Forest cover and change for the Eastern Arc Mountains and ... · Ukaguru, Rubeho, Udzungwa and...
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Forest cover and change for the Eastern Arc Mountains and Coastal Forests of Tanzania and Kenya circa 2000 to circa 2010 Final report Karyn Tabor, Japhet J. Kashaigili, Boniface Mbilinyi, and Timothy M. Wright
Transcript of Forest cover and change for the Eastern Arc Mountains and ... · Ukaguru, Rubeho, Udzungwa and...
Forest cover and change for the Eastern Arc Mountains and Coastal Forests of Tanzania and Kenya circa 2000 to circa 2010
Final report
Karyn Tabor, Japhet J. Kashaigili, Boniface Mbilinyi, and Timothy M. Wright
Table of Contents Introduction .................................................................................................................................................. 2
1.1 Biodiversity Values of the Eastern Arc Mountains and Coastal Forests ....................................... 2
1.2 The threats to the forests ............................................................................................................. 5
1.3 Trends in deforestation ................................................................................................................. 6
1.4 The importance of monitoring ...................................................................................................... 8
Methods ........................................................................................................................................................ 8
2.1 study area ............................................................................................................................................ 8
2.1 Mapping methodology ........................................................................................................................ 8
2.3 Habitat change statistics ..................................................................................................................... 9
2.4 Map validation .................................................................................................................................... 9
Results ......................................................................................................................................................... 10
3.1 Deforestation rates ........................................................................................................................... 10
3.2 Forest cover and change map ........................................................................................................... 11
3.2 Habitat cover and change statistics .................................................................................................. 12
Table 2. Forest cover and change for the Eastern Arc Mountains and Coastal forests (Tanzania and
Kenya). ................................................................................................................................................ 12
Table 3. Mangrove cover and change for the Eastern Arc Mountains and Coastal forests (Tanzania
and Kenya). ......................................................................................................................................... 12
Table 4. Forest cover and change for Kenya’s Districts. ..................................................................... 12
Table 5. Forest cover and change for Tanzania’s Districts. ................................................................. 13
Table 6. Rates of deforestation inside verses outside conservation areas in Kenya .......................... 14
Table 7. Rates of deforestation inside verses outside conservation areas in Tanzania ..................... 14
3.3 Validation .......................................................................................................................................... 14
Table 8. Validation Matrix ................................................................................................................... 14
Discussion.................................................................................................................................................... 14
Conclusion ................................................................................................................................................... 15
References .................................................................................................................................................. 17
Appendix A .................................................................................................................................................. 20
Table A1. Forest cover and change for Kenya’s KBAs. ........................................................................ 20
Table A2. Forest cover and change for Tanzania’s KBAs. ................................................................... 21
Table A3. Forest cover and change for Kenya’s protected areas. ...................................................... 22
Table A4. Forest cover and change for Tanzania’s protected areas. .................................................. 23
Table A5. Forest cover and change for Kenya’s forest reserves. ........................................................ 30
Table A6. Forest cover and change for Tanzania’s forest reserves. ................................................... 30
Introduction
1.1 Biodiversity Values of the Eastern Arc Mountains and Coastal Forests
The Eastern Arc Mountains
The Eastern Arc Mountains and Coastal Forests of Kenya and Tanzania (EACF) are very important due to their biological richness (BirdLife International, 2013), have been designated a hotspot by Conservation International, and are also considered one of WWF’s Global 200 priority ecoregions. Many thousands of species of plants and animals are found in these forests and nowhere else on earth (EAMCEF, 2012), and these include at least 100 species of birds, mammals, amphibians and reptiles; at least 500 plants and huge numbers of smaller creatures including butterflies and millipedes. The name “Eastern Arc Mountains” (EAM) originated as a handy way to describe the unique area of forests that contained many rare plants and animals (Lovett, 1998). The areas are characterized by a high level of species endemism, a severe degree of threat and exceptional diversity of plant and animal communities. The mountains span the crystalline rock mountains of eastern Tanzania and south-eastern Kenya that the biologists have long known about the extraordinary species richness. According to FBD (2006), the Usambara Mountains in north-eastern Tanzania were one of the first areas to be studied seriously, starting about 100 years ago. Gradually other areas such as the Uluguru Mountains were visited by scientists and also proven to be biologically very important. Studies in the Udzungwa Mountains started in the 1970s. These revealed that many of the species thought to be found only in the Usambara Mountains were also present in southern Tanzania. Studies in the Livingstone Mountains (also crystalline rock mountains) further south above Lake Nyasa showed that these mountains contain different species from the rest of the mountains and are thus not included in the definition of the Eastern Arc Mountains (Lovett, 1998). On the Tanzanian side, the Eastern Arc Mountains therefore include North Pare, South Pare, West Usambara, East Usambara, Nguu, Nguru, Uluguru, Malundwe, Ukaguru, Rubeho, Udzungwa and Mahenge (Figure 1). The hotspot is believed to contain at least 1,500 endemic plant species, 16 endemic mammals, 22 endemic birds, 50 endemic reptiles and 33 endemic amphibians (Lovett & Wasser, 1993; Burgess & Clarke, 2000; Myers et al., 2000). The 2002 IUCN Red List identifies 333 species as either critically endangered, endangered, or threatened in the hotspot. Twenty out of twenty-one African Violets found in the Eastern Arc Mountains are endemic. According to Siex et al. (2011), the Kihansi spray toad Nectophrynoides asperginis, endemic to the Uzungwa Scarp Forest Reserve, is now probably extinct in the world. Recent studies (Burgess, 2012) revealed rediscoveries of species previously thought to be extinct. For example Erythrina schliebenii Harms and Karomia gigas (Faden) Verdc., have been rediscovered in the little-known Namatimbili–Ngarama forest block located some 35 km inland of Kilwa in south-east Tanzania. Nectophrynoide swendaye, another hyper-endemic, was recorded at a new site,0.5 km from the original site in Uzungwa Scarp Forest Reserve (Siex et al., 2011) while the Critically Endangered Long-billed Tailorbird Artisornis moreaui was recorded at the Misalai village landscape (Leonard et al., 2010) thus extending the range for both species. A recent compilation of the past records of the African golden cat Caracal aurata, Africa's least known felid; suggest that this species’ range extends to Arabuko-Sokoke Forest on the Kenya coast (Butynski et al., 2012).
Figure 1.
BirdLife International, (2013) revealed the continued discoveries in the EAM in Tanzania, especially among reptiles and amphibians. Recent analysis of reptile and amphibian data collected since 1997 from the Eastern Arc Mountains mostly by the Natural History Museum of Trento has doubled the number of these species known to be endemic to the mountains (TFCG, 2010). The yet to be completed analysis has identified a number of evolutionary significant units, with c 300 new distribution records and c 70 nondescript endemics. These discoveries are said to double the number of amphibian species known to be endemic to the Eastern Arc Mountains (TFCG, 2010). In the South Ngurus 16 herpetofauna species strictly endemic to the mountain block have been identified and the list could be found in BirdLife International, (2013).
Figure 1 shows the Eastern Arc Mountains and the reserve network they contain (FBD, 2006)
Despite the discoveries, studies conducted by Borghesio et al. (2010) in 2009 and 2010 strongly suggest that a major population crash of the Critically Endangered Taita Apalis Apalis fuscigularis is underway. Compared with 2001, sighting rates in April-May 2009 had dropped by about 38%; repeated counts done in September-December 2009 and May-July 2010 showed even larger decreases, approaching 80%. This means that the global population of the species might now be only 60-130 individuals, almost all of which are located in a single forest, Ngangao, which is only about 120 ha (BirdLife International, 2013). Elsewhere, monitoring data collected over a three year period from 2005 to 2008 in 67 permanent transects in Arabuko-Sokoke forest by Virani et al. (2010) showed a steady but not statistically significant decline in Sokoke Scops-Owl densities. This equated to 22.5% decline over a 16 year period going back to density estimates of 1993. However the study did show that densities were relatively higher and degree of human disturbance lower at Dida where a PFM project has been implemented. The discovery of the Dakatcha population, with densities higher than those of the Arabuko-Sokoke and Usambara populations (Njoroge et al., 2011), has boosted the species survival chances, but it is still predicted to suffer rapid declines and shift in distribution due to climate change in the near future (Monadjem et al., 2012). Research conducted in 2010 on phylogeography of the Angolan black and white colobus monkey, Colobus angolensis palliatus, in Kenya and Tanzania highlighted the evolutionary distinctiveness of Kenyan populations of the species relative to Tanzanian populations. With the Tanzanian population better protected than the Kenyan population. IUCN red-list status of palliatus is now being reassessed and status change seems likely (Mcdonald and Hamilton, 2010). The Coastal Forests
A total of 6,200 km2 of the Eastern African Coastal Forests (Kenya, Tanzania and Mozambique) have been recognized as a distinct Global Hotspot for the Conservation of Biodiversity on account of high levels of both endemism (plants and several animal taxa) and species richness, both within and between the many constituents of small forest patches (WWF, 2006). The Coastal Forests found in Tanzania are a part of the ecoregion known as "Northern Zanzibar-Inhambane Coastal Forest Mosaic". This ecoregion extends from the Kenya–Somali border to the Tanzania–Mozambique border along the coast. The ecoregion includes forest patches found on the islands of Zanzibar, Pemba, and Mafia. These forests are characterized by a mosaic of vegetation types including evergreen forest, brachystegia woodland, scrub forest and dry forest. Mangrove forests are not considered to be Coastal Forest in this definition. Coastal Forests are distinct from the forests of the Eastern Arc in terms of climate, elevation, and dominant plant species. Most Coastal Forests are found at elevations between 0-50 m, 300-500 m, and even up to a maximum of 1100 m depending on ecological conditions (Burgess et al., 2000).Like the Eastern Arc Mountains Forests, the Coastal Forests are important for conservation due to the high levels of flora and fauna endemism. In Tanzania, there are 66 forest patches covering an area of 700 sq. km (Burgess et al., 2000). While the highest levels of biodiversity are found in the closed canopy forest, this only makes up about 1% of the total area of the Coastal Forest Mosaic (Clarke, 2000). Notwithstanding the small area covered by these forests, they retain high numbers of endemic plant and animal species. For example, the Coastal Forests Hotspot contains about 4,500 plant species in more than 1,000 plant genera, of which about 1,750 plant species and 27 genera are endemic (WWF, 2006). As noted above, most of the forest patches with proportion of the endemic plants are confined to single forests (for example, the Rondo Forest area in southern Tanzania has at least 60 strict endemics, the small Litipo Forest, also in southern Tanzania, has at least 30 strict endemics). The coastal forests in East Africa also contain high rate of fauna endemism which contains 10% of bird species, 19% of butterflies, 7% of mammals and the largest percentage covered with reptiles and amphibians (57% and 36% respectively). While the endemism within vertebrates is impressive,
rates of endemism are even higher in invertebrate groups such as millipedes (80% of all the forest species) and molluscs (68%) (Burgess and Clarke, 2000).
