Assessment of drivers of coastal land use change in Malaysia

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Assessment of drivers of coastal land use change in Malaysia

A.O. Olaniyi a,*, A.M. Abdullah a, M.F. Ramli a, M.S. Alias b

aDepartment of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 Serdang, Selangor, MalaysiabDepartment of Forest Production, Faculty of Forestry, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

a r t i c l e i n f o

Article history:Available online 28 June 2012

a b s t r a c t

Drivers of coastal land use change (CLUC) were assessed between 1990 and 2006 in Selangor, Malaysia.Land use maps for the state (Selangor, Malaysia) for 1990 and 2006 and their respective biophysical andsocio e economic data were obtained from the Malaysian departments of agriculture, survey andstatistics respectively. Vector maps of socio economic variables were prepared in a GIS environment. Allmaps (land use maps and their potential drivers) were gridded and compared at three spatial scalesusing logistic regression analysis. Results indicated that agricultural practises were particularlyresponsible for coastal land use change. Coastal lands were converted to agricultural uses as a result ofincreased accessibility, suitable slope and favourable climatic condition of the areas. Findings furtherindicated that the impact of urbanization is just becoming an important factor of coastal land use change.While efforts of the government at coastal resources restoration and conservation is commendable,a political will is still required in achieving sustainable development of the coastal areas in order to fulfilthe environmental component of the ongoing Vision 2020 economic development plan.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Extent of coastal land and resources in Malaysia

A coastal zone is a transitional region between the terrestrialand marine environment with features (Ong and Gong, 2001),whichmakes it one of the most productive ecosystems in the world(Woodroffe, 2002). A coast is composed of about 200 nautical milesseaward and 5 kme10 km landward. Based on this definition,Malaysian coastal area is estimated to consist of about 510,510 km2

(Ismail, 2011) sustaining about 4000 of 20,000 known species offish, 22 of 50 species of sea snakes, four of the seven species of seaturtles (Ong and Gong, 2001), 64 of 200 species of hard and softcorals and about 577,558 ha of mangrove (FAO, 2002; Tan andBasiron, 2000) (Fig. 1 and Table 1).

Of these coastal resources, mangrove forest and coral reef areimportant for providing natural breeding ground for fish (Ong andGong, 2001) timber for poles and charcoal (Chan et al., 1993) andbuffer against coastal erosion and flood (Chong, 2006). Themultiplefunctions supported by the coastal zone have lead to its extensiveexploitation (Dahuri, 2005; UNEP, 2001) for human economicbenefits (Kates et al., 1990; Turner and Meyer, 1994; Serneels andLambin, 2001; Li and Yeh, 2004) particularly in the developingeconomies where open market economic policies have caused

increase in demand for land (Okuda et al., 2004 cited in Abdullahand Nakagoshi, 2007) for growing exportable crops (Goh, 1982;Kumar, 1986; Abdullah and Nakagoshi, 2007).

1.2. Historical background of coastal land conversion in Malaysia

The history of coastal land conversion in Malaysia is dated to1950 when the Department of Irrigation and Drainage constructedearthen dyke along the coast of Peninsula Malaysia to reclaim theareas for agricultural purposes (Chong, 1998; MDoF, 2001; Chongand Sasekumar 2002). Also, the construction of the build e upareas, sea port, industrial park and airports in various parts ofPeninsula Malaysia (Chong, 2006) as a result of industrial andurban developments (UNEPe RRCAP, 2004) have also impacted thecoastal areas in the Peninsula Malaysia (Table 2, Fig. 2a).

The progressive conversion of the coastal land to alternativeuses has been documented to cause deforestation, pollution ofmarine and inland waters, coral reef destruction, coastal erosionand flood (Adeel and Pomeroy, 2002; Burke and Spalding, 2002;WWF, 2006). For instance, Lim (2005) reported a loss of 100,000 haof mangrove area to land development due to economic policies of1970e1980’s (McMorrow and Abdul Talip, 2001; Ibrahim, 2001;Choo, 2001).

