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Trajectory Analysis of Forest CoverChange in the Tropical Dry Forest ofBurkina Faso, West AfricaIssa Ouedraogo a , Patrice Savadogo a b , Mulualem Tigabu a , RoyCole c , Per Christer Oden a & Jean-Marie Ouadba ba Southern Swedish Forest Research Centre, Swedish University ofAgricultural Sciences (SLU) , Swedenb Centre National de la Recherche Scientifique et Technologique,Institut de l'Environnement et de Recherches Agricoles ,Ouagadougou , Burkina Fasoc Geography and Planning Department , Grand Valley StateUniversity , USAPublished online: 23 May 2011.

To cite this article: Issa Ouedraogo , Patrice Savadogo , Mulualem Tigabu , Roy Cole , PerChrister Oden & Jean-Marie Ouadba (2011) Trajectory Analysis of Forest Cover Change in theTropical Dry Forest of Burkina Faso, West Africa, Landscape Research, 36:3, 303-320, DOI:10.1080/01426397.2011.564861

To link to this article: http://dx.doi.org/10.1080/01426397.2011.564861

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Trajectory Analysis of Forest CoverChange in the Tropical Dry Forestof Burkina Faso, West Africa

ISSA OUEDRAOGO*, PATRICE SAVADOGO*{,MULUALEM TIGABU*, ROY COLE{, PER CHRISTER ODEN* &JEAN-MARIE OUADBA{*Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences (SLU), Sweden{Centre National de la Recherche Scientifique et Technologique, Institut de l’Environnement et deRecherches Agricoles, Ouagadougou, Burkina Faso {Geography and Planning Department, Grand ValleyState University, USA

ABSTRACT Forest cover decline is one of the most important environmental issues in the tropics.The present study was carried out in Burkina Faso, West Africa, and aimed at assessing thetrajectories of forest cover change and measuring landscape metrics of the trajectory classes inorder to better understand the processes of change. Landsat and ASTER images acquired over aperiod of 30 years were used for cover change detection and the Fragstats package was used tocompute landscape metrics with five unifying change classes. Results showed a substantialincrease in cropland with concurrent decline in forest cover. Deforestation represented 63% of thePercentage of Landscape (PLAND) in 2006, while reforestation accounted for only 28%. Bothof these classes had high Normalized Landscape Shape Index (NLSI) values, indicating that theywere present as scattered small patches. The old cultivation (30-year permanent cropland) wasaggregated (IJI ! 0) while deforestation exhibited highly interspersed patches. The old forestand old cultivation presented lower Area Weighted Fractal Dimension Index (FRAC_AM), butdeforestation and reforestation had the higher FRAC_AM. These results confirmed that therewas a high level of deforestation and fragmentation in southern Burkina Faso and justify the needfor a proper management plan to ensure the sustainable use of forest resources.

KEY WORDS: Landscape metrics, deforestation, dry forest, land cover change, Fragstats

1. Introduction

Forest cover decline is one of the most important environmental issues in the tropicswhere forest resources contribute significantly to the national economies and providethe majority of domestic energy and housing requirements (Geist & Lambin, 2001).However, in most tropical African countries, little effort has been made to efficientlyestimate forest extent and stocking to support the social, economic and environ-mental objectives related to forestry (FAO, 2006).

Correspondence Address: Issa Ouedraogo, Southern Swedish Forest Research Centre, Tropical Silvicultureand Seed Laboratory, Swedish University of Agricultural Sciences (SLU), PO Box 49, SE-230 53 Alnarp,Sweden. Email: issadeh@hotmail.com; issa.ouedraogo@slu.se

Landscape Research,Vol. 36, No. 3, 303–320, June 2011

ISSN 0142-6397 Print/1469-9710 Online/11/030303-18 ! 2011 Landscape Research Group LtdDOI: 10.1080/01426397.2011.564861

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Remotely sensed data have been used for forest cover change studies for decades(Zhou et al., 2008a, 2008b) and the availability of multi-temporal imagery has madelong-term land cover change analysis possible (Achard et al., 2002; Braimoh &Vlek, 2005; Focardi et al., 2008; Lambin et al., 2003; Ouedraogo et al., 2009). Thesestudies have demonstrated that remote sensing is an efficient tool for detectingchanges and for interpreting the causes of deforestation and the impacts of suchchanges at different spatial scales. Monitoring land cover change is also perceivedas one of the most important contributions of remote sensing technology to thestudy of global ecological and environmental change (Southworth et al., 2002).Some researchers (Borak et al., 2000; Crews-Meyer, 2004; Zhou et al., 2008a) haveused this technology to analyze landscape patterns and showed that patterns ofdeforestation and reforestation most frequently relate to human impacts and areextremely complex, due to changes occurring across multiple temporal and spatialscales.