1.2 The threats to the forests The EAM and Coastal forests are highly important for the livelihood and well-being of many Tanzanians. The Uluguru Mountains, for example, provide Dar es Salaam with its main water source. The forests also generate a significant percentage of Tanzania's electricity through hydroelectric power plants. Despite the high biodiversity values of the EAM forests and Coastal Forests, and their many and varied uses that include among others medicinal plants, fuel wood, building materials, food, and maintaining a regular water supply for towns and villages, the forests are being degraded rapidly. For example, WWF-EARPO (2006) identify the main threats to the Coastal Forests and Eastern Arc Mountains as being agriculture (subsistence and commercial), settlement, urbanization, lack of legal protection, fires, illegal logging (using temporary sawmills and selective logging of particularly valuable trees), hunting, grazing, pole cutting, firewood collection, charcoal burning, mining and medicinal plant collection. It is more apparent that as population continues to grow, the pressures on the forests will become even more significant. Above all, poverty is considered a root cause behind many of these problems. WWF Eastern Africa Coastal Forest programme (WWF- EARPO, 2006) ranked the threat status of several extraction activities and top of their list was charcoal, unsustainable logging for timber and poles, overharvesting for wood carving and unsustainable hunting and mining. High intensity of charcoal extraction occurs in areas close to towns e.g. a total of 6777 bags are reportedly transported to Dar es Salaam daily (Malimbwi et al., 2007). The catchment area for the sourcing of charcoal for Dar es salaam has expanded from 50 km radius in the 1970s, to 200 km in the 1990s (Ahrends et al., 2010) to over 1000-km today (Burgess, 2012). Unsustainable hunting for commercial and subsistence use has increased by 10% every year in Kenya’s coastal forests between 2008 and 2010 (2008:52%; 2009: 62% and 2010: 72%; Mwinami et al. 2011). This has contributed to the localized change of status of species like Golden-rumped Elephant Shrew (Rhynchocyon chrysopygus), and the critically endangered Ader’s Duiker (Cephalophus adersi), e.g, within Dakatcha woodlands (BirdLife International, 2013).
In Tanzania, illegal timber extraction by local people and by tea companies (Schaafsma, 2012) which use large amounts of firewood for drying tea also for tobacco curing has continued; although commercial timber extraction for export primarily to China was banned in the late1980s. However, heavy logging for hardwood for export has occurred in the coastal forests of the Rufiji, Kilwa and Lindi Districts. This threat is exacerbated by the expansion of settlements around coastal forests(Burgess et al., 2012).
Most of Kenya’s coastal forests have experienced uncontrolled firewood collection with the threat being bad at 71% of coastal sites in 2008 (Adhola et al., 2008) and increasing to 72% by 2010 (Mwinami et al. 2010). In many coastal IBAs, such as Dakatcha Woodland, firewood is collected on a commercial basis (Ruuska, 2012). In the forest surrounding Shimoni in the south coast of Kenya, resource extraction was determined between 2008 and 2012 (Wijtten et al., 2012). Results showed timber and pole extraction increased and there was a strong increasing trend in the encounter rate of charcoal pits even though the number of shelters, houses or evidence of chainsaw use did not increase significantly.
Titanium has been discovered on Kenya’s coast, from Kwale to Malindi District and underneath Arabuko-Sokoke Forest. The mining of titanium within forests is currently a major threat and there
are larger reserves where Tiomin Resources Incorporation plans to strip mine starting with an area of 64 km2 in Kwale District, which will be mined for at least 14 years. This will have impacts on Shimba Hills National Reserve, Arabuko-Sokoke, Dakatcha woodlands, Kaya Diani, Dzombo, Mrima hills and Kaya Kinondo forests (BirdLife International, 2013). Expansion of subsistence agriculture (mostly maize) has been responsible for the disappearance of most habitats of unprotected forest within Eastern Arc Mountain (Maeda et al., 2010). Agricultural expansion and intensification remain a key problem in the Tana River Delta, Tana River forests, Dakatcha woodlands, Mangea Hill and around Arabuko-Sokoke forest in Kenya and Ruvu South, Mang’alisa Forest Reserve in Rubeho Mountains, Kazimzumbi Forest Reserve, East Usambara, Pande Grassland Reserve & Rondo Forest Reserve, and Mlinga FRs, Pangani Falls, Tongwe Forest Reserve, Nyumburuni in Tanzania. All regions of Unguja and Pemba, and in particular the remaining patches of coral thicket forest, are severely threatened by a very high human population density (400 individuals/km2), which is increasing at an estimated rate of five percent per year (three percent intrinsic and two percent immigration) (Siex, 2011). Zanzibar is currently losing an estimated 1.2 percent of its forest each year (Burgess et al., 2008); shrinking forest patches and rapidly diminishing any potential to maintain and restore connectivity.
1.3 Trends in deforestation Previous studies on deforestation in the Eastern Arc Mountains are well document in FBD (2006) while a recent work on Coastal forest change analysis by Conservation International is provided in Burgess et al., (2010); Tabor et al., (2010) and Godoy et al., (2011). Tracking the history, using 1:250,000 land cover and use maps (Tanzanian Ministry of Natural Resources and Tourism, MNRT, 1996), and 1:250,000and 1:500,000 topographic maps, Newmark (1998) examined natural forest area, fragmentation and loss in the Eastern Arc Mountains. Their findings showed that remaining blocks of forest habitat were: Taita Hills (6km2), Pare Mountains (484 km2), West Usambaras (328 km2), East Usambaras (413 km2), Nguru (647 km2, incl. Nguu), Ukaguru (184 km2), Uluguru (527 km2), Rubeho (499 km2), Udzungwa (1960 km2) and Mahenge (291 km2). The maximum total area of natural forest (open as well as closed forest) in the Eastern Arc Mountains was, therefore, 5,340 km2. The median patch size was 10.2 km2 and the mean patch size was 58.0 km2. The original forest cover (2,000 years ago) on the Eastern Arc Mountains was estimated at around 23,000 km², of which only around 15,000 km2 had remained by 1900 and, based on Newmark’s (1998) findings, a maximum of 5,340 km² remained by mid-1990s. Losses were greatest, relative to original cover, in Taita Hills (98%), Ukaguru (90%), Mahenge (89%) and West Usambaras (84%). But, even in East Usambaras, Johansson and Sandy (1996) report huge shrinkage – from “originally” around 100,000 ha to around 45,000—50,000 ha in 1996. By mid-1990s (1994-1996), the Udzungwas and West Usambaras contained the largest numbers of patches (26 and 17 respectively) and only one mountain block (Ukaguru) had more or less continuous forest. At that time there were an estimated 94 forest patches in the Eastern Arc Mountains, but there was considerable degradation within forest patches. Of the closed forest that remained, only 27% had closed forest cover. With the exception of a few sites where there has been active intervention, the situation at present is far likely to have deteriorated more compared to the mid-1990s. Burgess et al. (2002), referring to the Uluguru Mountains, reported that forest area on the mountains declined from around 300 km2 in 1955 to 230 km2 in 2001, which was <40% of the potential forest area. The rate of forest loss was 1.7% per annum over 1955–1977 and 0.6% per annum over 1977–2000. They report further that forest clearing has concentrated in the sub-montane region, over altitudes of 600-1600 m. They reported that at present some middle altitudes contain almost no forest at all.
The finding by Tabor et al., (2010) revealed that in terms of change, a total of 424 km2(6%) of forest was cleared between ~1990 and ~2000; 53 km2 in Kenya and 371 km2 in Tanzania. Rates of forest loss were 8 times higher in unprotected areas than in protected sites such as Forest Reserves and National Parks. Key Biodiversity Areas had forest loss rates 2.5 times faster than protected areas while Alliance for Zero Extinction sites had the slowest rates of forest loss for the region. On the other hand, Godoy et al., (2011) findings showed that deforestation rates in coastal forests have slowed from 1.0 % yr-1 , or > 3735 ha yr-1during the 1990s, to 0.4 % yr-1, or > 1233 ha yr-1during 2000–2007. Despite lower deforestation rates in 2000–2007, the percentage forest lost from within reserved areas has remained steady at 0.2 % yr-1 for both time periods. Burgess et al.(2010) using data by FBD (2010) and Hall et al. (2009), have estimated that some 347 t ha-1 of carbon have been lost in Eastern Arc Mountain forests and mosaics of East African Coastal forests when carbon in pristine condition is compared with carbon in degraded forest up to 2000(Table 1). Percentage estimates by Burgess (2010) of the total carbon emissions per annum from the Coastal forest areas of Tanzania were 631,933 tCO2yr-1, for the period 1990-2000 and had declined to 198,154 tCO2yr-1by the period 2000-2007.
Table 1. Forested area and carbon storage status and change estimates for Tanzania’s forests
Table 1 shows the EACF Forest types in Tanzania with estimates of carbon (stem, branches and roots) in pristine and degraded forest and indicative loss through degradation (Burgess et al., 2010; BirdLife International, 2013)
According to Burgess et al. (2010), the past 20 years of deforestation have resulted in the loss of c. 34 million t of carbon, c. 1.7 million t per annum in the EAMs. Much of this comes from the woodlands and forests outside the network of PAs (FBD, 2007; Hall et al., 2009; Scharlemannet al., 2010). Degradation is estimated to reduce carbon storage by 65 million tons from the network of 150 reserves within the EAMs of Tanzania (Burgess et al., 2010). The continued loss in carbon storage associated with change in forest over a particular range will certainly lead to loss of species.
