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Landscape and Urban Planning xxx (2010) xxx–xxx

Contents lists available at ScienceDirect

Landscape and Urban Planning

journa l homepage: www.e lsev ier .com/ locate / landurbplan

and cover change under unplanned human settlements: A study of the Chyuluills squatters, Kenya

race Muriukia,b,∗, Leonie Seabrookc, Clive McAlpinec, Chris Jacobsona,ronwyn Priced, Greg Baxterc

The University of Queensland, School of Integrative Systems, Gatton, Queensland 4343, AustraliaKenya Agricultural Research Institute, P.O. Box 14733, Nairobi 00800, KenyaThe University of Queensland, Landscape Ecology and Conservation Group, Centre for Spatial Environmental Research,chool of Geography, Planning and Environmental Management, Brisbane, Queensland 4072, AustraliaDepartment of Sustainability and Environment, 3/8 Nicholson Street, East Melbourne 3002, Australia

r t i c l e i n f o

rticle history:eceived 8 March 2010eceived in revised form9 September 2010ccepted 18 October 2010vailable online xxx

eywords:quattersandscape analysisommunity assessments

a b s t r a c t

While population growth is widely acknowledged as an important driver of land cover change, the role ofunplanned human settlements is not adequately recognised. Many such settlements occur in the semi-arid lands of Kenya, which in the past had relatively lower human populations, but significant wildlifepopulations. Over the last four decades, the Chyulu Hills, adjacent to the Tsavo and Chyulu NationalParks, have experienced rapid land cover changes associated with migrant squatter settlements. Weused the Chyulu Hills to advance our understanding of the nature of land cover change under squattersettlements. We evaluated land cover change from aerial photographs and satellite images within aGeographic Information System (GIS) and combined it with landscape metrics and community surveys.Community perceptions of land cover change revealed comparable trajectories in major land cover typesderived from the GIS analysis. Landscape metrics showed fragmentation of native vegetation followed by

onservationISenya

coalescence to contiguous patches of cultivation as settlements increased. Land cover trajectories undersquatter settlements were influenced by historical land policies, protected area management as well asregional power structures. Our findings emphasize the importance of land use histories and communityinvolvement in evaluating and understanding land cover change. Resolution of squatter conflicts shouldtake cognisance of community perceptions, as well as the historical and political land use antecedents.

and sand i

We recommend humanenterprises, and research

. Introduction

Land cover change, often manifested through habitat loss andragmentation, is an important component of global environmentalhange (Nagendra et al., 2004). Changes have been associated withoil degradation and reduced ability of landscapes to sustain naturalesources and to provide ecosystem services. This can result in long-

Please cite this article in press as: Muriuki, G., et al., Land cover change undeKenya. Landscape Urban Plan. (2010), doi:10.1016/j.landurbplan.2010.10.0

erm environmental degradation and loss of biodiversity (Chhabrat al., 2006; Tilman et al., 1994). One of the key drivers of land coverhange in tropical regions is human migration (Wardell et al., 2003).ecent studies have indicated that intensifying climate change is

∗ Corresponding author at: The University of Queensland, School of Integrativeystems, Gatton, Queensland 4343, Australia. Tel.: +61 7 54601095.

E-mail addresses: [email protected] (G. Muriuki), [email protected]. Seabrook), [email protected] (C. McAlpine), c l [email protected]. Jacobson), [email protected] (B. Price), [email protected]. Baxter).

169-2046/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.landurbplan.2010.10.002

ocial capacity building of squatters towards non-land-intensive micro-nvestment in ecotourism.

© 2010 Elsevier B.V. All rights reserved.

likely to lead to more people being displaced in the poorest areasof the world, requiring new responses to immigration (Biermannand Boas, 2010), and rural migration is set to become more impor-tant in global environment change than previously thought (Carr,2009). Despite their importance, rural immigrants have receivedconsiderably less scientific attention compared to labour immi-grants, refugees and rural to urban migrants (Brown and Lawson,1985; Carr, 2009; Muriuki et al., 2010).

Squatters are those immigrants who illegally establish residencein areas deemed unsettled or with poorly defined tenure. In Kenya,they form a significant proportion of rural demography, and areconsidered a problem because of their large numbers, and thelack of a systematic framework to address the problem (Kanyinga,2000). The country’s growing population increases migration to

r unplanned human settlements: A study of the Chyulu Hills squatters,02

the arid and semi arid lands where land is apparently more read-ily available, but also where most protected areas occur. Conflictsbetween squatters and conservationists are a common outcomewhen they encroach on the protected areas leading to destructionand loss of livelihoods, and often, heightened resentment which

dx.doi.org/10.1016/j.landurbplan.2010.10.002


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urther compromises conservation efforts (Treves et al., 2006).ecause they generally invest minimal resources in land improve-ent and engage in exploitative farming practices (Huisman andinther, 1998), their settlements are commonly associated with

apid land cover change, in the short term at least. Where such rapidhanges occur close to protected areas, there can be significantmpacts such as illegal harvesting of natural resources and wildliferom the parks (Homewood et al., 2001), as well as transformationf the ecosystems. This can be critical for wildlife, whose dispersalreas and movement corridors become greatly transformed.

Approaches that integrate community land use history withpatial analysis using Geographic Information Systems (GIS) canrovide useful information on the patterns of change. This isecause communities possess knowledge and understanding of therocesses and causes of change in their environment, and oftenreat capacity to recall and estimate features of their environment.his can be greatly enhanced by the use of visual phenomena suchs photographs (Mapedza et al., 2003). Despite reported discrepan-ies between farmer-reported deforestation estimates and actualeforestation rates (Walker et al., 2000; Wynne et al., 2007), incor-oration of community and household estimates of deforestationan provide incisive entry points to the study of land cover change.ombining this with interpretation of landscape fragmentation canid in the identification of trends and likely processes that are tak-ng place (Kamusoko and Aniya, 2006; Southworth et al., 2004).

any land cover change studies combine different remotely sensedata products such as aerial photographs and satellite imagesAmsalu et al., 2007; Geneletti and Gorte, 2003). Examples fromfrica have utilised different combinations of community knowl-dge with remotely sensed products (Hessel et al., 2009; Mapedzat al., 2003; Mertens et al., 2000; Paré et al., 2008). Methodologicalhallenges, complicated by a lack of quality mapping products athe right times, scales and intervals can however hamper the suc-essful integration of community assessments with GIS. Many suchssessments therefore tend to be qualitative (Mapedza et al., 2003;ogt et al., 2006), and quantitative community assessments of landover change remains a challenge (Rindfuss et al., 2004).

