ArticlePDF Available

Land Cover Dynamics in the Kirisia Forest Ecosystem, Samburu County, Kenya African Wildlife Foundation (AWF)

Authors:
  • Food and Agriculture Organization of the United Nations (FAO) - Somalia

Abstract and Figures

The study characterized the status and trend of land cover transformation in Kirisia forest ecosystem between 1973 and 2015 using remote sensing and GIS. The dominant land cover types consisted of indigenous forest followed by shrub land and bush land. The findings showed a major increase in the built environment by 55.4% and an overall reduction in forest cover by 21.3%. Up to 83.9 km 2 of the original indigenous forest was lost between 1973 and 1986 due to severe fires. Thereafter, 23.7 km 2 of the remaining indigenous forest was lost between 1986 and 2000 mainly through charcoal burning, illegal timber logging and livestock forage harvesting. A slight recovery occurred between 2000 and 2015 with a 5% increase in indigenous forest cover mostly through natural succession by shrub land and bush land in the burnt forest areas especially following the 1998 El Nino period. The land cover change in the forest ecosystem was not exceptional in Kenya but mirrors similar changes that have been documented in other valued dry land watershed ecosystems in the country including the national water towers. The continued loss of forest cover is likely to affect the water recharge capacity in the watershed thereby creating severe water scarcity for the people in Mararal town as well as nearly 142,954 other individuals in the Kirisia region. Appropriate interventions are therefore needed to mitigate the negative land cover change in Ki-risia forest and restore its hydrological functions and water recharge capacity.
Content may be subject to copyright.
Advances in Remote Sensing, 2016, 5, 168-182
Published Online September 2016 in SciRes. http://www.scirp.org/journal/ars
http://dx.doi.org/10.4236/ars.2016.53014
How to cite this paper: Warinwa, F., Mwaura, F., Kiringe, J.W. and Ndubi, A.O. (2016) Land Cover Dynamics in the Kirisia
Forest Ecosystem, Samburu County, Kenya. Advances in Remote Sensing, 5, 168-182.
http://dx.doi.org/10.4236/ars.2016.53014
Land Cover Dynamics in the Kirisia Forest
Ecosystem, Samburu County, Kenya
Fiesta Warinwa1*, Francis Mwaura2#, John Warui Kiringe3, Antony Oduya Ndubi4
1African Wildlife Foundation (AWF), Nairobi, Kenya
2Department of Geography & Environmental Studies, University of Nairobi, Nairobi, Kenya
3Habitat Planners & Environmental Consultants Ltd, Nairobi, Kenya
4Regional Centre for Mapping of Resources for Development (RCMRD), SERVIR Eastern and Southern Africa
Project, Nairobi, Kenya
Received 19 May 2016; accepted 2 August 2016; published 5 August 2016
Copyright © 2016 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
The study characterized the status and trend of land cover transformation in Kirisia forest eco-
system between 1973 and 2015 using remote sensing and GIS. The dominant land cover types
consisted of indigenous forest followed by shrub land and bush land. The findings showed a major
increase in the built environment by 55.4% and an overall reduction in forest cover by 21.3%. Up
to 83.9 km2 of the original indigenous forest was lost between 1973 and 1986 due to severe fires.
Thereafter, 23.7 km2 of the remaining indigenous forest was lost between 1986 and 2000 mainly
through charcoal burning, illegal timber logging and livestock forage harvesting. A slight recovery
occurred between 2000 and 2015 with a 5% increase in indigenous forest cover mostly through
natural succession by shrub land and bush land in the burnt forest areas especially following the
1998 El Nino period. The land cover change in the forest ecosystem was not exceptional in Kenya
but mirrors similar changes that have been documented in other valued dry land watershed eco-
systems in the country including the national water towers. The continued loss of forest cover is
likely to affect the water recharge capacity in the watershed thereby creating severe water scarci-
ty for the people in Mararal town as well as nearly 142,954 other individuals in the Kirisia region.
Appropriate interventions are therefore needed to mitigate the negative land cover change in Ki-
risia forest and restore its hydrological functions and water recharge capacity.
Keywords
Kirisia Watershed Ecosystem, Forest Cover, Water Supply
*
This study was funded by the African Wildlife Foundation (AWF).
#Corresponding author.
F. Warinwa et al.
169
1. Introduction
Forest ecosystems have historically played a key role in supporting the livelihoods of many communities espe-
cially in developing countries. In Africa for instance, the importance of forests is epitomized by most hunt-
er-gather societies whose source of food and livelihood needs are largely obtained from animal and plant re-
sources in forests [1] [2]. Rural and urban societies also depend directly or indirectly on forests due to their high
reliance on natural resources [3]. Many communities in Africa still rely on forests for wild fruits, honey, con-
struction materials, herbal medicines and household income [2]-[4].
Kenya’s forest ecosystems are considered as valued assets because of their resource provisioning and ecosys-
tem services [5]-[7]. They also provide direct and indirect livelihood opportunities for society and contribute
significantly towards economic development [6]-[9]. Traditional economic valuation of forests has however
tended to concentrate on their consumptive benefits such as the extraction of timber products [6]-[8]. In return,
this consideration has encouraged agricultural and logging activities instead of the more sustainable non-con-
sumptive and indirect benefits thereby leading to widespread deforestation and huge losses of valued forest
ecosystem services [10] [11]. Watershed ecosystem services (WES) represents one of the most important roles
of forest ecosystems because such areas normally act as hydrologic powerhouses due to their ability to intercept
rainfall and supply water to urban and rural communities. Consequently, the Government of Kenya has recog-
nized the forest ecosystems in mountain areas as “Water Towers” whose management has recently been en-
trusted to the Kenya Water Towers Agency (KWTA) whose principal task is to oversee their management with a
view of sustaining social-economic development in the country.
Despite the high value of forests in Kenya, these areas have continued to suffer from wanton destruction
through unsustainable exploitation of their consumptive benefits thereby threatening their watershed ecosystem
services in a very serious way [6]-[10]. It was estimated that between the 1970s and 2010 alone, deforestation in
the country’s prime water towers amounted to over 500 km2 [12]. In Mt. Elgon, a 34% reduction in forest cover
was recorded between 1995 and 2006 alone while the Cherangani Hills recorded a reduction in forest cover from
465 to 240 km2 between 1973 and 2009 [13]. In the Mau escarpment which is a regional water tower in the
country, because it supplies water upto Sudan and Egypt, the forest cover declined from 4695 km2 to 4041 km2
between 1985 and 2010 which translated to an annual decline of about 8.7% [13] [14]. However, in the Aber-
dares Ranges which was another important watershed, the forest cover had been relatively stable and was esti-
mated at 2064 km2 in 1985 with only a minor decline to 2061 km2 in 2010 mostly through accidental fires [13].