1.4 The importance of monitoring If maintaining the biodiversity and ecological integrity of forests is a goal of management, then it is axiomatic that managers be fully informed about the forests being managed (Noss, 1999). A
primary requirement is to assess the status, condition, and conservation value of the forest in question - at whatever spatial scale - relative to other forests. Managers and policy makers need to be cognizant of the biological significance of the forests they manage in a broad context; otherwise they may inadvertently compromise global biodiversity goals by managing their forests inappropriately (Noss, 1999). Noss (1999) point out that assessment of status, condition, and conservation value should ideally be made at several spatial scales, and managers should have some understanding of the forests that occupied their region prior to intensive human activity. In this case, they must have a way of tracking changes in the status and condition of forests over time, extrapolating backward to historic conditions (maybe at several points in time) and forward to a range of potential future conditions. Given a reasonably clear picture of how forests in a region have changed over time and what the ecological consequences of those changes have been, managers and policy makers must decide what they want their forests to be in the future. Then they must measure their rate of progress toward the future condition deemed desirable and be cognizant of any departures from the desired trajectories. With the advances in remote sensing technologies, monitoring for the changes at different temporal scales have has been made possible. Landsat imageries are mostly freely available and other high resolution images can be sourced at some cost. It is now possible to capture real time data using drone planes mounted with camera; also technologies such as Google Earth have become very useful in monitoring.
Methods
2.1 study area We delineated the study area by the previous ~2000 classification of the region for the coastal forests (Godoy et al., 2011) and Eastern Arc Mountains (FBD, 2006). The area included twelve Landsat scene covering Tanzania and Kenya. Three-fourths of the area is contained in Tanzania and the remaining quarter in Kenya.
2.1 Mapping methodology The methods for updating the land-cover maps for the Eastern Arc Mountains and Coastal Forests of Tanzania utilized newly available data on tree cover loss from 2000-2012 published by Hansen et al. (2013) from the University of Maryland (UMD). This global forest cover change product was generated from a set of historical Landsat 5 and Landsat 7 imagery to create a cloud-free product that identifies areas that have undergone land cover transition between 2000 and 2012. For the purposes of this work, we only included the forest loss between 2000-2010 and excluded the forest gain class. We conducted a few pre-processing steps to the tree cover loss data set in order to update the existing map with the new loss product. First, pixels that experienced both loss and gain over the study period were removed, since the forest gain is not associated with a particular year and therefore could have occurred before or after the loss, and was not included in the analysis. Also, nodata pixels from clouds in the 2000 epoch are also considered nodata in the 2010 epoch as well since we could not verify forest cover in the previous epoch. Then the remaining pixels were reclassified so that the loss dataset only represented changes from 2000-2010. Finally, we applied a 3x3-majority filter to the loss pixels and the minimum patch size was set to 0.5 hectares in order to remain consistent with the existing land-cover product.
In order to update the existing land-cover map we identified and updated land-cover types eligible for transition based on the existing 2000 land-cover map. For this analysis Forests and Mangroves in 2000 were converted to Non-forest if they were identified as experiencing forest cover loss from 2000-2010. We validated the final updated land-cover map product using a combination of Landsat imagery from 2010 and high-resolution Google earth imagery. All of the GIS analysis for the map updating was conducted with ArcGIS 10.2
2.3 Habitat change statistics Statistics on rates of forest change were calculated in the same method at (Tabor et al., 2010). The forest change statistic is based only on observed forest to non-forest changes in the two time epochs and does not include areas that were cloudy in ~2000.. A percent confidence statistic wascalculated to represent the “cloudiness” of the area is based on the 2000 epoch. High confidence means that most of the forest and deforestation was observed with relatively little to no cloud cover. The annual rate of forest change was calculated based on a fractional loss of change (FAO, 1995), which is a more accurate statistic to calculate average percent change then dividing the forest loss percent by the number of year between the two dates. On average, percent forest loss will increase as the forested area shrinks over then 10-year period.
2.4 Map validation We validated the 2010 land-cover classification by visually inspecting a set of 1060 sampling points. The sampling points were chosen using a stratified random sampling method to ensure all land-cover classes were adequately represented. The validation points were classified based on a combination of Landsat imagery from 2010 and high-resolution imagery from Google Earth. The majority of the study area had at least 10-m SPOT 4 coverage while other areas had 5-m SPOT5 coverage. Some populated areas along the coast had Digital Globe coverage at ~65cm resolution. The combination of images sources provided a more robust and comprehensive accuracy assessment that either source alone (See Figure 2). Also due to the low cost of the Landsat and Google Earth imagery, we were able to sample across the entire study area, rather than be restricted to the relatively small area that would be covered by costly high-resolution imagery. In
the final validation assessment, some of the map classes were aggregate to assess the land-cover accuracy. We calculated a confusion matrix to include overall map accuracy, class reliability and class accuracy. Class reliability measures the extent to which a given location on the ground is accurately represented in the map. Class accuracy reflects how any given point in the map corresponds to the ground information. The purpose of these two different results is to reveal how different classes in the map are misclassified.
Figure 2 shows the study area in grey and the
validation sampling points in red. The orange boxes
represent the coverage of the SPOT images available
in Google Earth and the small boxes of various colours
(representing the year of acquisition) delineate the
footprint of the Digital Globe images available to aid
interpretation of the validation points.
Figure 2.
Results
3.1 Deforestation rates The resulting map of forest and mangrove cover and change is shown in Figure 3. The results indicate a total of 865,141 ha of forest and 71,439 ha of mangrove remain in ~2010 for the study area. Forest change between 2000 and 2010 are -0.15 %y-1 and mangrove change is -0.01 %y-1 with a total of 12,673 ha of forest loss and 89 ha of mangrove loss (Table 2 and Table 3). Two thirds of the total forest change occurred in Tanzania at a rate of -0.13 %y-1 and the remaining third in Kenya at a rate of -0.20 %y-1.
Tables 4 and 5 show the rates of changes within the districts of Kenya and Tanzania. The districts in Tanzania with the greatest rates of change are Mkuranga (-1.0 %y-1), Mtware Rual (-0.9 %y-1), Kisarawe (-0.5 %y-1), Mufindi (-0.3 %y-1). Districts with less than 1000 ha of forest loss between 2000 and 2010 are not included in this list because small changes in forest cover in districts with limited forest cover in ~2000 will have a large percent change result. The Districts in Kenya with the highest rates of change are Tana River (-1.2 %y-1), Malindi (-0.4 %y-1), and Lamu (-0.3 %y-1).
KBAs Deforestation rates for Kenya KBA’s are -0.02 %y-1 whereas rates outside the KBA’s are -0.3 %y-1. Only four percent of all deforestation is occurring inside KBA’s in Kenya (Table 6). Kenya KBA’s with the greatest rates of forest change are Marafa (-0.69 %y-1), Mangea Hill (-0.62 %y-1, Dakatcha Woodland (-0.55 %y-1), Shimoni Forest Reserve (-0.18 %y-1). (See Appendix A Table A1)
Deforestation rates for Tanzania’s KBA’s are -0.03 %y-1 whereas rates outside the KBA’s are -0.25 %y-1. Fifteen percent of observed deforestation in Tanzania is occurring within Tanzania’s KBAs (Table 7). Tanzania KBAs with the largest rates of change are all coastal forest KBAs and include Kisarawe District Coastal Forests (-1.13 %y-1), Rufiji Delta (-0.20 %y-1), Lindi District Coastal Forests (-0.13 %y-1). (See Appendix A Table A2)
Protected Areas Deforestation rates for Kenya protected areas are -0.00 %y-1 whereas rates outside the protected areas are -0.27 %y-1. Only four percent of deforestation is occurring inside protected areas (Table 6). Of the Kenya protected areas, Marenji contributes to 96% of all deforestation at -0.25 %y-1. (See Appendix A Table A3)
Deforestation rates for Tanzania’s protected areas are -0.05 %y-1 whereas rates outside the protected areas are -0.26 %y-1. Twelve percent of deforestation is occurring inside protected areas (Table 7). The Tanzania protected areas with the highest rates of deforestation are Marenda (-100.0 %y-1), Kazimzumbwi (-4.94 %y-1), Sao Hill (-3.34 %y-1), Masanganya (-2.73 %y-1), Ruvu South (-1.04 %y-1). (See Appendix A Table A4)
Forest reserves Deforestation rates for Kenya forest reserves are -0.01 %y-1 whereas rates outside forest reserves are -0.23 %y-1. In addition, only 1 percent of deforestation is occurring inside of reserved areas (Table 6). Madunguni Forest and Witu are the only Forest reserves with observed deforestation. However, four other reserves had no observations of forest due to cloud cover. (See Appendix A Table A5)
Deforestation rates for Tanzania forest reserves are -0.07 %y-1 whereas rates outside the forest reserves are -0.16 %y-1. Eleven percent of deforestation is occurring inside forest reserves in Tanzania (Table 7). The forest reserves with the highest rates of deforestation in Tanzania are Sao Hill (-5.6%y-1), Kazimzumbwe (-4.34%y-1), Sao Hill Extension (-1.9%y-1), Ruvu South (-1.0%y-1), in reserved areas with more than 100 ha of deforestation. (See Appendix A Table A6)
3.2 Forest cover and change map Figure 3.
Figure 3 shows forest and mangrove cover and change between circa 2000 and circa 2010. Non-forested
areas are shown in tan, forests are green, and mangroves in brown.
3.2 Habitat cover and change statistics
Table 2. Forest cover and change for the Eastern Arc Mountains and Coastal forests (Tanzania and Kenya).
total area (ha)
known forest
2000 (ha)
known forest
~2010 (ha)
forest change
~2000-~2010
(ha)
annual rate of
forest change
~2000-~2010 rate
(%y-1
)
confidence
observed forest (%)
28455809 877813 865141 12673 -0.15 67
Table 3. Mangrove cover and change for the Eastern Arc Mountains and Coastal forests (Tanzania and
Kenya).
total area
(ha)
known
mangrove
2000(ha)
mangroves
~2010 (ha)
mangrove
change ~2000-
~2010 (ha)
annual rate of
mangrove change
~2000-~2010 rate (%y-
1)
confidence
observed
mangrove (%)
28455809 71528 71439 89 -0.01 14
Table 4. Forest cover and change for Kenya’s Districts.
