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LAND COVER CHANGE AND WOODLAND DEGRADATION IN A CHARCOAL
PRODUCING SEMI-ARID AREA IN KENYA
Harun M. Kiruki
1,2
*, Emma H. van der Zanden
2
,Žiga Malek
2
, Peter H. Verburg
2
1
School of Environment and Natural Resources, South Eastern Kenya University, P.O. Box 170, Kitui, Kenya
2
Environmental Geography Group, Department of Earth Sciences, Vrije Universiteit, de Boelelaan 10871081HV Amsterdam, the Netherlands
Received: 21 December 2015; Revised: 8 May 2016; Accepted: 14 May 2016
ABSTRACT
Woodlands in Kenya are undergoing land cover change and degradation leading to loss of livelihoods. Uncontrolled charcoal production,
although a livelihood source for communities living in woodland areas of Kenya, leads to woodland degradation. We used Landsat imagery,
field plot data and household interviews to describe land cover change and the role of charcoal production in woodland degradation. An
unsupervised classification was used to determine land cover change from woodland to open/farmland, and five 16-km transects were used
to investigate the extent of charcoal production in the target woodlands. Semi-structured interviews were conducted on 117 households to
understand their perceptions on woodland cover change and the role of charcoal production. The overall accuracy of our classification
was 86%. Woodland areas decreased by 24% between 1986 and 2014. The trend of woodland area change compared well between remote
sensing and interview data. The density of kilns, a proxy for charcoal-led woodland degradation, varied across the sample plots. Despite
charcoal providing a livelihood for 66% of the households, the community felt that their environment, wealth and social relations have been
affected by land cover changes caused by charcoal production. Based on these results, we recommend that appropriate measures aimed at
improving the productivity of agriculture, adapting to climate change and reducing dependence on charcoal for sustenance should be encour-
aged to mitigate woodland cover loss and degradation. Copyright © 2016 John Wiley & Sons, Ltd.
key words: charcoal; woodlands; Landsat imagery; community perceptions; land use
INTRODUCTION
Tropical savannahs and woodlands are a major component
of the world’s vegetation, covering one sixth of the land
surface and over half of the African continent. They account
for about 30% of the primary production of all terrestrial
vegetation, playing a crucial role in the energy, water and
carbon balance (Ribeiro et al., 2013). Savannahs and wood-
lands provide a range of goods and services to humans in
general and to local rural communities in particular (Pote
et al., 2006; Kalema et al., 2015). Woodlands are therefore
a key source of livelihood for over 50 million people in
Africa (Campbell & Costanza, 2000). Ecological services
provided by woodlands include soil quality maintenance
through erosion and leaching protection (Ni et al., 2015;
Zhang et al., 2015) thus avoiding erosion-induced soil quality
deterioration that is a major impediment to global food and
economic security (Erkossa et al., 2015).
Woodlands and forests are key in regulation of water flow
regimes and maintenance of morphology of water bodies
(Keesstra, 2007; Keesstra et al., 2009), microclimate regula-
tion (Belsky et al., 1989), carbon sequestration (Kalema
et al., 2015) and economic benefits in the form of food
and energy (Malimbwi et al., 2010). Despite their impor-
tance, tropical savannahs and woodlands are given less
attention than tropical rainforest and other ecosystems in
the land cover and land use change literature (Grainger,
1999).
Tropical savannahs and woodlands are among the most
degraded and threatened ecosystems (MacFarlane et al.,
2015). Degradation represents the temporary or permanent
reduction in density, structure, species composition or
productivity of vegetation cover (Malimbwi et al., 2010).
It occurs when woodlands are not managed sustainably or
controlled through appropriate environmental regulatory
policies and frameworks (Bodart et al., 2013; Lemenih
et al., 2014). Woodland degradation can have different
drivers: overuse of resources, climatic conditions such as
increased drought frequency, urbanization and agricultural
expansion (Rocheleau et al., 1995; Le Polain de Waroux
& Lambin, 2012).
