Modelling land use change across elevation gradients in district Swat, Pakistan
Transcript of Modelling land use change across elevation gradients in district Swat, Pakistan
ORIGINAL ARTICLE
Modelling land use change across elevation gradients in districtSwat, Pakistan
Muhammad Qasim • Klaus Hubacek •
Mette Termansen • Luuk Fleskens
Received: 25 May 2011 / Accepted: 18 December 2012 / Published online: 11 January 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract District Swat is part of the high mountain
Hindu-Kush Himalayan region of Pakistan. Documentation
and analysis of land use change in this region is chal-
lenging due to very disparate accounts of the state of forest
resources and limited accessible data. Such analysis is,
however, important due to concerns over the degradation of
forest land leading to deterioration of the protection of
water catchments and exposure of highly erodible soils.
Furthermore, the area is identified as hotspot for biodi-
versity loss. The aim of this paper is to identify geophysical
and geographical factors related to land use change and
model how these relationships vary across the district. For
three selected zones across the elevation gradient of the
district, we analyse land use change by studying land use
maps for the years 1968, 1990 and 2007. In the high-alti-
tude zone, the forest area decreased by 30.5 %, a third of
which was caused by agricultural expansion. In the mid-
elevation zone, agriculture expanded by 70.3 % and forests
decreased by 49.7 %. In the lower altitudes, agriculture
expansion was 129.9 % consuming 31.7 % of the forest
area over the forty-year time period. Annual deforestation
rates observed were 0.80, 1.28 and 1.86 % in high, mid and
low altitudes, respectively. In the high-altitude ecosystems,
accessibility (distance to nearest road and city) had no
significant role in agriculture expansion; rather land use
change appears significantly related to geophysical factors
such as slope, aspect and altitude. In the low-elevation
zone, accessibility was the factor showing the closest
association with agriculture expansion and abandonment.
The analysis illustrates that land use change processes vary
quite considerably between different altitudinal and vege-
tation cover zones of the same district and that environ-
mental constraints and stage of economic development
provide important contextual information.
Keywords Multiple logistic regression � Remote sensing �Spatial analysis � GIS � Land use change � Deforestation �Agricultural expansion � Swat � Pakistan
Introduction
Land use change, especially deforestation and agricultural
expansion in developing countries have lead to a number
of environmental issues such as increasing greenhouse
gas emissions, biodiversity loss, soil degradation, and a
decreasing supply of forestry products (Turner and Meyer
1994; IPCC 2000; Lambin et al. 2001; Olff and Ritchie
2002; Fahrig 2003). The driving forces of land use change
are many and complex including various combinations of
geophysical, biophysical, technological and socioeconomic
factors (Holden and Sankhayan 1998; Rao and Pant 2001;
Semwal et al. 2004; Serneels and Lambin 2001; Braimoh
and Onishi 2007; Bawa et al. 2007; Zheng et al. 1997). To
analyse causes and impacts of land use, spatial modelling
has increasingly become important. Modelling land use
change allows a quantification of past land use change,
helps understand and challenge existing theories and
M. Qasim (&)
Abdul Wali Khan University, Mardan, Pakistan
e-mail: [email protected]
K. Hubacek
University of Maryland, College Park, USA
M. Termansen
Aarhus University, Aarhus, Denmark
L. Fleskens
University of Leeds, Leeds, UK
123
Reg Environ Change (2013) 13:567–581
DOI 10.1007/s10113-012-0395-1
beliefs about the determinants and drivers of land use
change, and provides a framework for estimating trends of
land use change into the future (Lambin 1997; Kant 2000;
Serneels and Lambin 2001; Braimoh and Onishi 2007;
Gobin et al. 2002).
Modelling land use change is usually meant to answer
questions such as, where are land use changes taking place
and at what rate are land cover changes likely to progress?
Environmental planners and managers are not only inter-
ested in the rates and impacts of land use change, but also
the location. For example, forest areas with the highest
probability of clearing or degradation in the near future
should receive priority for preventive action. It is increas-
ingly being recognized that accounting for spatial variation
is essential for understanding varying land use processes
(Nagendra et al. 2004; Peter et al. 2008). With the devel-
opment of improved data handling capability and increasing
access to spatial data, such analysis is now feasible even in
relatively undeveloped areas. Spatially explicit statistical
modelling has been shown to be an effective approach
whereby different types of land cover are classified from
remotely sensed data, and their spatial occurrence is cor-
related with location attributes using multivariate statistics
(Zheng et al. 1997; Angelsen 1999; Osborne et al. 2001;
Agarwal et al. 2002; Sankhayan et al. 2003).
Critics of spatial modelling have mentioned the danger
of improper interpretation and redundancy (Tischendorf
2001), lack of fit to the environmental outcome of interest,
that is, species-based observations (Lindenmayer et al.
2002) or ecological processes (Vos et al. 2001) and the lack
of treatment of habitat quality (Olsson et al. 2000). Fur-
thermore, spatial modelling approaches, with few excep-
tions (e.g. Valbuena et al. 2010), tend to place emphasis on
geophysical factors related to land use change processes,
ignoring socioeconomic and institutional factors and pro-
cesses. However, the methodology has been widely used,
and authors have also suggested that spatial modelling is a
key methodology in quantifying landscape pattern and in
providing a means of monitoring extent, rate and pattern of
change (Jones et al. 2001; Lindenmayer et al. 2002).
The driving forces of land use change are complex and
change over time. Factors range from droughts to climatic
changes (Olff and Ritchie 2002; Gorsevski et al. 2006;
Chowdhury 2006; Yang 1999) and from socioeconomic to
institutional factors (Mertens and Lambin 2000; Rao and
Pant 2001; Semwal et al. 2004; Bawa et al. 2007).Through
statistical and GIS analysis, researchers have found strong
relationships between deforestation and spatially dependent
factors such has neighbourhood characteristics, road
accessibility and proximity to residential areas (Ludeke
et al. 1990; Sader and Joyce 1988; Osborne et al. 2001).