In Vietnam, coastline erosion has been documented to occur atan annual rate of 50 m since the early twentieth century (Mazdaet al., 1997, 2002; Cat et al., 2006) as a results of the massiveconversion of mangrove vegetation consequent upon long-termimpacts of human activities since the nineteenth century (Cat

* Corresponding author. Tel.: þ60 102933451; fax: þ60 389466768.E-mail address: [email protected] (A.O. Olaniyi).

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et al., 2006). Coastal erosion in Sagar Island inWest Bengal, India, iscaused by several anthropogenic processes such as human settle-ment fuelwood cutting and aquaculture that remove themangroves and other coastal vegetation (Gopinath and Seralathan,2005).

Coastal erosion started in the northern coast of Java Island ofIndonesia in the 1970s when most of the mangrove forest wasconverted to shrimp ponds and other aquacultural farms (Bird andOngkosongo, 1980; Syamsudin and Riandini, 2000). Mangrovesdominating coastal locations exhibit less Ferosion than areas withnon-vegetated land or former mangrove areas. Artificial andnatural agents that induce mangrove loss and make coastal areassusceptible to erosion include excessive logging, direct land recla-mation for agriculture, aquaculture, urban development andsettlement (Kovacs, 2000; Wolanski, 1992; Mazda et al., 1997;Massel et al., 1999). Studies have found the degradation ofmangroves along the Merbok Estuary, Malaysia (Wu et al., 2001)has led to the intensification of tidal currents and the erosion ofMalaysian (Othman, 1994) and Southern Thailand (Thampanyaet al., 2006) coastlines.

1.3. Approaches in land use change analysis

Several studies have been conducted to assess the drivers ofland use change (Geist and Lambin, 2002; Lambin et al., 2003;

Olaniyi et al., 2011). Bilsborrow and Okoth-Ogondo (2005) exploredthe manner by which increased population could cause defores-tation. Cropper et al. (1999) found population pressure, roaddensity and proximity to the capital city as the major factorsresponsible for deforestation in Thailand. Chompitz and Gray(1996) and Nelson and Hellerstein (1997) also found accessibilityas the main driver of land use change in Southern Belize andCentral Mexico. Seto and Kaufmann, 2003 identified foreign direct

Fig. 1. Study area. Source: CIA, 2007.

Table 1Size of mangrove forest (ha) and length of coastlines (km) by states in Malaysia.

States Length ofcoastline (Km)b

GR(ha)a SL (ha)a Total (ha) Density(ha/km2)

Perlis 20 0 20 20 0Kedah 148 7248 400 7648 52Penang 152 451 500 951 6Perak 230 43500 150 43,650 190Selangor 213 15,090 4500 19,590 92Negeri Sembilan 58 454 200 654 11Melaka 73 166 100 266 4Johor 492 17,832 6500 24,332 49Pahang 271 2675 2000 4675 17Terengganu 244 1295 1000 2295 9Kelantan 71 0 100 100 1Sarawak 1094 73,000 59,000 132,000 121Sabah 1743 328,658 12,719 341,377 196Total 4809 490,369 87,189 577,558 120

GR ¼ Gazetted reserves.SL ¼ State land.

a Tan and Basiron, 2000.b Ooi, 1996.

Table 2Rate of urbanization (%) by states in Malaysia (1980e2000).

State/period 1980 1991 1995 2000

Johore 35.2 48 51.8 56.4Kedah 14.4 33.1 36.9 42.1Kelantan 28.1 33.7 35.7 39.4Melaka 13.4 39.4 44 49.8Negeri Sem 32.6 42.5 44.7 47.6Pahang 26.1 30.6 31 33.7Perak 26.1 30.6 31.9 33.7Perlis 32.2 54.3 60.5 67.8Pulau Pinang 8.9 26.7 30.4 35.3Sabah 19.9 32.8 35.2 38.2Sarawak 18 38 43.4 50.5Selangor 34.2 75 82.6 89.4Terrengganu 42.9 44.6 45.1 45.7Malaysia 34.2 51.1 54.7 58.8

Source: MDoS, 2010.

Table 3Area (ha) and % change in mangrove forest reserve by state.

