 Forest ........................................................................................................................ 31

6.5.2 Shrub ........................................................................................................................ 32

6.5.3 Grassland .................................................................................................................. 33

7. Discussion ......................................................................................................................... 34

7.1 Comments on the supervised images .............................................................................. 34

7.2 Objective 1: Analysing recent LULCC trends ............................................................... 37

7.3 Objective 2: Studying vegetation density changes ......................................................... 38

8. Project Limitations and Future Research .......................................................................... 42

8.1 Project limitations ........................................................................................................... 42

8.2 Future research ............................................................................................................... 42

9. Conclusion ........................................................................................................................ 43

Appendix i: Ethics Screening and Risk Assessment Forms ..................................................... 44

Appendix ii: Transformed Divergence Results ........................................................................ 47

References Cited ...................................................................................................................... 50

List of Tables

Table 5.1: Data set attributes of Landsat imagery used for LULCC analysis .......................... 16

Table 6.1: Land use class area statistics for the Mangabe Reserve .......................................... 23

Table 6.2: Land use class area statistics for the 10 km buffered supervised land use

classification maps of the Mangabe Reserve ........................................................................... 25

Table 6.3: Confusion matrix accuracy assessment for the 2013 supervised classification ...... 26

Table 6.4: Confusion matrix accuracy assessment for the 2014 supervised classification ...... 26

Table 6.5: NDVI statistical data of the Mangabe Reserve ....................................................... 29

Table 6.6: NDVI statistical data of the Mangabe Reserve; only forest pixels ......................... 30

Table 6.7: NDVI and LAI data of the 15 study sites within the Mangabe Reserve. ................ 30

Table 6.8: Mangabe Reserve LAI/NDVI correlation statistics for the years 2013 and 2014. . 31

Table 7.1: Mangabe Reserve mean NDVI and average monthly precipitation values. ........... 39

Table 7.2: Mean NDVI per land use class and mean LAI per land use class .......................... 40

Table 7.3: Mean LAI values for the Mangabe Reserve, based on a scenario of an average

precipitation rate of 137.5 mm/month (equal to the 2014 mean precipitation rate) ................. 41

List of Figures

Figure 2.1: GLOBCOVER 2009: Global land cover map for the year 2009 ............................. 4

Figure 2.2: Global Forest Change Map for the years 2000-2012 ............................................... 5

Figure 2.3: Forest cover changes in Madagascar from 1953 to 2000 ........................................ 7

Figure 2.4: Spatial distribution of forest loss in Madagascar between 2000 and 2012 .............. 8

Figure 2.5: Fallow species succession per tavy cycle ................................................................ 9

Figure 4.1: Location of the Mangabe Reserve, Madagascar, and the 15 study sites.. ............. 13

Figure 4.2: Monthly averages of precipitation/ rainfall days in Moramanga ........................... 14

Figure 4.3: Monthly averages of high and low temperatures in Moramanga .......................... 14

Figure 6.1: Column graph regarding the class area statistics of the supervised classification

maps for the Mangabe Reserve between the years 1976-2014 ................................................ 23

Figure 6.2: Supervised land use classifications of the Mangabe Reserve from 1976-2014 ..... 24

Figure 6.3: Supervised land use classifications of the Mangabe Reserve, including a 10 km

buffer zone ................................................................................................................................ 25

Figure 6.4: Spatial distribution of ground truth points with respect to the Mangabe Reserve. 27

Figure 6.5: Spatial distribution of correctly and incorrectly classified ground truth points .... 28

Figure 6.6: NDVI change map of the Mangabe Reserve between 1976 and 2014 .................. 29

Figure 6.7: Scatter plot of LAI for the 15 study sites compared to NDVI ............................... 31

Figure 6.8: Example of a forest study site ................................................................................ 32

Figure 6.9: Example of a shrub study site ................................................................................ 32

Figure 6.10: Example of a grassland study site ........................................................................ 33

Figure 7.1: Distinct change in LULC pattern between the two mosaicked Landsat MSS

images for the year 1976 .......................................................................................................... 34

Figure 7.2: Close-up of the mosaicked 1976 natural colour composite ................................... 35

Figure 7.3: Comparison of 2008 supervised classification image and 2008 natural colour

composite ................................................................................................................................. 36

Figure 7.4: Supervised 2013 classification versus 2013 natural colour composite.................. 36

Figure 7.5: Scatter plot of NDVI values and precipitation averages of the Mangabe Reserve 39

Figure 7.6: Scatter plot of LAI for the 15 study sites compared to NDVI ............................... 41

List of Abbreviations and Acronyms

AVHRR Advanced Very High Resolution Radiometer

DN Digital Number

DOS Dark Object Subtraction

ETM + SLC Enhanced Thematic Mapper + Scan Line Corrector

FAO Food and Agriculture Organization of the United Nations

ha hectare

IPCC Intergovernmental Panel on Climate Change

IUCN International Union for Conservation of Nature

km kilometre

LAI Leaf Area Index

LULCC Land Use/ Land Cover Change

m metre

masl metres above sea level

MERIS Medium Resolution Imaging Spectrometer

MODIS Moderate Resolution Imaging Spectroradiometer

MSS MultiSpectral Scanner

NDVI Normalized Difference Vegetation Index

OLS Ordinary Least Square

P4GES Paying For Global Ecosystem Services

RMS Root Mean Square

ROI Region of Interest

SPOT Satellite Pour l'Observation de la Terre

ToA Top of the Atmosphere

USGS United States Geological Survey

WGS/ UTM World Geodetic System/ Universal Transverse Mercator

Acknowledgements

The writing of this dissertation has been a wonderful learning experience, and I would like to

thank all those who have assisted me in the process. First of all, I would like to sincerely

thank my dissertation supervisor Dr. Nick Drake for his guidance and advice throughout my

project. I would also like to thank Dr. Mark Mulligan, Dr. Julia Jones, and Herimanitra

Patrick Rafidimanantsoa for their help in preparing and organising the field work conducted

in this project.

I would like to express my gratitude towards all staff of NGO Madagasikara Voakajy, and

specifically Voahirana Claudia Randriamamonjy, for their hospitality and logistical support in

Madagascar. Furthermore, I am forever grateful for the enthusiasm and determination of my

field guide Emile Razanakoto, my research assistants Cassandra Docherty and Mamy

Andriamanantena, and co-leader Shanti Winiewska, without whom I would not have been

able to collect my data.

I would like to thank the Royal Geographical Society and Kings College London for

providing financial support to my project, and I would like to thank the Ministry of

Environment and Forest of Madagascar for granting a research permit. Moreover, I would like

to thank the Geography Department of Kings College London for their assistance throughout

the year.

Last but not least, I would like to thank my close family and friends for their encouragement

and support.

Natalie L. Bakker 1332760 p. 1 of 57

1. Introduction Madagascar, the fourth largest island in the World, located around 400 km east of the African

coastline, is considered one of the richest countries in the world in terms of biological

diversity. There are around 8000 endemic species of flowering plants, and with 80% of biota

being unique to Madagascar, the island is often seen as a place of biological wonder

(Stampoulis et al., 2014). Due to recent anthropogenic influences and population pressure,

this unique biodiversity is, however, currently under major threat (Rogers et al., 2010).

Between 1950 and 2000 deforestation rates were near to 1% per year, and it has been

estimated that over 90% of Madagascars original forest extent has already been lost (Myers

et al, 2001; Harper et al., 2007). Under the severe pressures on its primary forest, Madagascar

was coined to be a biological hotspot in 1995; a biological hotspot is defined as a region

with a high biodiversity and high concentration of endemic species, having lost >70% of its

primary vegetation (Ganzhorn et al., 2001). As a biological hotspot, Madagascars remaining

primary forests are now amongst the top priority areas for biodiversity conservation (Myers et

al., 2000).