In Africa, woodland degradation results from grazing,
human-initiated fires for land clearing for agriculture and
vegetation removal for fuelwood, charcoal and building
material (Malimbwi & Zahabu, 2008; Ouedraogo et al.,
2010). These activities can affect the structure and produc-
tivity of woodland areas, for example, by altering the soil
properties (Yimer & Abdelkadir, 2010; Bruun et al., 2015;
Mohawesh et al., 2015) and the hydrological regimes
(Montenegro & Ragab, 2010; Yu et al., 2015). The way in
which charcoal production links to deforestation and degra-
dation has been debated (Chidumayo & Gumbo, 2013;
Coomes & Miltner, 2016). Charcoal can lead to the transfor-
mation of woodland to bush and bush to scrub, over very
*Correspondence to: Harun M. Kiruki, School of Environment and Natural
Resources, South Eastern Kenya University, P.O. Box 170 Kitui, Kenya.
E-mail: h.m.kiruki@vu.nl
Copyright © 2016 John Wiley & Sons, Ltd.
land degradation & development
Land Degrad. Develop. (2016)
Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.2545
large areas (Arnold & Persson, 2003). Charcoal making can
occur when the land is being cleared for agriculture and thus
may not be the main driver of degradation (Zulu & Richardson,
2013). In some cases however, charcoal production can be
linked directly to woodland degradation (Chidumayo &
Gumbo, 2013). Despite the unknown extent, woodland
degradation because of charcoal production has been
reported in Kenya (Kirubi et al., 2000; KFS, 2013).
The ongoing debate on the role and extent of fuelwood-
driven degradation in woodlands (Butz, 2013) indicates that
woodland degradation in arid and semi-arid areas is not
sufficiently investigated. This is partly because woodlands
are more difficult to monitor with traditional forestry tools
and remote sensing as compared with other forest types.
Woodland degradation is also more subtle than clear-cutting
a forest making it difficult to detect its occurrence and extent.
Moreover, woodlands are viewed as less commercially
important (Grainger, 1999).
To obtain a comprehensive understanding of woodland
degradation requires a combination of change detection by
remote sensing, physical field observations and sociological
data. The objective of this paper is to enhance the under-
standing of woodland degradation and to assess the role of
charcoal making in woodland degradation for a study area
in Kitui, Kenya using such a portfolio of methods. The
novelty of this paper lies in linking woodland degradation
and perceived change to livelihoods. We explicitly include
perceptions by local inhabitants as this can provide informa-
tion for planning (Vila Subirós et al., 2015) and gain support
for restorative work (Davies et al., 2010) and better manage-
ment of land cover change. We have chosen the woodlands
of Kitui, Kenya as these have been undergoing rapid socio-
economic, climatic and environmental change (Zaal &
Oostendorp, 2002; Lasage et al., 2008).
Within this context, the specific aims of this study are to
(1) investigate the spatial patterns and trends of local
woodland degradation and cover change; (2) link wood-
land degradation with the perception of local people on
the land cover/use change and the effect on their well-
being; and (3) explore the role of charcoal in woodland
degradation.
MATERIALS AND METHODS
The study area is located in Kitui County in Kenya about
150 km east of Nairobi (38°–23′, 38°–37′E and 1°–46′,
1°–58′S) and covers an area of 442 km
2
(Figure 1). The
altitude is about 550 m above sea level. The average yearly
rainfall for Kitui is around 1,000 mm with large local differ-
ences. The rainfall pattern is bimodal consisting of ‘long
rains’between March and May, while the ‘short rains’
season occurs between October and December (Lasage
et al., 2008). The vegetation of the study area is described
as Somalia–Masai Acacia–Commiphora deciduos bushland
and thicket within the Somalia–Masai ecoregion (Brink &
Eva, 2011) dominated by Commiphora spp., Acacia spp.
and Adansonia digitata L.
The study area borders Tsavo East National Park to the
south-east and Kitui South Game Reserve to the east. Mutha
market is the largest settlement in the area. The land in the
study area is privately owned through family lineages
although formal titling has not been done. Shifting cultiva-
tion is practised within the family land holdings, which are
generally a few kilometres apart. The population of the area
is 10,154 people in 1,865 households (KNBS, 2010) with an
average density of 27 person s km
2
(KCDP, 2013). We
selected the study site as it is located deep inside in the
charcoal producing area of Kitui County. Furthermore, it is
located at the intersection of two major roads connecting it
to the Nairobi and Mombasa highway, where the charcoal
is mostly transported to. Charcoal making was introduced
in the study area in 1998 by evictees from Chyulu Hills in
the neighbouring Makueni County about 120 km south-west
of the study area, which was gazetted as a national park in
1995 (Muriuki et al., 2011).