The Aberdares forest is protected through a 400 km all-round electrified fence.
Land cover change in Kenya has also been recorded in a number of dry land water towers. For instance, in Mt.
Marsabit, the forest cover was approximately 240 km2 in 1985 and declined thereafter to about 132 km2 in 2010
which was mostly attributed to the rapid expansion of Marsabit town [13]. A study by [15] in the area recorded a
32% reduction in forest cover between 1973 and 2005 due to increased sedentarization of pastoral communities
around the forest leading to a huge increase (700%) in agricultural encroachment of the forest. Elsewhere, stu-
dies in the Taita hills within the Taita Taveta County have showed that although significant land use changes
have taken place, the forest cover increased significantly from 270 km2 to 414 km2 between 1985 and 2010 as a
result of agro forestry and introduction of forest plantations [13] [16] [17]. The Taita hills covering approx-
imately 1000 km2 constitute the northern section of the Eastern Arc Ecoregion and the only one in Kenya.
The deforestation of critical watershed ecosystems has been established to cause significant changes in the
magnitude and seasonality of water discharge in springs, streams and rivers [18]-[20]. Numerous studies have
demonstrated that substantial changes in hydrological regimes can arise due to land cover changes especially the
decline in forests due to their influence on rainfall interception, evapotranspiration, infiltration and surface ru-
noff [21]. Rampant deforestation in Kenya’s water towers has consequently been observed to cause massive re-
duction in water discharge in some valued springs, streams and rivers thereby creating serious water supply def-
icits for people, livestock and wildlife especially during the dry season [7] [13]. The consequences of this food
security and local livelihoods are enormous, far-reaching, long-lasting and are likely to affect the realization of
Vision 2030 as well as the global Sustainable Development Goals (SDGs) especially SDG-6 on water and sani-
tation which aims at ensuring availability and sustainable management of water and sanitation for all. It is
therefore imperative that forest cover change is monitored on a regular basis especially within the country’s
treasured watersheds.
Kirisia Forest is a key dryland water tower in a region of water scarcity. The forest is the life-line for numer-
F. Warinwa et al.
170
ous households, thousands of livestock and a wide range of wildlife populations because Samburu County is
largely semi-arid and water deficient. The forest ecosystem is therefore critical in the provision of valued wa-
tershed services. Unfortunately, the environmental integrity of the ecosystem has continued to be threatened by
all manner of anthropogenic activities such as charcoal burning and illegal timber logging [22]-[25]. At the same
time, Kirisia forest has not been adequately considered in the previous land cover change studies undertaken in
Kenya and yet it is a critical lifeline ecosystem especially with regard to water supply for the Mararal town. The
implementation of the Lamu Port-South Sudan-Ethiopia Transport (LAPPSET) corridor project is likely to at-
tract more people to Mararal thereby leading to higher water demand. In this regard, an understanding of the
land cover status and dynamics in Kirisia Forest is important since it has a direct impact on the hydrological ca-
pacity of the watershed and provision of water to the Mararal town and other settlements in Samburu County.
Digital change detection techniques using multi-temporal satellite imagery has emerged as a reliable and cost
effective method in the monitoring of spatio-temporal landcover dynamics in watershed ecosystems [26]-[28].
This approach is convenient because it exploits the regular data acquisition capacity of space technology through
the use of satellites for the detection, mapping and quantification of land cover change. The aim of this study
was to: 1) analyse and characterize the current status of land cover in the Kirisia forest ecosystem using remote
sensing and geographic information system (GIS); 2) analyse recent land cover changes in the watershed; 3)
compare the land cover change with other areas in Kenya. The two technical limitations experienced in the study
included the use of an inconsistent temporal interval in the digital imagery time series with a focus on 1976,
1986, 2000 and 2015. This was due to the lack of cloudy free images in some years. Secondly, field data valida-
tion was only based on the most recent satellite image (2015) because it was not possible to go back in time and
ground truth for the digital images in the previous years.
2. Study Area
Kirisia Forest also known as Leroghi is located in the northern section of Kenya within Samburu Central
Sub-County of Samburu Country (Figure 1). The forest is situated within the Leroghi plateau and lies on the
northern end of the Laikipia plateau in Northern Kenya [22]-[24]. It is one of the oldest state forest reserves in
Kenya having been gazetted in 1933 [22] [29]. The forest, which is located at an altitude of 2000 - 2200 m, was
initially covering approximately 920 km2 but has now reduced to less than 780 km2 [22]-[24] [30]. The Leroghi
region within which Kirisia forest is situated is largely semi-arid and dominated by ecological zones IV-VI with
a mean annual rainfall of around 551 mm [22] [23] [29]. Due to its higher elevation and rainfall, the forest
serves as an important water catchment area, with surface water from the forest emerging downstream in the
form of springs and ephemeral streams and laggas [29]. The northern sections of the forest in areas such as Por-
ror usually receive more rainfall at 575 mm compared to the central and southern regions around Mararal town
and Baawa area which receive an average of 563 and 552 mm, respectively. The north eastern section of the
forest can therefore be considered as the hydrological powerhouse for the forest ecosystem [12].
[31] described the floral composition of Kirisia forest and indicated that it is characterized by diverse vegeta-
tion associations with four woody species dominating the top canopy, namely, Olea europaea spp africana
(34%), Juniperus procera (25%), Podocarpus falcatus (26%) and Croton megalocarpus (15%). The species
dominating the middle canopy are P. falcatus (12% - 45%), Olea. europaea spp a fricana (21% - 28%), Junipe-
rus procera (20%), Teclea simplicifolia (13% - 15%) and Croton megalocarpus (12%). According to [31], the
disturbed and rocky areas of the forest are characterized by small sized trees and shrubs such as Euclea divino-
rum, Carissa edulis, Rhus natalensis and Croton dichogamus. The forest supports a wide range of fauna includ-
ing avian species [32]. According to [22]-[24], the forest is endowed with diverse wildlife species such as the
cape buffalo (Syncerus cafer), elephant (Loxondonta africana), common zebra (Equus burchelli), olive baboon
(Papio anubis), common warthog (Phacochoerus aethiops), bush-buck (Tragelaphus scriptus), lion (Panthera
leo), spotted hyena (Crocuta crocuta), Maasai giraffe (Giraffa camoleopardis tippelskirchi), eland (Taurotagus
oryx) and giant forest hog (Hylochoerus meinertzhageni).