The spatial pattern of landscape change is an important indicator ofenvironmental monitoring and has been subjected to intensive investigations(Nagendra et al., 2004; Zhou et al., 2008b). Methods for studying the spatial patternof forest cover change include the measurement of landscape metrics of categoricaltrajectories derived from the multi-temporal images. These methods were recentlydeveloped to trace paths of land cover change for a given location (Zhou et al.,2008b). Examples of such studies include Mertens and Lambin (2000) in Cameroon,Petit et al. (2001) in Zambia, and Liu and Zhou (2004) in China. Zhou et al. (2008b)reported that the majority of spatial pattern analyses are based on the ‘snapshot’method, which attempts to determine the track of spatial patterns and hence inferthe ecological processes (Crews-Meyer, 2004; Southworth et al., 2002). However,this approach does not provide sufficient information to understand the causes ofland cover change. Mertens and Lambin (2000) suggested that landscape processescan be better measured and understood through an examination of the change ofthe trajectory patterns, rather than the ‘snapshot’ approach. Therefore, Zhou et al.(2008b) analyzed the spatial patterns of land cover change trajectories in northwestChina using the landscape metrics measurement approach and derivedaccurately ranked factors, both natural and human, driving land cover change inthe region. Their investigation extended the scope of categorical change trajectoryanalysis.

We used the recently improved landscape metrics measurement approach ofZhou et al. (2008b) to better understand the processes of change in the dry forestecosystem of southern Burkina Faso. We highlighted the importance of analyzinglandscape metrics measurement as an addition to the static land cover/land useclasses that are more commonly studied in southern Burkina Faso (e.g.Ouedraogo, 2006b; Ouedraogo et al., 2009; Pare et al., 2008). Our analysis wasperformed for To District of Sissili Province in southern Burkina Faso, thought tobe experiencing important forest losses conjointly due to uncontrolled farmlandexpansion, extensive wood extraction, overgrazing and bushfires (Ouedraogoet al., 2009). The basic approach was to derive and interpret spatial patternmetrics of multi-epoch trajectories of forest cover change using high resolutionimagery from different sources and background over a monitoring time frame of30 years.

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2. Materials and Method

2.1. Description of the Study Area

The study was carried out in To District, located in Sissili Province (108 570N to 118550N and 728 480W to 718 240W), southern Burkina Faso, West Africa (Figure 1).The district covers 1203 km2 and is characterized by a low relief with an averagealtitude of 300 m a.s.l. Phyto-geographically, To District belongs to the south-Sudanian ecological zone (Fontes & Guinko, 1995). The natural vegetationcomprises a mixture of mostly dry forest and tree savanna types. The unimodalrainy season lasts for about six months, from May to October. Based on datacollected from the in situ mini-weather station at Leo, the provincial city located14 km from the study site, the mean (+SE) annual rainfall during the study period(1975–2007) was 883+ 147 mm. Mean daily minimum and maximum temperaturesranged from 16 to 328C in January (the coldest month) and from 26 to 408C in April(the hottest month). According to the FAO’s soil classification system (Driessenet al., 2001), the most frequently encountered soil type is Lixisol (tropical ferruginoussoils), which is poorly to fully leached, overlying sandy, clayey-sandy and sandy-clayey material.

With a total of 47 666 inhabitants in 2006, distributed in 24 villages (INSD, 2007),To is the most populated district in Sissili Province. This is due to a high immigrationrate (mostly farmers) since the 1980s (Henry et al., 2003; INSD, 2007; Ouedraogo,2006b; Ouedraogo et al., 2009). The population comprises four main ethnic groups:Nuni, Wala, Mossi and Fulani. The first two ethnic groups have been living in thearea for centuries and are considered indigenous, while Mossi, originated fromcentral plateau in Burkina Faso and Fulani, herders from the northern region of thecountry are considered to be migrants because they were attracted to Sissili Provinceduring the 1980s in search of arable land and green pasture, respectively (Howorth &O’Keefe, 1999). The dominant farming system in the study area is traditionalcultivation of cereals (such as sorghum, millet and maize) and tubers (yam and sweetpotatoes), and animal husbandry. Over the last decade, there has been an intensecompetition between the traditional farming system and more lucrative productionsystems, involving cash crop production (cotton and cashew nut) and ranching (Pareet al., 2008). Trees are also cut by the local people for commercial fuel wood andconstruction poles. The district provides fuel wood and charcoal for the inhabitantsof Ouagadougou and Koudougou, located at 80 km and 150 km away, respectively.Non-timber forest products, such as fruits, leaves, tubers, perennial grass straw andhay, are also harvested.

