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Local farmers’ place-based forestbenefits and government interventionsbehind land and forest covertransitions in Zanzibar, TanzaniaNiina Käyhköa, Nora Fagerholma & Abbas J. Mzeeb

a Geography Section, Department of Geography and Geology,University of Turku, FI-20014 Turku, Finlandb Department of Forestry and Non-Renewable Natural Resources,Government of Zanzibar, TanzaniaPublished online: 10 Dec 2013.

To cite this article: Niina Käyhkö, Nora Fagerholm & Abbas J. Mzee , Journal of Land UseScience (2013): Local farmers’ place-based forest benefits and government interventions behindland and forest cover transitions in Zanzibar, Tanzania, Journal of Land Use Science, DOI:10.1080/1747423X.2013.858784

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Local farmers’ place-based forest benefits and governmentinterventions behind land and forest cover transitions in Zanzibar,

Tanzania

Niina Käyhköa*, Nora Fagerholma and Abbas J. Mzeeb

aGeography Section, Department of Geography and Geology, University of Turku, FI-20014 Turku,Finland; bDepartment of Forestry and Non-Renewable Natural Resources, Government of Zanzibar,

Tanzania

(Received 5 June 2012; final version received 21 October 2013)

Forest transitions cannot be separated from the overall changes in land uses and landcover patterns. On a local scale, these changes relate closely to values and preferenceswhich people set on different land use strategies. We have analysed the dynamics offorested land cover over the last 50 years in Zanzibar, Tanzania, in relation to farmers’material and non-material place-based forest benefits. Our results show that forestchange patterns are emerging from the adaptations of farmers’ traditional land usepractices to prevailing physical site conditions and accessibility and availability ofresources. External forces, such as government intervention in the form of planting andgazetting, have had a substantial influence on the increase in forest cover during thelast couple of decades but also challenged farmers to adapt to changing land useregimes. Our study implies that forest management and land use planning efforts, suchas community forest management processes implementing REDD+, would substan-tially benefit from a place-sensitive interpretation of forest transitions on a local scale.This would enhance genuine participation of the locals in producing place-based forestbenefit maps and expressing their values and preferences in terms of land use planning.

Keywords: land cover change; tropical forests; participation; GIS; landscape services;local scale

Introduction

Forests are complex socio-ecological systems, which are extensively overused throughoutthe globe, especially in the tropical regions. It has been estimated that about 6 millionhectares of primary forest is lost globally each year due to agriculture, logging, and otherhuman activities (FAO, 2010; Lambin et al., 1999). For many decades, governments andthe global community have tried to tackle the issue of forest deterioration, but themechanisms leading to forest degradation relate to complex dynamics of physical, social,economic, and cultural factors (Brink & Eva, 2009; Lambin et al., 2001; Paré, Söderberg,Sandewall, & Ouadba, 2008). While poverty and reliance on wood-based energy areglobally recognised bottlenecks (FAO, 2008), extensive conversion of forests into agricul-tural land is one of the main causes of forest losses in the tropics (Barbier, 2004). Externalsocio-economic changes drive and influence forest transitions on broader scales, but theinterlinked socio-ecological processes are the ones which trigger land use changes on local

*Corresponding author. Email: niina.kayhko@utu.fi

Journal of Land Use Science, 2013http://dx.doi.org/10.1080/1747423X.2013.858784

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scales. These dynamics materialise as various land use and land cover transitions and causetemporary and permanent forest losses in the landscape (Lambin & Meyfroidt, 2010).

Forest cover and land use transitions can be studied retrospectively with geographicaldata sets over the decades, and many practical efforts have shown the power of changedetection in the argumentation of forest losses and their sustainable management (Cabral,Vasconcelos, Oom, & Sardinha, 2010; Hartter, Lucas, Gaughan, & Aranda, 2008;Kennedy, Cohen, & Schroeder, 2007; Mertens & Lambin, 2000). The advantage ofdetailed, historically extensive land cover change analyses is that they may reveal thosesocio-ecological feedback mechanisms, which explain land use and forest changes (Brink& Eva, 2009; Lambin, Helmut, Geist, & Lepers, 2003). As forest cover changes are rarelyjust simple conversions from one cover type to another but rather continuous transition ofland characteristics through cyclical, linear, secular, and reversible processes with variabletime lags (Antrop, 1998; Bürgi & Russell, 2001; Käyhkö, Fagerholm, Asseid, & Mzee,2011), establishing meaningful information about forest changes is not a straightforwardprocess. Automated change detection tools often fail to see the cyclical, rapid and subtlechanges that are typical to forests with shifting land uses due to overgeneralised classifica-tions or imprecise sampling designs (Serneels, Linderman, & Lambin, 2007; Southworth,2004). Furthermore, mapping and measurement of forests can be carried out on differentscales, over varying time periods, and based on very controversial classification schemes(Hett, Castella, Heinimann, Messerli, & Pfund, 2012). For example, conversion of indi-genous forests to domestic forests (e.g. agroforests) is not necessarily visible in the changestatistics (Lambin & Meyfroidt, 2010). Identifying forest loss simply as deforestationwould be misleading since the dynamic land use regimes of domestic forests create gradualpatterns of regrowth and clearance. Eventually, what may improve the value of changedetection is the success of identifying what type of forest change patterns are typical to aparticular site or area, how do these patterns vary explicitly in space and time, and whatinternal and external mechanisms may drive these changes.

It has been stated that forest transitions cannot be separated from the overall changesin land uses and land cover patterns. It has also been acknowledged that land use changesrelate closely to values and preferences people set on different land use choices andstrategies (Barbier, Burgess, & Grainger, 2010). As land cover transitions occur onvarious spatial and temporal scales, interpretations of these dynamics are always sensitiveto site characteristics and level of detail (Rudel et al., 2005). For example, shiftingagriculture, which is extensively practised around the world as a highly variable crop-ping/fallow system, creates dramatically different consequences to local forest conditions(Hett et al., 2012; Mertz, 2009). On local scales, forest change patterns are manifestationsof underlying land valuation processes, which communities generate and produce overmany generations. Dependency on forest resources and everyday experiences of differentplaces have gradually evolved into commonly approved values and meanings attached toforests and resulted in various forest use practices (Ingold, 1993; Pickels, 1985; Relph,1976; Stephenson, 2008; Tuan, 1977; Williams & Patterson, 1996).