Kenya
Districts total area (ha)
known
forest 2000
(ha)
forest
~2010 (ha)
forest
change
~2000-
~2010 (ha)
annual
rate of
forest
change
~2000-
~2010 rate
(%y-1
)
confidence
observed
forest (%)
Garissa 1802169 77617 77607 10 0.00 56
Kilifi 450343 15443 15378 65 -0.04 46
Kitui 5104 0 0 0 0.00 100
Kwale 839493 16785 16741 44 -0.03 99
Lamu 661257 54509 53177 1332 -0.25 35
Malindi 785686 43328 41637 1690 -0.40 75
Mombasa 24031 15 15 0 0.00 18
Taita Taveta 772712 749 749 0 0.00 97
Tana River 2109828 9454 8348 1106 -1.24 7
Total 7450623 217900 213653 4247 -0.20 40
Table 5. Forest cover and change for Tanzania’s Districts.
Tanzania
Districts
total area
(ha)
known forest
2000 (ha)
forest
~2010 (ha)
forest
change
~2000-
~2010 (ha)
annual rate of
forest change
~2000-~2010
(%y-1
)
confidence
observed
forest (%)
Bagamoyo 853543 1251 1171 80 -0.66 7
Handeni 804470 1045 1032 13 -0.13 19
Ilala 33522 184 94 90 -6.52 8
Iringa Urban 14659 0 0 0 0.00 100
Kibaha 183563 1097 1064 33 -0.31 32
Kilindi 629404 21378 21376 2 0.00 100
Kilolo 898323 77160 77087 73 -0.01 100
Kilombero 1305388 40477 40474 2 0.00 100
Kilosa 1290681 56461 56441 20 0.00 100
Kilwa 1204383 81565 80554 1011 -0.12 100
Kinondoni 54747 137 133 5 -0.34 12
Kisarawe 485624 32555 30911 1644 -0.52 68
Kiteto 179844 227 227 0 0.00 100
Korogwe 313639 9869 9863 7 -0.01 99
Lindi Rural 631502 11704 11487 217 -0.19 94
Lindi Urban 23997 11 11 0 0.00 68
Liwale 2131096 71508 71492 16 0.00 95
Lushoto 408058 25245 25217 28 -0.01 100
Masasi 120130 63 63 0 0.00 25
Mkuranga 272195 15776 14261 1514 -1.00 37
Morogoro Rural 1297732 29232 29157 75 -0.03 98
Morogoro Urban 53518 64 64 0 0.00 100
Mpwapwa 459919 9537 9537 0 0.00 100
Mtwara Rural 385965 6702 6105 597 -0.93 95
Mtwara urban 19534 12 12 0 0.00 100
Mufindi 663585 24523 23907 615 -0.25 100
Muheza 420919 14904 14866 38 -0.03 95
Mvomero 624052 33400 33386 14 0.00 100
Mwanga 169098 2631 2631 0 0.00 100
Newala 160816 18 18 0 0.00 5
Njombe 453452 970 955 15 -0.16 100
Pangani 178516 29 29 0 0.00 1
Ruangwa 126136 471 471 0 0.00 77
Rufiji 1317731 72011 69785 2225 -0.31 78
Same 633749 14058 14033 25 -0.02 100
Songea Rural 69869 0 0 0 0.00 100
Tandahimba 210082 391 381 10 -0.25 73
Tanga 59410 12 12 0 0.00 8
Temeke 74898 92 92 0 0.00 2
Ulanga 1787440 3145 3089 55 -0.18 100
Total 21005186 659913 651488 12673 -0.13 86.26
Table 6. Rates of deforestation inside verses outside conservation areas in Kenya
Type of Conservation Area Inside (%y-1
) Outside (%y-1
)
Deforestation inside as a
percentage of total deforestation
(%)
Protected Areas 0.00 -0.27 4%
Forest Reserves -0.01 -0.23 1%
KBAs -0.02 -0.3 4%
Table 7. Rates of deforestation inside verses outside conservation areas in Tanzania
Type of Conservation Area Inside (%y-1
) Outside (%y-1
)
Deforestation inside as a
percentage of total deforestation
(%)
Protected Areas -0.05 -0.26 12%
Forest Reserves -0.07 -0.16 11%
KBAs -0.03 -0.25 15%
3.3 Validation The final validation matrix included forest, non-forest and mangroves in 2010 (Table 8) and the overall accuracy of the land-cover map was 81%.
Table 8. Validation Matrix
Classified land-cover map
Forest Non-forest Mangrove Accuracy
Forest 63 46 1 57%
Non-forest 93 741 49 84%
Mangrove 1 5 47 88%
Reliability 40% 94% 46% 81%
Table 8. shows the number of points correctly classified and the number of points misclassified for the ~2010 forest cover map. The last column is the class accuracy and the last row is the class reliability. The number in bold is the overall map accuracy.
Discussion This study focuses on the rates of deforestation in conservation areas. While there have been past
studies in this region, it is difficult to directly compare overall estimates of forested area and change
between different study extents. In this study deforestation rates of fractional percent per year are
slightly higher in Kenya than in Tanzania.
Deforestation rates inside conservation areas were generally much lower that outside reserves
areas. In Kenya, almost 95% of deforestation is occurring outside KBAs areas. In Tanzania, while
more deforestation occurs outside conservation areas, 15% of deforestation is occurring inside
Tanzania’s KBAs. The deforestation in Kenya may be from misclassification of the dense woodlands
Va
lid
ati
on
po
ints
along the coast as forest. In addition, the global forest cover change we used to updated the 2000
forest cover map may be incorrectly identifying vegetation seasonality of the coastal forests and
plantation harvesting in both Tanzania and Kenya as forest change. For example, deforestation was
mapped in Sao Hill which is the largest plantation forest in Tanzania.
The results of the analysis do reveal some known trends in deforestation rates in the region. For
example, high rates of forest loss in the Tana River District are likely due to the pressure of
agricultural expansion. Another area with high rates of forest loss is the Malindi district in Kenya
district, which is a known for Timonium extraction. Deforestation from these extractions were
observed in our analysis in the Shimba Hills and Abruki Sokoke forest. In Tanzania, this analysis
showed high rates of deforestation in the Rufiji and Lindi districts where there is ongoing illegal
timber extraction activities.
There are a few caveats to the precision of the forest cover and change results. First, cloud–free
imagery for this region is difficult to find, therefore not being able to observe forest cover and
change is a major issue. Only 40% of the potential forested area and forest change area were
observed due to cloud cover. Tanzania had more cloud free imagery with a total of 86% of potential
forested area and forest change area observed. In fact, the cloudiness of the 2010 epoch imagery
combined with the SLC-off striping of the Landsat 7 images was the main reason we chose to
update the 2000 epoch forest cover data with the cloud-free tree cover loss dataset.
Next, accuracy and reliability of the map classification should also be considered. Accuracy and
reliability were high for the non-forest class. The accuracy and reliability for the forest class are
low, however, not alarming given most land cover classification accuracies. The results show more
over-estimation of forest than under estimation. Therefore forest estimates in this analysis may be
considered optimistic. The confusion between the forest and non-forest class is most likely due to
confusion between forest and dense woodland (considered non-forest in the validation dataset).
The slight overestimation of forest cover in the ~2010 map could be partially the result of changes
in forest after 2010 and the date of the high resolution. The discrepancy between the classified map
and the date of the validation images was most likely the reason why the user’s accuracy of the
mangrove class is high, but the of the class, what is represented on the ground, is low. According to
the validation the map overestimated mangrove cover. These errors of commission could be from
mangrove loss or shoreline changes between the dates of the Landsat imagery and the dates of the
high resolution Google earth imagery. Another caveat in this validation is in the interpretation of
the validation points in the Google Earth imagery. The Google Earth coverage of very high
resolution imagery for this region has improved vastly over the past few years, there are still gaps
between the high resolution imagery.
Conclusion This study is a continuation in a series of monitoring products for this region using a consistent
data source and methodology. For the first time in the series of forest cover and change map
products, the analysis was performed on the entire extent of coastal forests in Tanzania and Kenya
and the humid forests of the Eastern Arc Mountains to create a seamless forest cover product. The
data produced from monitoring provides information on the site-level to enhance conservation
initiatives such as International Union for Conservation of Nature (IUCN) Red list assessments,
protected area effectiveness, and outcomes of conservation investments. These data also feed into
important assessments at the national level such as emissions baselines for REDD (Reduced
Emissions from Deforestation and Forest Degradation), informing policy scenarios, and setting
national sustainability targets.
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Appendix A
Table A1. Forest cover and change for Kenya’s KBAs.
Kenya KBAs
total
area (ha)
known
forest
2000
(ha)
forest
~2010
(ha)
forest
change
~2000-
~2010
(ha)
annual
rate of
forest
change
~2000-
~2010
rate (%y-
1)
confidence
observed
forest (%)
Arabuko-Sokoke Forest 40619 24625 24625 1 0.00 82
Boni National Reserve 129808 21971 21971 0 0 100
Buda Forest Reserve 668 394 394 0 0.00 100
Chale Island 41 0 0 0 0 0
Chuna Forest 3516 18 18 0 0 100
Dakatcha Woodland 35003 2354 2227 127 -0.55 78
Diani Forest 461 0 0 0 0 0
Dodori National Reserve 84774 1630 1630 0 0 99
Dzombo Hill Forest 560 109 109 0 0 100
Gede Ruins National Monument 44 0 0 0 0 0
Gongoni Forest Reserve 816 419 419 0 0 100
Kaya Bombo 43 0 0 0 0 0
Kaya Chonyi 202 0 0 0 0 0
Kaya Dzombo 304 203 203 0 0 100
Kaya Fungo 360 0 0 0 0 0
Kaya Gandini 223 58 58 0 0 100
Kaya Gonja 840 56 56 0 0 100
Kaya Jibana 596 0 0 0 0 0
Kaya Kambe 820 0 0 0 0 0
Kaya Kauma 75 24 24 0 0 91
Kaya Kinondo 54 0 0 0 0 0
Kaya Kivara 92 0 0 0 0 0
Kaya Lunguma 252 0 0 0 0 0
Kaya Mtswakara 4505 0 0 0 0 0
Kaya Muhaka 132 34 34 0 0 100
Kaya Puma 331 0 0 0 0 0
Kaya Ribe 76 0 0 0 0 0
Kaya Sega 278 0 0 0 0 0
Kaya Teleza 458 0 0 0 0 0
Kaya Tiwi 52 3 3 0 0 100
Kaya Ukunda 41 0 0 0 0 0
Kaya Waa 65 0 0 0 0 0
Kisite island 147 0 0 0 0 0
Kiunga Marine National
Reserve 24368 6 6 0 0 100
Lower Tana River Forests 23691 132 132 0 0 2
Lunghi 102783 10000 10000 0 0 66
Mangea Hill 7128 363 341 22 -0.62 53
Marafa 1538 134 125 9 -0.69 54
Marenji Forest 1538 1230 1226 4 -0.03 99
Mida Creek, Whale Island
and the Malindi - Watamu coast 10264 34 34 0 0 3
Mount Kasigau 855 189 189 0 0 100
Mrima Hill Forest 378 275 275 0 0 100
Mwache Forest Reserve 303 1 1 0 0 30
Sabaki River Mouth 581 3 3 0 0 43
Shimba Hills 21026 11529 11529 0 0 100
Shimoni Forests 1424 630 618 12 -0.18 100
Tana River Delta 7651 803 803 0 0 37
Tsavo East National Park 256173 0 0 0 0 0
Tsavo West National Park 261525 0 0 0 0 0
Witu Forest Reserve 3609 2118 2104 15 -0.07 81
Total 1031093 79345 79157 188 -0.02 79
Table A2. Forest cover and change for Tanzania’s KBAs.