2.2. Detection of Forest Cover Change

Four Landsat scenes and one ASTER (Advanced Spaceborne Thermal Emission andReflection Radiometer) image of the study area acquired over a period of 30 years(1976–2006) were used to detect forest cover change (Table 1). Each image was geo-rectified to UTM WGS 84 Zone 30 North coordinates using ground control points(Focardi et al., 2008) collected during fieldwork in January and February 2009. Afirst-order affine transformation was applied to all data, resulting in a root meansquare error below 15 m for the Landsat images (Braimoh & Vlek, 2005). The visible

Forest Cover Change Trajectory in Burkina Faso 305

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Figure 1. Location of the study area.

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and the near-infra-red bands (1, 2 and 3) of the ASTER image were resized to aresolution of 306 30 m to fit the Landsat scenes. Land-cover classification for theimages was carried out based on training sites, available topographic maps and in situobservations (Braimoh, 2004; Focardi et al., 2008; Ouedraogo et al., 2010). Trainingsites for supervised classification were collected at different periods of the year(January/February, April/May and July) to take into account the differentphenological stages of the vegetation of the study areas. The procedure first involvedradiometric rectification of the 1976, 1986, 1992 and 2000 images to the 2006 image(Hall et al., 1991), followed by a tasselled cap orthogonal transformation (Kauth &Thomas, 1976) of the original six bands in each image into three new dimensionalspaces, corresponding to soil brightness, green vegetation and moisture indices. Apartfrom yielding relevant training data for land use classification, the transformationalso improved visual discrimination of land use types. Classification was carried outon three bands of the tasselled cap transformation using the maximum likelihoodalgorithm. Four classification schemes, based on the classification of Lamprecht(1989), were used to assign pixels to four land cover classes; namely cropland,shrubland, woodland and dense forest (Table 2). As the cropland has several features,it could not be classified simultaneously. Therefore, we first made a detailedclassification using training samples for each individual component of the cropland(harvested and non-harvested maize, sorghum and cotton fields, yam fields, pastureand young fellows), and finally we grouped all components to make a single cropland

Table 1. References and date of acquisition of Landsat and ASTER images used in the presentstudy

Scene references Acquisition date Satellite Cloud cover

LM22100521976090AAA04 30 March 1976 Landsat MSSS 0%ETP195R52_5T19861118 18 November 1986 Landsat TM 0%L4_1992101819450 18 October 1992 Landsat TM 0%p195r052_7t20010714 14 July 2001 Landsat ETMþ 0%AST_L1A.003:2037879937 23 October 2006 ASTER 0%

Table 2. Land-use classification schemes

Land-use class Description

Cropland Farmland, harvested agricultural land, pastures and young fallow withsigns of human induced disturbances, such as felled tree trunks or stumps

Shrubland Land covered by shrubs, bushes, grass and young broadleaf plantlets (550trees/ha)

Woodland Open woodland with an upper stratum of deciduous trees of small tomedium size, with their crowns more or less touching above a sparsewoody stratum. The ground layer consists of undergrowth of shrubs andgrasses (75–150 trees/ha)

Dense forest Forests and closed woody vegetation, gallery forest and close lowlandwoody vegetation

Adapted from Lamprecht (1989).

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cover class. All image processing was done using ENVI (ENVI Version 4.2,Copyright 2005, Research Systems, Inc. 4990 Pearl East Circle) and the vectorizeddata were processed with MapInfo 7.5 (MapInfo Professional Version 7.5, ReleaseBuild 23, Copyright 1985–2003 MapInfo Corporation).