One way of investigating the interplay between people and forests is to considerforests as providing various goods and services for people (Haines-Young & Potschin,2013; MEA, 2003). Forests offer valuable commodities, such as timber, firewood, andwild fruits, and support several intangible benefits for most of the subsistence-basedcommunities in the tropics (FAO, 2010; Hartter et al., 2008; MEA, 2003; Solomonet al., 2007; UNEP, 2007). Forests also form the foundation for many supporting services,such as water circulation, on which human systems depend (Rudel et al., 2005). Theseecosystem functions and their realisation as services to people can be can be modelled on

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coarser scales using, for example, land cover data sets (e.g. FAO land cover classifica-tion), various forest and land transition models, and economic projections (Brink & Eva,2009; Verburg, Neumann, & Nol, 2011). However, most of the models and hypotheses offorest changes have been made at national and regional levels and there is a lack of local-scale studies (Lambin & Meyfroidt, 2010). To interpret such an amalgamated socio-ecological system, models of both forest cover transitions and forest services to peopleare needed. On a local scale, it is possible to study explicitly how forest cover or stockretrospectively changes in space and time (Hayes & Sader, 2001; Lambin et al., 2003;Mapedza, Wright, & Fawcett, 2003; Mertens & Lambin, 2000; Verburg, Overmars,Huigen, de Groot, & Veldkamp, 2006). It is also possible to collect place-specificinformation about how local farmers obtain different material and non-material benefitsfrom forests for their daily needs.

It has been successfully shown that community stakeholders are able to identify andmap different place-based ecosystem services (Bernard, Barbosa, & Carvalho, 2011;Black & Liljeblad, 2006; Brown, 2005; Brown, Reed, & Harris, 2002; Bryan,Raymond, Neville, & Hatton MacDonald, 2010; Carver et al., 2009; Raymond et al.,2009; Sherrouse, Clement, & Semmens, 2011). These sites can be interpreted as providingbenefits which people obtain from forests and ecosystems in general (Fagerholm, Käyhkö,Ndumbaro, & Khamis, 2012). Interpretation of place-based landscape services is reward-ing, as it opens access to direct knowledge about forest land uses and related values.Participatory mapping provides richer and more straightforward means of interpretation ofhuman systems compared to, for example, non-spatial participatory approaches, statisticaldata, or expert interviews (Corbett et al., 2006). The strength of empirical mappingmethods is that they are based on the true local knowledge of the distribution of landscapebenefits. Stakeholder involvement has also the potential to deepen the assessment andappreciation of the non-material, intangible benefits that the landscape and ecosystemsprovide to humans (Fagerholm et al., 2012; Vejre, Søndergaard Jensen, & JellesmarkThorsen, 2010). These cultural landscape services have quite often been limited tomapping few indicators, such as recreation and tourism (O’Farrell et al., 2010;Willemen, Hein, van Mensvoort, & Verburg, 2010).

We aim to interpret spatio-temporal dynamics of forested land cover over the last 50years in relation to place-based individual and collective services which local farmershave identified and mapped over the landscape. We work under the assumption thatthese place-based landscape benefits in a rural subsistence community are cumulativereflections of the direct and indirect values which humans place on the land and theforests. First, we aim to identify what type of forest transitions and change trajectoriesare typical in the study area over the last 50 years. Then, we analyse the mapped place-based landscape services spatially and aim to interpret how they interact with theidentified forest changes. Finally, we discuss how our spatial approach contributes tothe improved understanding and management of the local scale forest transitions andland cover changes and farmers’ associated land use preferences.

Materials and methods

Community forests of Zanzibar Island in Eastern Africa

The Coastal Forests of Eastern Africa extend from southern Somalia to southernMozambique, and are part of the Swahilian regional centre of endemism (Burgess &Clarke, 2000). These forests are extensively threatened by agricultural expansion and extrac-tion of forest materials. According to FAO (2010), Tanzania is one of the Eastern African

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countries facing severe deforestation. Due to human influences, once extensive coastal forestlandscape has fragmented, and old-growth forest patches can only be found scattered alongthe coastal region. Today, indigenous mature forests are estimated to cover less than 20% ofthe Zanzibar Islands, and these remnants concentrate mainly to forest reserves and nationalparks (Department of Commercial Crops, Fruits and Forestry [DCCFF], 2008). The con-temporary Zanzibar landscape is characterised by a mosaic of cultivated and managed forestland. Roughly speaking, the western part of the island is dominated by a mixture of urban,semi-urban, and agroforest landscapes, while the eastern part hosts vast areas of coral ragforests and thickets, which are managed through traditional community-based land useregimes (Kombo and Kitwana, 1997; Törhönen, 2004).

Forestry, together with agriculture and hunting, contributes to the national economy ofTanzania with approximately 30% of the gross domestic product (MFEA, 2010).However, the socio-economic importance of the forest products associated with bothnative and planted vegetation is relatively much higher for the subsistence-based econo-mies in the country’s rural population (Tamrini, 2009). Forests form the fundamental basisfor the agricultural and silvicultural practices and livelihoods of the local farmers. Inaddition to material benefits, forests host a diversity of intangible benefits in the form ofpractising traditional beliefs, aesthetic and intrinsic values, and social communication, allcontributing significantly to the well-being of the rural forest-dependent communities(Fagerholm & Käyhkö, 2009; Fagerholm et al., 2012; Sitari, 2005).

The Zanzibarian authorities, and also the local communities, have expressed concern overthe long-term sustainability of their forest resources. The first forest reserve was establishedby the government in the 1960s, and since then, forest deterioration has been slowed downby extensive plantations of mainly exotic forest trees and by establishment of several gazettedindigenous and planted forest reserves (DCCFF, 2008). Foreign donors, especially theGovernment of Finland, have actively been collaborating with the RevolutionaryGovernment of Zanzibar since the 1980s to establish forest-sector activities in Zanzibar(ZFDP, 1997). These interventions have not only involved supporting extensive plantationsbut also included establishment of forest administration and training of foresters to work forthe Zanzibar Government. Participation of the local communities in forest management wastaken into active agenda during the 1990s through Resource Use Management Agreements(RUMA). Currently such activities are strongly promoted in conjunction with REDD+piloting (Reducing Emissions from Deforestation and Forest Degradation; UN-REDDProgramme, 2009) and implementation of Community Forest Management Agreements(CoFMA) (MANR, 2011). Despite extensive plantations and community involvement insetting up their own plantations, forests are deteriorating due to intensive population growth(annual increase 3.1%; OCGS, 2010), poverty of the rural population, and increasing landuse pressures driven both by internal and by external factors (DCCFF, 2008).