Because land cover change becomes evident through changen its structure, it is often first manifested through fragmenta-ion. Landscape structure refers to the spatial relationships amongpatially discrete areas of land cover classes (patches) and cane characterised by its composition and configuration. Composi-ion describes the abundance and variety of patch types, whileonfiguration is the physical distribution and spatial characteris-ics of patches within a landscape mosaic (McGarigal and Marks,994). Landscape metrics provide a clear understanding of the pat-erns of conversion from natural to agricultural landscapes, and canherefore be good indicators of landscape resilience or dynamismLausch and Herzog, 2002). When combined with land cover changessessments and community land use histories, metrics can be aseful indicator of the pattern of change.

This paper aims to improve understanding of the nature ofand cover change under unplanned human settlements using thehyulu Hills, a contested landscape with a history of squatter occu-ation, as a case study. We identified the timing and pattern ofuman settlements, combined remote sensing results with trends

rom community assessments, and computed landscape metricso measure landscape fragmentation. We adapted proportionaliling, a semi-quantitative survey tool more commonly used ineterinary epidemiology, to assess land use by the community.e combined community land use histories with documented


hronicles and relate them to land cover change outcomes. Finally,e discuss the results in the context of the processes influencing

and cover change. We recommend capacity building programmesor the squatters, requisite investments for collaborative manage-

ent of the resources through ecotourism ventures, and value

PRESSn Planning xxx (2010) xxx–xxx

chain addition for small agribusinesses that rely on less landresources.

2. Materials and methods

2.1. Study area

The Chyulu Hills settlements fall within the eastern provinceof Kenya (Fig. 1). They cover approximately 800 km2 between37◦45′E to 38◦10′E and 2◦10′S and 2◦45′S. Characterised by lowand unreliable rainfall, infertile and often saline soils much ofthe Chyulu Hills are classified as arid or semi-arid (Ministry forthe Development of Northern Kenya and Other Arid Lands, 2008),but with patches of deep well drained soils and higher rain-fall. The settlements are bounded by two protected areas, theTsavo (20,000 km2) and Chyulu Hills National Parks (724.3 km2),a major highway connecting the capital city of Nairobi withthe port of Mombasa, and the National Range Research Cen-tre at Kiboko. The hills are in the transition zone betweenTsavo plains grasslands and the Chyulu forests, comprisingimportant ecosystem characteristics of the Eastern African wood-lands/savannah/forest bio-geographical transition. They are richin biodiversity, form an important corridor for the movementof elephants from the Tsavo into the Amboseli National Park,and are an important water catchment for the Mzima springs.Their diversity in wildlife, plants, as well as the scenic lava flows,cones and craters present outstanding examples of major stagesof the Earth’s history (Pócs and Luke, 2007). These and otherattributes of authenticity, conservation and universal values havebeen recognized in their nomination to a world heritage site(http://whc.unesco.org/en/tentativelists/5515).

The region however has a history of high-density squatter set-tlements, many of whom still lived in squatter camps (mikumbo)in 2008/2009. Absorbing illegal settlers from all the major eth-nic groups in Kenya, it has a track record of stark confrontationsbetween land hungry peasants, the Kenya Wildlife Service, civiladministration and local politicians (Freeman et al., 2004; Officeof the Vice President and Ministry of Planning and NationalDevelopment – Kenya, 2001). Between 1988 and 1990 many squat-ters were violently evicted from the Chyulu Hills National Park bythe Kenya Wildlife Service and apprehended for illegal occupationand harvesting sandalwood (Osyris lanceolata), a protected herb(Convention on International Trade in Endangered Species of WildFauna and Flora, 2006).

2.2. Community surveys

To determine the timing of settlement and history of land coverchange and assess community perceptions of land cover change wecarried out interviews in households using a structured question-naire. We interviewed a total of 188 households selected througha stratified random design using geographical location, time of set-tlement and tribe as strata. Focus group discussions were also heldat the village level for 15 of the 45 selected villages where house-hold interviews were conducted. Participants were selected withthe assistance of village elders and assistant chiefs, and coveringthe entire landscape. Typically, focus group discussions had 6–14participants, comprising of village members who were knowledge-able on the history and patterns of settlement in the village. Surveydata were reinforced by published work and government records


on the history of land policy change in the Eastern Province andChyulu landscape, spanning the British colonial occupation, landdevelopment after Kenya’s independence in 1963, and how changesin governments have affected land administration and governancein the Chyulu Hills.


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.3. Geographic database development

Aerial photographs are useful for historical land use/cover stud-es as they have high spatial resolutions and provide longer termoverage than satellite images. However, particularly in developing


ountries such as Kenya, air photo acquisition is sporadic in spatialnd temporal coverage, and metadata are not usually well docu-ented. To compensate for this shortfall, we combined historical

ir photos with satellite images (Table 1). The availability of imagest high resolution to combine with air photos influenced the date

u Hills study area.

choices of remote sensing products. Proximity of the study area toMt Kilimanjaro cast clouds over many images, and spatial resolu-tion disparities between air photos and other affordable products(Landsat) were large.


2.4. Land cover classification

The two sets of panchromatic aerial photographs takenin the dry seasons of 1967 and 1978 at scales of 1:50,000and 1:20,000 respectively were scanned at 400 dpi. An ortho-




4 G. Muriuki et al. / Landscape and Urban Planning xxx (2010) xxx–xxx

Table 1Remote sensing products used for land cover change assessment.

Product Date Details Remarks

Aerial photographs (37 prints) February 1967 1:50,000 scale Photos had moderate cloud cover,000 s

ial resial res

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Aerial photographs (45 prints) January 1978 1:20

SPOT XS 1/11/1999 SpatLandsat ETM+ 03/04/2001 Spat

raphic correction was done using the 90 m ASTER DEM datahttp://srtm.csi.cgiar.org). They were geometrically rectified andegistered to a 2001 Landsat ETM+ image (acquisition date3/04/2001 downloaded from http://glfccapp.umiacs.umd.edu).he photographs were merged to create orthophoto mosaics usingandscape Mapper 1.4© software. The orthophotos and all GISector layers of roads, rivers and administrative boundaries wereonverted to a common UTM projection (Zone 37S) and datum (Arc960, Clarke 1880 spheroid), based on Survey of Kenya topographicaps (Survey of Kenya, 1974). A SPOT 2 XS scene (acquisition date

/11/1999, 20 m resolution), delivered without geometric correc-ion (Level 1A processing) was geo-referenced to the Landsat imagesing image to image registration and a first order polynomialransformation and a 30 m nearest neighbour resampling method.he root mean square error was 18.22 m (0.607 of a pixel) for themagery, 38.3 m for the 1967 mosaic and 9.2 m for 1978. Limitedoverage by 1978 photographs limited analysis to the subsectionsf Kiboko and Mtito (Fig. 3).