2.3. Accuracy Assessment

Stratified random sampling methods were used to select, at different periods of theyear, reference sample points for classification accuracy assessment. Three hundredand twelve sample points were generated and transferred to a Geographic InformationSystems (GIS) program, in which they were overlaid with the classified images, as wellas the high-resolution visible and near-infra-red bands (1, 2 and 3) of the ASTERimage (Zhou et al., 2008a, 2008b). The high-resolution visible and near-infra-red bandsof the ASTER image served as a basis for the assessment of the accuracy together withhistorical panchromatic aerial photographs of the study area. The in situ datacollection is basically the ground truthing: delineation of homogeneous training sitesfor the supervised classification as well as the collection of sampling points for the geo-rectification of the images and the accuracy assessment. This was done using highlyprecise GPS, sample historical aerial photographs from 1976, 1986, 1991 and 2001.The topographic map helped to find the sites, and also contributed to fix the geo-rectification issues of the images, where mean root square after the geometricalcorrection is below half of their pixel resolution (15 m for Landsat and 7.5 m forASTER). The coordinates of reference points for the geo-rectification was measured inJanuary/February, but training sites for supervised classification and sampling pointsfor the accuracy assessment were collected at different periods of the year (January/February, April/May and July) to take into account the different phenological stagesof the vegetation of the study areas. A total of 312 sample points were randomlyselected for accuracy assessment, of which 15 points showed discrepancy between thepresent and historical images (satellite images and panchromatic aerial photographs).These points were clarified by interviewing elderly people (n¼ 30) living and farmingaround these sample points. The information obtained from them was used to correctthe land use classes at a particular time. The sample points (312) were then used tocompute the error matrix for each time period and land use class following theprocedure of Lillesand et al. (2008) and Congalton and Green (2009).

2.4. Trajectory Analysis

The term ‘trajectory of forest cover change’ refers here to successions of land covertypes for a given sample unit over more than two observations (Liu & Zhou, 2004;Mertens & Lambin, 2000; Petit et al., 2001; Zhou et al., 2008a, 2008b). The numberof trajectories (T) is given by T¼ nx where n is the number of land cover classes and xthe number of observations. In the present study, three time series (1976, 1992 and2006) were used to analyze forest cover trajectories. To simplify the analysis, eachland cover class was coded as C for cropland, S for shrubland, W for woodland andD for dense forest land. Thus, the three observations (time series) and the four landcover classes resulted in 64 trajectories (Figure 2). A forest cover change trajectorycan be specified for instance as C-C-C, meaning that the land was found to be

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cropland during the three observations (no change occurred) or S-W-D, meaning thatthe land was found to be shrubland in 1976, woodland in 1992, and dense forest in2006. To establish forest cover change trajectories, all classified images were vecto-rized and converted into ‘shp’ format for further use. With the Arcview GIS program,we clipped the vector files to bind them to the district borders. We then, successivelyintersected the three forest cover layers, starting from 1976. The data base from thegeo-processing was exported to Microsoft Excel for complementary analyses.

2.5. Landscape Metrics Computation

Landscape metrics of forest cover change trajectories were computed using theFragstat program (Spatial Pattern Analysis Program Quantifying LandscapeStructure, version 3.3 build 5). The program is designed to compute a wide rangeof landscape metrics for categorical map patterns (McGarigal & Marks, 1995;McGarigal et al., 2002; Nagendra et al., 2004; Southworth et al., 2002; Zhou et al.,2008b). The resulting file of the successive intersected vectors (‘shp’ format) wasimported to Idrisi Taiga (Idrisi Production, Copyright 1987–2009, Clark University,version 16.05) and converted to Idrisi raster format (‘rdc’) which is compatible withFragstats software. Four landscape metrics supported by Fragstats and commonlyused in ecological studies (Table 3) were computed, namely Percentage of Landscape(PLAND), Normalized Landscape Shape Index (NLSI), Interspersion and Juxtapo-sition Index (IJI) and Area Weighted Fractal Dimension Index (FRAC_AM).PLAND measures the abundance of the individual forest cover change trajectoryclasses; NLSI measures class aggregation; IJI describes how a given trajectory classspatially associates with other classes, and FRAC_AM measures the complexity ofthe shapes of class boundaries using the class area as a weighting factor.

All 64 trajectories were merged into five unified classes to simplify spatial patternmetrics analysis (Table 4). Old cultivation represents an area of cropland which didnot undergo any change during the 30-year period. Old forest refers here to forestcover types such as shrubland, woodland and dense forest which remainedunchanged during the study period. Agricultural deforestation is any forest coverrecently converted into cropland; non-agricultural deforestation represents any

Figure 2. All possible land cover trajectories in the study area. The thick lines show changetrajectories as C!C!C (bolded lines) indicating that the same land cover type was found onthe same point over the past 31 years; and a trajectory as S!W!D (dotted lines), meaningthat the land was found to be shrubland in 1986, woodland 1992, and dense forest in 2006.