To conserve its rainforest and biodiversity, Madagascar has recently been increasing the

number of protected areas. In 2003, only 3% (17,000 km2) of the total land area had a

protected status, divided over 47 protected areas (Schwitzer et al., 2013). Former president

Marc Ravalomana, who led the country between 2002 and 2009, was a supporter of protected

areas and declared to triple protected area coverage as part of the negotiations at the 2003

IUCN Durban World Parks Congress (Duffy, 2006). Under Ravalomanas government, 29

new protected areas were introduced, and by 2009, 8% of the total land area had a protected

status (Schwitzer et al., 2013). One of the priority areas for conservation of Malagasy

biodiversity that was identified was the Mangabe Reserve in the Moramanga district. As a

result of the political crisis that started in early 2009, procedures for it to acquire a definite

protected area status were hampered, and it currently resides as a temporary protected area.

At present, there is a lack of research regarding land use change dynamics in the Mangabe

Reserve. For effective land management and land use planning, it is, however, vital to

understand these dynamics. This study therefore aims to research recent land use/land cover

changes as well as forest density changes in and around the Mangabe Reserve, using remote

sensing techniques. Vegetation change trends between June 1976 and July 2014 shall be

analysed, and it will be investigated whether the awarding of a temporary protected status has

resulted in positive changes with respect to forest area in Mangabe.


2. Literature Review

2.1 Introduction to Land Use/Land Cover Change

Land use is the function of the land - i.e. the human activity carried out on the land -, whereas

land cover denotes the physical and biological cover of the land surface. IPCC defined Land

Use/Land Cover Change (LULCC) as changes in the way land is used due to human activities

(Stocker et al., 2013). LULCC leads to global environmental change, and is responsible for

various biological, social, and climatic consequences (Goldewijk, 2001). This includes, for

instance, impacts on biodiversity, alteration of soil quality, hydrological changes, and effects

on opportunities for ecosystem services (Ojima et al., 1994; Lambin et al., 2003).

Importantly, in the context of recent anthropogenic climate change, LULCC also affects

terrestrial sources and sinks of carbon (Goldewijk, 2001). According to Working Group Is

contribution to IPCCs Fifth Assessment Report, LULCC was responsible for the emission of

approximately 1.1 billion metric tonnes of carbon a year between the years 2000 - 2009

(Stocker et al., 2013). This is equal to around 10% of all carbon emissions. The most common

form of LULCC between 2000 and 2009 was deforestation, which was responsible for the

emission of 1 billion metric tonnes of carbon per year (Stocker et al., 2013). Deforestation has

been defined by the Food and Agriculture Organization of the United Nations (FAO) as

follows: the conversion of forest to another land use or the long-term reduction of the tree

canopy cover below the minimum 10 percent threshold (FAO, 2001, p.364).

2.2 Methodologies for measuring LULCC

Analysis of LULCC assists in the understanding of the causes and consequences of land use

change dynamics and provides a framework for land management strategies. During the past

decades, scientists have significantly advanced their knowledge with regards to measuring

LULCC, understanding causes of LULCC, and modelling LULCC (Lambin et al., 2003). This

was partly facilitated by the leading LULCC project of the International Geosphere-Biosphere

Programme in collaboration with the International Human Dimensions Programme on Global

Environmental Change (Lambin et al., 2003).

Currently, satellite imagery is the main data source for LULCC detection (Knorn et al., 2009).

Remote sensing techniques are applied to analyse these satellite images. In-situ measurements

have previously been used to detect and monitoring LULCC, however, this methodology has

presented difficulties when monitoring over large areas of land. Instead, in-situ measurements


are presently used complementary to satellite imagery, and function as a validation source by

providing ground truth data (Herold et al., 2009).

In remote sensing, land use change is detected through changes in radiance values. It is

assumed that radiance changes are largely the result of LULCC - as opposed to other factors

such as changes in atmospheric conditions, a change in the angle of the Sun, or differences in

soil moisture (Singh, 1989). Multiple techniques have been developed for LULCC detection

analysis. Lu et al. (2004), who reviewed change detection techniques, found that there are

three methods commonly applied: image difference, principal component analysis, and post-

classification comparison. In image differencing, two images of the same location at different

time periods are subtracted from each other pixel-wise, to calculate differences in radiance

values (Ilsever and nsalan, 2012). Principal component analysis is a multivariate analysis

technique in which spectral components are reduced to principal components - those showing

most variance in the original multispectral images - which can then be compared (Singh,

1989). For post-classification analysis, images are first classified, and subsequently compared

(Singh, 1989). When classifying the images, a distinction is made between unsupervised and

supervised classification methods. An unsupervised classification approach uses algorithms to

determine commonly occurring and distinctive reflectance patterns and groups pixels that are

similar to one another (atr and Berberolu,2012). With a supervised classification approach,

land use/ land cover classes are statistically described, after which the likelihood of a pixel

belonging to a certain class is assessed (atr and Berberolu,2012). The supervised post-

classification approach has been found to obtain highest accuracy of all methods (Mas, 1999).

2.3 Global LULCC datasets

There are multiple global datasets in relation to LULCC available. There are, for instance,

miscellaneous global land cover maps derived from satellite images using various remote

sensing techniques. Up until 1995, global land cover maps were of low resolution as they

were based on pre-existing maps, ground truth data, and generalized bio-geographic maps

(Friedl et al., 2010). In the 1990s, data from the space-borne sensor AVHRR made land cover

mapping based on remote sensing possible (Friedl et al., 2010). Remote sensing data sources

as well as techniques developed rapidly, enabling higher resolution mapping. From MODIS,

using a supervised approach, a global land cover map at 1 km spatial resolution could be

produced (Friedl et al., 2002). The European Commission's Joint Research Centre used

unsupervised classification techniques to produce their Global Land Cover map for the year


2000 from SPOT VEGETATION, also at a spatial resolution of 1 kilometre (Bartholome and

Belward, 2005). Most recently, the European Space Agency released a 300 metre spatial

resolution global land cover map for the year 2009 based on Envisats Medium Resolution

Imaging Spectrometer (MERIS) instrument, which is shown in Figure 2.1 (Arino et al., 2008).

Figure 2.1: GLOBCOVER 2009: Global land cover map for the year 2009, developed by the

European Space Agency. Map derived from Envisats Medium Resolution Imaging Spectrometer (MERIS) instrument, at a spatial resolution of 300m.


In terms of recent global datasets that specify change, there is the Global Forest Change map,

produced by the University of Maryland, Australia (Hansen et al., 2013). The map, shown in

Figure 2.2, depicts global forest extent and forest extent change (both loss and gain) between

2000 and 2012. Hansen et al. (2013) used remote sensing techniques to compose the map out

of 654,178 satellite images from Landsat 7. They reported a total forest loss of 2.3 million

km2 and a total forest gain of 0.8 million km

2 between 2000 and 2012. The only climate

domain showing a clear trend in forest extent change was the tropics, where forest loss

increased by 2101 km2 each year.

Figure 2.2: Global Forest Change Map, produced by the University of Maryland, Australia,

showing forest extent, forest loss, and forest gain worldwide for the years 2000-2012. The map is

a composite map of ~650,000 Landsat 7 images at a spatial resolution of 30 m. Figure modified

from Hansen et al. (2013).

Review articles comparing the various LULCC datasets conclude that LULCC maps have

good overall agreement, but show limited capability in discriminating mixed classes

(McCallum et al., 2006; Herold et al., 2008). Fritz et al. (2011) note that there is an especially

high disagreement between datasets on the spatial distribution of the forest and cropland

classes. Regional data providing higher spatial resolution can be used to obtain higher

accuracies in LULCC analysis.


2.4 LULCC in Madagascar and Mangabe

2.4.1 History of LULCC

Forests covered nearly all of Madagascar before human settlement. Now only a few natural

forests remain (Kull, 2000, p. 426). LULCC started in Madagascar after the arrival of

humans in 1500. The influence of human presence could be traced back through sedimentary

records, which show significant increases in fire frequency and a substantial spread of

grasslands from the 1500s onwards (Jarosz, 1993). Most LULCC in Madagascar took place

between 1895 and 1925, when French colonisers cleared roughly 70% of the primary forest

(Hornac, 1943). This was a result of increasing demands for forest products, rice, beef, as well

as expanding coffee cultivation (Jarosz, 1993). In 1927, the first protected areas were set up,

although lack of staff and unpopularity among rural communities prohibited proper

implementation (Randrup, 2010). Throughout the 1900s deforestation continued to take place,

mostly driven by population growth (Jarosz, 1993).