We used land cover types based on the Land Cover Classifi-ation System (LCCS) of the Food and Agricultural OrganizationGregorio, 2005), Survey of Kenya (Survey of Kenya, 1974, 1987) andn adaptation of physiognomic land cover descriptions (Table 2)y Thompson (1996). Thompson’s classification, adapted to anfrican environment, offers flexibility for project-specific classifi-ation and inclusion of ancillary non-remote sensing data sourcesnd user-designed sub-categories. This combination provided suf-cient breadth for grouping different vegetation types, whileaintaining a clear differentiation between classes to distinguish


and cover types using the combination of remote sensing products.To maintain consistency between the different remote sens-

ng products, we relied on visual interpretation. All data werexported to ArcMapTM 9.3, and used onscreen digitization. Com-only used approaches to interpretation of satellite images rely

able 2escription of land cover classes.

Land cover class Community local name (Kikamba) Survey of Kenya (1974;

Dense forests Kitheka Forests

Open forests Kitheka Woodlands/scattered tr

Shrublands Mubilisya Thickets/bush/scrub

Cultivation Uiimi Fields/villages

Grasslands Weu/Uiyi

Lava and rocks Kivuthi/Ing’alata Rocky outcrops/special s

Marsh/wetlands – Swamps/Marsh

cale Photos were of high quality, but flights didnot completely cover the study area

olution 20 m (HRV 2) Image identity K-J 142-355olution 28.5 m P167r062

on spectral signatures, which could not be used because black andwhite photographs lack multispectral signatures. We used stan-dard interpretation cues such as tone, texture, pattern, shape andlocation (Lillesand et al., 2008). The use of different sensor plat-forms with different spatial resolutions influenced the degree ofland cover class differentiation, and we used classes that wereclearly distinguishable on the coarsest resolution product (SPOTXS). For the GIS analysis of change, we classified land into 7 covertype classes (Table 2), excluding clouds and shadows. However,for community surveys, we further modified the initial 7 classesby combining open forests with shrublands, removing ambiguitybetween the often confused classes.

Accurate field verification of interpretation was not possible dueto the age of the aerial photographs and the rapidity of the landcover changes. However, fieldwork in 2008 and 2009 verified majorland cover types using 164 random ground control points, and acombination of interview data from group discussions for sites thathad not changed significantly over the settlement periods. This stepproduced maps with polygons of land cover types from the 1967and 1978 photos, and the 1999 SPOT imagery.

2.5. Change detection

We used a post classification comparison method to measureland cover changes. First, we clipped all three data sets to a com-mon area as determined by the 1978 cover, which was the mostlimited in spatial extent using ArcMap GIS. The vector data wererasterised using 20 m cell size, which is the coarsest resolution of


the remote sensing products used (SPOT XS). Second, we used zonalstatistics in ArcGIS Spatial Analyst’s tools to compute changed areasby cross tabulating pairs of time intervals i.e. 1967 and 1978, andbetween 1978 and 1999. Transitions were evaluated for geometricconsistency and small differences in polygon area (<0.02% for the

1987) classification Description

Land covered by dense trees and thick vegetation,appears rough, clumped and thick in imagery andphotography

ees Land covered by sparse forest trees, riverine and thickvegetation, appears rough and sparsely clumped onblack and white aerial photographyFew shorter trees, mixed with undergrowth andgrasses and short clumped or scattered herbs. Includedabandoned farmlandsFarmlands under crop and harvested fields, showingdiscernible patterns on imagery and photography.Included homesteadsGrass fields without scattered trees, smooth texture onphotography

urface Lava flows, rocky outcrops, rock deposits andcompletely denuded lands. May include severelydegraded lands. Rough texture, some with shinyreflectance, lava mostly blackWetlands follow streams and low lying plains, riparianvegetation clearly visible close to the river/stream,vegetation is sparse due to soil salinity and/poordrainage. Inland water reservoirs clearly identifiable.May be covered in sparse vegetation


http://srtm.csi.cgiar.org/

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hole class) originating from digitization differences in polygonoundaries and from old air photo registration to satellite imageryere ignored. This produced matrices of change between the data

et pairs. The instantaneous, annual rate of change in forest coveretween each sample period was calculated following Puyravaud2003), as:

= ln yi2 − ln yi1

t2 − t1(1)

= rate of change; ln Yi1 = natural logarithm of the area of land coverat time 1; ln Yi2 = natural logarithm of the area of the same landover at time 2, t2, t1 = time dates.

The classified maps were recoded into binary land coveraps of forest and non-forest land covers. These were added

ogether sequentially to produce a multi-temporal compositef eight possible trajectories between 1967, 1978 and 1999;orest–forest–non-forest (FFN); forest–non-forest-non-forestFNN); non-forest–forest–non-forest (NFN); forest–forest–forestFFF); non-forest–non-forest–non-forest (NNN); forest–non-orest–forest (FNF); non-forest–non-forest–forest (NNF); and non-orest–forest–forest (NFF). These trajectories were tested for areasf vegetation recovery such as from shrublands to open forests.

.6. Community land cover change assessment

Field surveys were held in 2008/2009. To assess local peo-les’ perception on land cover change, we adapted a participatoryethod of proportional piling, more widely used in veterinary epi-

emiology (Shiferaw et al., 2010). First, we recorded time lines ofajor historical events in each village (droughts, floods, political

vents), which were used to aid recall in vegetation mapping. Eachand cover class was also described by its local (Akamba) nameTable 2). Using 25 counters (maize seeds) and clear 9′′ × 9′′ colourhotos of each land cover class, participants were required to dis-ribute the counters among the pre-determined land cover classes.his was repeated for each decade. Community timelines did notatch the dates of aerial photos and imagery, but the techniqueas useful for trend assessment and historical land use. Constrain-

ng dates for communities to those of aerial photography wouldlso risk missing out information between these dates. Total tal-ies were computed, from which percentages were worked out forach land cover type in each decade, and mean rates of land coverhange similarly calculated using Eq. (1).

.7. Landscape fragmentation analysis

We followed a historical definition of the area under squatterccupation. This region has both physical and political peculiaritieshat predispose it to squatter settlements as described under studyrea. It covers the foothills area north of the Chyulu Hills range andelineated from the eastern Kenya plains by the Mombasa-Nairobiighway. It comprises of a mix of mosaics of land cover types, and

s generally considered more favourable for settlements due thenfluence of the hills creating more rainfall, pockets of deep volcanicoils from recent volcanic activity and often, trade in the economi-ally valuable natural vegetation such as sandalwood. Landscapeetrics describe the composition and arrangement of the vari-

us patches of land cover types in the landscape. This involvesrst partitioning the landscape into relatively homogeneous unitsith discreet boundaries (land cover classification), and comput-

ng indices that represent the arrangement and geometry of the


omponent patches within each land cover class (Southwortht al., 2004). Measured across time, metrics indicate fragmentationecause they quantify the amount and distribution of a particular

and cover class. These were measured by Class area (CA); Percent-ge of landscape (ZLAND), which is the percentage of the total

area.Sources: (Central Bureau of Statistics – Kenya, 1981, 1994, 2000; Statistics Division,Kenya, 1970). Sub-locations created after the census dates have no data.

landscape made up of the corresponding class area; Number ofpatches (NumP), which is the total number of patches in a class inthe landscape, and Mean patch size (MPS), which is the total areaoccupied by a particular patch class divided by the NumP of thatclass. The metrics were computed using Patch Analyst© (Rempelet al., 2008), which facilitated the spatial analysis of landscapepatches from vector data within ArcMapTM.

