International Journal of Remote SensingVol. 32, No. 18, 20 September 2011, 5229–5245

Systematic and random transitions of land-cover types in Burkina Faso,West Africa

ISSA OUEDRAOGO*†, PATRICE SAVADOGO†‡, MULUALEM TIGABU†,SIDZABDA DJIBRIL DAYAMBA† and PER CHRISTER ODÉN†

†Southern Swedish Forest Research Centre, Tropical Silviculture and Seed Laboratory,Swedish University of Agricultural Sciences (SLU), PO Box 101, SE-230 53, Alnarp,

Sweden‡Centre National de la Recherche Scientifique et Technologique, Institut de

l’Environnement et de Recherches Agricoles, Département Productions Forestières,03 BP 7047, Ouagadougou 03, Burkina Faso

(Received 14 July 2009; in final form 3 May 2010)

In-depth statistical analysis of forest transition between land-cover types over timecan reveal the dominant signals of landscape transformation, which are needed inorder to develop appropriate land management strategies. We applied a recentlydeveloped methodology to analyse the transition matrix of six land-cover classes,derived from 1986 and 2002 Landsat images of an area of 15 675 km2 in southernBurkina Faso. Results show that most landscape transformations followed a sys-tematic process. In addition, some transitions occurred as an apparently randomprocess, probably caused by uncommon or sporadic events. Degradation of wood-land to shrub-/grassland over 15.7% of the landscape, increases in biomass fromwoodland to dense forest on 10% of the landscape and conversion of 6% ofthe landscape from shrub-/grassland to cropland were the dominant signals offorest-cover transitions. From a planning perspective, the dominance of system-atic processes should facilitate regional land-use planning and sustainable forestmanagement in a context of immigration and agricultural intensification.

1. Introduction

Most tropical dry forest, which accounts for nearly 40% of all tropical forests, has beenseverely disturbed and fragmented in recent years (Hartter et al. 2008). Rapid popu-lation growth in the Tropics, generally associated with increased demands for foodand wood fuel, is consistently cited as a primary cause of forest loss (Ramankuttyand Foley 1999, Lambin et al. 2003). However, accurate estimates of potential forestarea in most of the tropical countries are lacking. This lack of precise figures canlead to poor planning strategies that may adversely affect sustainable forest manage-ment in Burkina Faso. Until recently, the ministry in charge of environmental issueshas used forest data estimated in the 1960s to quantify forest resources and potentialyields, which were often over-estimated (MECV 2004). Since the year 2000, attemptshave been made to map land cover for the whole country, using improved spatialinformation technology.

*Corresponding author. Email: issa.ouedraogo@ess.slu.se; issadeh@hotmail.com

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2011 Taylor & Francis

http://www.tandf.co.uk/journalshttp://dx.doi.org/10.1080/01431161.2010.495095

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5230 I. Ouedraogo et al.

The capability of remote sensing and Geographic Information Systems (GIS) formapping and monitoring land-use change is uncontested (Alo and Pontius 2008, Zhouet al. 2008). These techniques are being used increasingly to measure the spatial andtemporal patterns of land cover in many regions (e.g. Yang and Liu 2005, Vagen 2006,Mena 2008). This follows the recognition that land-use change is a major driver ofglobal change, through its effects on climate, ecosystem processes, biogeochemicalcycles and biodiversity (Vitousek et al. 1997). The commonly applied land-changeanalysis approach has involved comparing land-cover maps obtained from optical orradar images taken at different times and the construction of two-dimensional tran-sition matrix tables summarizing cover change between dates (Pontius et al. 2004,Braimoh 2006, Alo and Pontius 2008). This approach focuses on the amount, loca-tion and types of cover change, but it does not take into consideration the transitionprocesses involved. In that context, if the total forest area of a given study site isconstant, it is impossible to discern whether the landscape is stable or whether, forexample, deforestation is matched by equivalent forest regrowth (Pontius et al. 2004).These processes, however, can be deduced from appropriate statistical analyses of thequantitative information available in the transition matrix.