The main inhabitants of the Kirisia Forest are the Samburu people who are predominantly semi-nomadic pas-
toralists who are closely related to the Maasai. Most of the communities living adjacent to the forest are settled
in 13 group ranches which were part of the 159 group ranches established in the Rift Valley region of Kenya
under the Kenya Livestock Development Project of 1968-1980 [33]. These group ranches were also established
in Kajiado, Narok, Laikipia, Baringo and West Pokot. A group ranch is a form of communal land tenure consisting
F. Warinwa et al.
171
Figure 1. Location of Kirisia forest and its watershed in Samburu county.
of a common land title with shares allocated to the senior members of the beneficiary families. In addition to the
group ranches, Kirisia forest is closely associated with Maralal town which is the administrative centre for
Samburu County. The town is situated within the forest reserve and has a cosmopolitan population comprising
of the Samburus and other ethnic communities including the Kikuyu, Meru, Somali and Turkana [34]. Most of
the local people in the Kirisia region are pastoralists although mixed agriculture-livestock livelihood practices
are common in the humid areas to the north such as Porror where wheat, maize and other crops are grown [23]
[24]. Kirisia Forest is central to all the various communities in the area because it sustains their socio-economic
aspirations by providing diverse goods and services including but not limited to dry season livestock grazing
areas and watering sites, construction materials, honey, herbal medicine and wood-fuel among others [22] [24]
[34]. The extraction of such goods is likely to significantly affect the land cover status of the forest thereby af-
fecting the long term delivery of ecosystem services.
3. Methods
Remote sensing and GIS techniques were used to analyze and characterize land cover change in Kirisia Forest
through various steps which included: 1) delineation of watershed ecosystem boundary; 2) satellite land cover
change analysis and ground truthing; 3) comparative analysis with other land cover studies. The watershed
boundary was delineated from medium resolution (30 × 30 m) Landsat images and ASTER Digital Elevation
Model (DEM) using the ArcGIS 10.3 software. The 30m spatial resolution Landsat images were considered to
be detailed enough for the purpose of land cover status and change analysis. The images were identified by
overlaying the delineated watershed boundary in the Landsat image grid with subsequent selection of the images
that covered the study area, namely P169R059, P168R059 and P168R060 as shown in Figure 2.
Twelve Landsat images were acquired and then processed by mosaicking them into a single image for each of
F. Warinwa et al.
172
Figure 2. False colour composite images for the Kirisia forest region.
the four years with less than 10% cloud cover, namely 1973, 1986, 2000 and 2015. Thereafter, colour composite
images were processed for land cover interpretation and analysis with the aid of ground-truth information. The
land cover ground truthing was undertaken through field inspection missions undertaken in October and No-
vember 2015. The ground truthing was conducted through four inspection transects, namely: 1) Maralal-Enga-
ta-Nanyukie transect (45 km) on 30th October 2015; 2) Mararal-Ngari-Baawa transect (31 km) on 31st October
2015; 3) Maralal-Opiroi transect (16 km) on 31st October 2015; 4) the Maralal-Ngonyeki transect (10 km) on
1st November 2015. Most of the inspection points in Transects 1 and 2 were located at the edge of Kirisia
Forest while those in Transects 3 and 4 were traversing through the forest in easterly and northerly orienta-
tions from Mararal town. Figure 3 shows the GPS points for the 42 observation points in the four inspection
transects.
The land cover inspections involved a drive-through along each of the four transects with observation stops at
different points based on landscape and land-cover change. The elevation in each observation point was record-
ed using a Garmin GPS unit. In addition, rapid appraisal of the dominant land cover type was undertaken and the
key woody plant species identified. The specimen of the species which could not be identified on site were
clipped and preserved in a plant press and later identified at the herbarium in the School of Biological Sciences
at University of Nairobi. The final image interpretation was undertaken using the ground truth information
which entailed the downloading of the field GPS points, overlaying them on the satellite images and then under-
taking preliminary image classification using ArcGIS 10.3. The polygons were digitized on the satellite images
around the overlaid GPS points for areas with homogeneous spectral reflectance from which the land cover
classes were generated. Once the classification around all the GPS points was completed, the classification was
then extrapolated to the other parts of the watershed. Thereafter, the total area under each land cover type was
calculated in square kilometers followed by land cover change detection for 1973, 1986, 2000 and 2015. All the
analysis was done using the IDRISI land cover change modeler which also employs cross-tabulation to produce
F. Warinwa et al.
173
(a)
(b)
F. Warinwa et al.
174
(c)
(d)
Figure 3. The route and GPS points for the four land cover ground truthing inspection transects. (a) Transect
1-Maralal-engata-nanyukie transect (45 km). (b) Transect 2-Mararal-Ngari-Baawa transect (31 km). (c) Transect
3-Maralal-Opiroi transect (16 km). (d) Transect 4-Maralal-Ngonyeki transect (10 km).
F. Warinwa et al.
175
land cover change maps and area statistics.
4. Results and Discussion
Land cover ground truth inspection in the northern section (transect 1) showed that the Lpartuk area was pre-
viously dominated by a dense Juniperus-Olea evergreen forest but this had now been converted into human set-
tlements and farmlands. A large plantation of Eucalyptus globulus covering approximately 3 km2 had also been
established in Porror area. The forest section near the Samburu air flight radar was found to be relatively intact
and dominated by Juniperus procera, Olea Africana, Olinis rocheatina, Clutia abyssinica, Trimeria grandifolia,
and Rhus natalensis. Some remnants of the sandalwood (Osyris lanceolata) were also recorded in that section.
In the Ngorika area, large sections of the forest were destroyed by wildfires in the 1980s and were yet to recover
partly because the area had been encroached by illegal squatters. The riparian area along the Nachuda stream
was dominated by Juniperus procera, Podocapus, Olea Africana and Vepris simpicifolia and was heavily uti-
lized by elephants.