Forest Cover Change Trajectory in Burkina Faso 309

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Table

3.Lan

dscap

emetrics

foran

alyzingspatialpatternsoflandcoverchan

getrajectories

Abbreviation

Nam

eEquationa

Interpretation

PLAND

Percentage

of

Lan

dscap

ePLAND¼

Pi¼

Pn j¼1

a ij

A$100

Ameasure

ofproportional

abundan

ceofeach

classin

thearea,

rangingfrom

0to

100.

PLAND!

0when

classibecomes

increasinglyrare,whilePLAND¼100when

theentire

area

consistsofasingleclass.

NLSI

Norm

alized

Lan

dscap

eShap

eIndex

NLSI¼

e i%min

e imax

e i%min

e iA

measure

ofclassag

gregation,rangingfrom

0to

1.NLSI¼0

when

thearea

consistsofasinglesquareormaxim

ally

compactpatch

oftheclass;NLSIincreasesas

theclass

becomes

increasinglydisaggregated.

IJI

Interspersionan

dJuxtap

osition

Index

IJI¼

%Pm k¼1

!"e ik

Pm k¼1

e ik

#ln

"e ik

Pm k¼1

e ik

#$

lnðm%1Þ

$100

Ameasure

ofinterspersionoverthemaxim

um

possible

interspersionforthegivennumber

ofclasses,rangingfrom

0to

100.

IJI!

0when

theclassisad

jacentto

only

oneofthe

other

classes.IJI¼100when

theclassisequally

adjacentto

allother

classes(i.e.maxim

ally

interspersedan

djuxtap

osed

toother

classes).

FRAC_A

MAreaWeigh

ted

Fractal

Dim

ension

Index

FRAC

AM¼Pn j¼

1

!"2lnð0:25p ijÞ

lnða

ijÞ

#"

a ij

Pn j¼1

a ij#$

Ameasure

ofcomplexity

oftheshap

esofclassboundaries,

rangingfrom

1to

2.It

usesclassarea

asaweigh

tingfactor.

FRAC_A

M!1forshap

eswithvery

simple

perim

eterssuch

assquares,an

dFRAC_A

M!2forshap

eswithhighly

convo

luted,plane-fillingperim

eters.

Source:

McG

arigal

etal.(200

2).

aWhereiis

theclassofinterest;jis

thepatch

number

ofclassi;Pi¼proportionofthelandscap

eoccupiedbyclassi;a ij¼area

(m2)ofpatch

ij;

A¼totallandscap

earea

(m2);e i¼totallengthofedge

(orperim

eter)ofclassiin

term

sofnumber

ofcellsurfaces

(includingalllandscap

eboundary

andbackgroundedge

segm

entsinvo

lvingclassi);min

e i¼minim

um

totallengthofedge

(orperim

eter)ofclassiin

term

sofnumber

ofcellsurfaces;

max

e i¼maxim

um

totallengthofedge

(orperim

eter)ofclassiin

term

sofnumber

ofcellsurfaces;e ik¼totallength(m

)ofedge

inlandscap

ebetweenclassesian

dk;m¼number

ofclassespresentin

thelandscap

e,includingthelandscap

eborder,ifpresent;pij¼perim

eter

(m)ofpatch

ij.

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forest cover (dense forest or woodland) recently converted into lower forest cover,and reforestation is old cropland recently converted into any forest cover orimprovement of previous forest cover due to natural successions.

3. Results

3.1. Land Use Change

The overall accuracy of image classification ranged from 87% to 95% for all images(Table 5). In general, the area of cropland increased rapidly during the study period(Table 6 and Figure 3). It changed from 4% in 1976 to 43% in 2006 via 14% in 1986.The area of dense forest decreased considerably and dropped from 53% in 1976 to27% in 1986 and 17% in 2006. A large decrease was also observed in the area ofwoodland from 13% in 1976 to 7% in 2006. The area of shrubland experiencedchanges in two phases: it increased from 30% in 1976 to 53% in 1986 and 1992 andthen dropped back to 47% in 2001 and 32% in 2006.