The transition process analyses provide indications of whether the observed tran-sitions have been driven by systematic or random processes (Pontius et al. 2004).Statistically, a land-cover category is said to gain randomly from others if the gainsare in proportion to the availability of those losing categories. Conversely, a land-cover class is said to lose randomly to others if such losses are in proportion to thesize of the other gaining classes. Any large positive or negative deviation from theseproportions is referred to as a systematic transition (Pontius et al. 2004, Braimoh2006). Systematic transition involves regular or common process of change and tendsto be dictated by population growth, increased commercialization, frontier develop-ment or lack of environmental awareness. In contrast, random transitions are thoseinfluenced by coincidental or unique processes of change, characterized by abruptchanges accompanied by either recovery or non-recovery of the ecosystem (Lambinet al. 2003, Pontius et al. 2004, Braimoh 2006). Detection of such systematic and/orrandom transitions can help researchers and planners to focus on the most importantsignals of change, to investigate the possible drivers and, hence, to propose preventa-tive measures to avoid undesirable impacts of land-cover change (Pontius et al. 2004,Braimoh 2006).

The objective of the present study was to detect and examine dominant signals ofland-cover transformation in southern Burkina Faso, in order to support sustain-able forest ecosystem management, using in-depth statistical analysis of land-covertransition matrices developed by Pontius et al. (2004). The main reason for choos-ing southern Burkina Faso for the study was that the region was believed to haveexperienced high human pressure on natural resources and a high rural immigrationrate (Henry et al. 2003, Paré et al. 2008, Ouedraogo et al. 2009). Southern BurkinaFaso has been experiencing rapid population growth since the 1980s, caused by signif-icant immigration of farmers from drought-affected zones of the northern and centralareas of the country (Reenberg and Lund 1998, Gray 2005, Ouedraogo et al. 2009).This immigration is believed to have contributed to increasing forest degradation.This study is a continuation of recent works done in the area reported by Ouedraogo(2006b), Paré et al. (2008) and Ouedraogo et al. (2009).

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Land-cover transition in the Tropics 5231

2. Materials and methods

2.1 Study area description

The study was carried out in the Centre-Ouest region, one of Burkina Faso’s 13 admin-istrative regions (figure 1). The region is bordered to the west by the Sud-Ouest regionand a loop of the river Mouhoun, formally known as the Black Volta, to the northby the Nord and Plateau Central regions, to the east by the Centre and Centre-Sudregions and to the south by the Republic of Ghana. The study area, covering 15 675km2, lies between latitudes 10◦ 58′ N to 11◦ 52′ N and longitudes 2◦ 40′ W to 1◦ 12′ E.It covers three provinces (Nahouri, Sissili and Ziro) and is characterized by low reliefwith an average altitude of 300 m above sea level (asl). Phytogeographically, the areais situated in the Sudanian regional centre of endemism in the south Sudanian zone(Fontes and Guinko 1995). The natural vegetation comprises mostly dry forest andtree savanna community types. The climate is tropical with a unimodal rainy season,lasting for about 6 months from May to October. Based on data collected from thenearest in situ mini-weather station at Léo, the provincial city of Sissili, the mean(± Standard Error) annual rainfall from 1976 to 2007 was 883 ± 147 mm. Mean dailyminimum and maximum temperatures ranged from 16◦C to 32◦C in January (the cold-est month) and from 26◦C to 40◦C in April (the hottest month). According to the FAOsoil classification system (Driessen et al. 2001), the most frequently encountered soiltype is Lixisol (tropical ferruginous soils), which is poorly to fully leached, overlyingsandy, clayey–sandy and sandy–clayey material.

Figure 1. Study area.

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5232 I. Ouedraogo et al.

The population is composed of five main ethnic groups: Nuni, Wala, Lyele, Mossiand Fulani. The Nuni, Lyele and Wala groups have been living in the area forcenturies and are considered indigenous, while the Mossi, who originate from theCentral Plateau in Burkina Faso, and the Fulani, herders from the northern regionof the country, are considered migrants. The latter two groups were attracted to thesouthern region during the 1980s in search of arable land and green pasture, respec-tively (Howorth and O’Keefe 1999). The dominant agricultural production methodsin the study area are traditional subsistence farming systems with cereals (such assorghum, millet and maize), tubers (yam and sweet potatoes) and animal husbandry.However, over the last 10 years, there has been intense competition for land betweenthe traditional farming systems and more lucrative production systems. These systemsinclude extraction of fuel wood, poles and non-wood forest products, production ofcash crops (cotton and fruit-tree plantation) and ranching (Paré et al. 2008). SouthernBurkina Faso provides fuel wood and charcoal for the important nearby towns ofOuagadougou and Koudougou, located 80 and 150 km away, respectively. Non-timberforest products, such as fruits, leaves, tubers, perennial grass straw and hay, are alsoharvested from the forest.