These results illustrate that it is essential to include spatial
dependence in the analysis of land use change but also
that ‘simple’ studies with limited information can achieve
robust results that are helpful for the identification of areas
most susceptible to deforestation pressure.
Deforestation is one of the most important and most
intensively studied land use change processes. However,
there are still many regions with a limited number of
studies, among them the Hindu Kush Himalayan (HKH)
region of Pakistan (Ahmad and Mahmood 1998; Haenusler
et al. 2000). The forest area in Pakistan is relatively small.
According to FAO estimates, only 2.5 % of the country’s
area was forested in 2005. District Swat, where this study
was conducted, is part of the HKH region of Pakistan.
Qasim et al. (2011) observed that 70.9, 49.7, 30.5 % of the
forests disappeared in selected low-, mid- and high-eleva-
tion zones of the district between 1968 and 2007. In the
same study, the authors observed tremendous agriculture
expansion on sloping land with likely catalytic effects on
erosion and land degradation processes.
So far, very few attempts have been made to charac-
terize the land use change processes in the HKH region to
gain a better understanding of the extent to which land use
conversion processes are happening and how they vary
across environmental and anthropogenic gradients. In this
paper, we develop a logistic model to estimate the proba-
bility of land use change using spatially explicit multiple
regression analysis. We focus on geophysical, infrastruc-
tural and spatial factors such as slope, aspect, distance to
main roads, distance to built-up areas, distance to water
sources and neighbourhood interactions of different land
cover types to understand and explore the relationships
amongst these variables and major land use changes in
three distinct altitudinal zones of district Swat. These fac-
tors have been found to be important in mountain regions
as they significantly affect the suitability of land for dif-
ferent uses (Kammerbauer and Ardon 1999; Serneels and
Lambin 2001). The study focuses on the four major land
conversion types in district Swat, that is,. expansion and
contraction of agriculture and forest areas.
Methods
Profile of the study area
The study was carried out in district Swat, a mountainous
part of Khyber Pakhtunkhwa province of Pakistan that
consists of multiple valleys with scrub and/or coniferous
forests on the upper slopes and alpine pastures on the ridges.
The district is located between 34�3000000–35�5000000N and
72�0500000–72�5000000E, with an altitudinal range from 500
to 6,500 m above sea level. With a surface area of
5,037 km2 and a total population of 1.25 million (according
to the most recent 1998 Census, GoP 1999), the average
568 M. Qasim et al.
123
population density of district Swat is 248 people/km2 (this is
similar to densely populated countries in Europe).
The population growth rate is 3.37 % and average
household size is 8.8 persons. The rural population con-
stitutes 86.17 % of the total population of the district (GoP
1999). The population of Swat is comprised of various
castes and ethnic groups. Swat is mainly inhabited by
Yousafzai Pathans, Mians, Kohistanis, Gujars and Pira-
chas. Pashto speaking groups (Yousafzai Pathans, Mians
and Pirachas) live in the plains, while Gujars and Kohi-
stanis inhabit the mountainous areas in the north. The
population is almost entirely Muslim (99.67 %).
Agriculture is the main source of income for the
majority of the population. According to the 1998 census
(GoP 1999), 50.11 % of the working population is engaged
in agriculture, forestry, hunting and fishing, 13.75 % are
engaged in community, social and personal services, fol-
lowed by 11.9 % working in wholesale and retail trade and
restaurants and hotels. The male literacy rate is 43.16 %,
whereas the female literacy rate is 13.45 %. However,
within the district, the literacy rate is much lower in rural
areas than urban areas (GoP 1999).
For the purpose of this study, Swat was divided into
three distinct zones based on elevation, which also cor-
respond to the three different broad vegetation cover
types. The high-elevation zone (zone A, dark shade,
Fig. 1) extends from Fathipur Tehsil to the northern
boundary of Swat. The area is dominated by coniferous
forests and alpine pastures. Elevation extends above
6,000 m in some places. The area has lower population
density and less developed infrastructure compared to the
two other regions. The mid-elevation zone (zone B, grey
shaded in Fig. 1) extends from north of Mingora to
Fathipur Tehsil. The vegetation is mainly composed of
agricultural crops and some pine forest, and elevation
ranges from 1,000 to 2,000 m. The area has a few large
settlements. The low-elevation zone (zone C) starts from
the southern boundary of the district and extends roughly
up to Mingora and nearby lowlands on the east and west
side of river Swat. The area is densely populated and has
a well-developed infrastructure. The major vegetation
cover is agriculture and scrub forest. The elevation in this
area ranges between 500 to 1,000 m (zone C, light shade,
Fig. 1).
Modelling approach
Land use change is modelled as the occurrence of an event,
dependent on multiple explanatory factors, xi. A logistic
regression model is a model of odds ratios, that is,. the
probability of an event occurring in relation to the proba-
bility of the event not occurring. Mathematically, the
logistic model is written as:
lnPðxÞ
1� PðxÞ
� �¼ aþ
Xbixi ð1Þ
Fig. 1 District Swat on Pakistan’s map
Modelling land use change across elevation 569
123
Specifying the probabilities of the event as:
PðxÞ ¼ 1
1þ e�ðaþP
bixiÞð2Þ
Model parameters a and b can directly be interpreted as
changes in the odds, that is, the predicted change in odds
ratio for a unit change in the explanatory variable can be
computed as the exponential of the estimated parameter
values (eb). The parameter values are therefore a measure
of how much more likely or unlikely land use change is for
a unit change in the values of the independent variables
(Hosmer and Lemeshow 1989).
Aspect, slope, distance to roads, distance to built-up
areas, distance to water sources and neighbourhood of
different land cover types have been used as explanatory
variables. These variables represent important geophysical
and geographical factors that are hypothesized to have
influence on the observed land use change, as they are
likely to affect land use decisions (Fu et al. 2004; Keese
et al. 2007; Lorena and Lambin 2009; Sierra and Russman
2006; Lopez and Sierra 2010). Table 1 highlights these
variables in detail.