The southern section of the forest in the Baawa area (transect 2) was dominated by Croton megalocarpus,
Acacia xanthopholea, Juniperus procera, Olea Africana, Vepris simplicifolia, Akakanthera schimperi and Euc-
lea divinorum. This part of the forest was characterized by intensive use by the locals through domestic water
abstraction, livestock grazing and the watering especially in the dry seasons. The area was also heavily used by
elephants and other wildlife especially common zebra (Equus burchelli boehmi), Thompson gazelle (Gazella
thomsonii) and impala (Aepyceros melampus).
The central sections of the forest especially to the east of Mararal town (transect 3) was the most degraded
probably because of its close proximity to Mararal town. Most of the area had been converted from the pre-
viously dense evergreen forest to evergreen Euclea divinorum shrub. Most of the giant Olea africana trees had
been chopped off to provide forage for the livestock especially in the dry season while the Juniperus procera
had been logged for commercial house construction and household fencing posts. Active and widespread char-
coal burning of Olea africana was recorded along the Lorok riparian zone which was also characterized by in-
tensive livestock grazing. The Ngonyeki section of the forest to the north-east of Mararal town (transect 4) was
found to be largely intact and dominated by Olea Africana, Juniperus procera, Croton megalocarpus, and Po-
docarpus sp trees as well as Rhus natalensis and Euclea divinorum shrubs. This part of the forest was also heav-
ily utilized by elephants.
Table 1 shows the land cover change statistics based on satellite imagery analyses for 1973, 1986, 2000 and
2015 whose land cover maps are shown in Figure 4. Figure 5 shows the integrated land cover change pattern
for the 1973-2015 period. The dominant land cover types during this 42-year period comprised natural or indi-
genous forest, followed by shrub land, bush land and built environment consisting of urban areas and other hu-
man settlements. The results showed a major increase in the built environment by about 55.4%, and a significant
decrease in forest cover by 21.3%. Upto 83.9 km2 of the original indigenous forest was lost between 1973 and
1986 due to severe fire in the 1980s. The forest fires are said to have occurred in 1984 and 1996 in protest fol-
lowing the eviction by the government of illegal squatters in the forest. Such severe fires are environmentally
very destructive because they destroy mature trees which have taken many years to grow in a semi-arid area and
also hamper forest regeneration by killing the saplings. They can also alter the composition of woody species
with more fire tolerant species becoming more dominant.
Thereafter, 23.7 km2 of the remaining indigenous forest was lost between 1986 and 2000 mainly through
charcoal burning, illegal timber logging and livestock forage harvesting. A slight recovery occurred between
2000 and 2015 with a 5% increase in indigenous forest cover mostly through natural succession by shrub land
and bush land in the burnt areas especially following the 1998 El Nino period. Between 1973 and 2015, Kirisia
forest experienced a huge expansion in grassland, shrub land and bush land mostly due to the ecological impacts
of the wildfires of 1984 and 1996 also coupled by the increased degradation of the forest as indicated by the re-
duction in natural forest cover. The prevalence of these land cover types is an indicator that human activities in
the forest as well as climate change are gradually transforming the ecosystem in a negative way.
The findings showed the conversion of some sections of natural forest into Eucalyptus plantation in the Porror
area. The ground truth information indicated that a similar plantation had been established in the Angata Nanyo-
kie area in the 1940’s. The satellite image analysis showed that the Porror Eucalyptus plantation expanded by
approximately 1.7 km2 between 1986 and 2000 and thereafter declined by 0.3 km2 in 2015 due to intentional
F. Warinwa et al.
176
Table 1. Landcover change statistics for the Kirisia Forest (1973-2015).
Land cover type Area in km2 Overall change
(1973-1986)
Overall
change
(1986-2000)
Overall
change
(2000-2015) % Change
1973 1986 2000 2015
Indigenous forest 430.6 346.7 322.9 338.8 −83.9 −23.7 15.8 −21.3
Plantation forest 0.0 1.6 3.2 2.9 −1 .6 1.7 −0.3 57.0
Open woodland 23.0 52.4 60.8 55.5 −29 .3 8.4 −5.3 58.6
Wooded shrub land 1.0 1.0 1.1 0.9 0.1 0.1 −0 .2 16.9
Closed shrub land 416.4 18.2 19.3 19.1 398.3 1.1 −0.2 95.4
Open shrub land 67.1 35.2 35.2 35.4 31.8 0.1 0.2 47.2
Wooded bush land 70.6 152.1 151.3 151.5 −81.5 −0.8 0.2 53.4
Wooded grassland 0.0 1.8 1.9 1.8 −1.8 0.2 −0.2 100.0
Open grassland 72.9 91.6 91.4 92.9 −18.7 −0.2 1.6 21.6
Closed wooded bush
land 29.9 209.9 218.6 209.9 −179.9 8.7 −8 .7 85.7
Closed bush land 339.9 451.7 454.9 455.8 −111.7 3.2 0.9 25.4
Open bush land 290.6 323.1 323.3 322.4 −32.5 0.3 −1.0 9.9
Scattered bush land 17.3 52.3 52.6 49.8 −35 .0 0.3 −2.9 65.3
Crop fields 0.0 19.5 20.2 19.4 −19.5 0.7 −0 .7 100.0
Water body (dams) 0.1 0.3 0.3 0.3 −0.1 0.0 0.02 60.4
Built up area 2.5 4.8 5.0 5.6 −2.3 0.2 0.6 55.4
harvesting by KFS which was eventually resisted by the local people.
Table 1 shows a massive build-up in human settlements within the watershed due to human population
growth and urbanization in Maralal and other towns such as Kisima and Suguta Marmar which have attracted
immigrants from other parts of Kenya. It was established that human population growth has significantly con-
tributed towards the land cover change in Kirisia forest. Most of the communities in Samburu County are em-
bracing sedentary lifestyles instead of nomadic pastoralism. This has seen most people constructing modern
permanent houses which consume a lot of wood thereby decimating indigenous tree species especially Juniperus
procera. The increased harvesting of subsistence and commercial timber as well as firewood gathering and il-
legal charcoal burning have also continued to degrade the forest ecosystem.