States 1980a 1990a 2000b �% Conservationarea (ha)a

Perlis 0 0 0 0 0Kedah 9037 8034 7248 �19.8Penang 406 406 451 11.1Perak 40,869 40,869 43,500 6.4 42Selangor 28,243 21,983 15,090 �46.6 320Negeri Sembilan 1352 1061 454 �66.4Melaka 77 314 166 115.6Johor 25,619 16,697 17,831 �30.4 26Pahang 2496 2032 2675 7.2 56Terengganu 2982 954 1295 �56.6Kelantan e e e 0.0Sarawak 44,491 36,992 73,000 64.1 385Sabah 349,773 316,460 328,658 �6.0 4840Total 505,345 445,802 490,368 �3.0 5669

GR ¼ Gazetted reserves.SL ¼ State land.

a Tan and Basiron, 2000.b Ooi, 1996.

A.O. Olaniyi et al. / Ocean & Coastal Management 67 (2012) 113e123114


investment as the main drivers of land use change in Pearl River,China. Whereas, Othman et al. (2009) found urbanization as thedriver of land use change in the Langat Dengkil e Klang catchmentof Peninsula Malaysia. While, Olaniyi et al. (2011) reported

availability of labour as the main driver of agricultural land use inMalaysia.

Reasonable number of studies has been conducted on landuse and land use change in Malaysia. Evidence from these

Fig. 2. a. Rate of urbanization in selected states of Malaysia (1980e2000). Source: MDoS online database. b. Population data of selected states in Malaysia 1930e2010. Source: MDoSonline database.

Fig. 3. a. GDP of selected states in Malaysia (1970e2009). Source: MDoS online database. b. Sectoral contribution to the GDP in Malaysia 1970e2005. Source: MDoS online database.

A.O. Olaniyi et al. / Ocean & Coastal Management 67 (2012) 113e123 115


studies suggested that the land use and land use change andtheir associated drivers are dynamic (Serneels and Lambin,2001; Li and Yeh, 2004; Geist and Lambin, 2002; Lambin et al.,2003) and are therefore required to be studied progressively(Lambin et al., 2003; Chopra et al., 2009). Studies by (Abdullahand Nakagoshi, 2006; Abdullah and Nakagoshi, 2007; Abdullahand Hezri, 2008; Olaniyi et al., 2011) examined the impact ofsocio e economic policies on general land use change inMalaysia and coastal pollution (Ong, 2008) and none of thestudies addressed the drivers of coastal land use. The paucity ofliterature in this area make this research useful in identifying inquantitative terms, the drivers of coastal land use and thusprovide a link between these drivers of coastal land use andcoastal degradation recently experienced in some parts ofPeninsula Malaysia.

Therefore, the objective of this study is to investigate thebiophysical and socio economic drivers of coastal land usechange. The analysis of the relationships will give us the insightinto the factors that caused the coastal land use change for theprediction of the future coastal land use change. Malaysiancoastal area is of extreme socio e economic and environmentalvalues (WTTC, 2007; Honey and Krantz, 2007). MPP e EAS (1999)gave an estimate of a Malaysian mangrove swamps for storm andflood control as US$ $222,000/km (values in terms of US$ 1995prices). French (2001); Kremmer (2005) and Moller (2006),highlighted the roles played by the Malaysian coastal forest inprotecting the country during the 2004 tsunamis incidence (NST,2005).

Their studies indicated that mangroves swamps along theMalaysian’s coastlines protected the country from the killer waves(NST, 2005) unlike in the neighbouring countries {VietNam (Cat

et al., 2006); Indonesia (Tjardana, 1995); Sri Lanka (Samarayanke,2003); India (Gopinath and Seralathan, 2005) and Thailand(Thampanya et al., 2006)} where the clearance of the coastal forestshave increased the vulnerability of the coastal areas to erosion andflood.

Unfortunately, these coastal forests have been variouslythreatened by the socio e economic activities (tourism) (Othman,1994). The Malaysia’s tourism e a sub sector of the servicessector accounted for about 13.3% of the Malaysian GDP in 2006.The tourism sub sector is now the largest contributors to theservices sector and this figure is expected to rise over time(WTTC, 2007).