Tanzania KBAs
total
area
(ha)
known
forest
2000
(ha)
forest
~2010
(ha)
forest
change
~2000-
~2010
(ha)
annual
rate of
forest
change
~2000-
~2010
rate (%y-
1)
confidence
observed
forest (%)
Kisarawe District Coastal Forests 40001 5670 5060 610 -1.13 69
Udzungwa Mountain Range 543685 115701 115226 475 -0.04 100
Lindi District Coastal Forests 43971 4220 4166 54 -0.13 99
Kilwa District Coastal Forests 149813 20002 19963 40 -0.02 100
Rufiji Delta 126365 1258 1233 25 -0.20 27
South Pare Mountains 23215 11743 11719 24 -0.02 100
West Usambara Mountains 34140 21809 21789 20 -0.01 100
Selous Game Reserve 3330156 69102 69087 15 0.00 92
Uluguru Mountains 36263 24786 24776 10 0.00 100
East Usambara Mountains 38936 10655 10647 8 -0.01 99
Ukaguru Mountains 20755 14177 14171 6 0.00 100
Rufiji District Coastal Forests 13320 1507 1502 6 -0.04 87
Rubeho Mountains 44965 26702 26698 5 0.00 100
Mahenge Mountains 3379 1350 1349 1 0.00 100
Bagamoyo (Kikoka Forest Reserve) 1599 0 0 0 0 0
Bagamoyo District Coastal Forests 114623 242 242 0 0 34
Dar es Salaam coast 40074 2 2 0 0 0
Handeni District Coastal Forests 5782 61 61 0 0 13
Kilombero Valley 479827 26 26 0 0 100
Kimani River 60856 67 67 0 0 100
Kisiju 1265 9 9 0 0 100
Lindi (Nyangao River) 3673 59 59 0 0 75
Lindi Creek 6185 3 3 0 0 100
Lukoga Forest Reserve 261 0 0 0 0 0
Magombera Forest Reserve 702 0 0 0 0 0
Mikindani (Mnima) 1535 4 4 0 0 100
Mikumi National Park 332606 1712 1712 0 0 100
Mkomazi Game Reserve 316159 0 0 0 0 0
Mnazi Bay 55605 142 142 0 0 100
Mtwara District Coastal Forests 1970 472 472 0 0 100
Muheza District Coastal Forests 4018 14 14 0 0 6
Newala (Kitama) 11077 0 0 0 0 0
Newala District Coastal Forests 5414 2 2 0 0 28
Nguru Mountains 31813 24204 24204 0 0 100
Nguu Mountains 29698 15181 15181 0 0 100
North Pare Mountains 4326 1001 1001 0 0 100
Pangani District Coastal Forests 4861 2 2 0 0 0
Tanga North - Kibo saltpans 143 4 4 0 0 100
Tanga South 2281 29 29 0 0 15
Uvidunda Mountains 38056 456 456 0 0 100
Total 6003370 372374 371077 1298 -0.03 95
Table A3. Forest cover and change for Kenya’s protected areas.
Kenya Protected
Areas
total area
(ha)
known
forest 2000
(ha)
forest
~2010 (ha)
forest
change
~2000-
~2010 (ha)
annual rate
of forest
change
~2000-
~2010 rate
(%y-1)
confidence
observed
forest (%)
Arabuko Sokoke 36993 21841 21833 8 0.00 83
Arawale 45086 0 0 0 0 0
Boni 135169 21298 21298 0 0 100
Buda 629 219 219 0 0 100
Diani 10738 0 0 0 0 0
Dodori 72958 1319 1319 0 0 98
Gogoni 786 273 273 0 0 100
Gonja 314 0 0 0 0 0
Jombo 369 0 0 0 0 100
Kasigau 878 0 0 0 0 0
Kaya Chonyi 201 0 0 0 0 0
Kaya Dzombo 313 201 201 0 0 100
Kaya Jibana 116 0 0 0 0 0
Kaya Kambe 61 0 0 0 0 0
Kaya Ribe 75 0 0 0 0 0
Kisite 3415 0 0 0 0 0
Kiunga 24341 6 6 0 0 100
Lumo 7657 0 0 0 0 0
Mailuganji 1818 80 80 0 0 100
Malindi 624 0 0 0 0 0
Marenji 1102 220 214 5 -0.25 100
Mkongani North 889 0 0 0 0 0
Mkongani West 1502 427 427 0 0 100
Mombasa 14274 0 0 0 0 0
Mpunguti 1293 2 2 0 0 100
Mrima 334 154 154 0 0 100
Mwachi 826 0 0 0 0 0
Shimba Hills 22520 11156 11152 4 0.00 100
Taita Hills 3 0 0 0 0 0
Tana River Prima 11041 68 68 0 0 4
Tsavo East 264407 0 0 0 0 0
Tsavo West 228602 0 0 0 0 0
Watamu 20709 20 20 0 0 1
Witu 2314 1223 1223 0 0 77
total 912356 58508 58490 17 0.00 87
Table A4. Forest cover and change for Tanzania’s protected areas.
Tanzania
Protected Areas
total area
(ha)
known
forest 2000
(ha)
forest
~2010 (ha)
forest
change
~2000-
~2010 (ha)
annual rate
of forest
change
~2000-
~2010 rate
(%y-1)
confidence
observed
forest (%)
Amani 8627 2908 2906 2 -0.01 98
Baga I (Mzinga) 3587 3202 3202 0 0 100
Bagai 317 260 260 0 0 100
Balangai West 1073 755 755 0 0 100
Bamba Ridge 1142 541 541 0 0 100
Bassi 25 0 0 0 0 0
Bombo East I 1215 28 28 0 0 100
Bombo East II 479 4 4 0 0 100
Bombo West 3664 265 265 0 0 100
Bondo 2867 0 0 0 0 0
Bosha East Villa 1 0 0 0 0 0
Bosha North Vill 3 0 0 0 0 0
Bosha South Vill 4 0 0 0 0 0
Bunduki 67 40 40 0 0 100
Bunduki 3 4 0 0 0 0 0
Chamanyani 543 61 61 0 0 100
Chambogo 5476 909 909 0 0 100
Changandu 6705 0 0 0 0 0
Chilangala 1819 9 9 0 0 72
Chitoa 783 41 41 0 0 100
Chomme 14091 10024 10000 24 -0.02 100
Chongweni 102 0 0 0 0 0
Chumbe Island Co 0 0 0 0 0 0
Dabaga New 3757 3435 3435 0 0 100
Dar es Salaam 3210 2 2 0 0 1
Derema 4013 2712 2712 0 0 100
Dindili 1046 0 0 0 0 0
Disalasala 43 0 0 0 0 0
Forest Reserve N 1782 0 0 0 0 0
Forest Reserve N 2546 0 0 0 0 0
Forest Reserve N 5691 128 128 0 0 100
Forest Reserve N 10584 45 45 0 0 18
Garafuno 288 0 0 0 0 0
Gologolo East 4 0 0 0 0 0
Gologolo West 61 0 0 0 0 0
Gonja 94 41 41 0 0 100
Gulosilo 3663 173 173 0 0 100
Gwami 1311 0 0 0 0 0
Handei Village 174 5 5 0 0 100
Handeni Hill 561 130 130 0 0 100
Hebangwe 38 0 0 0 0 0
Idewa 298 189 189 0 0 100
Idodi Pawaga 27050 0 0 0 0 0
Ihanga 3588 0 0 0 0 0
Ijogo 0 0 0 0 0 0
Ikwamba 873 748 748 0 0 100
Image 9050 4930 4928 2 0.00 100
Iringa 594 0 0 0 0 0
Irunda 392 4 4 0 0 100
Iwonde 12449 1310 1310 0 0 100
Jasini 111 0 0 0 0 0
Jozani-Chwaka Ba 0 0 0 0 0 0
Kahe I 0 0 0 0 0 0
Kahe II 0 0 0 0 0 0
Kambai 983 373 373 0 0 100
Kambai Village 9 0 0 0 0 0
Kambona 111 0 0 0 0 0
Kamwalla I 122 45 45 0 0 100
Kamwalla II 309 85 85 0 0 100
Kanga 7166 6259 6259 0 0 100
Kankoma 72 0 0 0 0 0
Kasanga 431 3 3 0 0 100
Katundu 4619 336 336 0 0 100
Kazimzumbwi 5005 252 152 100 -4.94 13
Kianika 234 35 35 0 0 100
Kibambo 34 11 11 0 0 100
Kibao 116 77 77 0 0 100
Kichi Hills 14566 11786 11786 0 0.00 100
Kigogo 2631 2260 2260 0 0 100
Kihanga 56 2 2 0 0 100
Kihiriri 297 0 0 0 0 0
Kihuhwi 486 0 0 0 0 0
Kihuhwi Sigi 910 23 23 0 0 100
Kihuhwi Teak 34 0 0 0 0 0
Kihundu Village 41973 52 52 0 0 100
Kikale 372 1 1 0 0 41
Kikoka 1598 0 0 0 0 0
Kikole 2 1976 306 306 0 0 100
Kikole Village 457 0 0 0 0 0
Kikongoloi 269 0 0 0 0 0
Kilanga (Nilo) 5903 2632 2626 6 -0.02 97
Kilengwe 946 0 0 0 0 0
Kilindi 5379 1543 1543 0 0 100
Kilombero 129196 26339 26329 9 0.00 100
Kilombero Valley 745934 117 117 0 0 100
Kilungulungu 348 30 30 0 0 100
Kimboza 517 119 119 0 0 100
Kindoroko 640 477 477 0 0 100
Kingoma 1396 62 40 22 -4.23 92
Kipo 1201 0 0 0 0 0
Kiranga Hengae 487 317 317 0 0 100
Kiranzi Kitunguu 1156 1002 1002 0 0 100
Kiriguru 295 0 0 0 0 0
Kisangi Village 4061 1309 1289 20 -0.16 100
Kisima Gonja 1579 1204 1204 0 0 100
Kisinga Lugaro 17818 12133 12129 4 0.00 100
Kisiwani 111 0 0 0 0 0
Kitapilimwa 5868 0 0 0 0 0
Kitara Ridge 784 0 0 0 0 0
Kitivo North 69 12 12 0 0 100
Kitivo South 37 0 0 0 0 0
Kitope 4114 373 373 0 0 100
Kitulanghalo 4512 0 0 0 0 0
Kitulio 86 0 0 0 0 0
Kiverenge 1898 302 302 0 0 100
Kiwawa Village 4237 469 461 8 -0.17 100
Kiwengoma 3607 1167 1167 0 0 100