The findings in this study indicated a lower level of forest reduction at 21.3% compared to the estimates of
[35], who recorded a 46% reduction in the wider Samburu County for the 1976-2000 period, although his work
was focussed on the Mathew Ranges and Ndotto forest. The reduction in Kirisia forest cover was almost similar
to the decline in the Chyulu Hills where [12] estimated an 18.6% decline in natural forest mostly through the
negative impact of frequent wildfires. The 1984 and 1996 fires in Kirisia forest caused massive damage to the
ecosystem and the effects were still visible during this study. According to [36], Africa leads the world in terms
of the annual number of forest wildfires. In 2000 it was estimated that 1.75 millionkm2 of forest, woodlands,
and grasslands which constituted about 5.8% of Sub-Saharan Africa was lost through such fires. Most of the
fires were intentionally started in order to clear land for agricultural cultivation [36].
In Kenya, up to 40% of all the wildfires which occurred between 1990 and 2010 were associated with arson,
20% to negligence and carelessness and 40% due to unknown causes [37]. Unintentional fires in the country are
also associated with honey gatherers during the smoking of bees in forests. Apart from loss of biodiversity, for-
est fires have a significant negative hydrological impact because loss of forest cover reduces the natural recharge
capacity and increases flood hazards, surface erosion and siltation of rivers and water bodies. Previous studies
have confirmed that frequent forest fires can have significant impact on watershed hydrology. [37] reported a 66%
reduction in infiltration rate within a pine forest area in Arizona which initially resulted in an 800% increase in
stream flow from the burnt catchment immediately after the fire. This means that forest fires will tend to in-
crease the risk of flood hazards during the wet season and exacerbate the problem of water scarcity in the dry
F. Warinwa et al.
177
(a)
(b)
F. Warinwa et al.
178
(c)
(d)
Figure 4. Land cover change in Kirisia forest (1973-2015). (a) Kirisia forest cover in 1973. (b) Kirisia forest cov-
er in 1986. (c) Kirisia forest cover in 2000. (d) Kirisia forest cover in 2015.
F. Warinwa et al.
179
Figure 5. Integrated land cover change for the Kirisia forest watershed (1973-2015).
season due to the low recharge capacity induced by the loss of forest cover.
The rate of forest cover loss in Kirisia Forest was much lower than the 32% decline recorded by [15] in Mar-
sabit Forest between 1973 and 2005 during which upto 58.6 km2 of forest was lost. Both Marsabit and Kirisia
Forest are dryland water towers with very similar status in terms of their location within rapidly expanding ur-
ban centers which are both county headquarters. However, Marsabit forest is under more strict protection be-
cause of its designation as a national park under the Kenya Wildlife Service (KWS) and would therefore be ex-
pected to experience a lower rate of forest loss. The difference might be attributed to differences in the urban
population growth in the two towns. While Mararal town has an estimated population of 20,000, the current
population estimate for Marsabit town is about 30,000 people which translate to a much higher demand for a
wide range of forest resources. The lower rate of forest loss in Kirisia Forest can also be attributed to the con-
certed efforts by the Kirisia Community Forest Association (KCFA) which provides a critical platform for parti-
cipatory forest management (PFM) for the forest reserve although it is facing serious resistance by the people
including local politicians. On the overall, it is evident that rural communities are negatively impacting forest
ecosystems thereby jeopardizing their natural water supply which is more critical in comparison to other society
needs. This pattern is common in many other parts of Kenya [38]-[43].
5. Conclusions and Recommendations
The loss of forest cover in the Kirisia watershed ecosystem between 1973 and 2015 was enormous but quite
similar to the challenging situation in other valued watershed ecosystems around the country including the na-
tional water towers. The 21.3% reduction in forest cover within the watershed is unfortunate because it will
eventually affect the role of the watershed as a critical dryland water tower. The findings clearly showed that the
watershed ecosystem was experiencing negative forest cover dynamics despite the efforts by government and
remarkable grass root effort through the CFA to safeguard the forest. The main reason for the negative land
cover change in the watershed ecosystem is lack of public awareness and appreciation of the need to maintain a
good state of forest ecosystem health in order to continue enjoying the watershed ecosystem services. This fact
is usually taken for granted until it is too late. With declining forest cover, the hydrological functions of the wa-
F. Warinwa et al.
180
tershed ecosystem will be impaired leading to increased water scarcity especially in Mararal town and its envi-
rons. Maralal town is currently rated as among the fastest growing in Kenya with a growth rate of about 7% per
annum. The water demand in Mararal is likely to increase significantly as a result of the Lamu Port South Sudan
Ethiopia Transport (LAPSSET) corridor project and the on-going tarmacking of the Rumuruti-Mararal Highway
both of which will attract more investors into the town.
Although the major loss of forest cover in Kirisia Forest was attributed to the massive fires of 1980s and
1990s, substantial forest loss had continued to occur through illegal logging, charcoal burning, and harvesting of
livestock fodder. Consequently, a combination of strategies is needed to halt or reduce further loss. These in-
clude the following:
Initiating more meaningful collaboration between Samburu County Government and other key stakeholders
especially the Kirisia Forest CFA, Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), Kenya Wa-
ter Towers Agency (KWTA) and Water Resources Management Authority (WRMA) for more effective
conservation and restoration of the forest ecosystem. This effort should focus on creating awareness among
various forest users on the linkage between forest cover change and water supply so that the stakeholders can
appreciate and support forest conservation efforts in a more serious way.
Controlling the increasing problem of illegal logging and charcoal burning in the forest by enforcing the
Forest (Charcoal) Regulations (2009). The regulations are enacted by the government in order to enable the
KFS to regulate the production, transportation and marketing of forest products and ensure sustainable use of
forests. The strict enforcement of these regulations may reduce the rapid loss of valued species such as Olea
africana in the forest.
Regulating livestock grazing and harvesting of fodder in the forest by educating the local communities on
the need to reduce their livestock herds. Effective and negotiated forest use guidelines for livestock grazing
in the forest should also be put in place with a view of reducing livestock incursion into the forest including
the disturbance of key water sources.
Developing participatory fire prevention and control strategies especially for CFA members and educating
neighboring communities (and honey harvesters) on the environmental hazards associated with forest fires.
Supporting the implement the Kirisia Forest Management Plan which was developed in 2012 but has not
been effectively actualized.