Data and model validation
The data for the analysis of land use change is derived from
a time series of land use maps based on old aerial photo-
graphs (1968) and high-resolution satellite images (1990,
2007). ArcGIS was used for developing digital land use
and cover maps, categorized into 5 land cover types: for-
estland, agricultural land, rangeland, settlements and area
covered by permanent or perennial water bodies. Snow
covered areas were included as an additional land cover
type, but it was only relevant for zone A. The old aerial
photographs were scanned and mosaic files were created
for each zone, these were then geo-rectified using ArcMap.
Land use and cover maps were developed using the geo-
rectified aerial photographs and the satellite images via the
polygon formation technique in ArcMap, for each of the
three zones and for each year (1968, 1990 and 2007). Road
network maps were also created manually from the same
satellite maps and from the maps provided by ‘the Pakistan
Wetlands Programme Islamabad’.
The data record starts with the baseline of October 1968,
which forms a natural starting point given that Swat State
(then Princely State) was merged with Pakistan in 1969.
The next data point selected was 1990, which allowed to
establish conditions after a transition period of approxi-
mately two decades during which important institutional
changes took place and several development projects were
carried out in the area; for example, the Rural Development
Project (RDP), the Project for Horticulture Promotion
(PHP) and the Kalam Integrated Development Project
(KIDP). These projects mostly focussed on community
development, agriculture and road infrastructure in the
province of Khyber Pakhtunkhwa in general and in district
Swat in particular. Initiated in the early 1970s and
continuing until the mid-1990s, these projects were
instrumental in agricultural and wider rural development in
the area, and thus constituted an important additional set of
drivers of land use change. The last data point is 2007,
which represented the latest available data at the start of
this research. In total, the data covers a period of about four
decades. Hereafter, the time period between 1968 and 1990
Table 1 Explanation and importance of the explanatory variables
Explanatory
variable
Definition Importance
Aspect Slope azimuth Solar insulation, evapotranspiration, flora and fauna distribution
Slope Change in elevation divided by horizontal
distance
Erosion, run-off rate, vegetation, geomorphology, soil water content,
land capability class
Distance to main
roads
Typically smoothened or asphalted routes
between places for vehicular traffic
Accessibility, easy transportation, economic and industrial
development, etc
Distance to
secondary roads
Distance to urban
areas
Euclidean distance to any constructed area/
settlements
Areas close to settlements are directly in use for different human
activities and thus are more subject to change
Distance to
agricultural land
Euclidean distances to boundaries of agricultural
land, forest or rangelands
Neighbourhood dependence impacting protection and expansion of land
cover types
Distance to forest
cover
Distance to
rangeland
Distance to water
sources
Euclidean distance to natural water sources Availability of natural water sources for irrigation may impact the
suitability for agricultural purposes
570 M. Qasim et al.
123
is referred to as period 1 and the period between 1990 and
2007 as period 2.
The land use maps of the years 1968, 1990 and 2007
were brought together in a raster GIS to a common spatial
resolution of 25 m (for more details please read Qasim
et al. 2011). The maps were reclassified for one cover type,
that is, either forest or agriculture or rangeland etc.
Euclidian distances maps were calculated with a series of
1-m buffer layers starting from the boundary of each land
cover type and spreading further to cover the whole map.
All these maps were converted to point format and were
spatially joined using ArcGIS, and the data was exported to
SPSS for regression analysis.
Receiver Operating Characteristic (ROC) curves were
used for validation of the models, which is a commonly
used method for assessing the accuracy of a diagnostic test
or proposed model (Swets 1988; Williams et al. 1999;
Gorsevski et al. 2006). The area under the ROC curves
(AUC) provides a diagnostic that is used to distinguish
between alternative model specifications. The AUC varies
from 0.5 for a model that assigns the probability of land use
change at random to 1 for a model that perfectly assigns
land use change to the empirically observed locations
(Williams et al. 1999).
Limitations
A number of limitations were encountered in this research:
the aerial photographs, which have been used as baseline,
were not available for the whole of district Swat. In order
to track land use changes over a long period of time, it was
necessary to select areas within the three vegetation zones.
Thus, approximately 250 km2 areas from each zone have
been mapped. The sampling took into account that district
Swat is a narrow elongated valley divided into roughly two
equal parts by a river (river Swat) flowing from North to
South. Across section selected from each vegetation zone
comprising areas on both sides of the river provided good
samples to represent the whole district.
Using old aerial photographs, maps and their digital
counterpart is an established approach in modern carto-
graphic-based research (Tekle and Hedlund 2000; Kadio-
gullari and Baskent 2008). However, creating land use
maps by adopting different methodologies for similar data
from different data sources sometimes lead to different
results (Rao and Pant 2001). We might have obtained
better results if high-resolution satellite images of the
research area for the year 1968 were available. Therefore,
special attention was given to use the uniform methodology
of polygon formation for creating time series maps from
the available data sources.
It is out of the scope of the paper to discuss the socio-
economic drivers of land use change, which is an integral
part of the whole process. Another article, which is under
review for publication in Land Use Policy, however, dis-
cusses these issues in more detail.
Furthermore, the findings of the study are only limited to
district Swat and should only be carefully applied to other
parts of the Himalayan region with similar socioeconomic
conditions.
Results
Major land use changes
Deforestation
Analysis of land use change maps shows that most of the
deforested land in district Swat is either used for agricul-
ture (shown as agriculture expansion) or as rangeland
(Figs. 2, 3, 4). In zone C, 75.1 % of the forest area was
converted to rangeland in 40 years, whereas in zone A,
37.8 % of forest area was converted to rangeland, of which
2/3 took place in period 1(see Table 2).
Reforestation
Reforestation mostly took place in period 1, while in the
latter two decades, a very small area was reforested
(Figs. 2, 3, 4). The highest rate of reforestation was
observed in zone A, where 27.7 % of rangeland and
16.0 % of agriculture land was reforested in period 1
(Table 2). Reforestation in period 2 took place particularly
on range land, with 0.3, 0.4 and 4.1 % in zones A, B and C,
respectively, reforested. The extent of reforestation was,
however, negligible compared to the rate of deforestation.