Different methods were combined in order to analyse
woodland degradation: land cover change detection based
on the interpretation of satellite imagery, field-based
charcoal site identification and interviews with local land-
owners. Figure 2 summarizes the data sources and processes
followed to analyse the Kitui woodlands.
Landsat image analysis
We chose Landsat satellite images because they enabled us
to work on a local scale of 30-m resolution and cover the
desired time. Images for the years 1986 (26 August), 1999
(25 October), 2005 (22 August) and 2010 (1 June) were
used (USGS, 2015). For 2014, we used a cloud-free mosaic
from Hansen et al., (Hansen et al., 2013). The satellite
images were chosen based on time of the year, their clarity
Figure 1. The location of study area within Kitui County, Kenya (Landsat
image path 167 row 61). The image is in true colour, based on Landsat
bands 4, 3 and 2.
H. M. KIRUKI ET AL.
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
and major socio-economic changes in the area (e.g. 1998
Chyulu Hills eviction). Images for the dry months were
chosen to minimize the chances of error in classification
because of seasonal differences in vegetation. The satellite
images were pre-processed using ENVI 5.1 and ERDAS
Imagine 9.1 software. We masked clouds—cloud cover
however never exceeded 5% of the study area and was
limited to the south-east section.
The unsupervised Iterative Self-Organizing Data Analy-
sis algorithm was used to cluster image areas with com-
mon reflectance. We performed the classification in
ERDAS with the convergence value at 0.95 and 20 maxi-
mum iterations, resulting in 20 clusters. These were later
reclassified to three predetermined classes based on the
knowledge of the area and visual examination of colour
composites of raw satellite images (Stringer & Harris,
2014). The three classes were woodland, transitional wood-
lands and farmlands/open areas (Figure 3). It was not pos-
sible to map and analyse changes in the area occupied by
settlements. This was because of the spatial resolution of
Landsat images and the characteristics of mostly informal,
small settlements, with a low share of sealed surfaces. The
accuracy assessment was performed using a stratified ran-
dom sample of 200 units, which were visually interpreted
from the satellite images. We calculated the overall, user’s
and producer’s accuracy for each classified image for the
three land cover classes (Congalton, 1991; Foody et al.,
2002).
A post-classification change detection was applied to
analyse woodland degradation through space and time.
The images from the different time periods were compared
to create a change image map (Macleod & Congalton,
1998). This method has the advantage that various images
are classified separately hence reducing the challenges
associated with radiometric calibration (Singh, 1989;
Lillesand et al., 2004).
Charcoal and woodland degradation
To further assess the impact of charcoal on woodland degra-
dation, five parallel transects were aligned in a west–east
direction. The transects, which were 16 km in length and
1 km apart, started from the Kivandeni-Mutha Market main
road going towards the Kitui South Game Reserve boundary.
They represent a gradient of accessibility as we assumed that
accessibility might affect the degree of charcoal extraction
(Figure 1). Rectangular plots (50 by 20 m) of 0.1 ha were
sampled every kilometre along the transects, giving a total
of 75 plots. The 50 by 20 m plot size has been widely used
for assessments of shrub lands, tropical savannahs and
woodlands (Luoga et al., 2002; Kalema et al., 2015). For
every sampling plot, the global positioning system (GPS)
Figure 2. An overview of the research process.
Figure 3. (a) A typical representation of the land cover classes used: wood-
land, transitional woodland and open areas/farmland, (b) charcoal kiln and
extraction road and (c) tree stumps dug out for charcoal production.
CHARCOAL, LAND COVER CHANGE AND WOODLAND DEGRADATION IN KENYA
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
coordinates were captured and the number of charcoal kilns
was counted. A kiln is an insulated chamber for wood
carbonization made by covering a wood pile with herba-
ceous material and soil (Chidumayo & Gumbo, 2013, refer
to Figure 3b). Furthermore, the number of tree stumps and
species used for charcoal making within the plot was
counted and identified with the assistance of local guides
(Figure 3c). This plot level data was used as a proxy for
the contribution of charcoal towards woodland degradation.