2.2 Detection of land-cover change

Two Landsat scenes of the study area acquired 16 years apart (18 November 1986and 21 October 2002) were used to detect forest-cover change. Each image wasgeorectified to Universal Transverse Mercator (UTM) WGS 84 Zone 30 Northcoordinates using ground control points (Focardi et al. 2008) collected during field-work in January–February 2009. A first-order affine transformation was applied toall data, resulting in a root mean square (rms) error below 15 m for all Landsatimages (Braimoh and Vlek 2004). The procedure involved radiometric rectificationof the 1986 image to the 2002 image (Hall et al. 1991), followed by a tasselledcap orthogonal transformation of the original six bands in each image into threenew dimensional spaces, corresponding to soil brightness, green vegetation andmoisture indices. Apart from yielding relevant training data for land-use classifi-cation, the transformation also improved visual discrimination of land-use types.Land-cover classification of the images was carried out using the maximum like-lihood algorithm, based on training sites, available topographic maps, aerial pho-tographs and in situ observations. Six classification schemes, based on the land-coverclassification of Lamprecht (1989), were used to assign pixels to six land-coverclasses. The classes were bare land (infertile soil, not suitable for any vegetationgrowth), cropland (cultivated land), shrub-/grassland (land covered by shrubs, bushesand grass with cover ≤ 75 trees ha−1), woodland (open woodland; tree cover of75–150 trees ha−1), dense forest (closed canopy woody vegetation and gallery forest)and water body (mostly dammed).

Stratified random sampling methods were used to select 294 sample reference pointsfor the classification accuracy assessment. The reference points were generated andtransferred to a GIS program, in which they were overlaid with the classified images.A field check was made to test the accuracy of the reference points as well as theuse of high resolution aerial photographs very close to the periods when the satel-lite images were taken. Interviews with local people were also conducted to clarifyissues for which a clear relationship between the present and historical images couldnot be established. The overall accuracies of the classifications were 87.6% for the

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Land-cover transition in the Tropics 5233

Tab

le1.

Err

orm

atri

ces

for

the

supe

rvis

edcl

assi

ficat

ions

ofth

e20

02an

d19

86L

ands

atim

ages

.

Gro

und

data

Bar

ela

ndC

ropl

and

Shru

b-/gr

assl

and

Woo

dlan

dD

ense

fore

stW

ater

body

Tota

lP

rodu

cer’s

accu

racy

(%)

Use

r’sac

cura

cy(%

)

2002

(ove

rall

accu

racy

=93

%)

Bar

ela

nd31

20

00

033

9194

Cro

plan

d1

682

00

071

9196

Shru

b-/gr

assl

and

25

611

00

6995

88W

oodl

and

00

058

30

6195

95D

ense

fore

st0

01

248

051

9494

Wat

erbo

dy0

00

00

99

100

100

Tota

l34

7564

6151

929

4

1986

(ove

rall

accu

racy

=87

%)

Bar

ela

nd14

21

00

017

7482

Cro

plan

d4

575

10

067

8885

Shru

b-/gr

assl

and

16

700

00

7786

91W

oodl

and

00

453

40

6187

87D

ense

fore

st0

01

757

065

9388

Wat

erbo

dy0

00

00

77

100

100

Tota

l19

6581

6161

729

4

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5234 I. Ouedraogo et al.

1986 image and 93.4% for the 2002 image (table 1), which are more than acceptableaccording to Jansen et al. (2008) and Congalton and Green (2009). All image process-ing was done using Environment for Visualization Images (ENVI Version 4.2, ©2005,Research Systems, Inc. 4990 Pearl East Circle) and the vectorized data were processedwith MapInfo 7.5 (MapInfo Professional Version 7.5, Release Build 23, ©1985–2003MapInfo Corporation).

2.3 Data analyses

2.3.1 Land-cover transition matrix. Forest-cover transitions in the study area duringthe study period were analysed using a transition matrix obtained from cross-tabulation of the two raster maps (outlined above), using the methodology presentedby Pontius et al. (2004) to identify major signals of systematic land-cover change. Thegeneral structure of the transition matrix was arranged in such a way that the rowsand columns display the proportions of the six classes in 1986 and 2002, respectively(table 2). The diagonal cells (Cjj) indicate the proportions of land classes that did notchange (‘persistence’). Frequencies of persistence are required to compute two types ofchanges: the gains and the losses (Pontius et al. 2004). The off-diagonal entries, whichare Cjj (∀i �= j), indicate the proportion of the landscape that transited from class i toclass j between 1986 and 2002. The proportions of the landscape, Ci+ and C+j, thatwere occupied by class i in 1986 and class j in 2002 were computed using formulae (1)and (2), respectively:

Ci+ =n∑

i=1

Cij (1)

C+j =n∑

j=1

Cij (2)

where n is the total number of classes.The loss column shows the proportion of the landscape that experienced a gross loss

of class i between 1986 and 2002, whereas the gain row shows the proportion of thelandscape that experienced a gross gain of class j between the two years.