Agriculture expansion
Analysis of the land cover change maps shows that agri-
culture mostly expanded on rangeland and forestland. In
zone A, B and C, agriculture expanded by consuming 17.2,
30.8 and 51.1 % of rangeland, respectively. Agriculture
also expanded on forestland, mainly in zone C, where
31.7 % of forests were cleared for agriculture. In zone B,
agriculture consumed 10.5 % of forestland in period 1 and
1.1 % in period 2. In zone A, 11.4 % of forestland was
cleared for agricultural expansion (see Table 2).
Agriculture contraction
At the same time, we could also observe cases of agri-
cultural abandonment, which was mainly caused by
expansion of built-up land such as housing and infra-
structure and land degradation leading to a conversion of
Modelling land use change across elevation 571
123
agricultural land to rangeland (yellow coloured areas in
Figs. 2, 3, 4). In zone A 17.3 % and in zone B 18.2 % of
agriculture land was converted to rangeland over 40 years.
Similarly, in the three zones, respectively, 8.3, 11.2 and
8.6 % of agriculture land was consumed by expansion of
built-up areas over the study period (Table 2).
Geographical drivers of land use change
In low-, mid- and high-elevation zones of district Swat, the
described geographical (and geophysical) factors showed
significant impacts on agriculture and forest expansion and
contraction in varying patterns across the zones and time
periods. Multiple regression results at 25 m1 resolution are
presented in Tables 3, 4, 5, 6.
Geographical drivers of deforestation
The results show that aspect, slope, accessibility (distance
to nearest roads and built-up areas) and distance to
rangelands were statistically highly significant and played
an important role in predicting deforestation in the three
zones (see Table 3).
In zone A, aspect is negatively correlated to deforesta-
tion showing a more pronounced deforestation on northern
facing slopes maybe because the northern aspects have
usually larger forest stands with more mature trees. How-
ever, in zones B and C, the odds of deforestation increased
by 2 and 7 %, respectively, for one degree deviation from
southern facing slopes, which are highly preferred for
agricultural production. In zone A, main road accessibility
was positively correlated to deforestation, as the odds of
deforestation increased 1.6 times (in period 2), while in
zone C, it decreased by 0.71 times (in period 1) as the
distance to the main roads increased. Similarly, distance to
built-up areas showed different relationships across the
zones. For example in zone C (in period 2), the odds of
deforestation decreased 0.93 times, while in zone A, it
increased 2.63 times as the distance increased to built-up
areas. Forest in close proximity of rangelands was vul-
nerable across the gradient, especially during the second
period. For example, in zone A, the odds of deforestation
declined by 0.19 times in period 2 compared to 0.88 in
period 1 for each km distance from rangelands (Table 3).
This may be due to free and unrestricted access to forests as
in winter the local population practise free grazing, that is,
Fig. 2 Land use maps of
high-elevation region
(Kalam zone A)
1 We analysed data both at 25 and 50 m resolution. Very few
differences were found in the results at these two resolutions and thus
to avoid unnecessary lengthiness only multiple regression results at
25 m resolution are presented here.
572 M. Qasim et al.
123
cattle can feed anywhere they can find vegetation, and thus,
forests in rangeland neighbourhood are particularly sus-
ceptible.AUC values for specified deforestation models
vary between 0.61 and 0.86.
Geographical drivers of afforestation
In zone A, the probability of afforestation increased 1.03
and 2.44 times for each percentage increase in slope in
period 1 and 2, respectively. Similarly, in all three zones
relatively steepland was reforested, whereas flat or level
land was preferred for agricultural purposes.
Road accessibility has a significant impact on affores-
tation but surprisingly was positively correlated in zones B
and C, while negatively correlated in zone A. In the den-
sely populated zone C, the remaining forests are relatively
remote from main roads and built-up areas; this may be
because these areas are relatively safe from grazing cattle
and local population and have relatively low opportunity
costs. In period 1, the odds of finding reforested areas
increased by 1.3 times for each additional kilometre dis-
tance from the closest section of the main road in zone C,
whereas in zone A, the odds of reforestation decreased by
0.18 times for each kilometre distance to roads. Exactly,
the same pattern was found in relation to reforestation and
distance to built-up areas in this zone. Forest neighbour-
hood was consistently negatively correlated to afforesta-
tion. In the agricultural zones (zone C and B), afforestation
increased with increasing distance to water sources as areas
closer to water were preferred for agriculture, whereas in
zone A, distance to water was negatively correlated with
afforestation because of steep slopes around water sources
(rivers in mountainous areas), making these inaccessible
for forestry operations.
Geographical drivers of agriculture contraction
Table 5 shows that aspect was negatively correlated to
agriculture contraction in most cases; in zone C, the odds
of agriculture contraction decreased by 0.92 times for each
degree change towards southern slopes, as southern facing
slopes are preferred for houses in the Himalayas. Houses
built on southern aspect catch more sunshine and are
warmer in cold weather, reducing heating costs.