The present study is conducted in two neighbouring administrative regions of Cheju(pop. 1800) and Unguja Ukuu Kaebona (pop. 1320) (Figure 1). These communities are, forlarge part, subsistence-based, and approximately half of the population (54%) live below thebasic needs poverty line (OCGS, 2010). The tangible and intangible values and benefitsprovided by the forests are manifold. The western part of Cheju, with lowland, deep fertilesoil, hosts permanent cultivation, but otherwise local land use regimes are highly diversemixtures of agroforestry and forest management on porous but fertile coral rag soils. Thelandscape is dominated by semi-open grasslands, encroached evergreen, and semi-deciduousbushes, as well as high forests and natural thickets (Williams, Basha, Mtumwa, &Mussa, 1997). The study site lies partly within the Jozani–Chwaka Bay National Park(JCBNP, 5000 ha), which was established in its full extent in 2004 from the Jozani Forest

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Reserve, Unguja Ukuu Forest Plantation Reserve, and Chwaka Bay mangrove forest (CNR,1997; Tamrini, 2009). At present, the National Park restricts the use of its resources by localcommunities. To compensate the effects on the surrounding communities, a revenue genera-tion mechanism from visitor profits has been implemented (Tamrini, 2009). Collaborativeforest management in Zanzibar has the longest history in Cheju, where in cooperation withthe Government, the Cheju Shehia Forest Management Plan was first drawn up in 1997(Williams et al., 1997).

Land cover data and retrospective forest transitions

Land cover data were compiled from four sets of vertical aerial photographs of the studyarea from 1952–1953, 1977–1978, 1989–1990, and 2004–2005. These materials wereobtained from the Department of Urban and Rural Planning (DoURP, formerly DoSUP)in Zanzibar town. The contemporary panchromatic normal-colour images were combined

Figure 1. The tropical island of Unguja (1700 km2, population 700,000, OCGS, 2010) is thelargest of the Zanzibar Islands in Tanzania. The population is highly concentrated in the westernurban area, while the rest of the island remains rural. The case study area is located in the south-central part of the island.

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with historical panchromatic black-and-white photographs, which we processed into digitalstereo-models and orthoimages for land cover interpretation using Leica PhotogrammetricSuite in ERDAS IMAGINE (9.0; Intergraph Corporation 2012, Essen, Germany). Allimages were georectified and mosaicked into the UTM 37 South map coordinate system(Datum Arc1960, spheroid Clarke 1880).

Land cover information was created through a visual interpretation of the orthoimageson the scale of 1:8000. Forest land cover data were delineated from the digital four-sequence aerial photograph data (T1–T2–T3–T4) into vector-based map data using ArcGIS10 (ESRI, 2011). A simple land cover classification consisting of three classes (open land,semi-open land, and closed land) was used. This structural classification was based on theuse of within-patch tree/scrub density as a criteria for class separation (open land <25%scrub/tree cover, closed land >75%). Such a classification illustrates the horizontalgradient from open to closed forest covers typical to coral rag landscapes and shiftingagriculture (Käyhkö et al., 2011), but it does not specify any other types of land coverparameters (e.g. forest height and species). Individual vector layers were converted into10 m × 10 m resolution cell data, which was sufficient to sustain the minimum mappingunit of the digitised polygons. Each time layer was transformed into a shared point latticedatabase, which had the same point frequency as the original cells.

Retrospective change trajectories describing forest cover changes were calculated foreach cell from 2004 to 1953, resulting with 81 individual combinations of changetrajectories. These individual trajectories were classified into six different qualitativechange trajectory classes using a decision tree shown in Figure 2. These classes reflectedstability, gain, loss, and turnover transitions of land (forest) cover from 1953 to 2004.

Figure 2. Individual land cover change trajectories (n = 81) were classified into six differentqualitative change trajectory classes using a decision tree. These trajectories reflect stability, gain,loss, and turnover patterns of land cover changes. These trajectories were further scaled into degreeof land cover quality following the idea that the more forested or scrub-covered landscape has beenover the decades, the higher is the quality of the land cover.

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These classes were further scaled into a continuous variable indicating the degree of landcover quality in the area. This variable ranges from closed stable land cover areas (value1) to open stable sites (value 0) following the idea that the more forested or scrub-coveredthe landscape has been over the decades, the higher the quality is.

Place-based landscape services as generalised patterns of land uses and communitybenefits

Place-based landscape service data, describing the material and non-material benefits ofthe local communities, were collected during a participatory mapping campaign orga-nised in Cheju and Unguja Ukuu Kaebona in 2010–2011. During this campaign, 218community members (8% of the adult population) participated in individual semi-structured interviews mapping indicators for landscape services (Haines-Young &Potschin, 2010; Termorshuizen & Opdam, 2009) on an aerial image map. A detaileddescription of the community mapping campaign and data processing is given inFagerholm et al. (2012). For the purpose of this study, individual farmer landscapeservices were produced into generalised spatial data of land use patterns to reflectinteractions of the communities with the forests. Based on the spatial distribution,intensity, diversity, relationships, and descriptive attributes of the 18 mapped landscapeservice indicators, four data layers, namely community-based dominant land use andresources (a), community-based non-material landscape benefits (b), richness (c), andintensity (d), were established (Figure 3).

The dominant land use and resources layer (a) was interpreted from the 13 materiallandscape service indicators together with contemporary land cover and land use (LC/LU)data. First, a map of seven different LC/LU classes based on a visual interpretation of the2004–2005 orthoimages was established. This map was used to assist in the interpretationof the mapped service indicators and their geographical patterns, which were then general-ised into four consistent dominant land use and resource classes (1–4). The non-materiallandscape benefits layer (b) was interpreted from the five non-material landscape serviceindicators and was also reflected with the contemporary LC/LU information to establish aspatial generalisation into four classes (1–4). Layers a and b were manually delineated andclassified into spatial generalisations.

