Estructuras del discurso

Forest transition models demonstrate that long-term changes in forest cover can be broadly described using a U-shaped curve (see Section 2.6.2). In this chapter, I focus on the last century, testing if a forest transition forest is

e) c)

a) b)

evident during this period and, if it is, identify the possible pathway that brought about this transition.

Forest transitions are theorised, by Meyfroidt and Lambin (2011), to occur via five main pathways: the economic development pathway; the forest scarcity pathway; the state forest policy; the globalisation pathway; and the smallholder, tree-based land use intensification pathway (see Section 2.6.2 for a detailed description). In addition to these, I propose an additional pathway. Climatic conditions and events may affect both deforestation and forest establishment, and so these biophysical impacts should not be ignored in tropical regions (Perz, 2007). A forest-favourable climate pathway could be imagined under several scenarios. These actions could be: a) direct, whereby climate changes result in a critical transition to/from forest. For example, precipitation change is a key variable that may lead to transition between the stable states of forest, savannah and grassland (Hirota et al., 2011); or b) indirect, whereby climate changes result in abandonment of agricultural land. For example, a series of droughts over a short time span may lead to abandonment of agriculture after crop failure over successive years. Furthermore, heavy rains may lead to waterlogged soils and, ultimately, landslides. Thus, both increased and decreased precipitation can be theorised to result in agricultural abandonment and forest recovery. Additionally, edaphic conditions may also fall under this pathway. Agricultural fields may be abandoned as a result of impoverished soil and declining yields, however, forest regeneration may be able to occur even under these extreme conditions.

In this chapter, I will investigate whether the long-term changes in forest cover in Tanzania follow the U-shaped curve expected under the forest transition theory. If forest transition is detected, I will descriptively analyse the forest replenishment period and evaluate the ability of the economic development pathway, the forest scarcity pathway, the state forest policy pathway, the globalisation pathway and the proposed forest-favourable climate pathway to explain this transition. Due to data-deficiency, evaluation of the smallholder tree-based land use intensification pathway is beyond the scope of this study.

Figure 3.2 Region for land cover change analysis is the Eastern Arc

Mountain watershed in Tanzania (shaded) (Swetnam et al., 2011). Additional analyses were conducted for the mountain blocs themselves (striped), and for just the northernmost blocs (circled). Points locate towns and geographical features used to assess the spatial accuracy of historical maps.

3.5.2 Study Area

In order to maximise the historical land cover data available, I focus on two nested study areas: the Eastern Arc Mountains (hereafter, EAM) and their Tanzanian watershed, which cover 5.2 and 33.9 million ha, respectively (Figure 3.2; see pages 46-48 and Swetnam et al. (2011) for further details). The EAM are defined as ancient crystalline mountains within Tanzania and Kenya, under the climatic influence of the Indian Ocean (Lovett, 1990). Their

United Republic

of Tanzania

Kenya

watershed is a heterogeneous mix of cropland, savanna, miombo and forest, and contains the administrative and commercial capitals of Dodoma and Dar es Salaam. Ecosystems within the EAM are considered a global priority for biodiversity conservation, with high levels of plant and animal endemism (Burgess et al., 2007, Platts et al., 2008, Myers et al., 2000). The region provides numerous critical ecosystem services including timber, fuel, carbon storage, water provision and regulation, maintenance of soil quality, reduction of erosion, stabilisation of local climate, conservation of cultural values (including traditional medicine), hydroelectricity generation and nutrient cycling (Economic Research Bureau, 2006, FORCONSULT, 2005, Pfliegner and Burgess, 2005, Marshall, 1998). At the time of the last national census, the population of Tanzania was 34.4 million people (NBS, 2006), of which 2.2 million lived in the EAMs and 12.9 million lived within the wider watershed catchment. Over the last 14 years, the national population growth rate has been 2.9 % yr-1, increasing pressure on land and resources (NBS, 2006).

3.5.3 Data

3.5.3.1 1891 Map

In the late 19th century Dr Oscar Baumann was tasked by the Deütschostafrikanischen Society to map the topography and vegetation of northern Deutsch-Ostafrika. The 1891 map produced by Engler (1908-10) shows the location and extent of forest in the Usamabara and Pare mountains in the late 19th century at a scale of 1:2,000,000. I find the map to be highly accurate, showing the names and locations of settlements in areas where they still persist today. Prominent natural features of Tanzania (northern EAM [namely North Pare, South Pare, West Usambara and East Usambara] and Lake Jipe) are also identifiable on the map in the correct spatial location. In addition, national borders and coastlines are accurately illustrated. I categorise the reliability of the 1891 map as high, having been well validated on-the-ground by extensive German exploration in this region of Tanzania.