Acknowledgements
The funding was provided by the African Wildlife Foundation (AWF) for which special appreciation goes to the
County Director Ms. Fiesta Warinwa for her support and great ideas. The remarkable technical support provided
by WRMA through David Mumo, Kimeu Musau, John Kinyua and Inoti Mburugu is appreciated. Many thanks
also for the support, advice and valuable information provided by the Samburu County Director of Environment,
Water and Natural resources Mr. Benson Lengalen and the Managing Director of SAWASCO Mr. Mark Lcha-
runi. The Vice-secretary of the Kirisia Forest Community Forest Association Mr. Wilson Lekaaso was the field
guide and liaison for the research team and his instrumental support is highly appreciated. Finally, many to Ms.
Tiffany Mwake of Habitat Planners for providing incredible support in terms of information gathering, docu-
mentation and manuscript typesetting in addition to running many other errands.
References
[1] Scudder, T. (1971) Gathering among African Woodland Savannah Cultivators: A Case Study of the Gwembe Tonga.
Manchester University Press for the University of Zambia, Institute for African Studies, Lusaka.
[2] Food and Agriculture Organization (FAO) (2011) Forests for Improved Nutrition and Food Security. Food and Agri-
culture Organization of the United Nations. Rome.
[3] African Development Bank (AfDB) (2015) Payment for Environmental Services; a Promising Tool for Natural Re-
sources Management in Africa. Environment and Climate Change Department (ONEC), African Development Bank,
Abidjan.
[4] Maghembe, J.A., Kwesiga, F., Ngulube, M., Prins, H. and Malaya, F.M. (1994) Domestication Potential of Indigenous
Fruit Trees of the Miombo Woodlands of Southern Africa. In: Leaky, R.R.B. and Newman, A.C., Eds., Tropical Trees:
Potential for Domestication and Rebuilding of Forest Resources, Her Majesty’s Stationery Office, London.
[5] Ministry of Environment and Natural Resources (MENR) (1994) Kenya Forestry Master Plan. Nairobi, Kenya.
F. Warinwa et al.
181
[6] Wass, P. (1995) Kenya’s Indigenous Forests: Status, Management and Conservation. Gland.
[7] National Environment Management Authority (NEMA) (2013) Mau Complex at a Glance. NEMA, Nairobi.
[8] Mogaka, H.R. (2000) Economic Analysis of Forest Resource Values to Local Communities in Kenya: Comparative
Study Cases of Kakamega and Ntugi-Kijege Reserves. PhD Thesis, University of Strathclyde, Glasgow.
[9] Nkako, F.M., Lambrechts, C., Gachanja, M. and Woodley, B. (2005) Mau Forest Status Report. UNEP, Nairobi.
[10] Government of Kenya (GoK) (2009) Rehabilitation of the Mau Forest Ecosystem. A Project Concept Prepared by the
Interim Coordinating Secretariat, Office of the Prime Minister, on Behalf of the Government of Kenya.
[11] Kenya Forest Service (KFS) (2011) Sururu Participatory Forest Management Plan: 2011-2015.
[12] Kiringe, J.W., Mwaura, F. and Kimeu, M.M. (2015) Watershed Ecosystem Services and Water Management Tools” for
the Kirisia Forest Ecosystem in the Samburu Landscape. Report to the African Wildlife Foundation (AWF), Nairobi.
[13] GoK (2013) Analysis of Drivers and Underlying Causes of Forest Cover Change in the Various Forest Types of Kenya.
Ministry of Forestry and Wildlife, Nairobi.
[14] Ayuyo, I.O. and Sweta, L. (2014) Land Cover and Land Use Mapping and Change Detection of Mau Complex in
Kenya Using Geospatial Technology. International Journal of Science and Research (IJSR), 3, 767-778.
[15] Oroda, A.S.K. (2011) The Impact of Increased Population and Sedentarization of the Pastoral Communities on the
Land Cover and the Resources of Mount Marsabit Forest and the Surrounding Lands. Master of Environmental
Sciences Thesis, Kenyatta University, Nairobi.
[16] Bytebier, B. (2001) Taita Hills Biodiversity Project Report. National Museums of Kenya, Nairobi.
[17] Pellikka, P.B., Clark, P., Hurskainen, A., Keskinen, M., Lanne, K., Masalin, P., Nyman-Ghezelbash, P. and Sirviö, T.
(2004) Land Use Change Monitoring Applying Geographic Information Systems in the Taita Hills, SE-Kenya. Pro-
ceedings of the 5th African Association of Remote Sensing of Environment Conference, Nairobi, 17-22 October 2004.
[18] Karanja, A., China, S.S. and Kundu, P.M. (1986) The Influence of Land Use on the Njoro River Catchment between
1975 and 1985. Soil and Water Conservation in Kenya, University of Nairobi, Nairobi, Kenya.
[19] Donner, D.S. (Ed) (2004) Land Use, Land Cover, and Climate Change across the Mississippi Basin: Impacts on Se-
lected Land and Water Resources. American Geophysical Union, 249-262.
[20] Mustafa Y.M., Amin, M.S.M., Lee, T.S. and Shariff, A.R.M. (2005) Evaluation of Land Development Impact on a
Tropical Watershed Hydrology Using Remote Sensing and GIS. Journal of Spatial Hydrology, 5, 16-30.
[21] Marloes, L.M. (2009) Understanding Hydrological Processes in an Ungauged Catchment in Sub-Saharan Africa. PhD
Thesis, UNESCO-IHE and Delft University of Technology, Delft.
[22] Watai, M.K. and Gachathi, F. (2003) Conservation for Sustainable Utilization of Biodiversity through Enhanced
Access and Benefit Sharing with Forest Adjacent Communities in Kirisia Forest Samburu District. Unpublished Report,
KFS, Nairobi.
[23] Hitimana, J., Kiyiapi, J., Kisioh, H., Warinwa, F., Lenaiyasa, P., Kibugi, P., Mayienda, R. and Sumba, D. (2005) Link-
ing Socio-Economic Significance and Conservation for Kirisia Forest, Samburu, Kenya. Technical Paper to African
Wildlife Foundation (AWF), Nairobi.
[24] Anne, P. (2009) Preliminary Ecological Survey of Kirisia Forest Reserve, Samburu District. Report to Laikipia Wild-
life Forum for Conservation Enterprise Development Program.
[25] Powys, A. (2009) A Preliminary Ecological Survey of Kirisia Forest Reserve, Samburu District for Conservation En-
terprise Development Program. Unpublished Report, AWF, Nairobi.