The spatial intensity layer (c) was calculated through the density of landscape servicepoints (points/ha) within 200 m × 200 m and 600 m × 600 m cell area, and by using athreshold distance of 200 and 600 m, respectively. These reflected the local scale of datacollection and the broader landscape level clustering of the services. After comparingpreviously applied methods for creating density and abundance surfaces of landscapevalue points, the quadratic Kernel function (Silverman, 1986), creating a smoothly curvedcircular surface of point density for each point and summing the values in a raster cell,was selected as the method of analysis (Alessa, Kliskey, & Brown, 2008; Brown, 2005;Bryan et al., 2010; Sherrouse et al., 2011). The richness layer (d) was calculated from theoriginal mapped service points by summing the variety of service hits in each 200 m cell(potential max. 18). Richness is a simple measurement of how many different servicetypes are found within each cell, and as such, it indicates spatial variability of differentlandscape services (Farina, 2006). Land use patterns in layers a–d were visualised,analysed, and interpreted for their general spatial and statistical characteristics withArcGIS. Reflection was also made to the descriptive attributes related to the landscapeservices.

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Spatial analysis

The qualitative land cover change classes were analysed with ArcGIS and Fragstats 3.4(McGarigal, Cushman, Neel, & Ene, 2002). Three aggregated variables describing landcover changes at the spatial resolution of 200 m × 200 m cells (4 ha) were calculated forfurther analysis. First, variety statistics describing the number of different change trajec-tories for each aggregated cell (hypothetical maximum 81) was calculated. Second, theclassified qualitative change trajectories (classes 1–6) as well as the scaled land coverquality variable (0–1) were converted into aggregated cells through majority rule so thateach cell expressed the most common class values.

In order to spatially compare land cover change data and generalised place-based landuse pattern data, GIS overlay statistics (percentage cover of each change class within eachland use class) between qualitative change trajectories and selected material and non-material land use layers having sufficient spatial coverage were calculated. Furthermore,mean and standard deviation of land cover quality (0–1) and landscape variety (1–26)were calculated within each dominant land use layer class.

Figure 3. The process of creating generalised spatial data of land use patterns into four layers (a–d)based on the participatory mapping of 18 landscape services by local farmers.

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Straight-line Euclidean distance from the village centres was calculated from thebuilding data, which were visually interpreted, digitised, and delineated as areas based onthe 2004–2005 orthoimages. In landscape service data, the distance from home is usually asignificant variable explaining the spatial arrangement and valuation of different services(see also Brown et al., 2002; Fagerholm & Käyhkö, 2009). It was also expected to play animportant role when comparing distance to potential pressures of land cover changes.

Finally, using SPSS19 (IBM Corporation, 2010), the spatial correlation between land-scape change trajectory diversity, scaled degree of land cover quality, distance fromsettlement, landscape service richness (c), and intensity (d) were calculated. For theintensity, the correlation was calculated for both 200 and 600 m Kernel density for thewhole data set and separately for material and non-material services. In addition, the datawere split according to the each of the dominant land use and resources (a) and non-materiallandscape benefits (b) classes and the correlation was run between the same variables. Thecorrelation was calculated as bivariate Spearman’s rho (ρ), considered suitable for the dataas it allows outliers and does not require normal distribution (Ebdon, 1985).

Results

Land cover and forest transitions during the last 50 years

Approximately one fourth of the land area of Cheju and Unguja Ukuu Kaebona ischaracterised by long-term continuity of closed forest or scrubland (Figure 4, class 1).

Figure 4. Qualitative land cover change classes based on the change trajectory analysis show thatthe largest change classes are indicating either forest/scrubland (class 1) or open and semi-open landcover stability (class 2). Gains and losses of forest cover (classes 3 and 4) through reforestation anddeforestation are typically small and spatially dispersed areas in the landscape. Around 20% of theland has changed between open and closed conditions with different turnover trajectories (classes 5and 6). The figure represents also the boundary of the Jozani–Chwaka Bay National Park, coveringthe eastern part of the study area.

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The largest of these areas are the high forests towards the edge of the Chwaka Bay, aroundMapopwe village, and on the northern side of Kaebona, located inside the National Park.On the contrary to these forested land areas, large open and semi-open cultivated andgrassland areas (class 2) form one third of the total land area and concentrate to thenorthern and western part of the study area. These stability classes cover nearly 60% ofthe land area and have relatively low number of patches (104–180) and patch density(1.7–2.9) in the landscape. Also, the mean patch size is the highest: 7.98 ha for class 1 and19.81 ha for class 2.

Reforestation (class 3) is most extensive in the south-eastern part of the study area(inside the National Park), and is altogether more common than deforestation (class 4),which is scattered in the landscape and covers less than 10% of the land area. However,turnover trajectories (classes 5 and 6) are quite abundant in the landscape (19%), depictingcontinuous transformation of land cover between open, semi-open, and closed land coverconditions. These areas are subject to rapid changes and describe consecutive deforesta-tion and reforestation phases. Number of patches and patch density are highest for thegain, loss, and turnover trajectories, and vary between 362 to 458 patches and 5.9–7.5patch density, respectively. On the contrary to classes 1 and 2, these change classes havethe lowest mean patch sizes.

Highest number of change trajectories are found within the turnover trajectory classes5 and 6 and within the deforestation trajectory class 4 (Figure 4). Naturally, changetrajectories are less abundant within the stable open and semi-open areas (class 2).Higher number of trajectories (Figure 5a) matches with the areas of heterogeneous landcover quality (Figure 5b). Spatially, over 60% of the cells are characterised by less thansix different change trajectories (Figure 5a). The degree of land cover quality shows thetransition from high-quality forested areas (value close to 1) to those which have not beenforested over the last 50 years (values close to 0) (Figure 5b). Areas of low land coverquality are large extensive areas which cover over 30% of the landscape. Both the areas of

Figure 5. Two aggregated variables describing land cover changes at the spatial resolution of200 m × 200 m cells (4 ha). The number of change trajectories (a) was calculated for eachaggregated cell (hypothetical maximum 81). The degree of land cover quality (b) ranges fromclosed stable land cover areas (value 1) to open stable sites (value 0). Both figures represent also theboundary of the Jozani–Chwaka Bay National Park, covering the eastern part of the study area.