The concept of ‘swap’ implies simultaneous gain and loss of a given land class in thelandscape. The computation of swap requires the pairing of each pixel that loses witha pixel that gains (Braimoh 2006). Equation (3) defines the amount of swap, denotedSj, for each category j, as twice the minimum of the gain and loss.

Sj = 2 × min(Cj+ − Cjj, C+j − Cjj

)(3)

If the net change is zero, implying that gain is equal to loss, then the swap is twice theloss (Braimoh 2006).

2.3.2 Vulnerability to transition. The vulnerability of each land class to transition iscomputed using the gain-to-persistence ratio (Gp = gp−1), the loss-to-persistence ratio(Lp = lp−1) and the net change-to-persistence (Np = Gp – Lp). g, p and l represent gain,persistence and loss, respectively. Values of Gp and Lp exceeding one indicate that theland-cover class under consideration has a higher tendency to convert to other land

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Land-cover transition in the Tropics 5235

Tab

le2.

A6×

6la

nd-c

over

mat

rix.

2002

1986

Bar

ela

ndC

ropl

and

Shru

b-/gr

assl

and

Woo

dlan

dD

ense

fore

stW

ater

body

Tota

l198

6L

oss

Bar

ela

ndC

11C

12C

13C

14C

15C

16C

1+C

1+−

C11

Cro

plan

dC

21C

22C

23C

24C

25C

26C

2+C

2+−

C22

Shru

b-/gr

assl

and

C31

C32

C33

C34

C35

C36

C3+

C3+

−C

33W

oodl

and

C41

C42

C43

C44

C45

C46

C4+

C4+

−C

44D

ense

fore

stC

51C

52C

53C

54C

55C

56C

5+C

5+−

C55

Wat

erbo

dyC

61C

62C

63C

64C

65C

66C

6+C

6+−

C66

Tota

l200

2C

+1C

+2C

+3C

+4C

+5C

+61

Gai

nC

+1−

C11

C+2

−C

22C

+3−

C33

C+4

−C

44C

+5−

C55

C+6

−C

66

Not

e:C

isan

yco

nver

sion

from

one

cove

rcl

ass

toan

othe

r.

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5236 I. Ouedraogo et al.

classes than persist (Braimoh 2006), while negative values of Np indicate that the landclass has a higher tendency to lose area to other cover types than to gain from them.

2.3.3 Detection of systematic and random transitions. The identification of system-atic transitions within the transition matrix requires the calculation of transitioncategories in four steps (Pontius et al. 2004). The first step computes the expectedgain, Gij (gain of class i from class j), for each class under a random process of gainusing the following formula:

Gij = (C+j − Cjj

) (Ci+

1 − Cj+

), ∀i �= j (4)

Equation (4) assumes that the gain of each class and the proportion of each class in2002 are fixed, and then distributes the gain across the off-diagonal entries accordingto the relative proportion of the other classes in 1986. These expected gains representthe random process of gain.

The second step computes the differences between the observed and expected pro-portions of gain under a random process of gain. Large positive or negative deviationsfrom zero indicate that systematic inter-category transitions, rather than random tran-sitions, occurred between two land classes. The higher a positive computed difference,the larger the area affected by systematic gain of class j from class i and the higher anegative computed difference, the weaker the tendency of class j to gain systematicallyfrom class i (Pontius et al. 2004, Braimoh 2006).

The third step computes the expected loss, Lij (loss of class i to class j) under arandom process of loss using the formula:

Lij = (Ci+ − Cii)

(C+j

1 − C+j

), ∀i �= j (5)

Equation (5) assumes that the loss of each class is fixed and distributes the loss ineach row across the other classes relative to their proportions in 2002.

The final step computes the differences between the observed and expected pro-portions of loss under a random process of loss. Similarly, large positive or negativedeviations from zero indicate that systematic inter-class transitions have occurred,rather than random transition between two cover types. The higher a positive com-puted difference, the higher the tendency for class i to systematically lose to class j,and the higher a negative computed difference, the weaker the tendency for class i tosystematically lose to class j.

In general, if class i loses systematically to class j, and class j gains systematicallyfrom class i, then it can be concluded that there is a systematic process of transitionfrom i to j (Pontius et al. 2004, Alo and Pontius 2008).