2.4.2 Recent trends

There have been multiple studies analysing recent LULCC trends in Madagascar, most of

which used aerial photographs and satellite imagery between the years 1950-2000. In 1990,

Green and Sussman used aerial photographs, in combination with multiple Landsat satellite

images, to determine rates of deforestation in the eastern rainforests (where Mangabe is

located) for the years 1950 - 1985. They found that on average, 111,000 hectares (~1.5% of

original vegetation in 1950) of primary vegetation was lost each year. Furthermore, they

found that deforestation was mostly occurring in areas with high population density and low

topographic relief.

More recently, Harper et al. (2007), studied deforestation in Madagascar for the fifty years

between 1950 and 2000. Similar to Green and Sussman they used aerial photographs from the

1950s in combination with satellite imagery of later years from Landsat. Average rates of

deforestation as recorded by Harper et al. were 0.3% per year from 1950-1970, 1.7% per year

from 1970-1990, and 0.9% per year from 1990 - 2000 (see Figure 2.3). From the detailed map

it can be seen that deforestation occurred in the region of the Mangabe Reserve between 1953

-2000. Vgen (2006a) found similar trend patterns in his analysis of LULCC in the highlands

for the years 1972 - 2000.


Figure 2.3: Forest cover changes in Madagascar, based on aerial photographs and multiple

Landsat scenes with a spatial resolution of 30 m. The main figure shows deforestation between c.

1973 c. 2000, whereas the detailed maps go back to c. 1953. The purple box outlines the approximate area of the Mangabe Reserve. Modified from Harper et al. (2007).

For the years after 2000, limited research exist with regards to LULCC trends on a national

scale. Most studies focus on particular parts of the country: e.g. Zinner et al. (2013)

researched deforestation patterns in Menabe (central/west Madagascar) between 1973 and

2010, whereas Allnutt et al., (2013) focused on northeastern Madagascar for their LULCC

analysis for the years 2005-2011. There are no publications on LULCC specifically for the

area of the Mangabe Reserve. Deforestation patterns on a national scale as well as regional


scale can, however, be studied from the Global Forest Change map of the University of

Maryland, although quantitative results would require further analysis. Figure 2.4 provides a

spatial overview of forest loss in Madagascar and the Mangabe Reserve area between 2000

and 2012.

Figure 2.4: Spatial distribution of forest loss in Madagascar and close-up of the approximate Mangabe Reserve area between 2000 and 2012. Maps are derived from Landsat 7 imagery at a

spatial resolution of 30 m, as part of the Global Forest Change map by Hansen et al. (2013).

2.4.3 Drivers of LULCC

There are multiple drivers of LULCC in Madagascar, which include agricultural expansion,

mining, logging, wildfires and cattle ranching (Jarosz, 1993; Vgen, 2006a). In Mangabe, the

primary cause of deforestation is a slash-and-burn technique used for agricultural expansion,

which the Malagasy call tavy (Randriamamonjy, 2013). During the tavy process, primary

vegetation is cleared and burnt. The ash from the burnt vegetation provides nutrients to the

soil; enough for a farmer to grow crops for one to two seasons (Styger et al., 2007). The crop

production depletes the soil of its nutrients, after which the farmer moves to a different

location leaving the plot to regenerate. Once natural fallow has re-grown, a new slash-and-

burn cycle is started, which is repeated until the soil fertility cannot be restored anymore. The


infertile grassland that results from tavy is called tany maty. Soil fertility is determined by

the type of natural fallow that re-grows (Styger et al., 2007). The species succession that

occurs as a result of tavy, and which governs the agricultural yields, can be found in Figure

2.5. It should also be noted that the tavy process, as well as the process of clearing the crop

fields, occasionally leads to wildfires, reducing forest area even further.

Figure 2.5: Fallow species succession per tavy cycle starting from primary forest, indicating

dominant species (black arrow), as well as other associated species (dotted black arrow). Upland

rice yields associated with the different fallow species are also shown, and are in tonnes/hectare

(t/ha). Figure modified from Styger et al. (2007).

Kull (2000) explains that rapid population growth has pushed farmers to expand their

practices further into the forests, enabling them to seize market opportunities this is partly

fostered by tenure incentives and government policies. The most important commodity for

farmers in Madagascar is rice. Near to 70% of the Malagasy population depend on rice as

their primary source of income (Critical Ecosystem Partnership Fund, 2000). Other major

food crops include: cassava, corn, sweet potato, and banana (Kull, 1998).

Besides the agricultural expansion, an important cause of LULCC is mining, as Madagascar

contains many valuable minerals and gemstones (Critical Ecosystem Partnership Fund, 2000).

Commercial mining mainly focuses on gold, titanium, and sapphire (Critical Ecosystem

Partnership Fund, 2000). In the Mangabe Reserve, gold is mined along the main river flowing


through the middle of the temporary protected area (Randriamamonjy, 2013). Not only is the

forest cleared for mining purposes, it is also cut down for its natural resource timber. The

Mangabe Reserve is specifically exploited for its ebony and rosewood (Randriamamonjy,

2013). Moreover, a significant contributor to LULCC is logging for fuel wood and charcoal

by local communities, who use the wood for cooking purposes (Critical Ecosystem

Partnership Fund, 2000).

2.4.4 Effects of LULCC

Like in other areas undergoing LULCC, biological, social, and climatic consequences of

LULCC are to be found in Madagascar. As mentioned in the introduction, Madagascar is

known for its unique biodiversity. Of the endemic animal species living in Madagascar,

approximately 90% live exclusively in the tropical rainforests (Dufils, 2003). In recent years,

deforestation has threatened species survival and diminished biodiversity, mainly though

fragmentation of the forest and by destroying forest habitat (Harper et al., 2007). Brown and

Gurevitch (2004), who studied the long-term impacts of logging on forest diversity in

Madagascar, found that logging has greatly increased the percentage of invasive species and

that rare species have a high likelihood of becoming extinct. Hanski et al. (2007) report that

the most significant factor for explaining the extinction of endemic forest beetles is recent

forest loss, whilst Benstead et al (2003) found that many of the islands stream insect species

have become extinct due to deforestation and specialization of forest stream habitats.

The tavy process specifically has been associated with land degradation and decreasing soil

fertility (Vgen et al., 2006b). A substantial amount of nutrients can be lost in the process of

forest conversion, and significant decreases in available phosphorous have been recorded in

the highlands of Madagascar (Vgen et al., 2006b).


3. Research Aim and Objective

Even though the Mangabe Reserve is considered a priority area for biodiversity conservation

in Madagascar, the literature review shows that there is a lack of research on LULCC and its

implications with regards to this region. This study aims to bridge the knowledge gap by

providing a detailed regional analysis of LULCC trends in and around the Mangabe Reserve.

As such, two main objectives for this study were identified:

1) To analyse recent LULCC patterns

2) To study vegetation density changes

To analyse recent LULCC patterns, satellite imagery and remote sensing techniques will be

used. Supervised maps for the years 1976-2014 shall be created, such that LULCC can be

quantified via a post-classification technique. Specifically, it will be tested whether change

rates decreased after the implementation of a temporary protected status. It will also be

examined how LULCC within Mangabe relates to change rates outside of the reserve. An

accuracy assessment with ground truth data collected in the field will be performed to ensure

the validity of the maps. A detailed description of the land use classes will be given, for which

a combination of literature and observational field data shall be used. Most importantly, the

study wants to identify key vegetation species, and will outline which species are affected by

LULCC.

Regarding vegetation density changes of the area, the Normalised Difference Vegetation

Index (NDVI) will be studied by carrying out remote sensing methods. It shall be investigated

whether the NDVI decreases over time, and if so, whether this implies decreases in vegetation

density. In addition, Leaf Area Index (LAI) values obtained in the field will be analysed.