Fig. 3 Land use maps of mid-
elevation region (Malamjaba
zone B)
Modelling land use change across elevation 573
123
Slope was another strong explanatory variable for agri-
cultural contraction in the study area, as on steep slopes,
erosion and water scarcity problems are more prominent. In
zone A and C, agricultural contraction increased by 1.5
times for each 1 % increase in slope in period 1. Proximity
to built-up areas played an important role in agricultural
contraction. In zone A, the main reasons for agriculture
abandonment could be the tremendous expansion of built-
up area (hotels mainly) on agricultural land in the close
vicinity of main roads and soil erosion on marginal land in
hilly areas. In the three zones, the odds of observing
agricultural contraction increased 2.98, 1.20 and 1.17
times, respectively, for each additional kilometre from the
nearest main road in period 1. The positive correlation in
this case highlights that land closer to a main road was
preferred for agriculture mainly due to reduction of
Fig. 4 Land use maps of
low-elevation region
(Barikot zone C)
Table 2 Percentage interchanges of major land cover types in the three zones
% of one land cover change to any other Zone A Zone B Zone C
P1 P2 Total P1 P2 Total P1 P2 Total
Agric expansion Forest to agriculture 6.6 4.8 11.4 10.5 1.1 11.6 12.7 18.9 31.7
Rangeland agriculture 12.3 0.9 17.2 26.9 3.87 30.8 26.0 25.1 51.1
Agric contraction Agriculture to rangeland 12.4 4.9 17.3 4.7 13.5 18.2 22.2 13.2 35.5
Agriculture to built up 3.0 5.5 8.6 4.9 6.2 11.2 3.7 4.6 8.3
Agriculture to forest 16.0 0.1 16.1 0.3 1.4 1.7 3.7 2.5 6.2
Reforestation Agriculture to forest 16.0 0.1 16.1 0.3 1.4 1.7 3.7 2.5 6.2
Rangeland to forest 27.7 0.3 28.1 13.4 0.4 13.8 5.4 4.1 9.4
Deforestation Forest to rangeland 23.9 13.9 37.8 49.2 17.2 66.4 46.1 29.0 75.1
Forest to agriculture 6.6 4.8 11.4 10.5 1.1 11.6 12.7 18.9 31.7
P1: changes between 1968 and 1990
P2: changes between 1990 and 2007
574 M. Qasim et al.
123
transportation costs and easier access for agricultural
equipment.
In zone B, the probability of agriculture contraction in
forest neighbourhood decreased by 0.72 times for each
kilometre increase from the nearest forest boundary in
period 2. A plausible reason is the presence of forest on
steep slopes(forest on flat land was mostly deforested in
period 1);agriculture contraction in forest neighbourhood
in zone Bhence means contraction of agriculture on steep
slopes, possibly mainly due to higher soil erosion on steep
slopes. Similarly in zone C, each additional kilometre
distance from rangeland, decreased the odds of agriculture
contraction by 0.15–0.32 times in periods 1 and 2,
respectively.
Distance to water sources has a complex but interesting
relation with abandoning agricultural land. In zone C,
where there are more plain areas, the odds of finding
agricultural contraction increased by 1.19 times per kilo-
metre distance from a water source. In zone B, however,
agriculture contraction was negatively correlated (0.81
times/km) with distance to water sources. This could be
because rivers in this area are steeper and water flows at
high speed, causing flooding and erosion of the bordering
agricultural land.
Geographical drivers for agriculture expansion
In period 2, the odds of agriculture expansion decreased by
0.72, 0.93 and 0.96 times for each 1 % increase in slope in
high-, mid- and low-elevation zones, respectively (see
Table 6).
Agriculture expansion in relation to roads accessibility
varied across the gradients and time periods depending on
population, local markets and topographic characteristics
of each zone. For example, in zone C, which is more
developed, densely populated and having many business
centres, the areas close to main roads or big urban centres
were preferred for markets and built-up areas, and as the
road network extended deep into valleys (secondary roads),
it increased accessibility and helped people to bring more
land under agriculture (Table 6). In zones A and B, dis-
tance to main roads and agriculture expansion were nega-
tively correlated in period 1 and positively correlated in
period 2, showing that in the second period, agriculture
expanded further and further into the forest areas and
valleys even in the absence of roads. This is an unexpected
pattern of expansion of agriculture (i.e. away from roads),
and a possible explanation for this could be the weaker law
enforcement in remote areas.
Table 3 Multiple regression analysis of variables for deforestation
Variables Time
periods
Kalam region (zone A) Malamjaba region (zone B) Barikot region (zone C)
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Intercept P 1 -0.919 0.008 – 6.562 0.08 – 0.0037 0.03 –
P 2 -1.844 0.007 – -6.8385 0.2 – 1.87 0.05 –
Aspect P 1 -0.029 0.0005 0.97*** 0.018 0.0006 1.02** 0.071 0.0002 1.07***
P 2 -0.012 0.0007 0.98*** 0.0479 0.001 1.04** 0.063 0.00008 1.07***
Slope P 1 -0.059 0.0003 0.94** -0.061 0.003 0.94*** -0.046 0.0004 0.95***
P 2 -0.051 0.0004 0.95*** Non sig – – Non sig – –
Distance to
built up areas
P 1 -0.09 0.0001 0.91*** -0.174 0.0003 0.84** -0.20 0.00006 0.81***
P 2 0.97 0.0004 2.63** -0.71 0.0003 0.49** -0.071 0.00003 0.93**
Distance to
main roads
P 1 0.29 0.0002 1.33*** -0.707 0.0001 0.49*** -0.34 0.00004 0.71***
P 2 0.474 0.0002 1.6*** 1.02 0.0002 2.77*** -0.14 0.00006 0.86***
Distance to
sec roads
P 1 -0.13 0.0004 0.14*** 0.327 0.0002 0.72*** -0.34 0.00008 0.71***
P 2 0.002 0.0001 1.002* -0.74 0.0002 0.47** 0.025 0.00001 1.02**
Distance to
agriculture
P 1 0.39 0.0001 1.47** -0.032 0.0006 0.96*** -0.06 0.00006 0.94***
P 2 -1.01 0.0002 0.36*** -0.72 0.0002 0.49*** -0.52 0.00002 0.59***
Distance to
rangeland
P 1 -0.12 0.0001 0.88** -0.48 0.0001 0.62*** -0.57 0.00003 0.56***
P 2 -1.68 0.0004 0.19*** -7.35 0.001 0.001*** -1.23 0.00004 0.29***
Distance to
water body
P 1 -0.36 0.0002 0.69** 0.737 0.0001 2.08*** 0.20 0.00006 1.22***
P 2 -0.32 0.0002 1.38*** Non sig – – 0.031 0.00002 1.03**
AUC P 1 0.61 0.79 0.75
P 2 0.71 0.86 0.70
Significance level: * significant at 0.05, ** significant at 0.001, *** significant at \0.0001
Modelling land use change across elevation 575
123
Forest neighbourhood and agriculture expansion were
positively correlated in period 1 and negatively correlated
in period 2 across the gradient. For example, in zone B, in
period 2, the odds of observing agriculture expansion
decreased by 0.85 times for each additional kilometre
distance to the nearest forest boundary. In zone C, the
probability of agriculture expansion decreased by 0.57
times for each additional kilometre distance to the nearest
boundary of rangelands.