Kiwengwa Pongwe 0 0 0 0 0 0
Kizee Village 40 0 0 0 0 0
Kizigo 0 0 0 0 0 0
Kizingata 7 1 1 0 0 100
Koko Hill 99 0 0 0 0 0
Kolekole 286 0 0 0 0 0
Konga 19 0 0 0 0 0
Korogwe Fuel 10307 0 0 0 0 0
Kwamarukanga 240 0 0 0 0 0
Kwamgumi 1279 1146 1146 0 0 100
Kwamrimba 973 176 176 0 0 100
Kwangola 740 0 0 0 0 0
Kwani 2655 2 2 0 0 1
Kwasumba 3047 0 0 0 0 0
Kwekanda 124 0 0 0 0 0
Kwemazagati-kwta 4 0 0 0 0 100
Kwembago 5 0 0 0 0 0
Kwemnyese Villag 1 0 0 0 0 100
Kwezitu Village 6 3 3 0 0 100
Kwizu 2995 629 629 0 0 100
Ligamba 32 0 0 0 0 0
Liteho 1325 0 0 0 0 0
Litipo 1182 85 85 0 0 100
Liwale 199429 106 106 0 0 82
Lohombero/Luweng 40209 0 0 0 0 0
Longuza 1532 68 68 0 0 100
Lukoka Hill 262 0 0 0 0 0
Lunda-Mkwabi 437178 0 0 0 0 0
Lungonya 49612 528 526 2 -0.04 100
Lushoto 11 0 0 0 0 0
Lusungulu 2231 0 0 0 0 0
Mabundi Mtwange 138 0 0 0 0 0
Mafi Hill 4435 2148 2148 0 0 100
Mafia Island 6477 0 0 0 0 0
Mafwomero 3324 2899 2899 0 0 100
Magambazi 708 0 0 0 0 0
Maganda 43 0 0 0 0 0
Magombera 701 0 0 0 0 0
Magoroto 1126 6 6 0 0 100
Mahenge Scarp 720 0 0 0 0 0
Mahezangulu 290 273 273 0 0 100
Makame 0 0 0 0 0 0
Makonde Scarp I 3799 2 2 0 0 30
Makonde Scarp II 1616 0 0 0 0 0
Mamboto 493 129 129 0 0 100
Mamboya 234 0 0 0 0 0
Mamiwa Kisara So 7083 5248 5247 0 0.00 100
Manga 1669 66 66 0 0 100
Mangala 44 0 0 0 0 0
Mangalisa 5884 3133 3133 0 0 100
Mangrove No.20 1117 0 0 0 0 0
Mangrove No.22 1937 0 0 0 0 0
Mangrove No.23 4880 26 19 8 -3.34 10
Mangroves (Mikin 1879 0 0 0 0 0
Mangroves along 767 60 58 3 -0.48 90
Mangroves along 119796 1214 1093 121 -1.04 51
Mangroves-Bagamo 4418 0 0 0 0 0
Mangroves-Kilwa 41760 11 11 0 0 100
Mangroves-Kisara 1773 0 0 0 0 0
Mangroves-Lindi 6288 43 43 0 0 100
Manka 145 0 0 0 0 0
Marenda 213 0 0 0 -100.00 100
Masagati 1687 4 4 0 0 100
Masanganya 2354 344 261 83 -2.73 19
Masingini Catchm 0 0 0 0 0 0
Matapwa 15727 692 692 0 0 100
Maziwe Island 249 0 0 0 0 0
Mbinga Kimaji / 1954 463 460 2 -0.05 100
Mbogo 120 0 0 0 0 0
Mbwara Village 2286 208 208 0 0 100
Mbwegere 470 0 0 0 0 0
Mchekela 25 12 12 0 0 100
Mchonga 4 0 0 0 0 0
Mchungu 1216 188 188 0 0 96
Menai Bay 0 0 0 0 0 0
Mfumbia 803 47 47 0 0 100
Mfundia-bosha Vi 7 0 0 0 0 0
Mgambo 1342 259 259 0 0 100
Mhulu 806 182 181 1 -0.04 100
Migeregere Villa 1107 413 413 0 0 92
Mikumi 327014 1507 1507 0 0 100
Milawilila 19 0 0 0 0 0
Milindo 3080 134 134 0 0 100
Mindu 1379 21 21 0 0 100
Minja 533 403 403 0 0 100
Misalai Village 3 0 0 0 0 0
Misali Island 0 0 0 0 0 0
Mitundumbea 8722 2067 2045 23 -0.11 100
Mkangala 1498 2 2 0 0 100
Mkewe 628 0 0 0 0 0
Mkindu 4913 288 288 0 0 100
Mkomazi 333127 0 0 0 0 0
Mkongo 557 0 0 0 0 0
Mkoro 118 0 0 0 0 0
Mkulazi 63563 0 0 0 0 0
Mkuli Exten. 3157 375 375 0 0 100
Mkundi 748 0 0 0 0 0
Mkungwe 5556 1090 1090 0 0 100
Mkusu 4283 2307 2295 12 -0.05 100
Mlali 0 0 0 0 0 0
Mlinga 843 56 56 0 0 100
Mlungui 188 0 0 0 0 0
Mnazi Bay-Ruvuma 48987 100 100 0 0 100
Mninga 193 98 94 4 -0.46 100
Mohoro 1902 740 698 42 -0.58 100
Mohoro River 259 21 16 5 -2.47 100
Mombo 19 0 0 0 0 0
Morogoro Fuel 11413 0 0 0 0 0
Mpagalalu 135 1 1 0 0 100
Mpanga Village 26 4 4 0 0 100
Mramba 3210 426 426 0 0 100
Mselezi 399 0 0 0 0 0
Msingeho Hill 84 0 0 0 0 0
Msitu Mkuu 0 0 0 0 0 0
Msumbugwe 4571 2 2 0 0 0
Mtai 3182 1606 1606 0 0 100
Mtama 632 0 0 0 0 0
Mtanza 4045 0 0 0 0 0
Mtarure 58347 971 971 0 0 100
Mtibwa 817 0 0 0 0 0
Mtita 3556 318 318 0 0 42
Mtumbi 311 111 105 6 -0.56 100
Mtunguru 2558 0 0 0 0 0
Mufindi Scarp 17542 4026 4018 7 -0.02 100
Mufindi Tea Esta 35777 2541 2504 37 -0.15 100
Mvuha 491 218 218 0 0 100
Mwenga 999 0 0 0 0 0
Myoe 120 0 0 0 0 0
Mzashai 52 0 0 0 0 0
Mzogoti 147 0 0 0 0 0
Mzungui Village 41 7 7 0 0 100
Nagaliendele 405 0 0 0 0 0
Namakutwa/Namade 4709 1239 1235 4 -0.03 100
Nambinga 1590 420 420 0 0 100
Nambunju Village 2081 518 518 0 0 100
Name Unknown No. 737 8 8 0 0 100
Namikupa 1382 1 1 0 0 100
Nandimba 1252 0 0 0 0 0
Ndasha Hill 1214 0 0 0 0 0
Ndekemai 1511 990 990 0 0 100
Ndimba 2979 856 856 0 0 100
Ndolwa 1108 0 0 0 0 0
Ngarambe Tapika 75704 7011 6877 134 -0.19 100
Ngarma North 35224 14025 14025 0 0 100
Ngezi-Vumawimbi 0 0 0 0 0 0
Ngulakula 2280 60 60 0 0 83
Nguru North 14064 9784 9784 0 0 100
Nguru South 19732 17636 17636 0 0 100
Nguru ya Ndege 2487 37 37 0 0 100
Ngwasi 2050 20 18 2 -0.94 100
Nndawa Village 710 0 0 0 0 0
Nyaganje 19043 2 2 0 0 100
Nyamwage Village 129 33 33 0 0 100
Nyandiduma 101 0 0 0 0 0
Nyandira 87 0 0 0 0 0
Nyera/Kiperere 2 0 0 0 0 0
Nyumburuni 4568 274 272 2 -0.08 56
Pagale 16 0 0 0 0 0
Pala Mountains 10811 275 275 0 0 100
Pande 1250 105 105 0 0 82
Pangawe East 746 0 0 0 0 0
Pangawe West 198 0 0 0 0 0
Pemba Channel Co 0 0 0 0 0 0
Pindiro 3787 952 952 0 0 100
Pongwe 527 0 0 0 0 0
Pugu - Kisarawe 2388 339 317 22 -0.66 25
Pumula 1158 32 32 0 0 100
Ras Kiuyu 0 0 0 0 0 0
Rondo 14696 2942 2898 44 -0.15 99
Ruaha 27133 0 0 0 0 0
Ruawa 3026 98 98 0 0 100
Rudewa South 579 172 172 0 0 100
Rufiji-Mafia-Kil 130589 599 591 8 -0.13 18
Ruhatwe Village 1735 26 26 0 0 100
Ruhoi River 79765 197 187 11 -0.55 33
Rungo 22400 62 62 0 0 100
Rupiage 2422 35 33 3 -0.75 100
Ruvu 36594 1555 1470 85 -0.56 73
Ruvu South 32683 5084 4580 504 -1.04 100
Saadani 97723 21 21 0 0 5
Sali 1070 981 980 1 -0.01 100
Sao Hill 50955 1312 906 406 -3.64 100
Sasajila 1263 0 0 0 0 0
Selous 8572 3 3 0 0 16
Selous Game Rese 3403187 69283 69269 14 0.00 92
Semdoe/Msige 991 139 139 0 0 100
Sengoma 1348 318 318 0 0 96
Shagayu 8031 6121 6121 0 0 100
Shambalai 23 0 0 0 0 0
Shambangeda Vill 4 3 3 0 0 100
Shinkurufumi 304 0 0 0 0 0
Shume Magamba 13599 6356 6352 3 -0.01 100
Simbo-Bagamoyo 956 0 0 0 0 0
Soni Public Land 37 0 0 0 0 0
South Gendagenda 3225 60 60 0 0 13
Talagwe 1077 135 135 0 0 100
Tamburu 5251 2423 2415 9 -0.04 100
Tawi Village 2520 244 244 0 0 97
Tongomba New 3020 313 309 5 -0.15 100
Tongwe 1364 12 12 0 0 27
Udzungwa Mountai 179020 43160 43145 15 0.00 100
Udzungwa Scarp 14141 10293 10293 0 0 100
Ukutu 164750 1753 1746 6 -0.04 99
Ukwiva 35756 20736 20732 4 0.00 100
Ulabo 5 0 0 0 0 0
Uluguru 24570 22398 22394 4 0.00 100
Uluguru North 1142 278 274 5 -0.17 100
Uluguru South 2479 1766 1765 1 -0.01 100
Umba 4587 0 0 0 0 0
Uponera 344 3 3 0 0 100
Usangu 0 0 0 0 0 0
Uzigua 25236 0 0 0 0 0
Vigoregore 545 61 61 0 0 100
Vigoza 48 0 0 0 0 0
Vikindu 1760 66 66 0 0 11
Vugiri 339 188 188 0 0 100
Vumari 1850 244 244 0 0 100
Wami mbiki 235526 0 0 0 0 0
West Kilombero S 3678 1027 1027 0 0 100
Wotta 1157 611 611 0 0 100
Yelya Village 1299 326 326 0 0 100
Zaraninge 17040 223 223 0 0 77
Zinge 10 5 5 0 0 100
Ziwani 644 3 3 0 0 100
Total 8030467 400921 399077 1844 -0.05 95
Table A5. Forest cover and change for Kenya’s forest reserves.