4. Study Area

The Mangabe Reserve is located between -19.00 and -19.47 latitude and 48.08 and 48.42

longitude. The closest city, Moramanga, is approximately 5-20 km northeast of Mangabe, and

has near to 30,000 inhabitants. The Mangabe Reserve is situated in the Alaotra Mangoro

Region, which is part of the province of Toamasina (also known as Tamatave province), and

covers 27,732 ha of land (see Figure 4.1). Within Mangabe, mainly two ethnic groups, namely

Bezanozano and Betsimisaraka, reside. The local population consists mostly of farmers.

There are a few immigrants from different ethnic groups (Merina, Sihanaka, and

Betsileo) in the area, who work as traders.

Mangabe is known for its remarkable biodiversity. It has an exceptionally high degree of

endemism, takes an important function in maintaining forest connectivity in east Madagascar,

provides opportunity for ecological services, and contains many significant natural resources

used by local communities to survive (Randriamamonjy, 2013). It is home to at least seven

species of lemurs, all listed in the IUCN Red list of threatened species (IUCN, 2014).

Moreover, half of the golden Mantella frogs (Mantella aurantiaca) breeding ponds can be

found in Mangabe (Bora et al., 2008).

The importance of Mangabe and its biodiversity was recognized by the government, and in

2008 procedures were started to establish Mangabe as a protected area (Madagasikara

Voakajy, 2013). The borders of the Mangabe Reserve were determined at that time, and were

based on river systems and boundaries of primary forest. Since then it has acquired temporary

protected area status. As a temporary protected area it has conferred protection against mining

and timber exploitation activities (Scales, 2014). Local NGO and legal land owners

Madagasikara Voakajy have recently started to conduct ecological and environmental

research in the area, and are working towards obtaining definite protected status (IUCN

Protected Area Category VI) for the area (Madagasikara Voakajy, 2013).

Mangabe has a subtropical climate (Kppen climate classification Cfa); it has hot and

humid summers, and mild winters with frequent thick mists. The wet season is from October

to April, whereas the dry season lasts between May and August. Average annual rainfall in

Moramanga is 1400mm, and average annual temperature is 19.7 C. The monthly averages of

precipitation and temperature for Moramanga are shown in Figures 4.2 and 4.3 respectively.

The terrain is hilly, and the elevation ranges between 720 and 1100 masl.


Figure 4.1: Location of the Mangabe Reserve, Madagascar. The figure also depicts the campsites

where in close proximity ground truth data was collected, and the location of the 15 study sites.


Figure 4.2: Monthly averages of precipitation and rainfall days in Moramanga, Madagascar.

Figure modified from World Weather Online (2014).

Figure 4.3: Monthly averages of high and low temperatures in Moramanga in Madagascar.

Figure modified from World Weather Online (2014).


5. Methodology

5.1 Data

For the analysis of LULCC in the Moramanga region of Madagascar, focusing on the

Mangabe Reserve, multiple Landsat scenes were used. Satellite imagery from Landsat is

widely used for LULCC analysis due to its global extent, its relatively long-term continuous

data record, and its high spatial resolution (Knorn et al., 2009). Furthermore, a benefit of

Landsat is that its data is readily and freely available to download from the EarthExplorer

database of the United States Geological Survey (USGS).

In total six Landsat scenes were obtained from EarthExplorer for further analysis, ranging

between June 1976 and July 2014. All Landsat scenes - other than those for the year 1976 -

included both the Mangabe Reserve and the city of Moramanga. For 1976, two images were

obtained and merged to cover these areas. Landsat scenes were selected to be near to June, the

month in which fieldwork was conducted, and were aimed to contain as little cloud cover as

possible. The exception of the December 2013 image was chosen based on it being the most

recent (relatively cloud-free) image of the Mangabe Reserve at the time of fieldwork

preparations. The July 2014 image was the most recent image available at the time of

analysis. The 1976 image was the earliest relatively cloud-free scene of Mangabe on

EarthExplorer, 2000 was chosen to mark the beginning of the new century, and 2008 was

chosen as this was the year of the implementation of Mangabe as a temporary protected area.

Full details of the images can be found in Table 5.1.

The shapefile of the boundary of the Mangabe Reserve was acquired from the NGO

Madagasikara Voakajy.


Table 5.1: Data set attributes of Landsat imagery used for LULCC analysis of the Mangabe

Reserve in Madagascar. The data and metadata were obtained from the EarthExplorer

database, United States Geological Survey (2013a).

5.2 Pre-processing

5.2.1 Pre-processing by USGS

All data was downloaded as level 1 product, i.e. having been pre-processed by the Level 1

Product Generation System of the USGS. The 1976 image, an L1G data product, was

radiometrically and geometrically corrected by USGS using data gathered by the sensor and

spacecraft. All other images were L1T products, which also incorporate ground control points

into the radiometric and geometric correction and use a Digital Elevation Model for

topographic accuracy.

5.2.2 Scan line gap filling

Due to a scan line corrector failure on the 31st of May 2003, the Landsat 7 ETM+ SLC image

contained scan line gaps, resulting in a loss of approximately 22% of data. Scan line gaps can

be corrected using various techniques. The USGS (2013a) describes a mosaicking method,

whereby other images (either those without scan line gaps, or adjacent images that contain

Landsat Scene Identifier Landsat Satellite Image Date Acquired

No. of

Bands Spatial

Resolution

(m)

Path,

Row

LM21690731976157GDS07 L2 MSS June 5, 1976 4 60 169,73

LM21700731976158GDS07 L2 MSS June 6, 1976 4 60 170,73

LE71580732000110SGS00 L7 ETM+ SLC On

April 19, 2000 8 30 158,73

LE71580732008132ASN00 L7 ETM+ SLC Off

May 11, 2008 8 30 158,73

LC81580732013361LGN00 L8 OLI/TIRS December 27, 2013

11 30 158,73

LC81580732014188LGN00 L8 OLI/TIRS July 7, 2014 11 30 158,73


different scan line gaps) are used for filling. This technique, however, led to large

colour/brightness differences between the original image and the filled scan line gaps. Instead,

therefore, images were chosen to be pre-processed using the landsat gapfill data specific

utility option, which is an extension tool provided by Exelis. It can be enabled in ENVI

through installation of the plugin landsat_gapfill.sav. From the landsat gapfill option, the

single image gap-filling technique was used. This technique calculates the most likely

values using a triangulation interpolation method.

5.2.3 Digital numbers to top of the atmosphere reflectance values

All images had to undergo further radiometric corrections. Landsat assigns each pixel in its

images a value, which is known as a pixels digital number (DN), depicting its brightness. To

perform vegetation density analyses on the satellite images, these DN values need to be

converted to top of atmosphere (ToA) reflectance values. By measuring reflectance at the top

of the atmosphere, contributions from clouds and atmospheric aerosols and gases can be

included. To convert DN to ToA reflectance values, DN first have to be converted to radiance

values. From the Spectral Radiance Scaling Method the following formula was used:

= [ ) ( )]( + (1)

Where: L is the cell value as radiance (in W m-2

sr-1m-1), Qcal is the quantised calibrated

pixel value in DN, Qcalmin and Qcalmax correspond to the minimum and maximum quantised

calibrated pixel value in DN, Lmin is the spectral radiance scaled to Qcalmin, and Lmax is the

spectral radiance scaled to Qcalmax.

The spectral radiance values can subsequently be converted to ToA reflectance values using

Equation 2.

= (2) ( cos ) (2)

Where: is the planetary reflectance (unitless), d is the Earth-Sun distance in astronomical

units, ESUN is the mean solar exoatmospheric irradiances in W m-2

m-1, and s is the solar

zenith angle in degrees. The equations were applied using the Band Math tool in ENVI.

Parameter values were obtained from the Landsat handbook (NASA, 2013), as well as from

the metadata file provided with the level 1 product.


5.2.4 Atmospheric correction

To account for atmospheric effects, including molecular and aerosol scattering as well as

absorption by gases (e.g. water vapor, ozone, oxygen or aerosols), atmospheric correction of

Landsat imagery is necessary (Liang et al., 2001). Especially when performing a classification

and/or change detection analysis, atmospheric correction is a primary task (Song et al., 2001).