3.5.3.2 1908 Map

In the early 20th century Engler and Drude produced a series of works summarising the flora and ecological conditions of Africa. Engler’s area of expertise encompassed the tropical flora contained within German territories (which, at this time, Tanzania was) (Cowles, 1910). The 1908 map produced

Table 3.1 Land cover categories originally reported in maps and their coercion into the harmonised land cover categories (Forest,

Savanna-spectrum, Crop, Other). Harmonised

category

1908 map legend 1923 map

legend

1949 map legend 2000 map legend

Forest • 5 Tropical rainforests of the flat

plains and the mountains • 6 Cloud or high altitude forest • 7 Park-like grove of the

coastlines with high tree and shrub diversity

• 4 Alluvial land in rain-poor areas, often park-like • 2 Mangrove and Creekland

• Tropical rain forest • Temperate rain forest • Mangrove • 1 Forest • 1b Forest/woodland intermediate • 22 Montane Forest 1500- 2000m • 21 Sub-montane forest 1000-1500m • 2 Lowland Forest <1000m • 23 Upper-montane forest >2000m • 11 Mangrove forest • 15 Plantation Forest • 27 Teak plantation • 26 Rubber plantation Savanna spectrum

• 1 Dry forest (forest -steppe, Miombo forest) or tree-steppe with few grasses with low tree diversity (few dominating species) often growing in single-species patches) • 8 Dry woody scrub bush and

mountainous bush, sometimes with evergreen species, in some places with trees and often merging into bust-tree and grass steppe

• 9 Individual mountains with bush-kind vegetation • Dry forest • Thorn forest • Acacia tall grass savanna • High grass low tree savanna • Alpine meadow • Mountain grass • 2 Woodland • 5 Closed Woodland • 3 Bushland and Thicket • 3b Specialised thickets of

regional extent • 2b Woodland/Bush

intermediate • Ugogo catena

• Central plateau catena • 4 Wooded grassland • Rain-pond catena • 5 Valley grassland

• 5b Ridge and slope grassland • 6 Permanent swamp vegetation

• 5 Closed Woodland • 0.5*(19 Woodland with scattered cropland) • 3 Bushland • 0.5*(4 Bushland with scattered cropland) • 7 Forest mosaic • 13 Open Woodland • 8 Grassland • 14 Permanent Swamp • 0.5*(9 Grassland with scattered cropland)

• 12 Open grass prairies, with only very few trees or shrubs • 11 High altitude grassland and

high mountain steppe

alongside alpine scrub and rock in high altitude regions

• 8 Grassland

Crop • 8 Actively induced vegetation

by natives

• 8b Actively induced vegetation by aliens • 24 Sisal plantation • 25 Tea plantation • 6 Cultivation • 0.5*(19 Woodland with scattered cropland) • 0.5*(9 Grassland with scattered cropland) • 0.5*(4 Bushland with scattered cropland) • 28 Rice plantation • 29 Monocrop unspecified • 3 Sugarcane plantation

Other • 13 Steppe with few grasses,

often with rocks, sometimes also with low, mostly thorny shrubs and trees, then orchard steepe with few grasses

• Acacia desert grass savanna

• 7 Desert/Semi desert • 1 Unclassified • 2 Bare Soils • 1 Ice • 12 Ocean • 16 Rock outcrops • 17 Urban Area • 18 Water

by Engler (1908-10) to identify the spatial location of natural resources in Tanzania was widely considered at the time as both reliable and accurate (Cowles, 1910). The map illustrates land cover within the whole of Tanzania at a scale of 1:6,000,000, using a biome-type classification system consisting of 13 different land covers (Table 3.1). I find the 1908 map to be highly accurate, showing the names and locations of settlements in areas where they still persist today. Prominent natural features of Tanzania (EAM, Kilimanjaro, Lake Nyasa, Lake Tanganyika and Lake Victoria) are also identifiable on the map in the correct spatial location. Figure 3.3 shows that, prior to geo-referencing, the map image corresponded well to the digitised study area boundary, with national borders and coastlines accurately illustrated. I categorise the reliability of the 1908 map as high, having low spatial errors (maximum spatial error of <14km [Figure 3.4]; see Section 3.5.4.5).

3.5.3.3 1923 Map

Shantz and Marbut (1923) presented a generalised map of the vegetation in Africa at a 1:10,000,000 scale. The map uses a biome-type classification system consisting of 10 different land covers within my study area (Table 3.1), but 20 in total. The 1923 map was the first such continental estimate (Whitlow, 1985) but was criticised in the literature for the broad land cover categories used during the mapping process. Michelmore (1934) felt that land covers grouped together by Shantz and Marbut (1923) were in fact very different and distinct due to wide geographical separation and thus should not grouped. I find the 1923 map to be reasonably accurate, showing the names and locations of settlements in areas where they still persist today, as well as accurately representing the railway network present in Tanzania at the time. Prominent natural features of Tanzania (Kilimanjaro, Lake Nyasa, Lake Tanganyika and Lake Victoria) are also identifiable on the map in the correct spatial location. Figure 3.5 shows that, prior to geo- referencing, the map image corresponded well to the digitised study area boundary, although the national border with Kenya shows minor discrepancies. I categorise the reliability of the 1923 map as medium, having medium spatial errors throughout my study area (maximum spatial error of <23km [Figure 3.4]; see Section 3.5.4.5).