3. Results

3.1 Inter-category transitions

In general, there were important changes among all land-cover classes. Table 3 sum-marizes the proportions in 1986 and 2002, together with the gain, loss, swap and netchange of each land-cover type during the intervening period. In 1986, woodland was

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Land-cover transition in the Tropics 5237

Table 3. Proportions of landscape-cover change (%) in southern Burkina Faso.

Total1986

Total2002 Gain Loss

Totalchange Swap

Absolute valueof net change

Bare land 0.04 0.45 0.43 0.02 0.45 0.04 0.41Cropland 8.75 17.03 12.72 4.44 17.16 8.88 8.28Shrub-

/grassland 34.44 35.89 20.53 19.08 39.61 38.16 1.45Woodland 46.93 24.05 9.27 32.15 41.42 18.54 22.88Dense forest 9.81 22.51 17.05 4.35 21.40 8.70 12.70Water body 0.02 0.06 0.04 0.00 0.04 0.00 0.04Total 100.00 100.00 60.04 60.04 60.04 37.16 22.88

Note: Change accuracy = 82%.

the dominant forest-cover type (47%), followed by shrub-/grassland (34%), while thedense forest represented only 10% of the study area. However, by 2002, the woodlandcover had decreased to 24% while the area of shrub-/grassland remained approxi-mately the same. The dense forest cover had increased to 22% and the area of croplandhad more than doubled (from 9% in 1986, to 17% in 2002).

Between the two dates, the shrub-/grassland, dense forest and cropland gainedmore, while woodland and shrub-/grassland experienced more loss. The woodlandexperienced the highest loss, over 32% of the landscape, followed by shrub-/grassland,over about 19% of the landscape. The gain-to-loss ratio was the highest (3.9) fordense forest, followed by cropland (2.8). Change attributable to location (swap) washighest for shrub-/grassland, accounting for about 96% of the total change for shrub-/grassland, followed by cropland (52% of the change in cropland) and woodland (45%of the change in woodland). Change attributable to quantity (net change) was highestfor woodland (22.9%), followed by dense forest (12.7%) and cropland (8.3%).

3.2 Persistence and vulnerability assessment

The proportions of different land-cover classes that did not change (persistence) from1986 to 2002 are shown in the diagonal of table 4. Among natural vegetation types,

Table 4. Land-cover change (%) matrix 1986–2002 in southern Burkina Faso.

2002

1986Bareland Cropland

Shrub-/grassland Woodland

Denseforest

Waterbody

Total1986 Loss

Bare land 0.02 0.02 0.00 0.00 0.00 0.00 0.04 0.02Cropland 0.24 4.31 2.18 1.63 0.38 0.01 8.75 4.44Shrub-

/grassland 0.12 6.36 15.36 6.20 6.38 0.02 34.44 19.08Woodland 0.06 6.10 15.69 14.78 10.29 0.01 46.93 32.15Dense forest 0.01 0.24 2.66 1.44 5.46 0.00 9.81 4.35Water body 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.00Total 2002 0.45 17.03 35.89 24.05 22.51 0.06 99.99 60.04Gain 0.43 12.72 20.53 9.27 17.05 0.04 60.04

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5238 I. Ouedraogo et al.

Table 5. Gain-to-persistence (Gp), loss-to-persistence (Lp) and net change-to-persistence (Np) ratios of the land-cover classes in southern Burkina Faso.

Gain (g) Loss (l) Persistence (p) Gp Lp Np

Bare land 0.43 0.02 0.02 21.50 1.00 20.50Cropland 12.72 4.44 4.31 2.95 1.03 1.92Shrub-/grassland 20.53 19.08 15.36 1.34 1.24 0.09Woodland 9.27 32.15 14.78 0.63 2.18 −1.55Dense forest 17.05 4.35 5.46 3.12 0.80 2.33Water body 0.04 0.00 0.02 2.00 0.00 2.00

the proportion of persistence was highest for shrub-/grassland (over about 15% oflandscape that was shrub-/grassland in 1986) and woodland (about 15% of its area in1986). About 4% of the landscape occupied by cropland in 1986 remained unchangedin 2002.

The vulnerability of land classes to transition is shown in table 5. The gain-to-persistence (Gp) was higher than one for all land classes except for woodland, andthe highest (21.5) for bare land. Remarkably, the loss-to-persistence (Lp) was onlyhigher than one for woodland (2.18), shrub-/grassland (1.24) and cropland (1.03).The net change-to-persistence (Np) was negative for woodland (–1.55) and positivefor the other classes. The Np value was the highest (20.5) for bare land, followed bydense forest (Np = 2.33).