As the nation has mapped out strategies of increasing revenuesfrom the tourism sub sector by year 2020 (National Vision Policy e

EPU, 2009; Honey and Krantz, 2007) this is likely to have implica-tions for coastal land use. Therefore, a detailed analysis of drivers ofcoastal land use is required to provide a baseline data upon whichthe impacts of National Vision Policy on coastal land use can beassessed (Table 3).

2. Study area

The state of Selangor covers an area of about 8000 km2 (Fig. 1).Climate in Selangor is governed by two monsoonal winds, whichoriginate from the North East (OctobereFebruary) and the SouthWest (MayeSeptember). The state has a mean annual rainfall ofabout 2500 mm and average daily temperature and humidity of26 �C and 80% respectively (Loh et al., 2005). Selangor is one of themost populous (Fig. 2b) and the richest (Fig. 3a) state in Malaysia(Alias et al., 2010). For instance, the Malaysian Department ofStatistics (MDoS) (2010) reported that Selangor contributed 22% tothe National Gross Domestic Product (GDP) in 2009.

Agricultural sector has contributed significantly to the economicdevelopment of the state as it used to be the major source ofincome, employment and foreign exchange earner (Alam et al.,2010). Despite that the agriculture has been overtaken by themanufacturing and the services sector (Fig. 3b), it still remained animportant sector in the production of food for the growing pop-ulation (EoN, 2010).

Agricultural land use (Table 4, Fig. 4) had been identified as themajor cause of land use change in Malaysia (Abdullah andNakagoshi, 2007; Gillis, 1988; Goh, 1982). For example, rubbercultivation started in the country by early 20th century when only4% of the total landmass of Malaysiawas used for cultivating rubber(Voon Seth, 1999 cited in Abdullah and Hezri, 2008). The intro-duction of the land development schemes lead to an extensiveclearance of land for agricultural uses (Jomo et al., 2004 cited in

Table 4Land use for major agricultural crops in Malaysia 1960e2010 (’000 ha).

Year Rubber Oil palm Cocoa Coconut Paddy Fruits Total

1960 1753 55 1 246 468 40 25631965 2025 97 3 252 503 47 29271970 2019 291 7 271 703 55 33461975 1992 642 29 287 758 65 37731980 2007 1023 123 300 717 61 42311985 1953 1468 304 334 662 109 48301990 1881 2029 420 314 752 177 55731995 1679 2539.5 190.7 248.9 672.9 257.7 55892000 1560 3131 163.8 213.8 521.2 291.5 58812005 1395 3461 160.0 193.2 475.0 329.8 60142010 1185 3637 160.0 175.5 450.0 373.2 5981

Various sources; Arif and TengkuMohd Ariff (2001 :2) quoted in Rasiah and Shahrin(2007).

Fig. 4. Agricultural land use for major agricultural crops in Malaysia (1960e2010) source: MDoA, 2010.



Abdullah and Hezri, 2008) and by the year 2000, agricultural landuse in Malaysia has increased to about 31% compared to 1974(Abdullah and Hezri, 2008).

Extensive agricultural land use in Malaysia was found to beassociated with the implementation of economic policies {NewEconomic Plan (NEP) and First to Third Malaysian Plans} between1965 and 1980 (McMorrow and Abdul Talip, 2001; Jomo and otherscited in Abdullah and Hezri, 2008; Lim, 1997 cited in Siwar et al.,2006; Abdullah and Nakagoshi, 2007).

2.1. Logistic models in land use and land use change analysis

The relationship between land use and its driving factors isa complex, dynamic and non e linear process that is usuallyaccomplished by comparing land use with the location specificcharacteristics at a pixel level (Karimi et al., 2010). These relation-ships have been successfully measured with the use of logisticmodels (Equation (1)) (Serneels and Lambin, 2001; Verburg et al.,2002, 2004; Peppler-lisbach, 2003; Lesschen et al., 2005).