[26] Czajkowski, K. and Lawrence, P.L. (2013) GIS and Remote Sensing Applications for Watershed Planning in the
Maumee River Basin, Ohio. Springer Science + Business Media, Dordrecht.
[27] Butt, A., Shabbir, R., Ahmad, S.S. and Aziz, N.N. (2015) Land Use Change Mapping and Analysis Using Remote
Sensing and GIS: A Case Study of Simly Watershed, Islamabad, Pakistan. The Egyptian Journal of Remote Sensing
and Space Science, 18, 251-259. http://dx.doi.org/10.1016/j.ejrs.2015.07.003
[28] Rawat, J.S. and Kumar, M. (2015) Monitoring Land Use/Cover Change Using Remote Sensing and GIS Techniques: A
Case Study of Hawalbagh Block, District Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and
Space Science, 18, 77-84. http://dx.doi.org/10.1016/j.ejrs.2015.02.002
[29] Nyaligu, M.O. (2013) Water Resources Inventory and Assessment, Kirisia Forest National Reserve. Final Report to
African Wildlife Foundation (AWF), Nairobi.
[30] Jaetzold, R. and Schmidt, H. (1983) Farm Management Handbook Vol. II—Natural Conditions and Farm Management
Information, Part B Central Kenya (Rift Valley and Central Provinces). Ministry of Agriculture, Kenya, in Cooperation
with the German Agricultural Team (GAT) of the GTZ, Nairobi.
F. Warinwa et al.
182
[31] Beentje, H.J. (1990) The Forests of Kenya. Mitteilungen aus dem Institut für 14. Allgemeine Botanik in Hamburg, 23a,
265-286.
[32] Evans, M.I. and Fishpool, L.D.C. (2001) Important Bird Areas in Africa and associated Islands: Priority Sites for Con-
servation. Birdlife International, Pisces Publications, Cambridge.
[33] International Bank for Reconstruction and Development (IBRD) (1985) Kenya Second Livestock Development Project.
Credit 477-KE Completion Report.
[34] KFS (2012) Leroghi/Kirisia Forest Management Plan (2012-2016). KFS, Nairobi.
[35] Mwaura, F. (2006) Assessment of Natural Resource Utilization in Samburu and Laikipia with Special Emphasis on
Namunyak, Kalama and Naibunga Conservancies. Report for the AU-IBAR Dryland Livestock Wildlife Environment
Interface Project (DLWEIP-KENYA), African Conservation Centre (ACC), Nairobi.
[36] FAO (2013) A Fire Baseline for Tanzania. Sustainable Forest Management in a Changing Climate. FAO-Finland Fore-
stry Programme, Tanzania.
[37] Campbell, R.E., Baker, M.B. and Folliott, P.F. (1977) Wildfire Effects on a Ponderosa Pine Ecosystem: An Arizona
Case Study”. USDA Forest Service Papers, RM-191. Rocky Mountain Forest and Range Experimental Station, Fort
Collins, Colorado.
[38] O’Keefe, P., Raskin, P. and Bernow, S. (Eds) (1984) Energy and Development in Kenya: Opportunities and Con-
straints, Energy, Environment and Development in Africa. The Beijer Institute and Scandinavian Institute.
[39] Musoga, H. (1988) The Rural Energy Problem: A Case Study of Food Fuel in Shiswa Sub-Location, Kakamega Dis-
trict. MA Thesis, University of Nairobi, Nairobi.
[40] Mugo, F.W. (1989) Wood Fuel Demand and Supply in a Rural Set-Up: A Case Study of Naitiri Sub-Location, Bungo-
ma District. MA Thesis, University of Nairobi, Nairobi.
[41] Ogolla, B.D. and Mugabe, J. (1996) Land Tenure Systems and Natural Resources Management. In: Juma, C. and Oj-
wang, J.B., Eds., Land We Trust: Environmental, Private Property and Constitutional Change, Initiatives Publishers,
Nairobi.
[42] Mugabe, J., Njeri, M. and Mukii, D. (1998) Biodiversity Management in Kenya. In: Mugabe, J. and Norman, C., Eds.,
Managing Biodiversity: National Systems of Conservation and Innovation in Africa, African Centre for Technology
Studies (ACTS) Press, Nairobi.
[43] Lambrechts, C., Woodley, B. and Gachanja, M. (2005) Aerial Survey of the Threats to Leroghi (Kirisia) Forest Re-
serve. Nairobi.
Submit or recommend next manuscript to SCIRP and we will provide best service for you:
Accepting pre-submission inquiries through Email, Facebook, LinkedIn, Twitter, etc.
A wide selection of journals (inclusive of 9 subjects, more than 200 journals)
Providing 24-hour high-quality service
User-friendly online submission system
Fair and swift peer-review system
Efficient typesetting and proofreading procedure
Display of the result of downloads and visits, as well as the number of cited articles
Maximum dissemination of your research work
Submit your manuscript at: http://papersubmission.scirp.org/
Article
Full-text available
Understanding about land cover and land use (LCLU) changes, as well as the associated impacts on ecosystem service values (ESV) is extremely important in the management of coastal ecosystems globally. Thus, this study assessed temporal LCLU changes, the underlying socioeconomic drivers and dynamics of ESV in the coastal zone of Tanzania. The LCLU data for 2000 and 2010 were from the Globe Land 30 mapping products at 30-meter spatial resolution developed by National Geomatics Center of China, while 2016 images were produced from Landsat 8. Classification of images was done from Landsat TM/ETM+ for 2000, 2010, and 2016 years complemented with MODIS and Normalized Difference Vegetation Index time series, and Chinese HJ imagery. LCLU categories and ecosystem service coefficients were used to compute ESV on each LCLU categories. Between 2000 and 2016, farmland, shrub land, waterbody, and artificial surface expanded while forest, grazing land, and wetlands declined. The ESV increased on farmland, shrub land, and waterbody, while the decline was found on forest, grazing land, and wetlands. The ESV and the total population ratios declined from 80.4,63.8,and80.4, 63.8, and 46.0 million in 2000, 2010, and 2016, respectively. Perfect positive correlation was on LCLU change and ESV, population and households in crop farming, livestock keeping, and bioenergy use. Population pressure and socioeconomic activities have amplified the degradation of the coastal ecosystems. If not abetted, there is a danger of further impairments on these ecosystems. We advise to regulate population and socioeconomic activities to avoid more negative impacts of coastal LCLU change.