In the case of converted DN values to reflectance values, absolute (as opposed to relative)

techniques are used (Song et al., 2001). A simple and widely used absolute atmospheric

correction approach in LULCC analyses is the Dark Object Subtraction (DOS) technique

(Song et al., 2001). The DOS technique assumes that reflectance from dark objects, such as

clear water bodies, or shadowed dark vegetation, is purely due to atmospheric path radiance

(Chen et al., 2005). To reduce atmospheric influences it therefore subtracts the dark object

values across the scene (Chen et al., 2005). DOS was applied to the images using the Dark

Subtract function with a band minimum method in ENVI.

5.2.5 Geometric correction

Lastly, the images were geometrically corrected to ensure accurate pixel-based change

detection. Images were geo-referenced to WGS 84/ UTM zone 39S. After comparison of the

images, it was concluded that only the Landsat MSS (1976) images needed to be

geometrically corrected. 10 Ground truth points were created from the 2000 image, which had

a root mean square (RMS) error of 0.283096. The 1976.1 image was then warped using the

RST method, and re-sampled using the nearest neighbour option. To georeference the 1976.2

image, 10 different ground truth points (RMS error of 0.197927) were collected from the

1976.1 image, and the same approach was applied.

5.2.6 Mosaicking the 1976 images

As the 1976.1 image did not fully cover the Mangabe Reserve area, the 1976.2 image

containing the remaining area of Mangabe (captured a day later) was added by means of

mosaicking. The georeferenced mosaicking method was used, whereby all values of 0

were ignored in the analysis.


5.3 LULCC detection

For LULCC detection in the Moramanga region of Madagascar, the supervised post-

classification technique was selected. It is a widely used approach, has been found to produce

accurate results, and is beneficial as it can provide insight in the nature of the changes

(Mas,1999).

5.3.1 Determining land use classes and regions of interest

For the supervised land use classification, first the land use classes had to be determined.

Following advice from local NGO Madagasikara Voakajy and the P4GES project1, land use

classes based on the tavy process were chosen, which resulted in the following land use

classes: Water, Forest, including primary and secondary forest, Shrub, including all

categories of shrub fallows, and Grassland, for low grassland, agricultural fields, and

uncovered land. Furthermore, the categories Cloud and Shadow were taken into

account in images with cloud cover. From the natural colour composites of the satellite

imagery, regions of interest for each land use class were created. To ensure statistical

separability of the classes, the transformed divergence tool was applied with respect to the

ROIs. A value greater than 1.7 was considered to indicate good separability between ROI

pairs. For those classes whose separability value was


Moramanga). The buffer zone was applied to determine changes directly outside of the

Mangabe Reserve. The maps were first visually compared to generally indicate LULCC

patterns. To quantify LULCC of the Mangabe Reserve area over time, the distribution of land

use classes in terms of number of pixels (which could be converted to m2) was compared

between the classified images. Subsequently, LULCC within the Mangabe Reserve was

compared to LULCC in the buffer zone area. The congregate of cloud, shadow, and water

pixels from all years were masked out of each image, such that the remaining classes could be

compared over an equal area.

5.4 Accuracy assessment

In order to carry out an accuracy assessment for the supervised classification, ground truth

data were collected in the field. The latitude and longitude of the ground truth points were

recorded using TerraSync software on a Trimble Juno 3B device, and where the Trimble did

not provide satisfactory accuracy, a Garmin eTrex 10 was used. Accuracy values of the

latitude and longitude values were within 5 m for all ground truth points. A confusion (or

error) matrix was used to calculate the percentage of land use classes correctly allocated.

Confusion matrices are widely used to assess accuracy, and are known to be an easily

interpretable way to measure overall accuracy of classification maps (Foody, 2002).

Congalton, who reviewed techniques for assessing accuracy of classification maps in 1991,

estimated that approximately 50 points per land use class would be needed for reliable results

when using a confusion matrix, although more ground truth points would be preferable. As

such, the study collected 50+ ground truth points per land use class. Ground truth points were

allocated using a random stratified sampling technique, keeping within walking distance from

our campsites, and adjusted where needed based on accessibility.

5.5 NDVI

In addition to LULCC maps, vegetation density changes were estimated from Normalised

Difference Vegetation Index (NDVI). NDVI is calculated as follows:

= ( ) ( + ) (3)

Where: NIR is the ToA reflectance value for the near-infrared band, and RED is the ToA

reflectance value for the red (visible) band. The resulting ratio is strongly related to the

fraction of incoming photosynthetically active radiation absorbed by plant canopies (Myneni


and Asrar, 1994). It is widely used, and has been found to produce satisfactory results with

respect to describing vegetation density and condition (Baldi et al., 2008). An image

differencing technique was used whereby NDVI values from two images were subtracted

from each other to obtain changes in NDVI. This was subsequently converted to a NDVI

(representing vegetation density) change map. Furthermore, NDVI trends within the Mangabe

Reserve were compared quantitatively, which included annual mean, annual maximum, and

annual minimum NDVI. For this, like in the supervised classification change assessment, only

those pixels classified as forest, shrub, or grassland on all images were taken into account.

The methodology was repeated for the pixels of the forest land use class, to determine

whether primary forest had degraded into secondary forest.

5.6 LAI

In addition to NDVI, leaf area index (LAI) data was collected in the field, in order to assess

vegetation density changes. To measure LAI, two techniques were used: the inclined point

quadrat method for vegetation shorter than 1.5 m and hemispherical photography for

vegetation taller than 1.5 m. In total 15 study sites were visited, ranging across various NDVI

values. For each site 60 x 60 metres were laid out, to ensure it could be attributed to one pixel

(30 x 30 metres) on the satellite imagery. All LAI values were measured in m2/m

2 of ground.

5.6.1 Inclined point quadrat

The inclined point quadrat technique is an indirect method for measuring LAI developed by

Warren Wilson in the 1960s (Zheng and Moskal, 2009). Using a long thin needle (or stick)

vegetation is pierced, after which the number of contacts with vegetation is recorded. From

repeated measurements, under varying elevation-angles, LAI can then be estimated using

Equation 4. This equation is based on a radiation penetration model.

= (4)

Where: Ni is the number of contacts with the vegetation under elevation angle i, and Ki is the

extinction coefficient with elevation i. An average elevation angle of 32.5 was used, as this is

recommended elevation angle if a single canopy piercing was performed. At an elevation of

32.5, Ki is relatively constant, and equals 0.9. The inclined point quadrat method is simple,


cheap, and non-destructive; however, it requires a large sample size and is therefore time-

consuming (Jonckheere et al., 2004).

Plots of 1 by 1 metre were set up, after which the vegetation was pierced 30 times and the

number of contacts with a 1 metre long stick was documents. Five randomly allocated plots

were selected, such that in total 150 Ni values were recorded per study site. The average of the

Ni values was then input into Equation 4, resulting in an average LAI value.

5.6.2 Hemispherical photography

In hemispherical photography, LAI is calculated using photographs from a hemispherical

(fisheye) lens, which is typically placed beneath the canopy (it can also be placed above the

canopy). The photograph captures the canopy under a 180 field of view, storing information

such as position, size, density, and distribution of canopy gaps (Jonckheere et al., 2004).

These parameters can then be used to analyse canopy structure and compute LAI.

Hemispherical photographs were taken with a Nikon Coolpix 990 to which a fisheye lens was

attached. Per study site 10 photos were taken. The photographs were analysed with the

software CIMES-fisheye, a free programme offered by the University of Strasbourg. Using

CIMES, first gap fraction of the images was calculated, which could subsequently be used to

calculate LAI by the executable file LAICAM.

5.7 Characterising land use classes

To be able to provide a more detailed description of the land use classes, observational data

was recorded in the field. For each land use class, the most prominent species were

documented, along with the average vegetation height. The percent cover of these species

within the plot was also reported. The same 15 sites as those visited for their LAI values were

studied, ranging across land use classes, also accounting for differences in NDVI. A

photograph was taken for each study site.