The plot data was analysed using both inferential and
descriptive statistics, including Pearson’s correlation coeffi-
cient. We also compared the reported kiln density with the
land cover at the plot. As charcoal making involves felling
of preferred tree species, a single kiln usually has a ‘catch-
ment’distance of several metres depending on the density
of the tree species. To account for this, we extracted the land
cover at the sampling plot locations within a 50-m buffer
from the centre of the plot.
Comparison of woodland loss between remote sensing and
community trends
In order to compare community knowledge and perceptions
with remote sensing results, a total of 117 households were
interviewed using a semi-structured questionnaire. The
interviews were conducted between May and July 2015 by
the lead author and four assistants whose mother tongue is
the local language (Kamba). The assistants were trained on
using GPS equipment and questionnaire administration. The
questionnaire was also pre-tested to ensure appropriate word-
ing and meaning of the questions. Systematic sampling of
households was applied using eight transects evenly spread
across the study area. The households interviewed were sys-
tematically picked at a distance of 1km from each other along
the transect. If no household was present after 1 km, the
nearest household was picked. This method was adopted as
there was no reliable settlement data or maps of the area
available.
We targeted household heads (both male and female)
who have lived in the area for at least 20 years to receive
an accurate account of the past land use activities in the
study area. The respondents were asked to explain the land
cover changes within their holdings for the last 20 years,
by indicating the estimated size of woodland and farmland
within the land holding for 5-year time intervals. They
were also requested to give their views on the impact of
charcoal making on the land cover. Finally, the inter-
viewees were asked to rate the effect of land cover change
on their immediate environment, wealth and social rela-
tions on a Likert scale ranging from 1 (no change) to 6
(extreme change), as well as their motivation for the values
given. In-depth semi-structured interviews have success-
fully been used to study stakeholder perceptions in land
use and management (Teshome et al., 2014; Vila Subirós
et al., 2015). Finally, personal observations of land
cover/use types and sources of livehoods were recorded
to supplement the satellite, plot and community-based
information.
RESULTS
Landsat image analysis and accuracy assessment
The results of the Landsat image analysis show the conver-
sion from woodland and transitional woodland to open areas
(Figure 4). The conversions from woodlands to transitional
woodlands cannot always be related to human activity. For
example, in several areas, we observed swaps between
woodlands and transitional woodlands that were a conse-
quence of seasonality and different levels of dryness in the
area. The woodland area has been gradually declining in
the study area from 1986 to 2014, from a total of
31,084 ha in 1986 to 23,930 ha in 2014 (Table I). The
biggest change to the woodland land cover occurred
between 2005 and 2014. Transitional woodland also
increased but at a more moderate pace, from 9,966 to
11,572 ha. Although some changes to this class can be
associated to land cover change, often these changes are as
a result of seasonality of woodlands. This is also shown by
the fluctuation of the area covered by transitional woodland
and the low classification accuracy of this land cover class.
Overall, the results show an increase in farmland and transi-
tional woodland areas at the expense of woodlands. There
are few noticeable changes within the Kitui South Game
Reserve.
The user’s and producer’s accuracy for woodland range
from 84 to 98% (refer to Table II). The transition class has
the lowest user’s accuracy of 69 and 75% for the year
2010 and 2014 respectively. This is because of the difficulty
to map this particular land cover class.
Charcoal and woodland degradation
Evidence of charcoal making was recorded in 38 of the 75
sample plots. The number of kilns ranged from 1 to 6 (10–
60 kilns ha
1
) per plot, and kilns had an average area of
25 m
2
. The number of trees harvested per plot ranged from
Figure 4. Woodland change from original and transitional woodlands into
open areas in Kitui County, Kenya, for the period 1986–2014.
H. M. KIRUKI ET AL.
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
1 to 16 (10–160 stems ha
1
) with diameters ranging from
9.2 to 79 cm (recorded 30 cm off the ground). Almost
complete clear fell of trees is carried out on plots with high
density of preferred species, and little or no extraction is ob-
served on plots with low preferred tree species density. The
species harvested for charcoal making include Strychnos
spinosa Lam., Acacia nilotica L. Willd., Cassia abbrevieta
Oliv. and Acacia tortilis Forsk. Harvesting is normally done
by felling the tree at approximately half a metre off the ground
using an axe. In some instances, charcoal makers dig up the
stumps of the preferred charcoal species (Figure 3c). The
harvesting intensity, the alignment of extraction roads and
cutting of shrubs to use on kiln construction all contribute
to woodland degradation. Thirty-one plots (81.5%) with
charcoal kilns were found in the area where woodland or
transitional woodlands were the dominant land cover
(>51%). Only five plots were found in the area classified
as farms/open areas thus indicating the importance of transi-
tional woodlands and woodland land cover types as a source
of charcoal (Figure 5).