LogitðYÞ ¼ aþ ß1X1 þ ß1X1 þ ß2X2 þ ß3X3 þ ßkXk (1)

whereY, dependent variable; a, constant of the equation; b1. bk,

coefficients the independent variables; X1 . Xk, the independentvariables.

The spatial relationships between dependent and a set ofindependent variables are usually quantified in a two procedures.Significant variables contributing to the model will be selectedfrom a pool of several variables using a forward conditional logisticregression method and the construction of multiple regressionanalysis with the variables earlier selected (Verburg et al., 2004).The use of stepwise regression procedure removes the problem ofmulticollinearity amongst the predictor variables (Verburg et al.,2004).

The coefficients (b’s) of the logit model are the values for therespective driving factor for a particular land use type. Values lowerthan 1 mean that the probability will decrease upon an increase inthe value of the independent variables whereas, the values higherthan 1 indicates an increase in the probability upon an increase in

Vector data

Land use data:1990

Proximity: road, river

Infrastructure data

Demographic data

ASCII data (matrix format)

Data rasterization (arcGIS)

Logistic regression analysis (SPSS)

Data conversion (arcGIS)

Load into SPSS

Data vectorization (matLAB)

Drivers of coastal land use change

Raster data

ASCII data (vector format)

Elevation data

Slope data

Topographic data

Soil data

Climatic data

Land use data:2006

Fig. 5. Research framework.



the value of the independent variables (Munroe et al., 2002;Menard, 2001).

The measure of goodness of fit in logistic regression is evaluatedwith the Relative Operation Curve (Pontius and Schneider, 2001).ROC is a measure of how well the independent variables correctlypredict the value of the dependent variable. An ROC value higherthan 0.7 is considered acceptable in land use modelling (Pontiusand Schneider, 2001; Hosmer and Lemeshow, 2000).

3. Methodology

3.1. Data and sources

3.1.1. Land use dataThe 1990 and 2006 land use vector maps of Selangor used in this

studywere collected from theMalaysian Department of Agriculture(MDoA). Amount of change (in hectares) of mangrove forest, whichwas used as a proxy for coastal land use change between 1990 and2006, were estimated in a GIS environment.

3.1.2. GDP at district levelSocioeconomic data such population and housing density were

obtained from the Malaysian Department of Statistics (MdoS). Thepopulation and housing densities were supplied at the district levelwhereas the GDP was available at the state level. Before spatialanalysis was conducted, all data were converted to the same spatiallevel by disaggregating GDP data to district level (Luijten et al.,2006).

3.1.3. Population dataPopulation data for the study area were obtained from the

MDoS. Missing data for some districts were (2006, in particular)derived by using the method documented by Braimoh and Onishi(2007).

3.1.4. Bio e physical dataMain agro-climatic variables used in this study are precipitation

(number of raining day per annum) and temperature (minimum,average and maximum in �C) soil, elevation and slope. Thesedata were derived from data provided by the Malaysian Depart-ment of Agriculture and Malaysian Meteorological Departmentrespectively.

Soil pH and cation exchangeable capacity (CEC) for the studyarea were downscaled from the global data set of derived soilproperties (Batjes, 2000). Soil pH and CEC were selected becausethey have been considered the most functional soil property that isimportant in defining agricultural potentials of a piece of land (Staalet al., 2002).

3.1.5. Proxies of coastal land use changeElevation was included as a proxy for drainage (Monmonier,

1982) while slope data was used as a proxy for suitability ofa piece of land for agricultural purposes {the state’s statutory lawthat prohibits the conversion of any land with slope greater than20� to non e natural uses (MDoA, 2003)}. GDP per capita was usedas a proxy for the standard of living (Mertens and Lambin, 2000;Yin and Li, 2001) while, proximity to a means of transportationwasused as a proxy for access to information and market (Staal et al.,2002).

3.2. Statistical analysis

The methodology employed in this study (Fig. 5) involved thecomparison of the coastal land use (CLU) with the potentialexplanatory variables on a cell by cell basis (Zhang et al., 2010; Chenand Pontius, 2010) at three spatial resolutions namely 150, 200 and250 m. These scales were chosen because it is related to units offarm size maintained by different groups of land users in the studyareas.