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low and high quality coincide with the sites of long-term continuity of either open orclosed land cover conditions (Figure 4), and thus few land cover changes (Figure 5a).Between these extremes, land cover quality is highly variable and forms a fragmentedpattern in the landscape (Figure 5b).

Land use patterns and community forest benefits

The dominant land use and resources layer (a) indicates four typical forms of land usebased on the material landscape benefits. These are core benefits in village environments(1), multifunctional subsistence zone (2), permanent agriculture (3), and forest resourcereserve and gathering (4) (Figure 6a). Examples in Figure 7a–c illustrate the nature ofthese material benefits as initially mapped points. Many of the material landscape benefits,especially home garden cultivation and livestock keeping, can be seen to cluster in thevillage environments with a relatively restricted spatial extent (class 1, 3% spatial cover-age). These core areas of settlement are surrounded by an approximately a kilometre widezone (class 2) of co-existing material benefits covering nearly half (43%) of the studyarea. This zone is particularly important for the subsistence activities of the local com-munities, the most important of which are agroforestry, shifting cultivation, and livestockkeeping, followed by tree planting, collection of wild fruits, medicinal species, handicraftmaterials, and the extraction of coral rock and soil. Another class having primarysubsistence significance is the north-western area (class 3, 19% spatial coverage), whicharises as a distinctive entity in the classification with rice cultivation combined withlivestock keeping and wild fruits collection. The remaining areas (35%) have a funda-mental role as the forest resource reserve and gathering. These forested areas face thepressure of scattered pattern of fuel wood and construction material extraction, collectionof handicraft materials, and medicinal species, answering to the subsistence needs bynearly every household.

The non-material benefits layer (b) consists of four classes: key areas of communitywell-being (1), shared sites of cultural traditions (2), appreciation of natural features (3),and no particular non-material benefits (4) (Figure 6b). Examples in Figure 7d–f illustratethe nature of these non-material benefits as initially mapped points. The key areascontributing to the overall community well-being (class 1) are found in the settlementareas with sites for social interaction, possibilities for shopping, government services, andlocal culture cluster (class 1, 3% spatial coverage). The spatial extent is similar to that ofclass 1 in layer a, because the spatial generalisation followed the patterns of the settlementareas. The most significant non-material landscape benefits relate to graveyards and sitesfor practicing traditional beliefs, which are typically located nearby the settlement areasand shared between several families. These religious and sacred sites in nature weredelineated as a distinctive class (class 2) regardless of the limited spatial coverage of 1%of the studied landscape. Over half (58%) of the landscape belongs to class 3, indicatingthe appreciation of natural features through aesthetic and intrinsic landscape values. Theseare often related to high forest areas, forest plantations, individual trees, other subjectivelymeaningful natural features, or to the valuation of good and deep soil properties. Thefourth class (38% spatial coverage) consists of those areas that do not express anyparticular non-material benefits that would allow their generalisation as collective spatialpatterns.

When considering all 18 landscape services, the most intensive occurrence showsclustering in the settlement areas, particularly in the northern and southern sub-villages(intensity max. 17.5 points/ha, Figure 6c). This pattern reflects not only the densest

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settlement and multiple material benefits provided by the nearby village landscape butalso more significantly the tendency for people to gather to these core areas for availableservices, for social interaction, and for transportation to markets and urban areas. Whenlooking at the intensity layers for material and non-material benefits separately, the non-material benefits tend to cluster more strongly in the villages (highest intensities 9.58

Figure 6. Community benefits as generalised land use patterns (200 m cell size): community-baseddominant land use and resources (a), community-based non-material landscape benefits (b), intensityof all mapped landscape service points as Kernel density surface as points/ha (c), and richness of allmapped landscape service points (d). For layers a and b, figures in parentheses indicate the relative(%) spatial coverage. Each figure represents also the boundary of the Jozani–Chwaka Bay NationalPark, covering the eastern part of the study area.

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points/ha) compared to material benefits (highest intensities 8.83 points/ha). The richnesslayer (Figure 6d), indicating the co-existence of different landscape services, follows thesame pattern with the highest richness of 15 co-existing landscape services found in thenorthern settlement areas. When viewing the landscape as a whole, the general tendencyfor spatial clustering within and nearby the settlement areas seen in Figure 6c and d is alsoindicated by the low mean values and high standard deviations of intensity (mean, 0.58;SD, 1.42) and richness (mean, 1.37; SD, 2.26).

The forest stability trajectory (1) is relatively more common within the forest resourceuse reserve (A4) (Figure 8). While on average, the share of forest stability is around 23%(all land areas) and 19% within the subsistence zone (A2), over 40% of the land within theforest reserve areas (A4) is indicated with forest continuity from the 1950s to 2005. Onthe contrary to these forested areas, stability of open and semi-open land is naturally mostcommon within the permanent agricultural area (A3, 90%). The reforestation trajectory(3) is relatively more common within the forest reserve zone (A4, 22%) compared to

Figure 7. Spatial distribution of selected material and non-material landscape services mapped by218 farmers during the participatory GIS campaign. Figures in parentheses indicate the absoluteamount of mapped points. The boundary of the Jozani–Chwaka Bay National Park, covering theeastern part of the study area, is visualised on all maps.

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average 16% within the whole landscape. There is less deforestation (4) than on average(8%) within the permanent agricultural zone (A3, 4%), which is obvious since these areasare mainly open, and also within areas of no particular non-material benefits (B4, 5%). Onthe other hand, deforestation seems to be a relatively more common feature of thesubsistence zone (A2, 12%) and within the areas of appreciation of natural features (B3,15%). Turnover trajectories (5 and 6), which comprise 19% of the whole landscape, areespecially characteristic to the subsistence zone (A2, 26%), the areas where naturalfeatures are appreciated (21%), and the forest resource reserve zone (B3, 20%). Overlaystatistics show that the forest resource reserve (A4) and the subsistence zone (A2) have thehighest mean values of both the degree of land cover quality and the number of changetrajectories (Figure 9), while the permanent agricultural zone has the lowest values.