3. Results

3.1. Settlement patterns

In the period just preceding Kenya’s independence in 1963,immigrants started to arrive in the area (Jacobsen et al., 2004),many as hunters. A few remained as squatters (in Akamba, “kuk-wata mana”), and established small villages as collection centresfor charcoal trade. These pioneer squatters cleared the land withslash and burn techniques, cutting trees for charcoal burningand trade, and later for cultivation. Word of mouth encouragedother immigrants to what was seen as “free land”. Small villagecouncils (“atumia”) would allocate fresh immigrants land adja-cent to their plots as a shield from wildlife. The acreage a settlerhousehold eventually owned depended on family labour avail-able to clear and cultivate virgin land. In the 1980s and early1990s, a few squatter uncoordinated settlement projects added tothe already rapid population (Fig. 2) growth and more intensivesettlement.

Our survey results showed that land tenure for the most parthas remained insecure through the four decades. Interviews withhousehold heads revealed that only 5% of households had securetenure (title deeds from the Ministry of Lands), only 12% had the


next most secure form (letters of allotment from the Ministry ofLands), a majority (69.1%) only had numbers, informal letters andsale agreements and/or inheritance, and the rest (13.3%) had noofficial proof of legal occupation Land conversion was predomi-nately for subsistence. All households grew maize and beans, with





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ig. 3. Patterns of village establishment overlaid on 1967 land cover types (hashedreas were established between 2007 and survey dates (2008).

inor additions of pigeon peas, green grams, millet and sorghum.lthough majority of immigrants belong to the Akamba ethnicroup from the surrounding region, squatters and immigrants tohe Chyulu Hills came from all over Kenya, some as far as 300 kmway. Patterns of village establishment from the overlay of landover types in 1967 showed a relationship between time of villagestablishment and land cover type (Fig. 3).


Cultivation was negligible in 1967. The oldest villages werestablished in or adjacent to dense forests, with the exception ofhree villages which were established by early hunters and char-oal traders such as “Kaunguni” (literally “a hiding place”), whichater grew into market centres and depots for charcoal trade.

show extent of 1978 aerial photographs coverage). *No new villages in our survey

The association of village establishment and dense forests in apurely subsistence economy implies their association with forestresources (e.g. firewood and timber) and possibly, better soils forcrops.

3.2. Patterns of land cover change


At the landscape level, cultivation showed a westward patternof expansion from the early easterly settlements (Fig. 4a and b).Between 1967 and 1978, dense forests and wetlands experiencedthe fastest rates of decline (−8.7% and −7.9% respectively), whilecultivation grew by approximately 40%. There was a marked dif-





ito an

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Fig. 4. Land cover transitions in (a) Mt

erence between patterns of change in Mtito and Kiboko areasspecially between 1967 and 1978. Initial settlements occurred intito area, associated with the rapid perforation of the landscape

etween 1967 and 1978, and the relatively stable natural covern Kiboko area in the same period. Between 1980 and 1992, squat-ers who had encroached on the Chyulu National Park were evictednd they established camps in the study area. In the same period,lashes in Kalembwani areas between Maasai pastoralists and land


ungry peasants over grazing lands in the neighboring Kajiado dis-rict further increased squatter numbers (Gitunu, 1999). Some ofhese squatters were issued land by the government, and patternsf squatter settlements expanded westwards where unoccupiedand was available.

d (b) Kiboko areas of the Chyulu Hills.

3.3. Trends of land cover change

Out of the total study area of approximately 744 km2 in 1967,only about one third was settled, with less than 1% (0.2%) ofland under cultivation. By 1978, almost all the dense forests,with the exception of those within the protected areas, had beenconverted to cultivated plots. Analysis of the three-time periods(1967–1978–1999) revealed dominance of human-use through


conversion of forests, wetlands, and shrubland to cultivation(Fig. 5), and only small areas of native vegetation recovering intoopen forests.

A selective pattern of use between 1967 and 1978 were indi-cated through an initial loss of dense forests, wetlands, and





s in t

gicpoa

1ag1Ht

FFa

Fig. 5. Land cover trajectorie

rasslands to cultivation, and a lagged conversion of shrublandsn the next decade. Shrublands, the dominant land cover type,hanged minimally between 1967 and 1978, at a rate of −0.5%er annum. But between 1978 and 1999, they declined at a ratef −4.7%, which was more than twice the rate of dense forest losst −1.7% in the same time period.

We also found an increase in open forests between 1978 and999 in the area where the earliest squatters had settled (Mtitorea). The pattern was attributed to vegetation recovery and pro-


ression from shrublands to open forests (Figs. 5 and 6). Between988 and 1990, squatters who had been evicted from the Chyuluills National Park established camps in the villages adjacent to

he park (Gitunu, 1999; Muriuki et al., 2010). While squatting in

ig. 6. Composite maps of land cover trajectories in the Mtito and Kiboko areas of ChyuFN: Forest–Forest–Non-forest (deforested after 1978). FNN: Forest–Non-forest–Non-forefter 1978).

he Chyulu Hills, 1967–1999.

these villages, they were allowed to practice rudimentary cropping,but were not permitted to cut trees. This effectively removed theundergrowth vegetation from the shrublands. Starting 1992, thegovernment of Kenya announced small settlement projects for theevictees, a move that drew both genuine and phony squatters to getfree land, from within and outside of the Chyulu landscape (Okelloand Tome, 2007). The subsequent abandonment of the camps fromthe Mtito area for settlement in Muuni and elsewhere in the Chyululandscape resulted in fallowing of the formerly cultivated plots and


vegetation recovery (FNF) with a progression of the remnant shrubsto open forests (Fig. 6).

The largest percentage of land was converted to non-forest useafter 1978 (FFN). Areas where no change in natural vegetation

lu Hills, from 1967 to 1978 to 1999. FFF: Forest–Forest–Forest (stable vegetation).st (deforested before 1978). FNF***: Forest–Non-Forest–Forest (vegetation recovery





chang

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Fig. 7. Annualised percentage rates of major land cover

ccurred (FFF) were located mainly in the protected areas (thehyulu Hills and Tsavo National Parks), with the exception of smallillage fringes in the Mtito area where the earliest immigrants hadettled. A large area that was formerly part of the National Rangeesearch Centre of the Kenya Agricultural Research Institute (KARI)as used to settle more squatters and subsequently converted toon-forest use after 1978.