3.2. Landscape Metric Parameters

The selected four landscape metrics computed in Fragstats described the spatialpattern of forest cover change trajectory classes in the study area. Results of the

Table 4. Land cover change trajectories

Trajectoryclasses Definition Trajectoriesa

Old cultivation At least 30-yearpermanent cropland

C-C-C

Old forest At least 30-yearpermanent forest

S-S-S; W-W-W; D-D-D

Agriculturaldeforestation

Conversion from anyforest cover type tocropland

C-D-C; C-S-C; C-W-C; D-C-C; D-D-C;D-W-C; D-S-C; S-C-C; S-D-C; S-S-C;S-W-C; W-C-C; W-D-C; W-S-C; W-W-C

Nonagriculturaldeforestation

Conversion from oneforest cover type toanother forest covertype with reductionof cover intensity

C-D-S; C-D-W; C-W-S; D-D-S; D-D-W;D-S-S; D-W-S; D-W-W; S-D-S; S-D-W;S-W-S; W-D-S; W-D-W; W-S-S; W-W-S

Reforestation Conversion ofcropland to anyforest cover type

C-C-D; C-C-S; C-C-W; C-D-D; C-S-D; C-S-S;C-S-W; C-W-D; C-W-W; D-C-D; D-C-S;D-C-W; D-S-D; D-S-W; D-W-D; S-C-D;S-C-S; S-C-W; S-D-D; S-S-D; S-S-W; S-W-D;S-W-W; W-C-D; W-C-S; W-C-W; W-D-D;W-S-D; W-S-W; W-W-D

Total – 64

aC is for Cropland; D, Dense forest; S, Shrubland and W, Woodland.C-C-C indicates that the area was found to be cropland in 1976, 1992 and 2006.C-D-S means that the area was found to be cropland in 1976, dense forest in 1992 andshrubland in 2006.

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Table

5.Errormatricesforthesupervisedclassificationsofthelandcoverin

2006,2001,1992,1986

and1976

2006

2001

1992

1986

1976

PA

a(%

)UA

b(%

)PA

(%)

UA

(%)

PA

(%)

UA

(%)

PA

(%)

UA

(%)

PA

(%)

UA

(%)

Cropland

91.87

95.76

86.90

91.25

93.81

97.85

86.89

88.33

87.27

87.27

Shrubland

89.87

87.65

90.29

86.92

95.18

91.86

87.10

90.00

87.76

89.58

Woodlan

88.06

88.06

86.36

86.36

90.79

93.24

95.12

87.64

87.37

89.25

Dense

forest

95.35

89.13

93.22

93.22

98.21

93.22

92.11

95.89

95.31

89.71

Overallaccuracy

c91.03

89.10

94.23

90.38

89.10

aPA:Producer’saccuracy

isameasure

ofhow

accurately

thean

alystclassified

theim

agedatabycatego

ry(columns).Theproducer’saccuracy

detailstheerrors

ofomission.Anerrorofomissionresultswhen

apixelisincorrectlyclassified

into

another

catego

ry.Thepixelisomittedfrom

its

correctclass.

bUA:User’saccuracy

isameasure

ofhow

welltheclassificationperform

edin

thefieldbycatego

ry(rows).Theuser’saccuracy

details

errors

of

commission.Anerrorofcommissionresultswhen

apixel

iscommittedto

anincorrectclass.

c Overallaccuracy

isthenumber

ofcorrectobservationsdivided

bythenumber

oftotalobservation.In

thepresentcase

thenumber

oftotal

observationis312.

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Table

6.Forest

coverchan

gein

ToDistrict

Lan

dcoverclasses

1976

1986

1992

2001

2006

ha

%ha

%ha

%ha

%ha

%

Cropland(C

)4237.30

3.52

16635.00

13.82

27113.50

22.52

30762.40

25.56

52019.58

43.22

Dense

forest

(D)

63455.70

52.71

32875.80

27.31

23328.60

19.38

25515.90

21.20

20364.83

16.92

Shrubland(S)

36563.70

30.37

63665.97

52.89

63082.40

52.40

56447.80

46.89

39037.26

32.44

Woodland(W

)16

118.30

13.39

7198.23

5.98

6850.50

5.69

7648.90

6.35

8927.57

7.42

Total

120375.00

100

120375.00

100.00

120375.00

100.00

120375.00

100.00

120375.00

100.00

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measurements are shown in Table 7. During the study period, the unchangedcropland was negligible and the unchanged forest cover (shrubland, woodland anddense forest) represented only 9% of the study area. In contrast, forest clearing foragriculture accounted for 30% and forest conversion into other classes different fromcropland (non-agricultural deforestation) reached 33%, while improvement of forestcover amounted to 29%.

Figure 3. Pictorial representation of forest cover change in To district during the studyperiod.

