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: qasiminleeds@hotmail.co.uk

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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