3.5.3.4 1949 Map

In 1943, Gillman was appointed to prepare a map of the vegetation of Tanganyika Territory (Gillman, 1949). Gillman had visited the territory

regularly during the 30 year period leading up to this, accumulating a wealth of land cover data and combined these with detail reconnaissance (Gillman, 1949). The 1:2,000,000 map illustrates land cover within the whole of Tanzania to a high resolution, identifying many small fragments of isolated land covers, and uses a biome-type classification system consisting of 16 different land covers (Table 3.1). The 1949 map does not illustrate the names or locations of settlements, but does accurately represent the railway network present in Tanzania at the time. Prominent natural features of Tanzania (EAM, Kilimanjaro, Lake Nyasa, Lake Tanganyika, Lake Rukwa and Lake Victoria) are also identifiable on the map in the correct spatial location. The author provided spatially explicit indications of map reliability which were, on the whole, favourable (with 55% of the map classed as of ‘high reliability’, 25% as ‘medium reliability and 20% as low reliability (Gillman, 1949)). Figure 3.6 shows that, prior to geo-referencing, the map image corresponded well to the digitised study area boundary, with national borders and coastlines accurately illustrated. I categorise the reliability of the 1949 map as high, having low spatial errors (maximum spatial error of <18km [Figure 3.4]; see Section 3.5.4.5).

3.5.3.5 1955 Map

I obtained digitised estimates of forest cover in the EAM in 1955 from Hall et al. (2009). These estimates were derived from the ‘Tanganyika First Series’ 1:50,000 topographic maps and had been digitised by the Tanzanian National Resource Information Centre. The data are regarded to be of high reliability, however, may be slightly erroneous for the Nguru mountains due to data deficiency (it was not possible to obtain this sheet of the map and so the data were substituted with 1970s Landsat MSS land cover). Substituting 1970 land cover into this 1955 map was appropriate as experts believe most of the forest clearing in this area occurred prior to 1955 (Hall et al., 2009).

3.5.3.6 1970, 1990, 2000 and 2007 Maps

Similarly, I obtained digitised estimates of forest cover in the EAM in 1970, 1990, 2000 and 2007 from Hall et al. (2009). These maps were produced for the Tanzanian government from Landsat MSS and ETM+ satellite images by the Sokoine University of Agriculture using standard classification protocols (Harper et al., 2007). Cloud cover prevented land cover classification in the Uluguru, East Usambara and Nguru mountains and so SPOT images were used in these areas (see FBD (2006b) for a full description of methods). These maps are spatially accurate to 30m and so are regarded as very reliable.

Figure 3.3 The 1908 land cover map: a) shows the original map image, with my study area illustrated by a red outline; b) shows the

error corrected digitised map using original land cover categories; and c) shows the error corrected digitised map using harmonised land cover categories.

a)

Figure 3.4 The spatial displacement of the digitised geo-referenced maps of

the EAM watershed from a) 1908; b) 1923; c) 1949; and d) 2000 when identifiable points are compared to the same points on an independently derived map (Earth Tools, 2010).

a) b)

c) d)

Spatial Error (km)

Figure 3.5 The 1923 land cover map: a) shows the original map image, with my study area illustrated by a red outline; b) shows the

error corrected digitised map using original land cover categories; and c) shows the error corrected digitised map using harmonised land cover categories.

Figure 3.6 The 1949 land cover map: a) shows the original map image, with my study area illustrated by a red outline; b) shows the

error corrected digitised map using original land cover categories; and c) shows the error corrected digitised map using harmonised land cover categories.

a) _b)

a) _b)

Figure 3.7 The 2000 land cover map: a) shows the error corrected digitised map using original land cover categories; and b) shows

3.5.3.7 2000 Map

An additional 2000 map illustrates land cover within the whole of Tanzania to a high resolution, identifying many small fragments, and uses a biome- type classification system consisting of 30 different land covers (Table 3.1, Figure 3.7). The 2000 map was derived from an estimate of land cover in 1995 (produced at a 1:250,000 scale by combining satellite based assessment with rigorous on-the-ground validation (HTSL, 1997)). The 1995 map was produced by Hunting Technical Services by analysing mosaics of Landsat Thematic Mapper and SPOT images acquired between May 1994 and July 1996 and is thought to be accurate to the nearest 100ha (Wang et al., 2003). This original map was updated by local experts and tropical biologists, taking into account any land cover changes that had occurred between 1995 and 2000 (Swetnam et al., 2011). I categorise the reliability of the 2000 map as very high, having very low spatial errors (maximum spatial error of <9km [Figure 3.4]; see Section 3.5.4.5).

3.5.4 Methods