3.3 Transition processes

The off-diagonal elements in table 6(a) represent the expected gains in land cover ata given persistence, if the change processes were random and the differences betweenthe observed gains (table 4) and the expected gain are given in table 6(b). The differ-ence between the observed and expected gains under a random process of change forthe shrub-/grassland to cropland transition is 1.56%, indicating that the transition of6.36% of the landscape from shrub-/grassland to cropland (table 4) was due to system-atic processes of change. The differences between the observed and expected gain forwoodland to dense forest and for woodland to shrub-/grassland transitions are 1.42%and 1.00%, respectively. This indicates that shrub-/grassland and dense forest system-atically gained from woodland over about 16% and 10% of the landscape, respectively(table 4). The differences between observed and expected gains for dense forest to crop-land and cropland to dense forest transitions are relatively large and negative (–1.13and –1.27, respectively). This implies that when cropland gains, new cropland system-atically avoided gaining from dense forest and, conversely, when dense forest gains,new dense forest systematically avoided gaining from cropland.

The expected losses under a random process of loss are shown in table 7(a),and the differences between the observed and expected losses in table 7(b). Thedifference between observed and expected loss for shrub-/grassland to cropland tran-sition was 1.29%, indicating that shrub-/grassland systematically lost to cropland.Similarly, the differences between the observed and expected losses for woodland toshrub-/grassland and for woodland to dense forest transitions were 0.5% and 0.76%,respectively, indicating that woodland has tended to lose systematically to both shrub-/grassland and dense forest. The difference between observed and expected loss for

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Land-cover transition in the Tropics 5239

Tab

le6.

Inte

r-ca

tego

ryga

ins

inso

uthe

rnB

urki

naF

aso:

(a)

expe

cted

gain

s;(b

)di

ffer

ence

betw

een

obse

rved

and

expe

cted

gain

s.

2002

1986

Bar

ela

ndC

ropl

and

Shru

b-/gr

assl

and

Woo

dlan

dD

ense

fore

stW

ater

body

Tota

l198

6L

oss

(a)

Exp

ecte

dga

ins

unde

ra

rand

ompr

oces

sof

gain

(%)

Bar

ela

nd0.

020.

010.

010.

010.

010.

000.

050.

03C

ropl

and

0.04

4.31

2.74

1.53

1.65

0.00

10.2

75.

96Sh

rub-

/gr

assl

and

0.15

4.80

15.3

66.

026.

510.

0132

.85

17.4

9W

oodl

and

0.20

6.54

14.7

014

.78

8.87

0.02

45.1

130

.34

Den

sefo

rest

0.04

1.37

3.07

1.71

5.46

0.00

11.6

66.

20W

ater

body

0.00

0.00

0.01

0.00

0.00

0.02

0.04

0.02

Tota

l200

20.

4517

.03

35.8

924

.05

22.5

10.

0699

.98

60.0

4G

ain

0.43

12.7

220

.53

9.27

17.0

50.

0460

.04

(b)

Diff

eren

cebe

twee

nth

eob

serv

edla

ndsc

ape

tran

siti

ons

and

the

expe

cted

gain

s(%

)B

are

land

0.00

0.01

−0.0

1−0

.01

−0.0

10.

00−0

.01

−0.0

1C

ropl

and

0.20

0.00

−0.5

60.

10−1

.27

0.01

−1.5

2−1

.52

Shru

b-/gr

assl

and

−0.0

31.

560.

000.

18−0

.13

0.01

1.59

1.59

Woo

dlan

d−0

.14

−0.4

40.

990.

001.

42−0

.01

1.82

1.82

Den

sefo

rest

−0.0

3−1

.13

−0.4

1−0

.27

0.00

0.00

−1.8

5−1

.85

Wat

erbo

dy0.

000.

00−0

.01

0.00

0.00

0.00

−0.0

2−0

.02

Tota

l200

20.

000.

000.

000.

000.

000.

000.

010.

01G

ain

0.00

0.00

0.00

0.00

0.00

0.00

0.00

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5240 I. Ouedraogo et al.

Tab

le7.

Inte

r-ca

tego

rylo

sses

inso

uthe

rnB

urki

naF

aso:

(a)

expe

cted

loss

;(b)

diff

eren

cebe

twee

nob

serv

edan

dex

pect

edlo

ss.