The land cover type and the number of kilns on each plot
were examined in relation to the distance of each plot from
the main road. We found a weak relationship between the
number of kilns and the distance from the main road
(r=0.178, p= 0.284) and a positive relationship between
woodland cover and the distance from the main road
(r= 0.672, p<0.001).
Household characteristics and woodland cover loss
Out of the 117 respondents, 57 were female and 60 were male.
Their average age was 45 years, and 84% have only had basic
education. Eighty-eight % of the respondents ranked agricul-
ture as their main source of livelihood, while 7 and 6% respec-
tively ranked livestock and charcoal making as their main
source of livelihood. The average farm holding was 20ha,
while the average household size was seven people.
The respondents reported a downward trend of the wood-
land cover over time. Between 1995 and 2015, the woodland
cover was lost at rates of 4.8, 5.6, 10.8 and 17.2% for every
5 years respectively. The highest rates of woodland cover
loss were reported for the years 2005–2015 when the wood-
land area dropped from 2,093 to 1,545ha. Most of this area
was converted to farmland/open areas (Table S1).
Woodland area loss: comparison between remote sensing
and community interviews
We made a comparison of the woodland loss rates calculated
from the interviews and the remote sensing analysis (Figure
6). For both analysis, we set the initial state of the woodland
to 100% and computed the percentage change based on each
data type. The calculated rates of woodland reduction as de-
rived from the interviews are consistently higher than those
from the satellite imagery. In spite of this difference, the
trends are highly correlated (r=0.96, p= 0.03).
The interviews indicate that 74% of the households have
made charcoal for commercial purposes at one point in time,
and the number currently involved in charcoal production is
66% of the sampled households. The average production per
household is 22 bags per month (Figure 7). The average
weight of a charcoal bag is 35 kg (KFS, 2013) and is cur-
rently sold at Ksh 450 (€4.5).
Community perceptions on the consequences of charcoal
making
The perceptions on how charcoal making has contributed to
observed woodland degradation and landscape changes
within the last two decades vary (Figure 8a). Thirty-eight
% of the respondents believe that charcoal making has led
to a reduction in rainfall through the massive felling of trees.
Other reported associations with charcoal making are wood-
land area reduction (19%), increased soil erosion (12%) and
increase in dust and wind speed (8%).
Table I. Observed land cover changes from 1986 to 2014 in Kitui, Kenya
LUC type 1986 1999 2005 2010 2014
ha % ha % ha % ha % ha %
Woodland 31,084 70.3 30,714 69.5 29,180 66.0 27,266 61.7 23,930 54.1
Transitional woodland 9,966 22.5 9,018 20.4 10,540 23.8 9,363 21.2 11,572 26.2
Farmland 3,168 7.2 4,487 10.1 4,496 10.2 7,583 17.2 8,697 19.7
Table II. Accuracy assessments for the study area for the year 1986–2014
1986 1999 2005 2010 2014
User’s
accuracy
Producer’s
accuracy
User’s
accuracy
Producer’s
accuracy
User’s
accuracy
Producer’s
accuracy
User’s
accuracy
Producer’s
accuracy
User’s
accuracy
Producer’s
accuracy
Woodland 84 88 97 91 98 98 93 90 94 91
Transition 89 82 75 89 93 93 69 84 75 84
Farms 75 90 78 78 93 87 100 80 93 93
Overall accuracy 86 89.5 96 87.5 89
CHARCOAL, LAND COVER CHANGE AND WOODLAND DEGRADATION IN KENYA
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
The perceived consequences of charcoal making and
woodland degradation on community well-being are
reported on a Likert scale ranging from no change (1) to
extreme change (6) in Figure 8b. Less than 25% of the
respondents believe that woodland degradation has had no
impact on their immediate environment, wealth and social
relations between 2010 and 2015, while at least 70% of
the respondents ranked the effect as moderate to extreme
change for the last 10 years. The reported aspects of environ-
mental change mentioned overlap with the reported conse-
quences of charcoal production, including increase in
temperature, dust whirls and migration of bees. As a
45-year-old man from Kendoo village explained: “Bees have
migrated.I have six bee hives out of which only one is occu-
pied.The smoke from the kilns has driven the bees away”.