6. Results

6.1 LULCC detection (1976-2014) from supervised imagery

The supervised classifications of the Mangabe Reserve from 1976-2014 can be found in

Figure 6.2. The area cover per land use class for each year is presented in Table 6.1 and a

column graph is shown in Figure 6.1. In 1976 most of the Mangabe Reserve was forested

land. Over the years, this forest area has decreased significantly, and the Mangabe Reserve

went from approximately 83% forest in 1976 to 45% forest area in 2014. Most of the forest

has degraded into shrubland, and some shrubland has been transformed to grassland/tany

maty. Deforestation mostly occurred along the boundaries of Mangabe, specifically in the east

and west.

Table 6.1: Land use class area statistics for the Mangabe Reserve in Madagascar. Areas were

calculated from supervised land use classification maps; the land use classes cloud, shadow, and

water were masked out.

1976 2000 2008 2013 2014

Difference

1976-2014

Forest (km2) 197 140 102 77 106 -91

Shrub (km2) 27 84 104 105 98 71

Grassland (km2) 13 13 31 55 33 20

Total 237 237 237 237 237

Figure 6.1: Column graph regarding the class area statistics of the supervised classification

maps for the Mangabe Reserve in Madagascar between the years 1976-2014.

0

50

100

150

200

250

1976 2000 2008 2013 2014

Are

a (

km

2)

Forest

Shrub

Grassland


Figure 6.2: Supervised land use classification maps of the Mangabe Reserve in Madagascar. The

classifications are based on Landsat satellite imagery from years: a) 1976, b) 2000, c) 2008, d)

2013, e) 2014. Spatial resolution is 60 metres for a), and 30 metres for all others.

a) b) c)

d) e)


The land use class area statistics for the 10 km buffered supervised classification images from

2008 to 2014 (see Figure 6.3) can be found in Table 6.2. Contrary to LULCC within the

Mangabe Reserve for those years, the buffered images show a decrease in forest area. In the

10 km buffer zone surrounding Mangabe, a total of 6% of forest was lost between 2008 and

2014.

Table 6.2: Land use class area statistics for the 10 km buffered supervised land use classification

maps of the Mangabe Reserve in Madagascar. The values exclude the pixels within the Mangabe

Reserve. The land use classes cloud, shadow, and water were masked out.

Figure 6.3: Supervised land use classification maps of the Mangabe Reserve, Madagascar, with a

10 km buffer zone. The classifications were derived from Landsat imagery from a) 2008 and b)

2014, both with a spatial resolution of 30 metres. The boundary of the Mangabe Reserve is

shown in yellow.

2008 2014

Difference

1976-2014

Forest (km2) 300 283 -18

Shrub (km2) 413 424 11

Grassland (km2) 190 196 6

Total 903 903

a) b)


6.2 Accuracy assessment

Based on 186 ground truth points collected during fieldwork, the overall accuracy of the 2013

supervised classification image is 74.2% with a kappa coefficient of 0.611. The 2014

supervised classification image has an overall accuracy of 79.0% and a kappa of 0.686. The

forest land use class had highest accuracies, with producers accuracies of 82.8% and 86.2%,

and users accuracies of 94.1% and 94.3%, for 2013 and 2014 respectively. Producers

accuracy is the probability that a land use class is classified correctly on the classification

map, whereas users accuracy refers to the probability that a pixel classified as land use class

x is also class x on the ground. Shrub was most misclassified in 2013, with producers and

users accuracies of 62.0% and 56.4%. In 2014, grassland had lowest producers accuracy

(74.4%), although shrub remained lowest with respect to users accuracy (61.9%).

The confusion matrices for the 2013 and 2014 image can be found in Table 6.3 and Table 6.4.

The spatial distribution of the ground truth points, and whether they were correctly classified

or not, can be found in Figure 6.4 and Figure 6.5 respectively.

Table 6.3: Confusion matrix accuracy assessment for the 2013 supervised classification image.

Ground truth data Producers

accuracy (%)

Forest Shrub Grassland Total

Classified

data

Forest 48 2 1 51 94.1

Shrub 10 31 14 55 56.4

Grassland 0 17 59 76 77.6

Cloud 0 0 4 4

Total 58 50 78 186

Users accuracy (%) 82.8 62 75.6

Overall accuracy: 74.2%

Kappa: 0.611

Table 6.4: Confusion matrix accuracy assessment for the 2014 supervised classification image.

Ground truth data Producers

accuracy

Forest Shrub Grassland Total

Classified

data

Forest 50 3 0 53 94.3

Shrub 5 39 19 63 61.9

Grassland 3 7 58 68 85.3

Cloud 0 1 1 2

Total 58 50 78 186

Users accuracy 86.2 78 74.4

Overall accuracy: 79.0%

Kappa: 0.686


Figure 6.4: Spatial distribution of ground truth points with respect to the Mangabe Reserve area

in Madagascar.


Figure 6.5: Spatial distribution of correctly and incorrectly classified ground truth points for the

2013 and 2014 supervised classification images.


6.3 NDVI change detection

The NDVI change map for 1976 as initial state and 2014 as final state is presented in Figure

6.6. Minimum, maximum, and mean NDVI values can be found in Table 6.5. The quantitative

NDVI analysis specifically for the forest class is shown in Table 6.6. NDVI values fluctuated

over the years, with mean NDVI ranging from 0.64 to 0.70, and no clear trend can be inferred.

Figure 6.6: NDVI change map of the Mangabe Reserve, Madagascar, between 1976 and 2014.

Table 6.5: NDVI statistical data of the Mangabe Reserve, Madagascar, after having masked out

land use classes: cloud, shadow, and water.

1976 2000 2008 2013 2014

NDVI min 0.511 0.150 0.260 0.529 0.771

NDVI max 0.996 0.852 0.857 0.869 0.874

NDVI mean 0.654 0.701 0.685 0.640 0.676


Table 6.6: NDVI statistical data of the Mangabe Reserve, specifically for the pixels classified as

forest in all images.

1976 2000 2008 2013 2014

NDVI min 0.459 0.558 0.414 0.334 0.377

NDVI max 0.776 0.808 0.817 0.835 0.823

NDVI mean 0.660 0.712 0.715 0.733 0.701

6.4 LAI data and correlation with NDVI

LAI and NDVI data of the 15 study sites can be found in Table 6.7. LAI and NDVI are

moderately correlated, with an R squared coefficient of determination of 0.378 for 2013, and

an R squared of 0.421 for 2014. Full correlation statistics are shown in Table 6.8; the related

scatter plot is presented in Figure 6.7. The LAI values deduced from hemispherical

photographs were substantially stronger correlated with NDVI than those obtained using the

inclined point quadrat method.

Table 6.7: NDVI and LAI data of the 15 study sites within the Mangabe Reserve, Madagascar.

LAI values are in m2/m

2 ground. The method for estimating LAI is either the inclined point

quadrat method (IPQ), or hemispherical photography (HP).

Site no. LULC LAI Method LAI NDVI 2013 NDVI 2014

1 Grassland IPQ 1.649 0.48 0.45

2 Grassland IPQ 1.593 0.47 0.36

3 Shrub IPQ 3.056 0.52 0.57

4 Shrub IPQ 3.517 0.48 0.6

5 Shrub HP 2.900 0.68 0.69

6 Shrub IPQ 1.652 0.6 0.64

7 Shrub IPQ 3.237 0.63 0.66

8 Forest HP 3.142 0.74 0.71

9 Forest HP 3.094 0.68 0.69

10 Shrub HP 2.870 0.69 0.69

11 Grassland IPQ 1.493 0.62 0.63 12 Grassland IPQ 1.459 0.52 0.64

13 Forest HP 3.468 0.8 0.75

14 Forest HP 3.975 0.76 0.76

15 Forest HP 3.663 0.77 0.78


Table 6.8: LAI/NDVI correlation (Pearsons r and R squared) statistics for the years 2013 and 2014 with regards to the Mangabe Reserve area in Madagascar.

2013 r 2013 r2 2014 r 2014 r2

LAI/NDVI correlation 0.615* 0.378* 0.649* 0.421*

LAI/NDVI correlation for HP 0.768* 0.590* 0.910* 0.828*

LAI/NDVI correlation for IPQ -0.004 -0.00 0.289 0.084

* Significant to the level p


Figure 6.8: Example of a forest study site.