The spatial relationship between the landscape change data (trajectory diversity, scaledqualitative trajectories) and the existence of landscape services (richness, material, non-material) shows rather weak correlation (Table 1). High landscape change trajectorydiversity and qualitative trajectories are related to high intensity of landscape services

Figure 8. Share (%) of different qualitative land cover change trajectories (1–6) within selecteddominant land use (a) and non-material landscape benefit (b) classes and within the total landarea (All).

Figure 9. Mean and standard deviation of the degree of land cover quality (left) and number ofchange trajectories (right) in each dominant land use zone.

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Table1.

Spatialrelationshipbetweentheland

scapechange

trajectory

diversity,d

egreeof

land

coverqu

ality,d

istancefrom

settlem

ent,land

scapeservicerichness,

andintensity

calculated

asSpearman’srhocorrelation.

Richn

ess

(0–15)

Intensity

(200

mcell,

points/ha)

Intensity

(600

mcell,

points/ha)

All

(0–17.5)

Material

(0–8

.8)

Non

-material

(0–9.6)

All(0–5

)Material

(0–3.4)

Non-m

aterial

(0–2.9)

Distancefrom

settlem

ent

Trajectorydiversity

(1–2

6)0.07

9**

0.07

1**

0.13

3**

NS

0.16

6**

0.20

5**

0.08

4**

−0.33

9**

Qualtrajectory

scale

(0–1

)−0.07

0**

−0.08

8**

−0.09

4**

NS

−0.08

3**

−0.10

8**

NS

0.08

7**

Distancefrom

settlem

ent

−0.48

1**

−0.52

1**

−0.54

4**

−0.34

3**

−0.62

9**

−0.64

2**

−0.50

5**

–

Notes:C

orrelatio

nsmarkedwith

**aresign

ificantatthe

levelo

f0.01

and*atthelevel0

.05.

The

correlationcoefficientiscatego

risedas

astrong

correlationwhenρ≥0.5(darkgrey),

moderatecorrelationwhen0.3≤ρ<0.5(m

edium

grey),andweakcorrelationwhen0.1≤ρ<0.3(light

grey).Veryweakandnotsignificantspatialrelatio

nships

(NS)areindicated

with

nofill.

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only at the resolution of 600 m, showing a weak positive correlation (0.166* and0.159**). This trend is due to the material landscape services, which show the spatialrelationship also at the 200 m resolution of intensity. The result indicates that increasedland dynamics is weakly related to the occurrence of material landscape benefits.Landscape service intensity also shows a weak spatial correlation to land cover opennessfor the 600 m material landscape service data (–0.108**).

When the distance from settlement is compared to the landscape change data, there isa moderate negative spatial relationship to trajectory diversity (–0.339**), meaning thathigh trajectory diversity is associated with the settlement areas. In general, the distance tosettlement correlates negatively with landscape service richness (moderately) and intensity(strongly), meaning that the most intensive clustering and richness of landscape benefits isfound within and nearby the settlement areas. This especially applies to the intensity ofmaterial benefits (–0.544** for 200 m and –0.642** for 600 m material intensitycompared to –0.343** and –0.505** non-material intensities, respectively).

Discussion

Forest dynamics and interlinked place-based benefits

This study showed that there are different types of local-scale forest transitions, whicharise from the overall land use choices and associated activities of the local farmers. Dueto multiple farming strategies of the families, these emerging forest change patterns areeither spatially clustered or highly fragmented in the landscape. The overall patterns offorest dynamics seem to be long-term socio-ecological adaptations to prevailing physicalsite conditions and accessibility and availability of resources. Thus, they also reflectadaptations to practical realities of poor infrastructure and non-mechanical subsistencefarming, where most of the activities associate spatially to closeness of farmers’ homesand villages (see also Fagerholm et al., 2012). Our results also indicate that externalgovernment intervention in the form of planting and gazetting forests have had a sub-stantial influence on the increase in forest cover during the last couple of decades.However, these same interventions have weakened farmers’ accessibility and rights toland and limited the diversity of their forest benefits.

Site-specific continuity (stability) of forested environments is an indication of sub-stantially lower land use pressure on the land or use of farming practices which do notremove forest cover. In practice, such areas can be found only within remote, old-growthindigenous forests or amongst the core areas of agroforestry activities. The mechanismswhich sustain forest cover in these two example areas are therefore different. While theabundance of fruit trees and multistory agroforestry sustain forest cover in the vicinity ofthe farmlands, old-growth indigenous forests have sustained, since their edaphic siteconditions are unsuitable for farming (Käyhkö, Orjala, & Mustelin, 2008; Kombo &Kitwana, 1997). Forest areas in general are an important reserve for material benefits suchas fuel wood and collection of various forest materials (poles, handicrafts, medicinalspecies, etc.), but especially old-growth forest remnants are appreciated for their aestheticand intrinsic values and because they form an important source of non-material benefits tolocal farmers.

Permanent cultivation and associated smallholder farming and forest managementhave generated stability of open and semi-open land covers for the study area. Sincethere is an extensive good quality farming area, the deep alluvial plain, in the north-western part of the study area, it allows the establishment and maintenance of permanent

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farming and provides valuable alternative for swidden cultivation practices. It most likelydecreases the pressures to clear forests to farmlands in the subsistence zone and alsowithin the forest reserve. However, maintenance of swidden and permanent farmingpractices in different edaphic site conditions secures availability of food for the poorfarmers (Eilola, Käyhkö, & Fagerholm, in press). These well-established farming strate-gies are able to support different crop varieties, and as long as these communities dependstrongly on subsistence economies, we do not foresee any major changes of these farmingpatterns and consequently forests.

Semi-permanent cultivation (crop rotation mixed with grazing and tree plantations) ongrassland/scrubland dominates areas in the north and south-east of the study area. Thistype of a land use strategy clearly diversifies forest and tree cover transitions on open andsemi-open land and may explain why there are less evident forest losses within thesubsistence zone. More likely, scrublands are gradually converted into semi-open domes-tic forests (Michon, De Foresta, Levang, & Verdeaux, 2007; Wiersum, 2004). These areasmanifest multiple co-existing material benefits, having interestingly hardly any non-material benefits for the farmers.