Reduction in harvest volume of maize and green grams
(mungbeans) and the number of livestock are key reasons
why respondents think that their wealth has gone down
considerably. A 60-year-old man reported that: “I had 30
herd of cattle but now I have none.I used to harvest a lot
of honey but now the bees are nowhere.I used to harvest
15 bags of maize but now I harvest 1 bag”. Perceived
reduction in rainfall amounts and predictability has severely
affected agricultural activities, and as a consequence, com-
munal activities such as voluntary farmer self-help groups
(mwethya) have since stopped. Exchange of agriculture-
based gifts such as food items and livestock has also re-
duced. Overall, individuals have focused their time on daily
survival efforts through non-farm activities such as charcoal
making, small businesses and local employment. This shift
in social relations is best captured through the sentiments
Figure 5. Location of sample plots where charcoal kilns where found and
their corresponding land cover types.
Figure 6. Comparison of woodland cover loss trends calculated from com-
munity interviews and satellite imagery.
Figure 7. Households involved in charcoal making and average production
per month.
Figure 8. Community perceptions on (a) the effects of charcoal making on
the landscape and (b) on the environmental conditions, wealth and social
changes.
H. M. KIRUKI ET AL.
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
of a 60-year-old man from Ithango village: “we no longer
share gifts,the mwethya no longer function and we no lon-
ger visit each other.We wake up early in the morning to
search for food and return home late to sleep”.
DISCUSSION
Land cover change from satellite imagery
Although the use of satellite imagery to derive information
on woodland degradation has its drawbacks, an 86% overall
accuracy of our land cover classification indicates a sufficient
level of accuracy to show real changes (Treitz & Rogan,
2004). Our accuracies are comparable to other studies in
the region, for example, in Ugandan woodlands (Mwavu
& Witkowski, 2008). Nevertheless, post-classification maps
are subject to uncertainty, where the errors of individual
maps propagate when different maps are compared to
analyse land cover change (Olofsson et al., 2013). This
can impact the validity of the observed locations and spatial
pattern of woodland cover change. Additional confidence in
the results is gained as the spatial pattern of woodland
change follows a logical pattern near or adjacent to roads
and settlements and is not randomly scattered across the
landscape. Moreover, the change trends correspond well
with the field measurements and interview results that each
have their own, different uncertainties. By comparing
different ways of measuring woodland change and degrada-
tion through a triangulation of evidence, we overcame the
limitations of each individual approach.
The decrease of woodland area and an increase in
farmlands in the study area are indicative for the rapidly
changing land use regime in the area attributable to an
increase in farmlands and shifting cultivation. This increase
is partly because of population increase that rose from 3
persons km
2
in 1989 (CBS, 1994) to 11 persons km
2
in
1999 (KNBS, 2010) and drought adaptation mechanisms
of the local population. Households have responded to the
decreased rainfall amounts by clearing increasingly bigger
farmlands hoping to compensate for lowered output per unit
area. Our field observations point to shifting cultivation as
one of the causes of woodland loss. Farmers use a particular
portion of the farm holding for a period of time and clear a
new woodland area for farming once the previous one is
exhausted. The effects of shifting cultivation on reducing
woodland cover have been reported in other African wood-
lands (Luoga et al., 2005; Kalema et al., 2015).
Conversion of woodlands to farmlands/open areas can
have adverse effects on soils. Conversion of woodland to
other land use has been noted to reduce soil organic carbon
by 22–30% and total nitrogen by 19% for a study in Ethiopia
(Yimer & Abdelkadir, 2010). An adequate amount of soil
organic matter is considered essential for sustainable
agriculture, and its reduction decreases crop productivity
(Yimer et al., 2007). Loss of vegetation cover can lead to
the formation of soil sealing that increases the risk of runoff
and soil erosion. Mohammad & Mohammad (2010) reported
high soil loss in cultivated fields as compared with land
under woody vegetation, an observation they attributed to
the breakdown of soil aggregate stability, loss of vegetative
cover and exposure of the soil particles to direct impact of
rain drops.