In zone C, the relationship between distance to water
sources and agriculture expansion changed over the two
periods. In period 1, areas in close proximity of water
sources were brought under agriculture, but in period 2,
agriculture expanded predominantly in areas away from
natural water sources. This could be due to the fact that in
period 2 human-made irrigation sources (tube wells) came
into use for irrigation, which were not mapped but
observed during the field survey. In zones A and B, we see
the same pattern, agriculture expanded in close proximity
of natural water sources in period 1, but in the second
period, agriculture expanded in areas away from the
mapped water sources (mostly forest areas) where spring
water or snow melt irrigates the crops.
Discussion
Agriculture expansion and deforestation in district Swat are
the result of a variety of factors. Our models show the
importance and variation of the physical drivers of land use
change across the zones.
Land use changes on sloping land
As expected, agricultural activities were found to prevail in
mainly flat areas probably due to ease of agricultural
practices, such as ploughing and irrigation, better quality of
soils, and closer proximity to human settlements. It is
generally accepted that deforestation may be widespread in
areas where slopes are relatively gentle and thus better
suited for agriculture (Ochoa-Gaona and Gonzalez-Espin-
osa 2000; Zeleke and Hurni 2001; Chen et al. 2001; Stage
and Salas 2007; Koulouri and Giourga 2007).
In the high-elevation zone (zone A), in period 2, com-
mercial agriculture became more prominent and expanded
onto steep slopes in close vicinity of forests. This agri-
cultural expansion in both zones A and B increased to high-
gradient land despite the high risk of soil erosion. In
Table 4 Multiple regression analysis of variables for afforestation
Variables Time
periods
Kalam region (zone A) Malamjaba region (zone B) Barikot region (zone C)
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Intercept P 1 -1.211 – -5.375 0.07 – -5.658 0.05 –
P 2 -3.417 – -1.541 0.07 – -4.659 0.05 –
Aspect P 1 -0.086 0.0006 0.91** Non sig – – Non sig – –
P 2 0.015 0.002 1.02*** Non sig – – Non sig – –
Slope P 1 0.0249 0.004 1.03*** 0.0966 0.0006 1.10* 0.024 0.002 1.02**
P 2 0.891 0.002 2.44*** 0.0489 0.003 1.05** 0.015 0.006 1.02*
Distance to
built up areas
P 1 -0.717 0.0002 0.49** 0.382 0.0002 2.28*** 0.202 0.0001 1.22**
P 2 -0.315 0.0005 0.73*** 0.134 0.0003 1.14*** 0.632 0.0002 1.88***
Distance to
main roads
P 1 -1.736 0.0002 0.18*** 0.449 0.0002 1.57** 0.263 0.00001 1.3**
P 2 -0.833 0.0005 0.43** 0.269 0.0001 1.31*** 0.0772 0.00001 1.08***
Distance to
sec roads
P 1 -0.025 0.0002 0.97** -0.923 0.0002 0.40*** 0.597 0.0001 1.8***
P 2 -0.066 0.0002 0.93*** 0.0614 0.0002 1.06* 0.318 0.0001 1.37***
Distance to
agriculture
P 1 0.555 0.0002 1.47** 0.883 0.001 2.42** -0.779 0.0002 0.45***
P 2 0.412 0.0005 1.5** -0.113 0.0003 0.89*** 0.277 0.0002 1.32***
Distance to
forest
P 1 -0.804 0.0002 0.45** -0.396 0.0003 0.67*** Non sig – –
P 2 -5.690 0.002 0.0034** -5.04 0.001 0.01*** -1.455 0.0004 0.23**
Distance to
rangeland
P 1 -1.141 0.0002 0.32*** -4.698 0.003 0.01*** -5.096 0.001 0.006***
P 2 -4.411 0.0007 0.012*** -0.474 0.0005 0.62** -3.085 0.001 0.05***
Distance to
water body
P 1 -1.696 0.0003 0.18*** 0.671 0.0001 1.95** 0.262 0.0001 1.29***
P 2 -0.777 0.0005 0.46** 0.228 0.0002 1.26** 0.166 0.00001 1.18**
AUC P 1 0.89 0.95 0.74
P 2 0.87 0.90 0.84
Significance level: * significant at 0.05, ** significant at 0.001, *** significant at \0.0001
576 M. Qasim et al.
123
mountainous areas, soil erosion and thus loss of nutrients
during the monsoon season is a continuous process, but it
becomes more problematic when inclination increases,
which may be the reason for the abandonment of agricul-
ture on sloping lands in district Swat. Presently, zone A and
zone B, the mountainous region of the district, have most
of the pine and mixed pine forests (on steep slopes). Fur-
ther deforestation in these two zones will mean deforesta-
tion on steep slopes and deforestation of the water
catchment areas for lowland rivers, thus potentially leading
to sedimentation.
According to FAO (2005) about 14.2 million ha of land
in the northern hills of Pakistan are subject to severe ero-
sion, with 20–40 tonnes/ha/year soil loss in certain areas of
Tarbela watershed, neighbouring to district Swat. At the
current rate of sedimentation, Tarbela dam is estimated to
be completely filled in 100 years and will lose the capacity
to store water, generate hydro-power and provide irrigation
water to the plains; the lost benefits of this was estimated to
be 2.3 billion rupees (24.74 million US$) annually (FAO
2005). Thus, deforestation and soil erosion not only affect
the local areas but also bring destruction on a wider scale to
downstream areas. People in lowland areas depend on the
watersheds for water supply as well as generation of
hydroelectric power and have a stake in prolonging the life
and capacity of water dams by curbing siltation.