Table 5Potential drivers of coastal land use (CLU).

Data Unit Reference

Land use maps1990 & 2006 3 spatial resolutions (150, 200 and 250)mSoil variablesSoil pH ln [H] Staal et al. 2002Soil CEC meq/100 g Staal et al. 2002Soil suitability FAO suitability classes Verburg and Chen (2000)Soil terrain FAO soil terrain classes Verburg and Chen (2000)Soil physical units % Fraction of different soil particles Verburg and Chen (2000)Soil series FAO soil series classes Verburg and Chen (2000)Bio - geophysical variablesSlope Approved slope classes (degrees) Verburg and Chen (2000)Elevation Elevation classes (m) Diogol and Koomen (2010)Numbers of raining days No of days with precipitation above 50 mm Verburg and Chen (2000)Total amount of rain per year mm/year Verburg and Chen (2000)Average temperature �C Verburg and Chen (2000)Minimum temperature �C Verburg and Chen (2000)Maximum temperature �C Verburg and Chen (2000)Demographic variablesTotal population Both rural and urban population Verburg and Chen (2000)Percentage rural population % Agricultural population Verburg and Chen (2000)Percentage urban population % Non agricultural population Verburg and Chen (2000)Rural work force Agricultural population with age 14e65 Verburg and Chen (2000)Urban work force Non agricultural population with age 14e65 Verburg and Chen (2000)Total population density Person/km2 Verburg and Chen (2000)Accessibility variablesDistance to lake, river Average distance from lake (km) Verburg and Chen (2000)Distance to major road Average distance from major road (km) Muller and Zeller (2002)Distance to minor road Average distance from minor road (km) Muller and Zeller (2002)Distance to river Average distance from river (km) Muller and Zeller (2002)Major road density Line density of major road infrastructure per grid cell Muller and Zeller (2002)Major rail density Line density of major rail infrastructure per grid cell Muller and Zeller (2002)Minor road density Line density of minor road infrastructure per grid cell Muller and Zeller (2002)



Table 6a) Variables in coastal land use model. b) Detailed drivers of coastal land use in year 2006. c) Detailed drivers of coastal land use in year 1990.

a

Variables 1990 2006

250 m 200 m 150 m 250 m 200 m 150 m

A. Socio e economicsAge above 65 5.28 e e e e e

Dependency ratio 0.43 �1.88 e e e e

Housing density e � e e e 0.21Percentage rural

residents�0.33 e e e e e

Rural work force e 1.02 e e �0.02 e

Distance to lake �0.18 e e e e e

Distance to major road e 1.37 e e e 0.38Distance to minor road e 12.34 0.27 1.15 2.35Minor road density �0.04 e e e e e

Major river density e e e e 0.43 e

Primary GDP e L0.30 L1.08 �0.33

B. Land use typesAnimals husbandry LU e e e e e 0.87Build up LU e e e L21.3 e e

Coconut LU e e L1.34 e e e

Degraded forest LU e e e e e 1.11Forest LU 0.37 e L3.11 L19.1 e e

Idle grass LU e e 0.96 L0.78 e 0.69Oil palm LU e �2.02 L2.08 L4.52 e 0.72Other LU e �3.09 e e e 1.07Paddy LU e e L1.99 L2.05 e e

Rubber LU e e L2.24 L18.9 e 0.90Vegetables LU e e e L1.60 e 0.69Water body LU e e e 1.01 e