Charcoal as a driver of land degradation
Whereas population growth and the demand for cropland
were the main drivers until 1999, charcoal burning became
a significant driver of woodland degradation afterwards as
evidenced by the large proportion of households who
engage in charcoal production, the large number of kilns
counted in woodland cover area and evidence of trees
harvested for charcoal making. This is supported both by
our field observations and interviews. It is not possible to
attribute the drivers of woodland degradation directly from
remote sensing data. The spatial pattern and distribution of
woodland degradation derived from the remote sensing
images after 1999 suggests charcoal burning among the
major factors of degradation. Patches of woodland degrada-
tion after 1999 are smaller as compared with the ones
between 1986 and 1999. The larger patches are likely the
result of land clearing for agriculture, while the smaller ones
are more characteristic for charcoal production. Neverthe-
less, it is difficult to point out one single driver, as often
areas that had been cleared for charcoal production are after-
wards used for cropland.
The degree of degradation is highly place-specific and
depends on the density of preferred charcoal making
species. Almost all the charcoal kilns were observed in
woodland cover areas suggesting that charcoal making is
an activity not directly associated with land clearing for
agriculture. The role of charcoal making in land degradation
has also been reported elsewhere (Luoga et al., 2002; Luoga
et al., 2005; Chidumayo & Gumbo, 2013). As charcoal
makers construct the kilns near the tree cutting site, kilns
further contribute to degradation as they remain bare for a
long time (Dons et al., 2015). Removal of vegetation cover
and subsequent burning around charcoal kilns can result in
soil fertility reduction. This can occur in numerous ways:
soil erosion with nutrient loss on bare grounds, reduction
of soil organic matter through volatilization and loss of soil
microbial biomass because of increased decay and loss of
heat-sensitive microbes (Certini, 2005; Zhang et al., 2015).
The effect of woodland degradation is further
compounded by the presence of uncontrolled extraction
roads that involves clearing of vegetation all the way to
the kiln site. Furthermore, selective harvesting of preferred
charcoal species may completely wipe away some species,
changing the structure of the woodland and its ability for self
regeneration (Butz, 2013). Many woodland and dryland
species regenerate through coppicing (Sawadogo et al.,
2002; McLaren & McDonald, 2003), and therefore, digging
up their stumps severely compromises this ability
(Malimbwi & Zahabu, 2008).
It is difficult to relate woodland degradation to charcoal
even with using relatively high-resolution Landsat images,
as charcoal making is a phenomenon occurring at subpixel
CHARCOAL, LAND COVER CHANGE AND WOODLAND DEGRADATION IN KENYA
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
size scale. To overcome this challenge, very high-resolution
satellite imagery such as Quickbird and Ikonos are being
used to analyse charcoal-related degradation (Oduori et al.,
2011; Dons et al., 2015).
Extended droughts and low farm productivity are cited as
some of the factors enhancing charcoal making in the savannah
areas and Kitui County in particular (PISCES, 2010). With
no other source of livelihood, poverty-striken residents turn
to charcoal making for subsistence and commercial
purposes, as shown by other examples in Africa (Malimbwi
& Zahabu, 2008; Kalema et al., 2015). People are attracted
to charcoal production as it requires neither formal educa-
tion nor large capital investment while at the same time
charcoal is a cash product with a large market ready to
absorb the entire production (Malimbwi & Zahabu, 2008).
The weak relationship between kilns and distance from
the main road contrasts sharply with what is normally
expected in wood resource extraction (Albers & Robinson,
2013). Studies in South Africa (Pote et al., 2006) have
shown that the amount of harvesting decreases with distance
from settlements as the most favoured species are removed
first from accessible areas. The pattern in our study can be
attributed to the nature of land ownership in the study area,
local vegetation variations and the commercial aspect of
charcoal making. Land is individually owned, and thus,
owners can exert some degree of control on how trees can
be used. The commercial nature of charcoal extraction,
fuelled by demand for charcoal in cities such as Nairobi
and Mombasa, has encouraged middlemen to venture deep
into the woodlands, leading to charcoal production all over
the woodlands. Charcoal can be sourced from long distances
as long as the price increases result in marginal gain
(Ahrends et al., 2010)
Comparison of woodland loss between remote sensing and
community interviews
The high degree of similarity of woodland cover trends
between remote sensing and from interviews indicates that
the community is well aware of their surroundings and the
land use changes. Combining interviews and remote sensing
provides more reliable and comprehensive data on land use
and land cover as various aspects of land use and cover
change, their causes and effects can be clarified and verified.