Kenya
Forest Reserves
total area
(ha)
known forest
~2000 (ha)
known forest
~2010 (ha)
forest change
~2000-~2010
(ha)
annual rate of
forest change
~2000-~2010
rate (%y-1)
confidence
observed
forest (%)
Arabuko Sokoke 37033 22040 22040 0 0.00 82
Arawale 52546 0 0 0 0.00 0
Buda 679 398 397 0 0.00 100
Gongoni 843 421 421 0 0.00 100
Gonja 862 55 55 0 0.00 100
Kaya Chonyi 202 0 0 0 0.00 0
Kaya Dzombo 314 204 204 0 0.00 100
Kaya Jibana 115 0 0 0 0.00 0
Kaya Kambe 61 0 0 0 0.00 0
Kaya Ribe 75 0 0 0 0.00 0
Lunghi 39736 2036 2036 0 0.00 49
Madunguni Forest 800 283 254 29 -1.07 70
Marenje 1484 1108 1107 0 0.00 99
Mkongani North 1095 949 949 0 0.00 100
Mkongani West 1362 833 833 0 0.00 100
Mrima 378 272 272 0 0.00 100
Mwache 354 2 2 0 0.00 39
Mwaluganji 1715 84 84 0 0.00 100
Witu 3915 2110 2096 15 -0.07 81
Total 143571 30792 30748 45 -0.01 79
Table A6. Forest cover and change for Tanzania’s forest reserves.
Tanzania
Forest Reserves
total area
(ha)
known forest
2000 (ha)
forest ~2010
(ha)
forest change
~2000-~2010
(ha)
Annual rate
of forest
change
~2000-~2010
rate (%y-1)
Confidence
observed
forest (%)
Angai 142430 0 0 0 0.00 0
Baga I 3486 3192 3192 0 0.00 100
Bagai 27847 261 261 0 0.00 100
Balangai West 1060 759 759 0 0.00 100
Bamba Ridge 1254 534 534 0 0.00 100
Bassi 25 0 0 0 0.00 0
Bombo East 1 1215 28 28 0 0.00 100
Bombo East 2 479 4 4 0 0.00 100
Bombo Makole 775 0 0 0 0.00 0
Bombo West 3675 262 262 0 0.00 100
Bondo 1936 0 0 0 0.00 0
Bunduki 1 and 2 262 95 95 0 0.00 100
Bunduki 3 86 0 0 0 0.00 0
Chamanyani 861 70 70 0 0.00 100
Chambogo (A) 5937 869 869 0 0.00 100
Chambogo (B) 853 0 0 0 0.00 0
Changandu 8922 0 0 0 0.00 0
Chilangala 1758 9 9 0 0.00 72
Chitoa 2220 0 0 0 0.00 0
Chome 13494 9870 9846 25 -0.03 100
Chongweni (A) 2432 0 0 0 0.00 0
Dar es Salaam 3125 2 2 0 0.00 1
Derema 3662 2888 2888 0 0.00 100
Dindili 1050 0 0 0 0.00 0
Disalasala 1038 0 0 0 0.00 0
Diwale 214 20 20 0 0.00 100
Forest Reserve N 3801 0 0 0 0.00 0
Forest Reserve N 6694 124 124 0 0.00 0
Forest Reserve N 35919 12 12 0 0.00 17
Forest Reserve N 2149 157 157 0 -1.80 100
Forest Reserve N 12086 45 45 0 0.00 0
Forest Reserve N 3816 0 0 0 0.00 0
Forest Reserve N 9115 0 0 0 -1.01 51
Forest Reserve N 7175 0 0 0 -0.47 90
Forest Reserve N 1425 0 0 0 0.00 0
Forest Reserve N 5927 0 0 0 0.00 0
Forest Reserve N 700 61 58 3 -3.40 10
Forest Reserve N 94984 1213 1096 117 0.00 100
Forest Reserve N 12806 581 485 96 0.00 100
Forest Reserve N 3985 26 18 8 0.00 88
Garafuno 1302 0 0 0 0.00 0
Gendagenda 6597 60 60 0 0.00 13
Gologolo East 4 0 0 0 0.00 0
Gologolo West 61 0 0 0 0.00 0
Gonja 94 40 40 0 0.00 100
Gulosilo 8992 173 173 0 0.00 100
Gwami 6646 0 0 0 0.00 0
Handei 1813 4 4 0 0.00 100
Handeni Hill 558 134 134 0 0.00 100
Hebangwe 4451 0 0 0 0.00 0
Idewa 303 189 189 0 0.00 100
Ihanga 4041 0 0 0 0.00 0
Ijogo 2284 0 0 0 0.00 0
Ikwamba 872 754 754 0 0.00 100
Image 8838 4881 4879 2 0.00 100
Iringa 1316 0 0 0 0.00 0
Irunda 4391 3 3 0 0.00 100
Iwonde 1823 45 45 0 0.00 100
Iyondo 3311 32 29 3 -0.83 100
Jasini 135 0 0 0 0.00 0
Kahe I 647 0 0 0 0.00 0
Kahe II 409 0 0 0 0.00 0
Kalunga 621 0 0 0 0.00 0
Kambai 990 374 374 0 0.00 100
Kambona 201 0 0 0 0.00 0
Kanga 7064 6263 6263 0 0.00 100
Kankoma 70 0 0 0 0.00 0
Kasanga 418 0 0 0 0.00 100
Katundu 2703 335 335 0 0.00 100
Kazimzumbwe 5149 286 183 102 -4.34 15
Kibao 116 76 76 0 0.00 100
Kibwezi 67 0 0 0 0.00 0
Kichi Hill 13986 11788 11788 0 0.00 100
Kigogo 2471 2256 2256 0 0.00 100
Kiguha 29 0 0 0 0.00 0
Kihanga 57 0 0 0 0.00 0
Kihiliri 328 0 0 0 0.00 0
Kihuhwi 485 0 0 0 0.00 0
Kihuhwi Sigi 910 36 36 0 0.00 100
Kihuhwi Teak 42 0 0 0 0.00 0
Kikale 585 1 1 0 0.00 31
Kikoka 1565 0 0 0 0.00 0
Kikongoloi 304 0 0 0 0.00 0
Kilanga Hengae 430 354 354 0 0.00 100
Kilengwe 928 0 0 0 0.00 0
Kileo East 307 0 0 0 0.00 0
Kilimanjaro 110636 0 0 0 0.00 0
Kilindi 4591 1619 1619 0 0.00 100
Kilole 262 0 0 0 0.00 0
Kimboza 467 148 148 0 0.00 100
Kindoroko 630 474 474 0 0.00 100
Kingoma 1192 63 40 23 -4.47 93
Kipo 1203 0 0 0 0.00 0
Kiranzi Kitungur 1145 986 986 0 0.00 100
Kiriguru 462 0 0 0 0.00 0
Kisima Gonja 1496 1210 1210 0 0.00 100
Kisingarugaro 17047 11974 11970 4 0.00 100
Kisiwani 111 0 0 0 0.00 0
Kitapilimwa 5877 0 0 0 0.00 0
Kitara Ridge 1056 0 0 0 0.00 0
Kitivo North 86 12 12 0 0.00 100
Kitope 3994 410 410 0 0.00 100
Kitovo South 36 0 0 0 0.00 0
Kitulang'halo 4511 0 0 0 0.00 0
Kitulio 87 0 0 0 0.00 0
Kiutu 10 0 0 0 0.00 0
Kiwengoma 3382 1165 1165 0 0.00 100
Kizee 40 0 0 0 0.00 0
Kizinga 6 1 1 0 0.00 100
Koko hill 101 0 0 0 0.00 0
Kolekole 285 0 0 0 0.00 0
Korogwe Fuel 9175 0 0 0 0.00 0
Kwamarimba 973 181 181 0 0.00 100
Kwamarukanga 248 0 0 0 0.00 0
Kwamgumi 1293 1142 1142 0 0.00 100
Kwangola 32 0 0 0 0.00 0
Kwani 2263 2 2 0 0.00 1
Kwasumba 1312 0 0 0 0.00 0
Kwediboma 295 115 115 0 0.00 100
Kwekanda 127 0 0 0 0.00 0
Kwembago 8 0 0 0 0.00 0
Kwizu 2843 600 600 0 0.00 100
Lake Duluti 88 0 0 0 0.00 0
Ligamba 189 0 0 0 0.00 0
Lionja 26614 0 0 0 0.00 0
Liteho 1283 0 0 0 0.00 0
Litipo 350 58 58 0 0.00 100
Longuza 1739 53 53 0 0.00 100
Luhombero Luwegu 39564 0 0 0 0.00 0
Lukoka 1346 0 0 0 0.00 0
Lunda-Mkwabi 690873 0 0 0 0.00 0
Lungonya 66893 550 548 2 -0.04 100
Lushoto 11 0 0 0 0.00 0
Lusunguru 2210 0 0 0 0.00 0