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Old cultivation has the smallest NLSI (0.0949) followed by old forest (0.1456).Agricultural deforestation, non-agricultural deforestation and reforestation had highNLSI values. Old cultivation showed a null IJI while the other classes had an IJIhigher than 50. In general, old cultivation and old forest had lower FRAC_AMcompared with other trajectories. The agricultural deforestation and non-agricultural deforestation had the highest FRAC_AM values of 1.2415 and 1.2821respectively.

4. Discussion

In general the area of cropland continuously increased at the expense of decliningforest cover (shrubland, woodland and dense forest) during the study period. Thiscorroborates previous studies in Burkina Faso in general, and in the southernregions of the country in particular (Howorth & O’Keefe, 1999; Ouedraogo, 2006b;Ouedraogo et al., 2009; Pare et al., 2008). Many factors could explain the largeincrease of the area of cropland. First, agriculture in Burkina Faso is the mainstay ofthe economy, and more than 85% of the population of the country depends onsmall-scale farming systems for their livelihood (Breusers, 1998). Second, climatechange has impacted soil and biophysical qualities in a way that reduces agriculturaland livestock production, most importantly in the northern and central regions ofthe country (Gray, 1999, 2005; Reij et al., 2005). The change in rainfall pattern, thatis, increased irregularity and unpredictability of rainfall makes crop productionchallenging. Consequently, this has resulted in uncontrolled large-scale movement offarmers and livestock herders from the drought-affected areas in the north tosouthern Burkina Faso since the 1980s (Breusers, 1998; Henry et al., 2003;Ouedraogo et al., 2009; Reenberg & Lund, 1998; Youl et al., 2008). Last, but notleast, the increased cropland in the study area might be related to the recentdevelopment of agricultural production systems. This reflects the important role ofcotton production in the nation’s economy (Ouattara et al., 2008), the emergence oflarge-scale commercial farming in the region (M. Ouedraogo, 2003; I. Ouedraogo,2006b; Ouedraogo et al., 2009; Pare et al., 2008), and the local population growth(Ouedraogo et al., 2009, 2010).

Dense forest and woodland accounted for 66% of the study area in 1976 butdropped to only 24% in 2006. Previous studies in southern Burkina Faso haveshown similar decreases in forest cover from 1986 to 2000 (Howorth & O’Keefe,1999; Ouedraogo, 2006b; Pare et al., 2008). The decrease of these specific forest

Table 7. Spatial pattern metrics of forest cover change trajectories

Trajectory classes PLAND (%) NLSI IJI FRAC_AM

Old cultivation 0.07 0.0949 0.00 1.0614Old forest 8.57 0.1456 50.68 1.0932Deforestation due to agriculture 30.12 0.2516 61.22 1.2415Deforestation due to factors differentfrom agriculture

32.50 0.2739 62.10 1.2821

Reforestation 28.74 0.2084 59.17 1.1532

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covers could be due to the fact that southern Burkina Faso supplies wood andcharcoal to Ouagadougou and Koudougou, the two nearby cities that have a highdemand for wood and charcoal (Ouedraogo, 2006a, 2006b). Indeed, 90% of the 1.7million inhabitants of these cities (INSD, 2007) depend on wood and charcoal fortheir energy requirements (Kramer, 2002). This is extracted from dense forest andwoodland. Tree cutting in dense forest and woodland has converted most of theseforest cover types into shrubland, hence resulting in considerable increase in the areaof shrubland during the 2000s. The fertile soils of dense forest and woodland areas(Jeune Afrique, 1998) are as suitable for agriculture as the shrublands, which areeasier to clear.

Computed spatial pattern metrics of forest cover change trajectories showeddifferences between classes for the four landscape metrics: PLAND, NLSI, IJI andFRAC_AM. The low proportion of PLAND for old cultivation (0.07%) suggeststhat 30 year-old cropland was very rare. This reflects the prevalence of shiftingcultivation in the study area that involves fallowing of cultivated lands whileclearing the forest for new ones. Old forests were also rare (PLAND¼ 9%),indicating that most forest covers were a result of re-growth from fallow areas orreconversion from one forest cover type to another. Gross deforestation had aPLAND of 63%, while reforestation accounted for nearly 29%. This gives a netdeforestation of 34%, showing the high level of deforestation in the region. Theseresults are consistent with reports that have claimed that serious deforestation isoccurring in southern Burkina Faso (Ouedraogo et al., 2009; Pare et al., 2008). Inaddition to the farming activities which have contributed to deforestation(PLAND¼ 30%), many other factors may also be associated with the changesin southern Burkina Faso (PLAND¼ 33%). These could be the combination ofenergy requirements (wood and charcoal production), over-grazing, bush fires(Ouedraogo, 2006b; Ouedraogo et al., 2009; Pare et al., 2008) and rainfallirregularity in the study zone (Yameogo, 2005). Indeed, the magnitude of landcover change is reported to be significantly related to inter- and intra-annualrainfall variability in several ecosystems across sub-Saharan Africa (Vanackeret al., 2005).