2002

1986

Bar

ela

ndC

ropl

and

Shru

b-/gr

assl

and

Woo

dlan

dD

ense

fore

stW

ater

body

Tota

l198

6L

oss

(a)

Exp

ecte

dlo

sses

unde

ra

rand

ompr

oces

sof

loss

(%)

Bar

ela

nd0.

020.

000.

010.

000.

000.

000.

040.

02C

ropl

and

0.02

4.31

1.92

1.29

1.20

0.00

8.75

4.44

Shru

b-/gr

assl

and

0.13

5.07

15.3

67.

166.

700.

0234

.44

19.0

8W

oodl

and

0.19

7.21

15.1

914

.78

9.53

0.03

46.9

332

.15

Den

sefo

rest

0.03

0.96

2.01

1.35

5.46

0.00

9.81

4.35

Wat

erbo

dy0.

000.

000.

000.

000.

000.

020.

020.

00To

tal2

002

0.39

17.5

534

.49

24.5

822

.90

0.07

99.9

960

.04

Gai

n0.

3713

.24

19.1

49.

8017

.44

0.05

60.0

4

(b)

Diff

eren

cebe

twee

nth

eob

serv

edla

ndsc

ape

tran

siti

ons

and

the

expe

cted

loss

(%)

Bar

ela

nd0.

000.

02−0

.01

0.00

0.00

0.00

0.00

0.00

Cro

plan

d0.

220.

000.

260.

34−0

.82

0.01

0.00

0.00

Shru

b-/gr

assl

and

−0.0

11.

290.

00−0

.96

−0.3

20.

000.

000.

00W

oodl

and

−0.1

3−1

.11

0.50

0.00

0.76

−0.0

20.

000.

00D

ense

fore

st−0

.02

−0.7

20.

650.

090.

000.

000.

000.

00W

ater

body

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Tota

l200

20.

06−0

.52

1.40

−0.5

3−0

.39

−0.0

10.

010.

00G

ain

0.06

−0.5

21.

39−0

.53

−0.3

9−0

.01

0.00

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Land-cover transition in the Tropics 5241

the woodland to cropland transition was large and negative (–1.11%), implying thatwoodland systematically avoided losing to cropland. In the same manner, croplandtended to systematically avoid losing to dense forest; shrub-/grassland systematicallyavoided losing to woodland, and dense forest systematically avoided losing to cropland(table 7(b)).

4. Discussion

The results show that the woodland represented the most dominant natural vegeta-tion cover in 1986, but in 2002 this cover type was the least represented. This wasdue to the degradation of this land-cover class to shrub-/grassland (16%) and itsimprovement to dense forest (10%). Degradation of woodland to shrub-/grasslandin southern Burkina Faso was probably due to logging for firewood and charcoal(Ouedraogo 2006a, 2006b), whereas its improvement to dense cover occurred in pro-tected areas. The apparent increase in vegetation cover could be the result of theenvironmental policies introduced during 1983–1987. A strong interest in environ-mental protection led to the institution of three programmes designated to preventillegal cutting, bushfires and free mobility of livestock in the forest (Yameogo 2005,Ouedraogo et al. 2009), as well as the demarcation of protected areas. These rules werestrongly enforced, which may have contributed to regrowth in the protected forests.The profound change in woodland amounted to a loss of 32% of landscape area in2002. In 2002, cropland area was more than doubled. Gain in cropland was associ-ated with a progressive expansion of the agricultural frontier as the demand for foodincreased together with local population. The expansion of commercial agriculture inthe region also contributed to this change. Similar findings of increasing cropland havebeen reported in Sissili province (Paré et al. 2008, Ouedraogo et al. 2009) and northernGhana (Braimoh 2006), which is geographically close and ecologically similar to ourstudy area.

The assessment of vulnerability to transition showed that Gp values were higherthan one for all land-cover classes except woodland, indicating that all cover types(except woodland) had a tendency to gain rather than persist. The highest Gp value, of21.5, for bare land indicates that despite its small proportion in the landscape in 2002(0.5%), this cover type has tended to increase in the study area. This could be linkedto changes in major economic activities in the region. Examples are the increase ofcash-crop production, with its associated land clearing and increased developmentfor human needs around settlements. In addition, the expansion and upgrading ofthe national and regional road network, which increases bare land by complete landclearance (Forman and Alexander 1998), might have contributed to the increased Gp

of this class. The lowest Gp value (0.63) for woodland indicates that this class hastended to lose to the other cover types. This is confirmed by the high Lp value (2.18)for woodland. The Lp of 1.03 for cropland indicates that cropland that was eitherfallowed or abandoned accounted for as much as 50% of the cropland area present in1986. This is an indication of the persistence of shifting cultivation.