Maasi 81 0 0 0 0.00 0
Mabundi Mtwange 139 0 0 0 0.00 0
Mafi Hill 4493 2147 2147 0 0.00 100
Mafleta 361 0 0 0 0.00 0
Mafomwero 3250 2885 2885 0 0.00 100
Magadu 30 0 0 0 0.00 0
Magambazi 263 0 0 0 0.00 0
Maganda 44 2 2 0 0.00 100
Magoroto 1126 6 6 0 0.00 100
Magotwe 684 0 0 0 0.00 0
Mahenge Scarp 720 0 0 0 0.00 0
Mahezangulu 270 270 270 0 0.00 100
Mahuta 1425 0 0 0 0.00 0
Makangala 999 2 2 0 0.00 100
Makonde Scarp 1 3637 2 2 0 0.00 27
Makonde Scarp 2 4994 0 0 0 0.00 0
Makonde Scarp 3 956 0 0 0 0.00 0
Mamboto 178 16 16 0 0.00 100
Mamboya 234 0 0 0 0.00 0
Mamiwa Kisara 6913 5237 5237 0 0.00 100
Manga 1643 69 69 0 0.00 100
Mangalisa 5880 3078 3078 0 0.00 100
Manka 146 0 0 0 0.00 0
Marenda 270 1 0 1 -97.46 100
Masagati 374 4 4 0 0.00 100
Masangan 2354 339 261 78 -2.57 18
Masasi 1530 0 0 0 0.00 0
Matapwa 11199 513 488 25 -0.51 100
Matehi 39617 0 0 0 0.00 0
Mazashai 53 0 0 0 0.00 0
Mbalwe Mfukulemb 35787 2552 2514 38 -0.15 100
Mbangala 594 0 0 0 0.00 0
Mbinga 1898 414 412 3 -0.06 100
Mbogo 204 0 0 0 0.00 0
Mbwegere 369 0 0 0 0.00 0
Mchonda 7178 0 0 0 0.00 0
Mchungu 437 72 72 0 0.00 90
Mfundia 2191 47 47 0 0.00 100
Mgambo 1339 261 261 0 0.00 100
Mgololo 21767 16 16 0 0.00 100
Milindo 8643 7882 7876 6 -0.01 100
Milonge 16 16 16 0 0.00 100
Mindu 3256 22 22 0 0.00 100
Minja 1835 0 0 0 0.00 0
Mitarure 34717 990 990 0 0.00 100
Mitundumbea 7642 2084 2061 23 -0.11 100
Mkangadala 366 0 0 0 0.00 0
Mkewe 639 0 0 0 0.00 0
Mkindu 2794 286 286 0 0.00 100
Mkongo 888 0 0 0 0.00 0
Mkoro 117 0 0 0 0.00 0
Mkulazi 66106 0 0 0 0.00 0
Mkuli 2944 369 369 0 0.00 100
Mkungwe 3237 1089 1089 0 0.00 100
Mkusu 4299 2314 2302 12 -0.05 100
Mlali 6478 0 0 0 0.00 0
Mlalivira 35 0 0 0 0.00 100
Mlinga 842 56 56 0 0.00 100
Mlola 6092 0 0 0 0.00 0
Mlungui 1101 0 0 0 0.00 0
Mninga 193 99 94 5 -0.48 100
Mombo 58 0 0 0 0.00 0
Morogoro Fuel 11421 0 0 0 0.00 0
Mpalalu 107 1 1 0 0.00 100
Mpanga Village 28 8 8 0 0.00 100
Mramba 3662 524 524 0 0.00 100
Mselezi 212 0 0 0 0.00 0
Msinko Hill 84 0 0 0 0.00 0
Msumbugwe 4106 2 2 0 0.00 0
Mtai 3729 1610 1610 0 0.00 100
Mtama 704 0 0 0 0.00 0
Mtanza 3903 0 0 0 0.00 0
Mtanza Msona 11327 15 15 0 0.00 64
Mtibwa 1071 0 0 0 0.00 0
Mtita 1903 343 343 0 0.00 44
Mtumbi 324 110 105 6 -0.52 100
Mtunguru 684 0 0 0 0.00 0
Mufindi Scarp Ea 14923 3147 3139 7 -0.02 100
Mufindi Scarp We 1723 887 887 0 0.00 100
Muhoro 1855 833 788 45 -0.55 100
Muhulu 1202 169 169 0 0.00 100
Muhuwesi 175458 0 0 0 0.00 0
Mvuha 694 308 308 0 0.00 100
Mwenga 694 0 0 0 0.00 0
Myoe 111 0 0 0 0.00 0
Mzogoti 162 0 0 0 0.00 0
Nagaga 1649 0 0 0 0.00 0
Naliendele 400 0 0 0 0.00 0
Namakutwa Nyamul 3773 1225 1222 4 -0.03 100
Nambiga 741 389 389 0 0.00 100
Namikupa 2654 2 2 0 0.00 100
Nampekeso 25821 27 27 0 0.00 100
Nandembo 5406 0 0 0 0.00 0
Nandimbo 772 0 0 0 0.00 0
Nawenge 252 0 0 0 0.00 0
Ndasha 1235 0 0 0 0.00 0
Ndelemai 1373 988 988 0 0.00 100
Ndimba 1831 675 675 0 0.00 100
Ndolwa 1065 0 0 0 0.00 0
New Dabaga 1785 1661 1661 0 0.00 100
Ngalonga 246 0 0 0 0.00 0
Ngarama 28578 14256 14249 7 0.00 100
Ngarama South 2108 78 78 0 0.00 100
Ngarusambu 113 0 0 0 0.00 0
Ngindo/North Eas 19342 0 0 0 0.00 0
Ngongwa-Busangi 3078 206 206 0 0.00 100
Ngulakula 1740 66 66 0 0.00 80
Nguru North 13150 9959 9959 0 0.00 100
Nguru South 20238 17655 17655 0 0.00 100
Nguru Ya Ndege 2571 37 37 0 0.00 100
Ngwasi 2657 18 16 2 -1.06 100
Njogi 2014 0 0 0 0.00 0
North East Unden 159406 0 0 0 0.00 0
Nyandiduma 122 0 0 0 0.00 0
Nyandira 25097 0 0 0 0.00 0
Nyanganje 11838 2 2 0 0.00 100
Nyera Kiperere 107397 0 0 0 0.00 0
Nyumburuni 2970 274 271 2 -0.08 55
Pagale 18768 0 0 0 0.00 0
Pala Mountain 10919 307 307 0 0.00 100
Pangawe East 6567 0 0 0 0.00 0
Pangawe West 236 0 0 0 0.00 0
Pindiro 4157 1164 1164 0 0.00 100
Pongwe 1186 0 0 0 0.00 0
Pugu 2308 346 325 22 -0.64 26
Pumula 539 59 59 0 0.00 100
Rau 1534 0 0 0 0.00 0
Rondo 8658 2891 2863 29 -0.10 99
Ruande 570 80 80 0 0.00 100
Ruawa 1922 74 74 0 0.00 100
Rudewa 529 168 168 0 0.00 100
Ruhoi River 74424 201 190 11 -0.54 34
Rungo 18632 21 21 0 0.00 100
Ruvu 37596 20886 20881 5 0.00 100
Ruvu North 30472 1435 1351 85 -0.61 71
Ruvu South 29235 5038 4554 484 -1.01 100
Sakila 267 0 0 0 0.00 0
Sali 1069 967 966 0 -0.01 100
Sambasha 17 0 0 0 0.00 0
Sao Hill 30541 678 382 296 -5.57 100
Sao Hill Extensi 21986 612 506 106 -1.89 100
Sasajila 1265 0 0 0 0.00 0
Segoma 1344 318 318 0 0.00 96
Semdoe 989 138 138 0 0.00 100
Shagayu 7907 6123 6123 0 0.00 100
Shambalai 22 0 0 0 0.00 0
Shikurufumi 312 0 0 0 0.00 0
Shume Magamba 13633 6357 6354 3 -0.01 100
Simbo 2 947 0 0 0 0.00 0
Talagwe 1091 139 139 0 0.00 100
Tamburu 6450 2391 2384 7 -0.03 100
Tongeni River 299 0 0 0 0.00 0
Tongomba 3917 548 541 7 -0.13 100
Tongwe 1353 11 11 0 0.00 26
Udzungwa Scarp F 15981 11043 11043 0 0.00 100
Ulangambi 2077 1880 1880 0 0.00 100
Uluguru North 8375 6997 6991 6 -0.01 100
Uluguru South 17543 15688 15684 4 0.00 100
Uponera 344 4 4 0 0.00 100
Utete 959 147 147 0 0.00 100
Uzigua 21143 0 0 0 0.00 0
Vigoregore 511 62 62 0 0.00 100
Vigoza 48 0 0 0 0.00 0
Vikindu 1805 60 60 0 0.00 11
Vugiri 323 185 185 0 0.00 100
Vumari 1735 247 247 0 0.00 100
Wotta 1202 612 612 0 0.00 100
Zinge 10 6 6 0 0.00 100
Ziwani 603 2 2 0 0.00 100
TOTAL 2961598 246429 244717 1712 -0.07 96