C. BiophysicalCEC sub soil e 0.76 0.09 e e

CEC top soil e �0.68 e e e

Elevation e 1.62 e e e

Slope 0.06 0.51 e 0.46

D. SoilSoil class 0.71 0.43 e e 0.22 e

Soil group �0.77 e L0.28 e �0.73 e

Soil physical unit �2.56 e e e e e

Soil series 1.83 e e e �3.17 e

Soil suitability e e e e 3.77 e

Soil terrain �0.91 e e e e e

E. ClimaticMaximum temp e e e e �0.19 e

Relative humidity e �0.51 e 0.65 e e

Numbers of raining days e e e 0.49 0.34 0.17Total rain e 1.18 e e �0.001 e

Constant �8.96 �20.03 L10.82 L7.84 �5.38 �7.43R2 0.79 0.47 0.81 0.69 0.67 0.37ROC 0.99 0.98 0.99 0.97 0.95 0.91

b

Variables b S.E. Wald df Sig. Exp(B) R2 ROCaBuild e up LU �19.93 248.251 0.006 1 0.936 0 0.688 0.965Oil palm LU �4.522 0.074 3685.898 1 0 0.011Paddy LU �2.049 0.069 875.478 1 0 0.129aRubber LU �18.933 433.438 0.002 1 0.965 0aCoconut LU �21.267 574.299 0.001 1 0.97 0Horticulture LU �1.599 0.038 1768.516 1 0 0.202aForest LU �19.103 221.767 0.007 1 0.931 0Idle grass LU �0.776 0.038 425.234 1 0 0.46No of raining days 0.492 0.008 3601.402 1 0 1.635Relative Humidity 0.646 0.019 1178.153 1 0 1.907Rural work force 0.266 0.008 1080.769 1 0 1.304Primary GDP �1.076 0.015 4912.606 1 0 0.341Constant �7.836 0.1 6200.896 1 0 0

(continued on next page)



Land use map of the study area for year 1990 and 2006 wereobtained from the Malaysian Department of Agriculture. The mapswere reclassified to map out the mangrove area, which was used asproxy for the coastal areas. Discrete data of potentialsocioeeconomic and biophysical factors (such as populationdensity, rural and urban work force, sectoral GDP, temperature)driving coastal land use change were reformatted to digital maps.Data of slope, elevation and soil maps were equally collected invector format and all data were rasterized at the same spatialextent and geographical coordinates (Table 5).

Raster data were later converted into ASCII files to make themsuitable for statistical analysis (binary logistic regression). EachASCII data representing each variable was converted into a columnformat using a short programwritten in MATLAB language (Luijtenet al., 2006).

The column data representing twenty seven potential spatiallyexplicit independent variables hypothesized to affect coastal landuse change and one for the dependent (CLUC) were assembled inSPSS version 18. Variables driving CLUC were selected with a step-wise forward regression using binary logistic regression analysis at0.05 significance level (Verburg et al., 2004).

4. Results

Results for the binary logistic regression analysis of the drivingfactors of coastal land use in Selangor, Malaysia were presented on(Table 6aec). Actual factors driving coastal land use were presentedon Table 6b and c. For the year 1990, the driving variables of thecoastal land use change can be broadly categorised into three viz:agricultural land use (coconut, rubber, paddy, oil palm, forest);accessibility (distance to the minor road) and biophysical factors(CEC, slope and soil group).

In 1990, the identified drivers of coastal land use change includeagricultural land use (primary GDP, oil palm, paddy, rubber,coconut and horticultural crops); accessibility (distance to theminor road) elevation (slope) and edaphic factor (soil group, CECsub soil). While, in the year 2006, important drivers of coastal landuse change include agricultural land use (primary GDP, oil palm,

paddy, rubber, coconut and horticultural crops); urbanization(build up areas); availability of labour (rural work force) climatic(numbers of raining days and relative humidity).

From this study, it was observed that some driving factors (agri-cultural land use) are common the year 1990 and 2006. However,despite that the agricultural land use drivers are common in the twotimes (1990 & 2006), the coefficients of the drivers are higher in theyear2006 than in the1990 (Table6a). This implied that thedrivers arehaving greater impacts on the coastal land use change in the year2006 than in 1990. Moreso, there are some variables (distance to theminor road, slope, soil group, CEC sub soil) that are specific to the year1990while (buildup, ruralwork force, horticulture, numberof rainingdays, relative humidity) are specific to the year 2006.

Urbanization (build e up and horticultural land uses) andclimatic factors (number of raining days and relative humidity)were additional drivers of costal land use change in the year 2006.The increase in urbanization and industrialization of Malaysia haslead to an increase in the demand for horticultural crops and thishas resulted to a greater environmental impact on the coast forcultivating more horticultural crops.