.4. Community perceptions of land cover change

The trends of land cover change by the community surveys wereomparable to GIS assessments, with minor differences in percent-ge rates of change (Fig. 7). Cultivation was reported to increase atdecreasing rate by communities. They reported similar patternsf change in dense forests, grasslands, and to a lesser extent, lava


nd rocks, which were said to be decreasing under population pres-ure and commercial harvesting for road construction and building.here was a significant difference between reported rates of changen shrublands by the community and empirical findings from GIS.his difference was attributed to lack of clarity in differentiating

able 3ragmentation indices for major land cover types in 1967, 1978, and 1999 (percentage in

Metrics Cultivation Dense forest

Class area (ha) 1967 47 (0.2)* 3416 (12)1978 3545 (13) 1309 (5)1999 16081 (57) 895 (3)

Mean patch size (ha) 1967 5 831978 10 2621999 3216 895

Number of patches 1967 10 471978 370 51999 5 1

es: (a) from community assessments and (b) from GIS.

open forests and shrublands by the community, which necessitatedan aggregation of the two land cover types. There were no compa-rable results for wetlands due to its confinement in a small portionof the landscape. Variations in trends of land cover change betweenvillages were minor, and the community had clear understandingof patterns of change in land cover, and demonstrated accuracy inhistorical events that were significant and influential in shaping thelandscape, such as adverse climatic events and land use policies andconflicts between conservation of wildlife and their settlement.

3.5. Landscape fragmentation

Between 1967 and 1978 the number of patches of dense forestswas reduced from 47 to 5 and the mean patch size increased from83 to 262 ha (Table 3). Forest class area and percentage reduced


by almost two thirds (from 12 to 5%). In the same period therewere no major changes in shrub class metrics. As cultivationincreased in area, the number of patches increased from 10 to 370,decreasing to 5 large contiguous patches in 1999, becoming thepredominant land cover interspersed by smaller patches of other

total landscape in parentheses).

Wetlands Open forest Shrubs Grasslands

631 (2) 1465 (5) 20718 (73) 53 (0.2)264 (1) 1613 (6) 19701 (70) 065 (0.2) 2177 (8) 7062 (25) 057 122 1594 1832.9 108 3284 065 167 706 011 12 13 38 15 6 01 13 10 0


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over types. The mean patch size for cultivation in 1978 (9.5 ha)as comparable to the average household acreage reported

n community group discussions for the decade (mean = 9 ha,E = 0.531). Wetlands reduced in area, patch size and number ofatches through both time intervals.

Smaller forest fragments were lost first, leaving large contigu-us patches shown by reduced numbers of patches and increasedean patch sizes in this class. Between 1978 and 1999, as dense

orests decreased in area, cultivation increased significantly andhe number of cultivated patches reduced from over 300 to 5, a coa-escence of small isolated plots to a contiguous mass of cultivatedelds. The landscape perforation evidenced through the landscapeetrics can be explained by preferential and selective patterns

f village establishment where settlements were first establisheddjacent to forests. The patterns of land cover change were alsoon-uniform across time, and tallied with landscape fragmenta-ion occasioned by selective village establishment and an east–westquatter migration pattern. Temporal changes in landscape met-ics were indicative of the processes driving the pattern, as landnitially became fragmented through perforation into small culti-ated plots, which then coalesced into large contiguous patches.he factors influencing landscape fragmentation are akin to thoseesponsible for land cover change, as this took place in a periodhen the areas were experiencing rapid population growth. The

andscape underwent responses to human settlements and popu-ation growth through fragmentation and land cover change, and its not clear yet how these transformations might affect the wildlifehat use it as a dispersal area.

. Discussion

The settlement of squatters and spontaneous migration haveeen shown elsewhere to play a role on patterns of land coverhange (Braimoh and Vlek, 2006; Unruh et al., 2005). Squatteronflicts in the Chyulu Hills date back to the 1920s, when landas first alienated by the colonial government to pave way forhat is now part of Tsavo West National Park. Between 1945

nd 1956, the Kenya Government attempted formal settlementchemes in parts of Makueni district. This was found necessary inrder to relieve pressure from the highly populated areas, whichad been compacted under colonialism (Lawrence and Mwanzia,004; Murton, 1999). British settlers had established large ranchesnd arable farms, effectively reducing the indigenous land reservesy over half (Wisner, 1977). Over the following years both officialnd unofficial settlements grew (Makin and Pratt, 1984). Changesfter Kenya’s independence in 1963 including reopening of partsf Chyulu for settlement and completion of the Mombasa-Nairobiighway gave impetus to settlement in the Chyulu landscapeMuriuki et al., 2010). The consequent cluster village projects werestablished with county council and political support or simplyhrough word of mouth from relatives and friends, but withoutormal encouragement from the Ministry of Lands and SettlementMakin and Pratt, 1984). Wisner (1977) reported that official settle-

ents of populations in other parts of the Makueni district providedsociological matrix for a far more significant growth in unofficial

ettlements in adjoining regions, mainly the Chyulu Hills. By mid-980s, population growth in Kibwezi, which comprises part of thehyulu landscape, achieved an annual growth rate of 8%, whichas twice the national average (Carnegie Quarterly, 1987), a rate

hat decreased slightly thereafter, but remained high compared


o the rest of Makueni district (which stood at 3.06% p.a in 2001)Office of the Vice President and Ministry of Planning and Nationalevelopment – Kenya, 2001). Our results, based on population cen-

us reports across four decades showed a doubling in populationensities in every decade. The only exception was in Mangelete


location, the sublocation with the oldest settlements, between1969 and 1979. The human carrying capacity for the region, basedon a full subsistence lifestyle where all requirements are derivedfrom the land, was previously estimated at 50–100 persons/km2

(Bernard and Thom, 1981), but by 1999, population densitiesranged between 98 and 161 persons/km2 in various localities inthe study area (Central Bureau of Statistics – Kenya, 2000).

Contestations over land use between squatters and the KenyaWildlife Service became protracted after gazetting of Chyulu HillsNational Park (Okello and Tome, 2007). This was done in twophases, with the lower Chyulu Hills being upgraded to nationalpark status in 1983 (400 km2), followed by the upper Chyulu Hillsextension (380 km2) in 1995. Both were done without adequateconsultation with the surrounding community, and without pro-viding for adequate compensation for displaced households. Thishad the additional effect of heightening resentment and could haveinfluenced wanton vegetation clearing and destruction. To this daylarge numbers of people repeatedly return to the hills as squatters,prompting the government to continuously evict them. The dynam-ics of vegetation and land cover change as a result of these humanmovements and conflicts over access have not previously beenassessed, and we postulate that they have had a significant influ-ence on patterns and rates of landscape change in the Chyulu Hills.