Among all land cover trajectory classes old cultivation had the smallest NLSIvalue (0.095), confirming that it exhibited a highly concentrated and aggregatedspatial pattern with patches surrounding the settlements, as reported by Ouedraogoet al. (2009). The old forest was also aggregated. Deforested areas and reforestationhad the highest NLSI values indicating that they were spatially scattered in smallpatches. This means that current agricultural practice is still dominated by small-scale and diffused farming systems that have significant impacts on environmentalsustainability and forest fragmentation. The NLSI also indicates a lack of propermanagement plan regulating wood cutting and grazing in the forest. The NLSI fornon-agricultural deforestation, for instance, was 0.274, suggesting uncontrolledexploitation of the forest in time and space.

Old cultivation had an IJI of zero, indicating that patches of this trajectory classwere aggregated and spatially adjacent to few other trajectory classes. This is in linewith studies that have shown that in the 1970s, southern Burkina Faso was sparselypopulated by the native Nuni people who were practicing subsistence farming withminimal environmental impact (Howorth & O’Keefe, 1999; Ouedraogo et al., 2009).

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Old forest, deforestation and reforestation classes had IJI values above 50, showing atendency to be associated each with other trajectory classes with highly interspersedpatches.

The measurement of the Area Weighted Fractal Dimension Index showed lowervalues for both old cultivation and forest, indicating less complex patch shapesthan the other trajectory classes. This also suggests that patches were lessexpanded in the study area (PLAND¼ 9%). Conversely, deforestation (bothagricultural and non-agricultural activities) had the highest FRAC_AM (1.242 and1.282 respectively), suggesting more complex patch shapes, expanded around theother classes in 2006 (PLAND¼ 63%). Reforestation also had a high FRAC_AMvalue, compared with the unchanged trajectory classes (old cultivation and oldforest).

It should be noted that when PLAND increases, NLSI, IJI and FRAC_AM alsoincrease (Table 7), suggesting that they might be correlated. This could be due to thefact that the number of land use categories used has a significant effect on the valuesof some selected landscape metrics (Shen et al., 2003). It also lends credence to Penget al. (2007) who performed correlation tests between 24 different landscape metricsand found a high magnitude of relationship between them. Therefore, further studiesthat use other landscape metrics or other spatial pattern measures may contribute tominimizing the suspected correlative effects on the results while depicting thecombination of pattern metrics that would be optimal for future trajectory analysisof land cover change.

5. Conclusions

The shrub- and woodlands in southern Burkina Faso have been subjected todeforestation caused mostly by farming, other land uses, and biophysical factorswhile most of the dense forest cover has been converted to shrubland. The forestcover in some areas has been expanded but such change falls far short of reversingthe process of deforestation and forest degradation. Methodology that combinesmulti-temporal remotely sensed imagery to detect forest cover trajectories and thecomputation of landscape metrics of the change trajectories helped to efficientlyestimate and describe the dominance and spatial distribution of the changes in thearea studied. Interpretation of the metrics of spatial patches described the nature ofchange trajectories and served as a basis to predict the spatial pattern of forestchange. The categorization of the 64 trajectories of the cover change into fiveunifying classes for the landscape metrics analysis might have somehow affected thedata, as pointed out by Peng et al. (2007). Therefore, there is a need to systematicallyinvestigate how landscape metrics behave with respect to changing class categories.Further investigations must also be done to find out the role of policy, socio-economic and climate change on the trajectory process in the study area. Finally, werecommend that a management plan be prepared for the remaining forests andwoodlands in the study area, focusing on forest regeneration and planning of treecutting for energy. This should be done through dialogue with stakeholders andparticipatory forest management involving local people in conservation andreforestation programs. In addition, intensification of agricultural practices isessential to halt the conversion of other land use classes to cropland.

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Acknowledgements

Funding for this study was provided by Swedish International DevelopmentCooperation Agency (Sida). We thank Djibril S. Dayamba who commented the firstdraft of the manuscript. We also thank Dr John Blackwell and Sees-editing Ltd forlinguistic improvements.

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