Reforestation is a constantly occurring process through natural regeneration of coralrag forest. It is especially evident amidst shifting cultivation, where low-lying scrublandoccupies the land soon after ceased swidden and rotating cultivation (Hett et al., 2012). Itis also typical within the tree-based farmland, where scrubs grow amongst planted trees.However, due to a relatively rapid rotation between the plots (usually 1 to 5 years),regenerated forest hardly ever grows to mature stands (Käyhkö et al., 2011). Substantialamount of reforestation is related to either family- or group-based afforestation activitiesor large government-run afforestation schemes, which have taken place since the 1980s(Käyhkö et al., 2008; Omar, 1994; Sitari, 2005; ZFDP, 1997). These plantations of exoticspecies, mainly Acacia auriculiformis and Casurina equisetifolia, have been successful inthe sense that a large area in the south-west has been converted from once completelyopen and extensive grassland area into forested land. Family- and group-based afforesta-tion activities have diversified the forest-related benefits and brought monetary economiesinto local farmers’ lives. They have also provided an alternative source of energy forcooking, but the locals seem to prefer indigenous wood for cooking and grow trees mainlyfor selling (Käyhkö et al., 2011; Sitari, 2005).

Deforestation is primarily caused by clearing forested land for different agricultural uses.However, usually transitions into open land are less evident, since most of the clearings aretemporary due to slashing and burning or gradual modification of the indigenous vegetationinto tree-based farmland. Both of these land use practices are typical to multifunctionalsubsistence zone and forest areas in the vicinity of the villages. As shifting cultivationcomprises consecutive phases of deforestation and reforestation, the sites of deforestation inour analysis are mainly recent clearings of forested land likely to swap back into closedland. Deforestation is also evident within the government forest plantations, which areconstantly harvested and replanted under the government forest plantation schemes.Permanent deforestation does not seem to be a major dilemma in the study area, but thecyclic pattern of land management obviously degrades forested environments ecologicallyand eventually also socially.

Turnover trajectories are typical patterns of forest transitions related largely to swiddencultivation practices. These are usually family-run activities on small plots of land, andthus generate heterogeneous and dynamic forest/open/semi-open/forest transition patterns.Shifting cultivation is characteristic throughout the subsistence zone and in some parts ofthe forest resource zone (mainly in the north-eastern and southern part). Some patches of

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turnover trajectories are created through conversion of forest through clearing back intosmall-holder farmland. This is typical to areas in the vicinity of the villages. On the otherhand, gazetting the extension of the Jozani–Chwaka Bay National Park stopped shiftingcultivation, and some of these areas were naturally regenerated into scrublands. Inaddition, some areas were afforested by local communities and the government soonafter the establishment of the conservation area.

When observing the collective contribution of the farmers’ values and preferences onthe land and the forests, one can say that material landscape benefits have a spatiallyscattered influence on forest patterns. This is primarily due to family-level preferences anddominance of subsistence economies. Non-material benefits, on the other hand, tend tohave more spatially clustered patterns indicating shared place-based values, especiallywithin and nearby the villages. These key areas of community well-being manifest sharedcultural traditions and social interaction in combination with home gardening and keepingof domestic animals (Eilola et al., in press). The subsistence zone contributes essentially tofamily livelihoods, and it simultaneously creates high pressure on natural resources,particularly forests and semi-open grasslands. This is also seen as the high trajectorydiversity of land cover. Deforestation is not necessarily the main threat here, since forestsare gradually converted into permanent, family-run, tree-based farmland with mixtures ofplanted and indigenous trees and crops, thus sustaining closed and semi-open domesticforest patterns over the decades to come (Michon et al., 2007).

Government intervention has created an interesting interplay between internal andexternal forces behind forest transitions in the study area. First, government interventionthrough large plantations has had a clearly positive influence on the total forest stock inthe area over the last decades. While generally speaking Zanzibar seems to be losing itsforest cover (DCCFF, 2008), here the forest cover has substantially increased. Small-scaleplantations do not have as evident role in forest recovery, but they have had an importantrole in engaging local farmers and groups of women and men in tree planting and incomegeneration activities. In general, they also raise farmers’ awareness about forest manage-ment issues (Tamrini, 2009).

The National Park regulations restrict the use of forest benefits among the area’sinhabitants. However, while local farmers are allowed to collect dry wood from some ofthe National Park’s zones, they are also partially forced to cut and log forest materialssimply due to limited resource base in the vicinity of their homes. At the same time,intangible cultural and social values attached to these forests work towards the apprecia-tion of forest resources. While material uses are the ones which caused a direct threat offorest losses and degradation, non-material benefits are creating indirect circumstances forforest maintenance and conservation arising from the communities.

Forest areas within the established National Park reflect quite an interesting commu-nity–forest–government interplay. Many of the outcomes of these interactions are still tobe seen, since the recent CoFM agreements and their follow-ups may bring forth new landuse issues to discuss, especially in how land uses within forested and non-forested areasare planned. For the local farmers, all land is potential ‘forest land’ since it may be subjectto regeneration of trees any day. This local interpretation of what constitutes a forestdiffers drastically from how forested land is defined in physical terms and interpreted asan entity in spatial planning. As Zanzibar is lacking local-level land use planning practice,it seems that CoFM is used as a surrogate. This is rather peculiar, as CoFM giveslegislative and institutional entitlement only to forest management (MANR, 2011), butin practice it is used by authorities to execute overall land use planning on community-managed land. This will surely challenge endeavours towards local- and district-level land

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use planning, where forest management would be subordinate to overall land use plans,which do not exist. Perhaps it concretely draws the government’s and other stakeholders’attention towards the critical issue of long-term forest sustainability. In this type of highlydiverse and multifunctional human–forest systems, explicit measurements of forest stocktoday will be outdated in some years’ time.