More secure land tenure arrangements may hold the potentialto raise investment in the available land, and also reduce the prob-ability of violent conflicts between squatters and protected areas.The piecemeal approach to squatter settlements acts to furtherencourage squatter migration within the landscape, creating a spi-ral which encourages prospecting squatters to migrate in the hopeof eventually getting land.

Our results show a landscape undergoing rapid change com-mensurate with higher than average population growth ratesresulting from migration and squatter settlements. Even thoughpopulation density could have played a big role in changing theland cover, we consider the carrying capacity narrative insufficienton its own to explain such rapid change, given the history of landpolicy administration from colonial to present day. Furthermore,the delayed action of the Ministry of Lands in land adjudication,piecemeal and reactionary squatter settlement programmes andthe management and expansion of the protected areas of the Tsavoand Chyulu Hills National Parks have in various ways impacted onthe land cover outcomes. We agree with diverse authors that envi-ronmental problems must be viewed within the political, historical,and economic contexts within which they occur (Bryant and Bailey,1997; Wardell et al., 2003), because not only do they impinge onthe social and power structures, but they also influence long-termland use plans.

By evaluating the historical and political land use antecedentswe conclude that land use in the Chyulu Hills should be evalu-ated as more than a product of land scarcity and squatter conflicts.It must pay attention to the policy and political environmentthrough which land and settlements have been managed in thepast. Interventions must recognise and appreciate the role of his-torical discourses, land and settlement policies, and politics inmanaging and resolving squatter conflicts.

Where availability of quality mapping products at the righttimes and intervals and map-reading ability are a real challenge,the engagement of communities to quantitatively assess land coverchange, and their integration with land use histories can pro-vide useful insights on the patterns and drivers of land coverchange. Other studies using combinations of surveys and remote


sensing techniques (McCracken et al., 1999; Wynne et al., 2007)found mostly slight discrepancies, and reiterated the importanceof integrating remote sensing into property-level analysis. Besidescontributing to this knowledge pool we circumvented the chal-lenge of triangulation, while providing detail about the timing and


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otential causes of land cover change, and which the remote sens-ng products alone could not do. The ability to quantitatively assessates and trends of land cover change from surveys is a gradua-ion from purely qualitative assessments. It can provide a relativelyheap and fast appraisal of trends in land cover change, and cane refined to reflect geographical locations and therefore improve

ts accuracy. Interventions that stem from sound assessments andncorporation of community findings such as is demonstrated inhis paper can raise the impacts of development projects directedt community levels.

. Recommendations

Policies that incorporate the multiplicity of players in the land-cape, including active engagement of the communities can revealpportunities for sustainable livelihood options that take intoccount the rates and patterns of change in the landscape. Forhose still in squatter camps and the large majority who have noefinitive official documentation of tenure, their precarious legalttachment to the land could potentially affect their motivation inong-term investments in the land. In the introduction we assertedhat squatters have few incentives to invest in land improvementnd protection, due to the lack of meaningful tenure, and insteadngage in exploitative resource uses. Land tenure issues have beenhown to influence land cover change and deforestation (Fearnside,001; Munroe et al., 2004; Walker et al., 2002), as well as the sus-ainable use of natural resources.

Community suggestions for alternative livelihood sourcesncluded beekeeping, small ruminant (sheep and goat) enterprises,nd improvements in the value chains for indigenous poultry.ome of these initiatives have been started with donor support,nd this study recommends involvement of squatters in planning.e support these proposals and recommend human and social

apacity building towards non-land-intensive micro-enterprisesor the squatters and other immigrants. Substantial improvementsn infrastructure, agricultural extension services and value additiono these micro-enterprises could alleviate the burden on land androvide opportunities for more diverse livelihood opportunities tohe community. As a minimum, addressing the increasing squat-er problem, as well as institution of co-management programmesetween the wildlife service and the squatters have been suggestedefore (Okello and Tome, 2007).

This study further recommends research and investment in eco-ourism ventures. A study of tourism potential ranked the Chyuluills 23rd out of the top 40 protected areas in Kenya (Okello et al.,005). This was despite the very low investment in marketing and

nfrastructure for and therefore resultant low tourist numbers. Thisan be attributed to among others, Kenya’s bias towards wildlifeourism particularly large game, the contestations on access andse of the landscape resources, and years of political machina-ions and land maladministration. We argue in support of Okellond Tome (2007), that with requisite initiatives, the Chyulu Hillsandscape holds potential for collaborative management of theatural resources with a special emphasis on the promotion ofcotourism ventures. The scenic landscape, biodiversity, diverseulture of immigrants, proximity to the Tsavo National Park andnational highway are advantageous opportunities. Ecotourismodels in adjacent districts to the Chyulu Hills (Kajiado and Taita)

re built on similar attributes (Himberg, 2006).


. Conclusions

By quantifying changes and depicting trends in land coverhange, this study provides the first assessment of patterns of landover change under unresolved contestations in the Chyulu Hills

PRESSn Planning xxx (2010) xxx–xxx 11

landscape, a model that is replicable in many parts of the worldas a result of changes in population mobility. The processes anddrivers of change in the Chyulu Hills and the possible role of squat-ter settlements are not unlike the broader processes driving landcover change in other parts of the world, where internal populationmovements and settlements arising from diverse causes (policy,catastrophes, and economic forces) are seen as catalysts for landcover change. Analysis of remote sensing products alone cannotreveal the complexity of driving forces that contribute to land coverchange. They can however provide a framework for a more nuancedanalysis of the actors at play. In this paper we have provided ahistorical approach that brings to the surface the gamut of likely fac-tors and antecedents associated with a rapidly changing landscapewithout implicating greater or lesser roles of any. The implicationsof these land cover change patterns and very high rates of changevis-a-vis the ability of the land to support such a rapidly growingpopulation in this semi-arid environment are of great concern.

In such a historically contested landscape, understanding localpeoples’ perceptions of the land cover change process can be criticalfor gaining support for co-management. It is important to incor-porate not only the characteristics of settler households but alsotheir own assessment and perceptions of change in planning andimplementation of policies that influence the land they occupy.

Despite reducing costs of remote sensing data and computingcosts, timely availability of quality time series data, particularly forhistorical studies remains a constraint in many developing coun-tries. Alternative approaches such as those adopted in this study canbe useful in exploring land cover change, especially in places wherespatial data is limiting and where historical evidence of changeis required. The lack of structure or permanence in informal set-tlements, rapid turnover and high mobility makes their detection,analysis and impact measurement more difficult. This may call fora set of tools that not only captures shorter time intervals, but alsoexpensive longitudinal research that follows immigrants and cap-tures data that explains changes in their landscape. Further workto explicitly quantify the different roles of the multiple actors driv-ing the land cover change will provide information on the role ofsquatters among the suites of other factors responsible for change.