Our results show that the transition of landscape from shrub-/grassland to croplandwas due to systematic processes of change, with cropland gains occurring preferen-tially from shrub-/grassland and not from dense forest and woodland. Hence, localfarmers seem to preferentially clear shrub-/grassland, which is less labour demand-ing for clear-cutting (Murphy and Lugo 1986), to make space for new farmland. Thissuggests that, at a regional scale, farming activities although recently amplified by

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5242 I. Ouedraogo et al.

rural migration (Ouedraogo et al. 2009) do not cause declines in woodland or indense forest. Conversely, shrub-/grassland was systematically gained from woodland,therefore indicating that firewood and charcoal produced in southern Burkina Faso tomeet energy requirements in the biggest nearby cities (Ouagadougou and Koudougou)(Krämer 2002, Ouedraogo 2006a), come preferentially from the woodlands.

The concurrent incidence of systematic gains and losses (tables 6(b) and 7(b)) leadto the conclusion that the major signals of change (figure 2) are: (i) the degradationof woodland to shrub-/grassland over about 15.7% of the landscape, (ii) an increasein biomass from woodland to dense forest over 10% of the landscape, and (iii) theconversion of shrub-/grassland to cropland over 6% of the landscape. Systematic tran-sitions between woodland and dense forest will impact carbon sources and sinks in theecosystem (Braimoh 2006). The regrowth of woodland may have been stimulated bythe management systems in the protected zones in the study area.

In addition to systematic processes of change, the spatial patterns of randomforest-cover change can provide potentially useful information on forest-cover changeprocesses in hotspot areas (Pontius et al. 2004, Braimoh 2006), as illustrated infigure 3. In areas indicated by boxes Aa and Ab (figure 3), there was an improve-ment of shrub-/grassland to woodland. This kind of transition occurred mostly inprotected areas (Aa) and in village forests (Ab), where vegetation is protected fromlogging, grazing and farming. In areas indicated by box B, there was a conversionof woodland to cropland. This could be due to the recent massive and uncontrolledmigration of farmers from drought-affected areas in the northern and central zones ofthe country to the southern, western and eastern regions (Reenberg and Lund 1998,Gray 2005, Ouedraogo et al. 2009). Moreover, it could reflect the spatial stigmatization

Figure 2. Systematic forest-cover transition in southern Burkina Faso.

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Land-cover transition in the Tropics 5243

Figure 3. Random forest-cover transition in southern Burkina Faso (Aa and Ab indi-cate improvement of shrub-/grassland to woodland in protected forest and village forest,respectively; B indicates uncommon conversion from woodland to cropland; and C, sporadicconversion from dense forest to cropland).

of the international returned migrant Burkinabe who were working in cocoa, coffeeand banana plantations in Côte d’Ivoire, stimulated by the political crisis that hasaffected that country since the 2000s. Most of the returning migrants were resettled bythe government in southern Burkina Faso and, during their first years of settlement,they cut the woodlands to make charcoal for cash generation then converted the cutareas to farmland (Ouedraogo et al. 2009). Even the protected areas were not safefrom this process. Finally, box C indicates areas where dense forest was sporadicallyconverted to cropland, mainly yam plantations, which needs deep soils and conditionsthat are more humid than other types of crops grown in the area.

5. Conclusions

The methodology applied in this article provides the means to obtain a better under-standing of the dynamic processes affecting forest cover in southern Burkina Faso.Our findings indicated that systematic rather than random processes were the maindrivers of landscape change. New cropland was preferentially derived from shrub-/grassland, rather than woodland and dense forest. The results also indicated thatdense forest has increased in size as a transition from woodland due to natural succes-sion. This conflicts with the widely held belief that forest resources in southern BurkinaFaso are facing total depletion. Further investigations of forest-cover transitioninside and outside protected areas in southern Burkina Faso are needed to eluci-date the changes, and underlying causes, in more detail. From a planning perspective,the dominance of systematic processes should facilitate regional land-use planning

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5244 I. Ouedraogo et al.

and sustainable forest management in a context of immigration and agriculturalintensification.

AcknowledgementsFunding for this study was provided by the Swedish International DevelopmentCooperation Agency (Sida). The authors thank Pascaline Coulibaly-Lingani forhelpful collaboration during fieldwork and data-processing periods and also JohnBlackwell and Sees-editing Ltd for linguistic improvements.

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