Retrospective change analysis combined with place-based mapping of forest benefits

This study demonstrated that retrospective land use and land cover change analysiscombined with place-based mapping of forest services has potential in enriching theinterpretation of local-level human–forest interactions. One of the advantages is that itdraws our attention to functional interactions of stakeholders and forests and links thesespecifically to places and forest areas in the landscape. It also directs forest interpretationtowards mapping and explaining forest change processes rather than measuring foreststock and cover at one moment in time. However, both of these models of forest coverchange and place-based services are generalised models of human–forest interactions, andthus have limited capability of explaining complex socio-ecological interactions behindforest dynamics.

The land cover change trajectories used in this study were based on spatio-temporalcartographic models of forest transitions. In our study, we chose to work towards mean-ingful trajectories, and thus used a semi-qualitative method in the initial establishment ofthe trajectories (e.g. Käyhkö & Skånes, 2008). We also established quantitative indicators,such as the number of trajectories, and used both of these to interpret key transitionswhich had taken place in the landscape and the qualities thereof. Our results are artefactsof the used land cover classification and change detection methods, and naturally do notrepresent the only possible models of forest transitions. For example, ‘stability’ of closedforest cover does not necessarily mean that there have been no changes in the land coverduring the analysis period, since temporal sampling is rather sparse and changes may havehappened unnoticed. However, it does indicate that the capacity of the forested land tosustain and renew is better within these stability trajectories than in some other areas.Furthermore, the fact that the correlations between the change trajectories and diversity ofservices were weak may simply demonstrate that the increased number of benefits doesnot need to lead into fragmentation and dynamics of land cover as not all benefitstransform or lead into harsh land conversions. It may tell, however, that simple quantita-tive indicators of land cover dynamics are weak representations of the nature of changesthat have taken place. Altogether, we believe that qualitative transitions were able to showthe most characteristic directions of change in a subsistence-based community forest area,and most importantly, they were able to show where such changes occurred in thelandscape. This spatial perspective is fundamental since it allows a genuine local-level,spatially specific discussion regarding farmer–forest–government interactions.

Our study has shown that similarities in forest change patterns do not necessarilyimply that causes to transitions are the same (Lambin & Meyfroidt, 2010). Therefore, wemay state that our landscape service or benefit mapping approach was very useful inlinking the site-specific transitions to their potential causes and, even more interestingly,to their possible consequences. Instead of using individual mapped points, we chose towork at the landscape level and established collective and spatially generalised interpreta-tions of mapped service points through their spatial patterns and descriptive attributes.These generalised patterns reflected concentrations of both material and non-materialhuman benefits and were able to indicate benefit characteristics in a map form as well

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as capture the sensitivity related to the benefits. As an example, aesthetic values meandifferent things when indicated for the forest areas compared to village areas. Althoughthere are different, even fully automated, ways of establishing generalisations frommapped services and physical land patterns (Sherrouse et al., 2011), we feel that simplequantification of the service points in relation to land covers would have limited ourinterpretations of their meaning and significance. We considered this interpretative deli-neation to reflect the actual spatial characteristics of the landscape services and theirrespective land cover and land use dynamics.

Long-term viability of community and government-managed forests on local scales

This study implies that forest management and land use planning on local scales wouldsubstantially benefit from a place-sensitive interpretation of forest transitions and genuineparticipation of the locals to produce their place-specific forest benefit maps (Guariguata,Sist, & Nasi, 2012; Termorshuizen & Opdam, 2009). We have shown that communityforests possess multidimensional values to local farmers and that these values havedistinctive spatial order and structure, which interact with the dynamics of the physicalland patterns. Some of these benefits can be translated into direct land use pressures,which lead to either permanent or temporary forest losses and simultaneously to establish-ment of new semi-open and closed land conditions (Sloan, 2008). However, to mostextent, they just simply reflect well-established customs and shared values of the localcommunities and demonstrate a complex human system adapted to socio-ecologicalrealities over the generations (Lambin & Meyfroidt, 2010). As there is large variabilityin the actual transitions and their realisations, their significance depends on what landcover conditions prevailed before the changes and what forest conditions are expected toprevail afterwards and how these are associated to human benefits (Farley, Ojeda-Revah,Atkinson, & Eaton-González, 2012).

The efforts to tackle forest loss and degradation have insufficiently addressed the roleof human–forest interactions and human values behind land use strategies and especiallytheir spatial connotations on local scales. REDD+ has been one of the latest interventionsof the global community and governments to tackle the tropical forest managementchallenges (Parker, Mitchell, Trivedi, & Mardas, 2009). REDD is expected to addresshuman–forest interactions especially on local scales and to build operative ways tocompensate countries and farmers for the prevention of forest losses (Parker et al.,2009). There are high hopes that participation of local actors in forest managementwould be able to implement the REDD+ mechanisms. It may provide income from forestprotection to local communities, but these co-benefits are not guaranteed due to lack ofparticipation of the locals in the process and due to underestimating subsistence commu-nities’ values regarding forests (Mertz, 2009; Mustalahti, Bolin, Boyd, & Paavola, 2012).However, the success of REDD, or any other local-level forest intervention to that matter,is dependent on whether our understanding of the forest–farmer interactions is wellestablished, and whether we are able to truly engage people into forest management. AsZanzibar is acting as one of the pilot sites for the REDD+ implementation, we foresee thatthe current structures of participation embedded in the CoFM process will not be able tosolve how local values could genuinely be incorporated into the socially justified forestmanagement practices (MANR, 2011).

Our interpretation of the soundness and sustainability of human–forest systems areoften directed by the external notions and expectations of what constitutes a betterenvironment and what drives sustainable development. These notions are driven by global

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concerns over the losses of tropical forests and its consequences to climate change andfurther to human well-being. Although we may clearly see the linkages between forestoveruse, poverty, and prevalence of subsistence economies, we know that the main broad-scale threats to forest losses are related to commercialisation of forestry and agriculture(Barbier, 2004). While such broad-scale land conversions often lead to permanent forestlosses, realities on local scales are much more dynamic and adaptable to changing social,economic and environmental conditions (Wiersum, 2004).

AcknowledgementsWe would like to thank the Government of Zanzibar, Department of Forestry, and Non-RenewableNatural Resources (DFNR) for their guidance, facilitation, and assistance of this research inZanzibar. The work was financially supported by the Academy of Finland (SA 8109210),University of Turku, Department of Geography and Geology, and the Laboratory of ComputerCartography (UTU-LCC).

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