Acknowledgements

This research was jointly supported by research grants from theSchool of Integrative Systems and the School of Geography, Plan-ning and Environment of The University of Queensland, Australia,and tuition from the International Postgraduate Research Scholar-ship (IPRS). The first author also benefited from a Research TravelGrant of the Graduate School, University of Queensland. We wish toacknowledge the Kenya Arid and Semi-Arid Lands (KASAL) projectof the Kenya Agricultural Research Institute, who provided supple-mentary support during fieldwork.

References

Amsalu, A., Stroosnijder, L., de Graaff, J., 2007. Long-term dynamics in land resourceuse and the driving forces in the Beressa watershed, highlands of Ethiopia. Jour-nal of Environmental Management 83, 448–459.

Bernard, F.E., Thom, D.J., 1981. Population pressure and human carrying capacityin selected locations of Machakos and Kitui districts. The Journal of DevelopingAreas 15 (3), 381–406.

Biermann, F., Boas, I., 2010. Preparing for a warmer world: towards a global gover-nance system to protect climate refugees. Global Environmental Politics 10 (1),60–88.

Braimoh, A.K., Vlek, P.L.G., 2006. Scale-dependent relationships between land usechange and its determinants in the Volta Basin of Ghana. Earth Interactions 8,


1–23.Brown, A.L., Lawson, V.A., 1985. Rural-destined migration in third world settings: a

neglected phenomenon? Regional Studies 19 (5), 415–432.Bryant, R.L., Bailey, S., 1997. Third World Political Ecology. Routledge, London.Carnegie Quarterly, 1987. Starting Where the People Are: The African Medical and

Research Foundation (AMREF), vol. 32. Carnegie Corp., New York, NY.


ING

L

1 d Urba

C

C

C

C

C

C

F

F

G

G

G

H

H

H

H

J

K

K

L

L

L

M

M

M

M

M

M

ARTICLEModel


2 G. Muriuki et al. / Landscape an

arr, D.L., 2009. Population and deforestation: why rural migration matters. Progressin Human Geography 33 (3), 355–378.

entral Bureau of Statistics – Kenya, 1981. Kenya Population Census 1979. Govern-ment Printer, Nairobi.

entral Bureau of Statistics – Kenya, 1994. Population Census 1989, vol. 1. Govern-ment Printer, Nairobi.

entral Bureau of Statistics – Kenya, 2000. National Housing and Population Census,vol. 1. Government Printer, Nairobi.

hhabra, A., Geist, H.J., Houghton, R.A., Haberl, H., Braimoh, A.K., Vlek, P.L.G., et al.,2006. Multiple impacts of land-use/cover change. In: Lambin, E.F., Geist, H.J.(Eds.), Land-use and Land-cover Change: Local Processes and Global Impacts.Springer, Berlin, New York, pp. 71–116.

onvention on International Trade in Endangered Species of Wild Fauna and Flora, C.,2006. Reports of Regional Representatives – AFRICA. CITES, Geneva, Switzerland,2–6 October 2006.

earnside, P.M., 2001. Land-tenure issues as factors in environmental destructionin Brazilian Amazonia: The case of Southern Pará. World Development 29 (8),1361–1372.

reeman, E., Lawrence, M., Christofferson, C., Kiilu, J.M., March 3–6, 2004. TheImpacts of Migration on Establishment of Social Capital and Environmen-tal Degradation in Kibwezi, Kenya. Third World Conference Foundation,Chicago.

eneletti, D., Gorte, B.G.H., 2003. A method for object-oriented land cover classifica-tion combining Landsat TM data and aerial photographs. International Journalof Remote Sensing 24 (6), 1273–1286.

itunu, A.M.M., 1999. Livestock production and food security in a changing socio-cultural environment due to involuntary relocation of agro-pastoralists intosemi-arid areas of Makueni district-Kenya. Ph.D. dissertation, University ofWales, Bangor.

regorio, A., 2005. Land Cover Classification System: Classification Concepts andUser Manual. Food and Agricultural Organization of the United Nations, Rome.

essel, R., van den Berg, J., Kaboré, O., van Kekem, A., Verzandvoort, S., Dipama,J., Diallo, B., 2009. Linking participatory and GIS-based land use planningmethods: a case study from Burkina Faso. Land Use Policy 26 (4), 1162–1172.

imberg, N., 2006. Community-based Ecotourism as a sustainable developmentoption in the Taita Hills, Kenya. M.Sc. Dissertation, Department of Geography,University of Helsinki, Finland.

omewood, K., Lambin, E.F., Coast, E., Kariuki, A., Kikulai, I., Kiveliai, J., et al., 2001.Long-term changes in Serengeti-Mara wildebeeste and land cover: pastoralism,population, or policies? Proceedings of the National Academy of Sciences 98(22), 12544–12549.

uisman, H., Winther, L., 1998. Squatters and Settlers in Conflict? Land Use Rightsand Economic Strategies of Farming Households in Zimbabwe. Department ofGeography of the Developing Countries, Utrecht University, The Netherlands.

acobsen, M., Lawrence, M., Sutton, J., March 3–6, 2004. “Vandu vaa ni vaseo muno”:Land Disputes and Village Identity in Kibwezi, Kenya. Third World ConferenceFoundation, Chicago.

amusoko, C., Aniya, M., 2006. Land use/cover change and landscape fragmentationanalysis in The Bindura District, Zimbabwe. Land Degradation and Development18, 221–233.

anyinga, K., 2000. Re-distribution from Above: The Politics of Land Rights andSquatting in Coastal Kenya, vol. 115. Nordic African Institute, Uppsala.

ausch, A., Herzog, F., 2002. Applicability of landscape metrics for the monitoringof landscape change: issues of scale, resolution and interpretability. EcologicalIndicators 2, 3–15.

awrence, M., Mwanzia, E., 2004. Lessons Under the Mango Tree: Making Place Outof Space and Community Out of Place in the 21st century. The School of SocialWork Theory and Practice of the Inter-University Centre, Dubrovnik, Croatia.

illesand, T.M., Kieffer, R.W., Chipman, J.W. (Eds.), 2008. Remote Sensing and ImageInterpretation, 6th ed. John Wiley and Sons, Hoboken, NJ.

akin, M.J., Pratt, D.J., 1984. Land use and Development in the Chyulu Area of Kenya.Surrey, England: Land Resources Development Center. Overseas DevelopmentAdministration. Project Report 135.

apedza, E., Wright, J., Fawcett, R., 2003. An investigation of land cover change inMafungautsi Forest, Zimbabwe, using GIS and participatory mapping. AppliedGeography 23 (1), 1–21.

cCracken, S.D., Brondizio, E., Nelson, D., Moran, E.F., Siqueira, A., Rodriguez-Pedraza, C., 1999. Remote sensing and GIS at farm property level: demographyand deforestation in the Brazilian Amazon. Photogrammetric Engineering andRemote Sensing 65 (11), 1311–1320.

cGarigal, L., Marks, B.J., 1994. FRAGSTATS Manual: Spatial Pattern Analy-sis Program for Quantifying Landscape Structure. Oregon State University,Corvallis, OR, Retrieved from http://www.umass.edu/landeco/pubs/mcgarigal.marks.1995.pdf.


ertens, B., Sunderlin, W.D., Ndoye, O., Lambin, E.F., 2000. Impact of macroeconomicchange on deforestation in south Cameroon: Integration of household surveyand remotely-sensed data. World Development 28 (6), 983–999.

inistry for the Development of Northern Kenya and Other Arid Lands, 2008. AridLands Resource Management Project. Environmental Audit 2008. ESF Consul-tants, Nairobi.