6.5.2 Shrub

The species Philippia floribunda (known by Malagasy as Anjavidy, see Figure 6.9) was by far

most abundant in the shrub land use class. Fern Ptederium aquilinum occurred at four out of

the six sites, where it occupied around 20-30% of the land. Moreover, Psiadia altissima was

an associated species. Height of vegetation at shrub sites varied between 0.5 and 2.5 metres.

Figure 6.9: Example of a shrub study site; the shrub on the photo is Anjavidy (average height 1

metre).


6.5.3 Grassland

The most predominant grass genera encountered at grassland sites were Aristida and

Imperata. Vegetation height ranged between 0 and 0.5 metres. Ptederium aquilinum was also

an associated species with grassland. Figure 6.10 shows a grassland study site.

Figure 6.10: Example of a grassland study site.


7. Discussion

The results will be discussed with respect to the two main objectives presented in Chapter 3.

Before discussing how the data relates to achieving these objectives, however, unusual and/or

unexpected outcomes of the data shall first be discussed.

7.1 Comments on the supervised images

There are a number of striking features in the supervised classification images from 1976-

2014. In the 1976 image, there is the vertical line (classified as cloud), running through the

middle of Mangabe. Although there are numerous anomalies associated with Landsat MSS,

none of them are similar to what is occurring here (USGS, 2013b). As the line is not

completely straight, and because it does not occur on earlier/later images of the area, it is

suggested that this line is due to a contrail formed by an aircraft. Contrails have similar

properties to cirrus clouds, which would explain the classification. Another noticeable feature

of the 1976 image is the sudden change in LULC pattern within Mangabe as a result of

mosaicking (see Figure 7.1). The different LULC pattern of the June 6th

image as compared to

the June 5th

LULC pattern can be attributed to the presence of detector failure lines, as well as

high cloud and shadow cover on that particular image. This is illustrated in Figure 7.2.

Figure 7.1: Distinct change in LULC pattern between the two mosaicked Landsat MSS images

for the year 1976.

June 5th

1976 image

June 6th

1976 image


Figure 7.2: Close-up of the mosaicked 1976 natural colour composite, with the June 5th

image in

the top right corner (bordered by the yellow line) and the June 6th

image on the left and bottom.

The boundary of Mangabe is shown in red. The figure depicts that the image from the 6th

of

June contains detector failure lines. Moreover, it shows that the June 6th

image comprises of

significantly more cloud and shadow area than the June 5th

image.

In the 2008 image classified image, a relatively large amount of pixels in the north of the

Mangabe Reserve are classified as shadow, whereas no pixels in vicinity of the shadow areas

are classified as cloud. A closer look at the classified versus the natural colour processed

image reveals that the cloud areas associated with the shadow areas have been blurred in the

process of gapfilling (see Figure 7.3), causing the misclassification.

Lastly, in the 2013 image, a large amount of pixels in the south of the Mangabe Reserve have

been classified as grassland, which seems to be an irregularity when compared to the other

years. Inspection of the natural colour composite of the 2013 image showed that hazy

cirrocumulus clouds were interfering, and caused this misclassification. Figure 7.4 portrays

these hazy clouds. In the discussion of the area statistics of the land use classes, it shall be

taken into account that for the 2013 classification, grassland was overestimated, while shrub

and forest were underestimated.


Figure 7.3: Comparison of 2008 supervised classification image (left) and 2008 natural colour

composite (right). The zoom of the natural colour composite shows that the filling of the scan

line gap (between the two vertical black lines) has resulted in the blurring of clouds, after which

these pixels were misclassified as grassland (see zoom window 1).

Figure 7.4: Supervised 2013 classification versus 2013 natural colour composite. The natural

colour composite shows the hazy clouds, which caused misclassification in the supervised image.


7.2 Objective 1: Analysing recent LULCC trends

From the supervised land use classification presented in Figure 6.2, it is clear that between the

years 1976 and 2014 relatively rapid LULCC took place, with an average deforestation rate of

1.5% per year. Between 2008 and 2013 deforestation rates were highest, with an average of

5.7% of forest lost each year. The period from 2013 to 2014 was the only time frame during

which forest extent increased.

The high deforestation rates from 2008-2013 could be explained by Madagascars political

stability. Following the coup dtat in January 2009, the country fell into an economic

stagnation (World Bank, 2013). Foreign aid dropped by ~30%, and income per capita in 2013

equalled the level of income per capita in 2001; 92% of the population lived under $2 a day

(World Bank, 2013). Not only was logging now attractive being relatively profitable, the

government also issued permits for the export of precious wood to listed exporters,

temporarily legalising precious wood trade (Global Witness and Environmental Investigation

Agency, 2010). Increased trade of rosewood and ebony in the period from 2009 onwards has

been noted in the literature (Schuurman and Lowry, 2009; Barrett et al., 2010; Global Witness

and Environmental Investigation Agency, 2010). Allnut et al. (2013) further report an

increase in illegal mining activities as a result of the 2009 political crisis. The crisis lasted

until July 2013, and is therefore in strong accordance with the LULCC trends found as a result

of the post-classification analysis.

Whilst it can be argued that after July 2013 deforestation as a result of political instability was

mitigated, the sharp increase in forest extent in the short period of time between 2013 and

2014 seems highly unlikely. The misclassification of pixels in the south of the Mangabe

Reserve on the 2013 image, as discussed in section 7.1 and illustrated in Figure 7.4, is

suggested to play a large role in this. Where in other years a loss of forest area was

accompanied by an increase in shrub land, in 2013 this was not the case. Instead, a strong

increase of grassland was observed. This increase in grassland area can partly be attributed to

the large amount of pixels classified as grassland in the south. These have been identified to

be partially incorrect (see Figure 7.4), and some pixels should have been classified as forested

land. The actual forest area in 2013 was thus greater than the table presents, which means that

the true change was less drastic than is reported. A minor increase in forest extent is plausible

given the recent acquisition of a temporary protected status of the Mangabe Reserve along

with increased efforts by NGO Madagasikara Voakajy to conserve the primary forest and

promote reforestation. Especially when compared to the buffered supervised classifications


(where deforestation continued to occur), it shows that the implementation of these measures

had a positive influence on the forest within Mangabe.

The most important form of LULCC between 1976 and 2014 was the conversion of forested

area to shrubland, although a fair amount of shrubland was also converted into grassland. In

total, the land use class shrub increased by 168%, whereas land use class grassland increased

by 54%. The LULC changes are in line with the tavy process discussed in the literature

review (see section 2.4.2). Predominant species listed in this study were also previously

identified by Du Puy and Moat in 1996, who described Madagascars primary vegetation

classes including evergreen uapaca woodlands and montane philippia shrubland. Psiadia

altissima has been associated with early stages of the tavy, occurring after one or two fallow

cycles, and Ptederium aquilinum was determined to start to appear from the third fallow cycle

onwards (Styger et al., 2007). Furthermore, both grassland genera Aristida and Imperata have

been linked to later stages of the tavy process leading up to the dead land called tany maty

(Styger et al., 2007).

Apart from the irregularities discussed in section 7.1, the LULC classification maps were

relatively accurate, with an overall accuracy of 79.0% for the 2014 image. Especially the

forest class produced high accuracies (both producers and users), although improvements

could be made with regards to the classification of shrub. Overall, it can be concluded that the

LULC classification maps provided satisfactory data for recent LULCC trend analysis in the

Mangabe Reserve. The results show explainable and logical patterns with regards to recent

LULCC trends in the Mangabe Reserve, and are also in strong accordance with the literature

on this matter.

7.3 Objective 2: Studying vegetation density changes

No clear trend in NDVI over the years could be detected, and so no clear statements with

regards to the degradation of primary forest into secondary forest can be made from the data

in this study. The lack of a trend in NDVI - even though clear LULCC patterns are observed

from post-classification analysis - is likely due to climatic reasons. As NDVI is related to

photosynthetic activity, and vegetation growth in tropical regions is largely dependent on

water availability, it is hypothesized that the NDVI results were related to the amount of

precipitation that fell in particular years, rather than being governed by LULCC.