Our findings are reflected in other studies in the Hindu
Kush Himalayan region. For example, Tiwari (2000)
reported that about 6 billion tonnes of soil are lost annually
from their original site in Himalayas. If this trend is
allowed to continue, about one third of arable land of the
region will be lost within 20 years. Other studies demon-
strated that the felling of trees and over-grazing increased
the peak discharge of run-off causing severe soil erosion on
slopes in the mid-elevation zone of the central Himalayas
(Singh 1981; Wakeel et al. 2005). The deforested slopes of
the Himalayas are less capable of absorbing and holding
the rainwater and, consequently, a large part of the rainfall
drains down the fragile mountain slopes, devastating the
lower lying plains by recurrent floods. In the central
Himalayas, the highest increase in area affected by flood as
a result of deforestation was in the Sharda (31.4 %) fol-
lowed by Ramganga (19.2 %) and Kosi (15 %) basins in
the Indian Himalayas (Tiwari 2000).
Table 5 Multiple regression analysis of variables for agriculture contraction
Variables Time
periods
Kalam region (zone A) Malamjaba region (zone B) Barikot region (zone C)
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Intercept P 1 -3.006 – -0.174 0.05 – -2.553 0.06 –
P 2 -1.739 – 1.357 0.02 – -2.501 0.04 –
Aspect P 1 -0.0284 0.0003 0.97*** -0.0124 0.001 0.98** -0.0732 0.0002 0.92**
P 2 -0.011 0.0007 0.98*** -0.016 0.0004 0.98*** Non sig – –
Slope P 1 0.42 0.002 1.5** 0.0184 0.001 1.02*** 0.422 0.002 1.5*
P 2 Non sig – – 0.0468 0.0009 1.05** 0.0927 0.0009 1.09***
Distance to
built up
areas
P 1 -0.555 0.0006 0.57*** 0.152 0.0002 1.164*** 0.087 0.0001 1.09***
P 2 0.651 0.0005 1.90** 0.403 0.00001 1.50*** 0.0439 0.0002 1.05**
Distance to
main roads
P 1 1.091 0.0002 2.98*** 0.183 0.00002 1.20** 0.157 0.00001 1.17***
P 2 Non sig – – 0.0003 0.00001 1.003** 0.036 0.00001 1.04***
Distance to
sec roads
P 1 0.083 0.0002 1.09** 0.123 0.0001 1.13* 0.828 0.0002 2.28**
P 2 -0.121 0.0001 0.88*** 0.226 0.00001 1.25*** -0.044 0.0002 0.95***
Distance to
forest
P 1 Non sig – – -0.830 0.0003 0.44*** 0.413 0.0002 1.51***
P 2 Non sig – – -0.333 0.00001 0.72*** -0.039 0.00001 0.96*
Distance to
rangeland
P 1 Non sig – – -3.37 0.0007 0.03*** -1.907 0.0006 0.15**
P 2 -0.213 0.0005 0.81* -2.910 0.0002 0.05** -1.143 0.0005 0.32***
Distance to
water body
P 1 0.65 0.0006 1.92*** -0.241 0.0001 0.79** 0.106 0.0001 1.11***
P 2 -0.175 0.0004 0.84** -0.216 0.00001 0.81* 0.176 0.00001 1.19**
AUC P 1 0.76 0.78 0.80
P 2 0.71 0.83 0.58
Significance level: * significant at 0.05, ** significant at 0.001, *** significant at \0.0001
Modelling land use change across elevation 577
123
Accessibility, neighbourhood interactions
and deforestation
In zone C, accessibility to main roads and distances to
markets (urban areas) were important variables in explain-
ing deforestation and agriculture expansion. Possible rea-
sons could be the availability and access of farm machinery
and the associated ease of applying fertilizers to soils.
Similarly, short distances to markets reduce transportation
cost and also reduce time for shifting fresh vegetables, the
high-income commodity of farmers, to the market. During
the first period, conversions took mainly place in close
vicinity of built-up areas. But in the second period, urban
areas as well as agriculture expanded gradually at the cost of
forests on fertile and less sloping lands in remote areas. The
higher deforestation in zone C has likely been driven by the
fact that the region is densely populated and has an exten-
sive road network. We also observed a stark conversion
from areas under water to agricultural land through building
of retaining walls and other drainage measures. Such
retaining walls were specifically found in zone C and point
to higher levels of investment to create agricultural land and
facilitate improvements towards more mechanized agri-
culture. In this zone (zone C), range lands close to agri-
cultural land were brought under agriculture using
agricultural machinery showing a significance of accessi-
bility and rangeland neighbourhood interaction as well.
The theory, attributed to von Thunen, that agriculture
expansion is controlled by the distance to the market, as a
proxy for transportation costs is only partially supported by
our results. We found the model applicable to the agri-
cultural expansion in zone C but not in zone A. Agriculture
expansion in zone A was more pronounced away from
main roads, where transportation costs were comparatively
high, and hence determined by suitability of land for
agriculture due to other factors than distance to market and
transportation cost. High land rents (land suitability) in our
study were correlated with the climatic condition of the
high-elevation zone, which makes the land suitable for off-
season vegetable production. Off-season vegetables pitch
very high prices in the market and thus not only overcome
the extra transportation costs but give enough income to the
farmers, encouraging them to extend their agricultural land
into remote areas.