Also, several other studies have successfully compared
satellite-derived land cover changes with interview data
from multiple stakeholders ranging from farmers, communi-
ties, businesses and institutions. In these studies, the inter-
views focussed on identifying the drivers of change
(Malek et al., 2014; Ariti et al., 2015), the impact of changes
(Garedew et al., 2009) or the stakeholder perception of
(Yiran et al., 2012; Ariti et al., 2015). The argument is that
whereas remote sensing can provide quantitative data on the
magnitude of land cover change, it is the stakeholders who
can inform on the driving forces of change and how the
changes affect their lives. However, interviews are time-
consuming and may be subjective, and for periods further
back in time, it is more difficult to gather reliable results
(Garedew et al., 2009). As stakeholders base their decisions
on their perception of the drivers and impacts of land use
and land cover change (Ariti et al., 2015), there is the need
to link the observed changes with the driving forces to
inform effective and sustainable land use planning (Yiran
et al., 2012). Our study provides an example of the analysis
of woodland degradation at a local level using a portfolio of
methods complementing each other and thus enabling a
comprehensive understanding of woodland degradation
and the role of charcoal production in specific.
In order to reduce woodland degradation, incentives for
landowners to maintain woodland areas should be combined
by disseminating technical information and alternative
management options, such as beekeeping. This will
reduce woodland degradation and dependence on charcoal
for sustenance. Extending forestry services to tree planting
for charcoal and wood production could reduce the
pressure on existing woodlands, as examples of local non-
governmental organizations (NGO) such as Katulu have al-
ready shown. Moreover, encouraging the use of alternative
charcoal production technologies, such as the casamance
charcoal kiln and briquetting, can increase the efficiency of
charcoal production by reducing the number of trees neces-
sary to produce a given volume of charcoal hence slowing
down land cover change (Mekuria et al., 2012). Finally,
pressures on woodland can be reduced by increasing agri-
cultural productivity, for instance through the introduction
of drought-tolerant crops, manure for fertilization, seasonal
forecasts and promotion of soil and water conservation
techniques (Recha et al., 2014).
CONCLUSIONS
Landsat imagery analysis, field transects and household
interviews provide an in-depth understanding on woodland
cover change and degradation. Our study has shown that
woodlands are being converted to farmlands and are
degraded mainly through charcoal making as result of a
host of physical and socio-economic factors. The commu-
nity is well aware of the woodland degradation and land
cover change and relate degradation of the land cover to
their perceived worsening livelihoods. To reduce woodland
cover loss, our results indicate that there is a need for ap-
propriate policy and technological measures to reduce
woodland cover change and degradation. Such measures
should target improving the productivity of agriculture to re-
duce shifting cultivation practice, adaptation to climate
change and reducing dependence on charcoal for sustenance
in order to address the combined drivers of woodland deg-
radation and loss.
ACKNOWLEDGEMENTS
This study was supported by the Ms Grietje Wille Legacy
under the A Sustainable Approach to Livelihood Improve-
ments Project, a joint cooperation between VU University,
the Netherlands and SEKU University, Kenya. Additional
H. M. KIRUKI ET AL.
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)
support was obtained from the European Research Coun-
cil under the European Union’s Seventh Framework
Programme ERC Grant Agreement no. 311819
(GLOLAND). We thank Dr Julia Schindler of VU Univer-
sity, Dr Kariuki Chege and Dr Peter Njuru both of SEKU
for their invaluable advice. We also thank Festus Kakuma
for organizing field logistics. The authors declare no con-
flict of interest.
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SUPPORTING INFORMATION
Additional supporting information may be found in the
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Table S1. Woodland cover loss for the years 1995–2015 as
determined from community interviews
H. M. KIRUKI ET AL.
Copyright © 2016 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2016)