To test this hypothesis, precipitation data for the Mangabe area was obtained from the Global

Precipitation Climatology Centre (2014), which is the German contribution to the World

Climate Research Programme (WCRP) and to the Global Climate Observing System (GCOS).

GPCCs Landsurface full data product version 6 at 0.5 provided monthly precipitation data

up until 2010, whereas GPCCs Landsurface Monitoring product at 1.0 was used for data

after 2010. The monthly precipitation data was averaged over the six months prior to the

satellite image, to include both dry and wet season months; the values can be found in Table

7.1. NDVI and precipitation were subsequently correlated. This resulted in a Pearsons r

squared of 0.902, significant to the p


With respect to the spatial NDVI changes presented in Figure 6.6, it can be concluded that the

observed positive changes in NDVI can be attributed to higher precipitation rates in 2014 as

compared to 1976. It should be noted that the extreme positive changes (those of more than

0.6) are the result of clouds in the 1976 image.

Apart from NDVI, vegetation density was also measured in terms of LAI. Multiple sources

report near-perfect correlation between LAI and NDVI with a Pearsons r of >0.9 (Gamon et

al., 1995; Wang et al., 2005; Fan et al., 2009). From the results on LAI and NDVI correlation

for the Mangabe Reserve area presented in Table 7.1/ Figure 7.5, it can therefore be inferred

that the LAI estimates determined using hemispherical photography are reliable, but those

obtained via the inclined point quadrat method are less accurate.

To be able to specifically calculate the effect of LULCC (discussed in 7.2) on vegetation

density changes, it is necessary to control for precipitation. As none of the precipitation rates

of the years studied were equal, this provided difficulties. Instead, therefore, hypothetical

mean LAI values for the different years were computed by assuming an average monthly

precipitation rate of 137.5 mm/month (equal to the 2014 average monthly precipitation rate)

for all years. The 2014 average monthly precipitation rate was used, as the LAI values (see

Table 6.7) were with respect to precipitation values of this year.

First, average LAI values for the land use classes forest, shrub, and grassland, were calculated

using the ordinary least square (OLS) of LAI and NDVI. As the LAI values from the inclined

point quadrat method were less accurate, only those based on the hemispherical photography

LAI were used when computing the OLS (see Figure 7.6). Mean NDVI values for each land

use class were calculated from the 2014 NDVI image by using masks. Mean NDVI values

and mean LAI values derived from the OLS can be found in Table 7.2.

Table 7.2: Mean NDVI per land use class as estimated from the 2014 NDVI image, and mean

LAI per land use class, calculated from the OLS presented in Figure 7.6. All values are specific

to the Mangabe Reserve area in Madagascar.

NDVI mean LAI mean

Forest 0.697 3.031 Shrub 0.685 2.911 Grassland 0.569 1.767


(5)

Figure 7.6: Scatter plot of LAI for the 15 study sites compared to NDVI as calculated from the

2014 Landsat images. The linear ordinary least square (OLS) function along with its equation is

also shown.

To compute the hypothetical mean LAI values for 1976, 2000, 2008 and 2013 under the 2014

precipitation rate, the 2014 average LAI values per land use class were used in combination

with the quantified LULCC presented in Table 6.1. The equation is shown in Equation 5, the

results can be found in Table 7.3.

=% 3.031 + % 2.911 + % 1.767

100

Table 7.3: Estimated mean LAI values for the Mangabe Reserve, Madagascar, based on a

scenario of an average precipitation rate of 137.5 mm/month (equal to the 2014 mean

precipitation rate).

From Table 7.3 it can be concluded that if precipitation rates had been equal to those in 2014

in all years, vegetation density would have decreased by 4.78% between 1976 and 2014 as a

result of LULCC. This assumes that average LAI per land use class stayed relatively stable

over time. It is, however, deemed likely that average LAI within land use classes also

decreased over the years due to degradation of primary to secondary forest within the forest

class. Therefore, the decrease of vegetation density from LULCC between 1976 and 2014 is

expected to be even greater than 4.78%.

y = 9.853x - 3.835

R = 0.828

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0.68 0.7 0.72 0.74 0.76 0.78 0.8

LA

I

NDVI

OLS

1976 2000 2008 2013 2014

LAI mean 2.949 2.918 2.815 2.686 2.809


8. Project Limitations and Future Research

8.1 Project limitations

Although LULCC patterns in the Mangabe area of Madagascar could be depicted and

quantified, there are a number of improvements that could be made with regards to LULCC

analysis and specifically the methodology used in this study.

With regards to the post-classification method for LULCC detection, the accuracies of 74.2%

and 79.0% and for the 2013 and 2014 could still be improved through post-classification

corrections. The use of ancillary data and knowledge-based logic rules allowed Manandhar et

al. (2009) to improve their accuracies from supervised classifications of Landsat imagery

significantly. Their overall classification accuracy increased from 72% to 91% for a Landsat

1985 classification image, whereas a classified image from 2005 was improved from 79% to

87%. Furthermore, the post-classification method was limited by the generalisation of its land

use classes. A more detailed analysis of LULCC would be possible if all seven stages of tavy

were included, and would allow for a more precise advice on regeneration years.

In terms of vegetation density change analysis, the collection of LAI data could have been

improved by a larger dataset and by the use of more effective approaches. Monetary

constraints restricted the study to the inclined point quadrat method along with hemispherical

photography, even though more effective methods exist: e.g. utilising a LAI 2000 Plant

Canopy Analyzer, or a Tracing Radiation and Architecture of Canopies instrument (Chen et

al., 2006; Olivas et al., 2013). Furthermore, a larger sample size would allow for a more

accurate correlation analysis with NDVI.

8.2 Future research

Building upon the findings of this study, it is suggested that future research focuses on

assessing high risk areas of LULCC and modelling of LULCC in and around the Mangabe

Reserve. Areas prone to LULCC, especially those with high biological, ecological, and

environmental importance, should be determined in order to decide which areas are most in

need of immediate protection and supervision. Moreover, now that past LULCC trends have

been studied, it is important to assess future scenarios to make an informed decision on land

management strategies.


9. Conclusion

From six Landsat satellite images, recent LULCC trends within the Mangabe Reserve, as well

as recent LULCC trends within the 10 km buffer zone surrounding the Mangabe Reserve,

could be determined utilising a supervised post-classification method. Between 1976 and

2014, forest area within Mangabe decreased by 46.4%. After the acquisition of a temporary

protected status in 2008 a minor increase of forest extent could be observed, whereas the 10

km buffer zone supervised classification showed that deforestation continued to occur outside

of the boundaries of Mangabe. From the supervised classifications it could be determined that

the most important form of LULCC within Mangabe between 1976 and 2014 was the

conversion of forest area into shrubland as a result of the tavy process.

The performed NDVI analysis could not provide conclusive evidence regarding vegetation

density changes, as the NDVI values were strongly influenced by precipitation rates. From a

hypothetical scenario in which precipitation rates were equal to 2014 precipitation rates, it

could be estimated that at mean LAI values, and thus vegetation density, decreased by

approximately 4.78% between 1976 and 2014 as a result of LULCC within Mangabe.


Appendix i: Ethics Screening and Risk Assessment Forms


Appendix ii: Transformed Divergence Results


1976 Pair Separation (least to most);

Shrub and Forest - 1.63041318

Shadow and Shrub - 1.76228076

Shadow and Forest - 1.80707069

Shrub and Grassland - 1.82549045

Water and Shadow - 1.92297893

Forest and Grassland - 1.94409724

Shadow and Grassland - 1.96303341

Water and Shrub - 1.99450828

Water and Forest - 1.99636591

Water and Grassland - 1.99993342

Cloud and Grassland - 2.00000000

Water and Cloud - 2.00000000

Cloud and Forest - 2.00000000

Cloud and Shadow - 2.00000000

Cloud and Shrub - 2.00000000

2000 Pair Separation (least to most); Shrub and Forest - 1.91931226

Shadow and Shrub - 1.98078615

Shrub and Grassland - 1.99047086

Shadow and Forest - 1.99593685

Forest and Grassland - 1.99658643

Water and Shadow - 1

analysing recent land use land cover change.pdf

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