Article
Full-text available
Evaluation of watersheds and development of a management strategy require accurate measurement of the past and present land cover/land use parameters as changes observed in these parameters determine the hydrological and ecological processes taking place in a watershed. This study applied supervised classification-maximum likelihood algorithm in ERDAS imagine to detect land cover/land use changes observed in Simly watershed, Pakistan using multispectral satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 respectively. The watershed was classified into five major land cover/use classes viz. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water. Resultant land cover/land use and overlay maps generated in ArcGIS 10 indicated a significant shift from Vegetation and Water cover to Agriculture, Bare soil/rock and Settlements cover, which shrank by 38.2% and 74.3% respectively. These land cover/use transformations posed a serious threat to watershed resources. Hence, proper management of the watershed is required or else these resources will soon be lost and no longer be able to play their role in socioeconomic development of the area.
Article
Full-text available
Digital change detection techniques by using multi-temporal satellite imagery helps in understanding landscape dynamics. The present study illustrates the spatio-temporal dynamics of land use/cover of Hawalbagh block of district Almora, Uttarakhand, India. Landsat satellite imageries of two different time periods, i.e., Landsat Thematic Mapper (TM) of 1990 and 2010 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site and quantify the changes in the Hawalbagh block from 1990 to 2010 over a period of 20 years. Supervised classification methodology has been employed using maximum likelihood technique in ERDAS 9.3 Software. The images of the study area were categorized into five different classes namely vegetation, agriculture, barren, built-up and water body. The results indicate that during the last two decades, vegetation and built-up land have been increased by 3.51% (9.39 km2) and 3.55% (9.48 km2) while agriculture, barren land and water body have decreased by 1.52% (4.06 km2), 5.46% (14.59 km2) and 0.08% (0.22 km2), respectively. The paper highlights the importance of digital change detection techniques for nature and location of change of the Hawalbagh block.
Article
Full-text available
The Mississippi Basin is the third largest drainage basin in the world and is home to one of the most productive agricultural regions on Earth. Here we discuss how land use/land cover change and climatic variability may be affecting some key environmental processes across the Mississippi and how these, in turn, affect the flow of selected ecosystem goods and services in the region. Specifically, we consider the recent history of land use/land cover change, crop yields, basin river flow and hydrology, and large-scale water quality in the Mississippi Basin. We find that agricultural activities may have had a profound influence on the basin and may have shifted the flow of many ecosystem goods and services into agricultural commodities, at the expense of altering many of the important biogeochemical linkages between atmosphere, land, and water.
Article
Full-text available
Understanding how the land use change influence the river basin hydrology will enable planners to formulate policies to minimize the undesirable effects of future land use changes. Land cover changes increase impervious ground surfaces, decrease infiltration rate and increase runoff rate, hence causing low base flow during the dry seasons. Efficient tools such as satellite remote sensing and Geographic Information System (GIS) are currently being used to manage the limited water resources. The need for spatial and temporal land-cover change detection at a larger scale makes satellite imagery the most cost effective, efficient and reliable source of data. The ability of GIS makes it an important and efficient tool for spatial hydrologic modeling. In this study, Satellite data and GIS were integrated with a spatial hydrological model to evaluate the impacts of land development in the Upper Bernam River Basin of Malaysia. HEC-1 (Hydrologic Engineering Center) model was calibrated and validated using actual flow data from the outlet of the watershed. The model performance was checked by means of four criteria viz., mean absolute error (MAE), root mean square error (RMSE), Theil's coefficient (U) and coefficient of determination (R 2 ) obtaining values of 0.14, 0.18, 0.097, and 0.86, respectively. From the hydrographs, it was found that the change in peak flow between the years 1989 and 1993 was 28% while it was 11% between the years 1993 -1995. The reduction of the time to peak was 7% for the same years. The model can be run for any future land development plans to investigate the hydrological impacts in order to avoid the shortage of irrigation water and mitigate the risk of floods occurrence.
Chapter
The Master Plan proposes urgent studies on new patterns for forestry administration in Kenya which would put an end to deforestation and improve the management of government-controlled indigenous forests and forest plantations. A development program for farm forestry is based on biomass surveys that revealed an increasing trend in trees on farms. Somewhat unexpectedly, but largely because of the favorable development in farm forestry, there was an overall positive balance in woody biomass in the country as a whole. Drylands cover 88% of the land area in Kenya and contain more than twice as much wood as all closed-canopy forests combined. They have to be managed by taking into account their prominent role in forest product supplies and their regional variation. A reform of forest policy and legislation is the most crucial immediate step in the further development of forestry institutions in Kenya. The new policy will facilitate the separation between a government forest authority and various forest management organizations, if this becomes an accepted development goal.
Chapter
The Maumee River watershed is the largest drainage basin that discharges into the Great Lakes. Although the watershed is largely a rural landscape, several major urban-industrial cities, including Fort Wayne and Toledo are located along the river. Many water quality concerns are present, especially non-point rural runoff that contributes significant amounts of sediment into the Maumee River. There is an important need to collect, organize and assess the available information on the watershed conditions and to better determine the status of the changes with land uses, crop rotation, and implementation of conservation tillage practices within this watershed. A partnership between the University of Toledo and US Department of Agriculture NRCS lead to several GIS and remote sensing products including annual land cover and crop rotations via remote sensing techniques, establishment of a Maumee Watershed Project Area GIS database, and providing educational and informational outreach with other project partners, resource managers, and the general public.
Article
Abstract A wildfire of variable severity swept through,717 acres (290 ha) of ponderosa,pine forest in north-central Arizona in May,1972. Where the fire was intense it killed 90% of the small trees and 50% of the sawtimber, burned 2.6 in (6.5 cm) of forest floor to the mineral soil, and induced a water-repellent layer in the sandier soils. The re- duced infiltration rates, which greatly increased water yield from severely burned areas during unusually heavy fall rains, caused soils to erode and removed,some,nutrients which had been rnineral- ized by the fire. Water yields have declined each year toward prefire levels. Soluble nutrients leached into the surface soil during fall rains were,subsequently,removed,by a record snowmelt. Succes- sional changes provided up to 1,650 lblacre (1,850 kglha) of herbage production compared,to about 515 lblacre (577 kglha) in unburned forest. USDA Forest Service Research Paper RM-191 Wildfire Effects on a Ponderosa Pine