Munroe, D.K., Southworth, J., Tucker, C.M., 2004. Modeling spatially and temporallycomplex land-cover change: the case of western Honduras. The ProfessionalGeographer 56 (4), 544–559.

Muriuki, G.W., Jacobson, C., McAlpine, C., Seabrook, L.M., Price, B., Baxter, G., 2010.Migrating, staying, or moving on: migration dynamics in the Chyulu Hills, Kenya.Population, Space and Place, 10.1002/psp.619.

Murton, J., 1999. Population growth and poverty in Machakos District, Kenya. TheGeographical Journal 165 (1), 37–46.

Nagendra, H., Munroe, D.K., Southworth, J., 2004. From pattern to process: land-scape fragmentation and the analysis of land use/land cover change. Agriculture,Ecosystems & Environment 101 (2–3), 111–115.

Office of the Vice President and Ministry of Planning and National Development– Kenya, 2001. Makueni District Development Plan 1994–1996. GovernmentPrinter, Nairobi.

Okello, M.M., Tome, S., 2007. The Chyulu Hills: Raison d’Etre and consequences ofcontested proprietorship of an idyllic resource oasis. In: Wishitemi, B., Spence-ley, A., Wels, H. (Eds.), Culture and Community: Tourism studies in Eastern andSouthern Africa. Rozenberg Publishers, Amsterdam, pp. 123–137.

Okello, M.M., Wishitemi, B., Bobby, E.L., Lagat, B., 2005. Tourism potential andachievement of protected areas in Kenya: criteria and prioritization. TourismAnalysis 10 (2), 151–164.

Paré, S., Söderberg, U., Sandewall, M., Ouadba, J.M., 2008. Land use analysis fromspatial and field data capture in southern Burkina Faso, West Africa. Agriculture,Ecosystems & Environment 127 (3–4), 277–285.

Pócs, T., Luke, Q., 2007. East African Bryophytes, XXV: Bryological records from theChyulu range, Kenya. Journal of East African Natural History 96 (1), 27–46.

Puyravaud, J.-P., 2003. Standardizing the calculation of the annual rate of deforesta-tion. Forest Ecology and Management 177 (1–3), 593–596.

Rempel, R.S., Carr, A., Elkie, P., 2008. Patch Analyst for ArcGIS® . Centre for NorthernForest Ecosystem Research, Ontario Ministry of Natural Resources. LakeheadUniversity.

Rindfuss, R.R., Walsh, S.J., Turner II, B.L., Moran, E.F., Entwisle, B., 2004. Linking pixelsand people. In: Gutman, G., Janetos, A.C., Justice, C.O., Moran, E.F., Mustard, J.F.,Rindfuss, R.R., Skole, D.L., Turner II, B.L., Cochrane, M.A. (Eds.), Land Change Sci-ence: Observing, Monitoring, and Understanding Trajectories of Change on theEarths Surface, vol. 6. Kluwer Academic Publishers, Dordrecht/Boston/London,pp. 379–394.

Shiferaw, T., Moses, K., Manyahilishal, K., 2010. Participatory appraisal of foot andmouth disease in the Afar pastoral area, northeast Ethiopia: implications forunderstanding disease ecology and control strategy. Tropical Animal Health andProduction 42 (2), 193–201.

Southworth, J., Munroe, D., Nagendra, H., 2004. Land cover change and landscapefragmentation – comparing the utility of continuous and discrete analyses fora western Honduras region. Agriculture, Ecosystems & Environment 101 (2–3),185–205.

Statistics Division, Kenya, 1970. Kenya Population Census 1969. Ministry of Financeand Economic Planning, Nairobi.

Survey of Kenya, 1974. Kibwezi Sheet 174/4. Survey of Kenya, Nairobi.Survey of Kenya, 1987. Kibwezi Sheet SA-37-10. Survey of Kenya, Nairobi.Thompson, M., 1996. A standard land cover classification scheme for remote sensing

applications in South Africa. South African Journal of Science 92, 34–42.Tilman, D., May, R.M., Lehman, C.L., Nowak, M.A., 1994. Habitat destruction and the

extinction debt. Nature 371 (6492), 65–66.Treves, A., Wallace, R.B., Naughton-Treves, L., Morales, A., 2006. Co-managing

human-wildlife conflicts: a review. Human Dimensions of Wildlife: An Inter-national Journal 11 (6), 383–396.

Unruh, J., Cliggett, L., Hay, R., 2005. Migrant land rights reception and “clearing toclaim” in sub-Saharan Africa: a deforestation example from Southern Zambia.Natural Resources Forum 29 (3), 190–198.

Vogt, N., Bahati, J., Unruh, J., Green, G.M., Banana, A., Gombya-Ssembajjwe, W.,Sweeney, S., 2006. Integrating remote sensing data and rapid appraisals forland cover change analysis in Uganda. Land Degradation & Development 17, 31–43.

Walker, B.H., Moran, E.F., Anselin, L., 2000. Deforestation and cattle ranching in theBrazilian Amazon: external capital and household processes. World Develop-ment 28 (4), 683–699.

Walker, B.H., Wood, C.H., Skole, D.L., Chomentowski, W., 2002. The impact of landtitling on tropical forest resources. In: Walsh, S.J., Crews-Meyer, K.A. (Eds.), Link-ing People, Place and Policy: A GIScience approach. Kluwer Academic Publishers,Boston, pp. 131–153.

Wardell, D.A., Reenberg, A., Tøttrup, C., 2003. Historical footprints in contemporaryland use systems: forest cover changes in savannah woodlands in the Sudano-Sahelian zone. Global Environmental Change 13 (4), 235–254.

Wisner, B., 1977. Man-made famine in Eastern Kenya: the interrelationship ofenvironment and development. In: O’Keefe, P., Wisner, B. (Eds.), Land use and


Development; African Environment Special Report 5. International African Insti-tute, London, pp. 194–215.

Wynne, R.H., Joseph, K.A., Browder, J.O., Summers, P.M., 2007. Comparing farmer-based and satellite-derived deforestation estimates in the Amazon basin usinga hybrid classifier. International Journal of Remote Sensing 28 (6), 1299–1315.


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