Table 6 Multiple regression analysis of variables for agriculture expansion
Variables Time
periods
Kalam region (zone A) Malamjaba region (zone B) Barikot region (zone C)
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Parameter
estimate
Standard
error
Odd ratios
sign. level
Intercept P 1 0.9884 – -1.0898 0.02 – 0.1409 0.02 –
P 2 -1.8720 – -1.732 0.05 – 0.07245 0.02 –
Aspect P 1 0.025 0.0008 1.03*** 0.042 0.0005 1.04* 0.062 0.0004 1.06***
P 2 0.090 0.0002 1.09*** 0.043 0.001 1.04*** -0.095 0.0004 0.90***
Slope P 1 -0.098 0.0007 0.90*** -0.076 0.0007 0.92** -0.0965 0.001 0.91***
P 2 -0.332 0.0009 0.72*** -0.072 0.006 0.93** -0.034 0.001 0.96***
Distance to
built up
areas
P 1 -0.061 0.0002 0.94*** -0.475 0.00007 0.62** 0.0342 0.00006 1.03***
P 2 -0.463 0.0003 0.63*** 0.235 0.0002 1.26** 0.159 0.00006 1.17***
Distance to
main roads
P 1 -0.643 0.0003 0.53*** -0.0198 0.00001 0.98*** -0.0583 0.00004 0.94***
P 2 0.215 0.0003 1.24*** 0.021 0.000003 1.02*** -0.0194 0.00003 0.98***
Distance to
sec roads
P 1 -0.184 0.00001 0.83*** -0.181 0.000001 0.83** -0.292 0.00007 0.75***
P 2 0.172 0.00001 1.19** 0.042 0.0001 1.04*** -0.302 0.00001 0.74***
Distance to
agriculture
P 1 -1.21 0.0003 0.30*** 0.151 0.00007 1.16*** -0.270 0.0001 0.76***
P 2 -1.09 0.0004 0.33*** -5.09 0.0009 0.01* -0.231 0.0001 0.79***
Distance to
forest
P 1 0.247 0.0006 1.28** 0.062 0.0001 1.06** 0.376 0.0002 1.46***
P 2 -5.349 0.001 0.005** -0.154 0.0001 0.85*** -0.0339 0.00006 0.96***
Distance to
rangeland
P 1 -0.889 0.0003 0.41*** -0.706 0.0002 0.49*** Non sig –
P 2 -0.900 0.0005 0.41** -3.484 0.001 0.03*** -0.566 0.0003 0.57***
Distance to
water body
P 1 -0.563 0.0003 0.57*** -0.137 0.0001 0.87** -0.177 0.00006 0.84***
P 2 0.562 0.0003 1.75*** 0.38 0.000001 1.46** 0.0517 0.00001 1.05***
AUC P 1 0.73 0.76 0.93
P 2 0.64 0.85 0.89
Significance level: * significant at 0.05, ** significant at 0.001, *** significant at \0.0001
578 M. Qasim et al.
123
In Pakistan, in general and in district Swat in particular,
the conversion of natural forest ecosystems to agriculture
has caused a rapid degradation of habitats and thus biodi-
versity loss. Habitat fragmentation is a key conservation
concern in many countries and is strongly associated with
loss of biodiversity (Olff and Ritchie 2002; Fahrig 2003).
Landscape change often leads to fragmentation of habitats,
affecting both structure and function through loss of original
habitat, reduction in habitat patch size and increasing iso-
lation of patches (Fahrig 1997; Botequilha and Ahern 2002).
To date, no systematic and comprehensive assessment with
the aim of objectively ranking the biodiversity importance
of Pakistan’s natural ecosystems has been made. However,
based on various reports (Mallon 1991) and the opinions of
recognized authorities, at least 10 ecosystems of particular
value for their species richness and/or unique communities
of flora and fauna are threatened due to increased accessi-
bility and agriculture expansion in Swat valley.
Other studies often find neighbourhood effects and
accessibility to dominate; for example, Vagen (2006) found
that accessibility (distance to villages and roads) and ele-
vation were the most important predictors of deforestation
in the highlands of Madagascar. In our study, intensive
cultivation of slopes increased by about 3400 ha (=65 %)
during the study period, a significant part of which came
from conversion of grassland to agriculture. This trend was
found to be indicative of increasing pressure on available
land resources in the region. Neighbourhood effects can
also be found in other case studies. For example, Haack
and Rafter (2006) analysed land use changes in Kathmandu
Valley of Nepal between 1978 and 2007. Their statistical
analysis of land use maps showed that over 140 km2 of
forests and farmland was converted to urban land use over
22 years driven by population pressure and roads network
expansion. Braimoh and Onishi (2007) identified that
accessibility, spatial interaction effects and policy variables
were the major determinants of industrial and agricultural
land use change in Lagos, Nigeria. Wyman and Stein
(2010) integrated remote sensing, household survey data
and spatial modelling to assess drivers of deforestation in
Belize. Their results showed that deforestation rates total-
led 30 % between 1989 and 2004 and that areas closer to
roads were more likely to be deforested. Further similar
results where physical accessibility and neighbourhood
interactions play an important role in land use change are
shown by Castella et al. (2005) and Skole et al. (1994).
Water availability
Proximity to permanent irrigation water sources improves
the suitability of land for agricultural purposes. However,
our model predicted different patterns of agricultural con-
traction and expansion in periods 1 and 2 in relation to
water access in the three zones of the district. In period 1,
agriculture expanded in close vicinity to water sources
(river Swat). In the second period, however, agriculture
expanded in areas further away from water sources. The
reason is likely to be that in the zones A and B, agricultural
land is rain-fed or otherwise irrigated by spring water and
snow melt. While in zone C, retaining walls have been built
in the early 1980s to control river bank erosion due to water
flow, which helped agriculture expansion in period 1. In
period 2, in areas away from natural water resources, irri-
gation is done by tube wells, which were only found in very
few places in zone C. Vashisht (2008) observed that ground
water abstraction by artificial means in the Himalayan and
Shiwalik foot hill region is negligible, but in these moun-
tainous regions survival of agriculture and biodiversity
during the lean period of the year entirely depends on
existence of spring water and snow melt.
Conclusion
According to our study, land use change processes vary
quite considerably between different altitudinal and vege-
tation cover zones, and environmental constraints and stage
of development provide important contextual information
influencing the effects of different drivers. In high-altitude
ecosystems (Zone A), accessibility (distance to nearest
road and city) did not have any significant role in agri-
culture expansion; rather land use change appeared sig-
nificantly related to geophysical factors such as slope,
aspect and altitude. In the low-elevation zone (Zone C),
accessibility through proximity to urban areas and markets
was the main factor showing the closest association with
agriculture expansion and abandonment. This contrast
shows the importance of higher granularity when trying to
explain land use processes over larger landscapes but also
provides an important warning when one attempts to apply
findings and insights to seemingly similar areas. Even
though the focus on biophysical factors provides some very
interesting insights, further analyses using more contextual
data and socioeconomic data are required to gain a fuller
picture of the drivers of land use change.
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