The increase in the population and urbanization of the coastalareas in Malaysia has been observed to mimic the growing trends ofincreasing population of the coastal land areas around theworld (U.S.Census Bureau, 2009; NOAA, 2004; Timmerman and White, 1997;EEA, 2006) and this has been related to an increase in global tourism.For example, Mediterranean coast is expected to receive 350 millionseasonal touristsby theyear2025(Hinrichsen,1999). The coastal townof Cancun in Mexico has been transformed as a result of increasedresident population (Burke et al., 2001). These changes in the demo-graphicpopulationof thecoastal zoneshave severely impactedcoastallandscapes by the construction `of the infrastructures needed tosustain residential, commercial and tourist activities (EEA, 2006).

5. Discussion

Findings from this research indicated that the drivers of coastalland use were dynamic over 1990 and 2006. For instance, around1990, this research identified agricultural land use, accessibility and

Table 6 (continued)

c

Variables b S.E. Wald df Sig. Exp(B) R2 ROC

CEC sub soil 0.09 0.001 7177.532 1 0 1.094 0.813 0.985Slope 0.514 0.004 20,188.63 1 0 1.672Soil group �0.279 0.003 10,954.623 1 0 0.756Distance to minor road 12.339 0.335 1360.474 1 0 228,517.68Primary GDP �0.3 0.005 3359.422 1 0 0.741Forestry LU �3.112 0.028 12,243.389 1 0 0.045Oil palm LU �2.082 0.018 13,525.912 1 0 0.125Paddy LU �1.985 0.033 3685.072 1 0 0.137Rubber LU �2.235 0.028 6177.137 1 0 0.107Coconut LU �1.336 0.02 4587.935 1 0 0.263Idle grass LU 0.961 0.021 2146.29 1 0 2.615Constant �10.819 0.333 1053.508 1 0 0

The best model that best describes the relationships between CLU and its driving factors were obtained at the spatial scales of 150 and 250 for 1990 and 2006 land use analysisrespectively. This assertion is evidenced in the ROC being 0.985 and 0.965 for coastal land use analysis (CLUA) in 1990 and 2006 respectively.Explanation of statistic codes in Table 6aec.b ¼ coefficient of independent variables.Scales at which the best models were obtained for 1990 and 2006 analysis.SE ¼ is the standard error around the coefficients.Wald ¼ is a measure of the relative importance of each predictor in the model.df ¼ is the degrees of freedom.Sig (p Value) is a measure of how significant a (predictor) is ? if less than 0.05 it mean that we would reject the null hypothesis and hence agreed that a predictor is makinga significant contribution to the model.Exp (b): is the change in the odds ratio associated with a unit change in the predictor variable.R2 Nagelkerke’s R2 is a measure of relationship between the predictors and the prediction.ROC (Relative Operation Curve) is a measure of how well the independent variables correctly predict the value of the dependent variable.

a insignificant.



soil factors as primary drivers of coastal land conversion whileagricultural land use, urbanization and climatic factors are impor-tant factors of coastal land use change in the year 2006. This findingagreed with the report of Abdullah and Hezri (2008), who foundthat agricultural expansion in Malaysia, between the years1960e1990 was largely responsible for the landscape dynamics inthe country (Abdullah and Nakaghozi, 2008). Whereas, urbaniza-tion and climatic factors are becoming important driving factor ofCLU by the year 2006 (Abdullah and Nakagoshi, 2007; Othmanet al., 2009).

However, during 1990, the availability of expansive idle grass-land, which was available for immediate conversion into agricul-tural use, minimized the pressure on coastal land conversion.However, the absence this potential agricultural (idle grass) land bythe year 2006 lead to extreme pressures on coastal land forconversion into other (agricultural and urbanization) uses. Thefindings of this research agreed with the observations of Abdullahand Nakagoshi (2008) and Othman et al. (2009) who identifiedindustrialization and urbanization as the major drivers of coastalland use change in Malaysia.

6. Conclusion

This study identified and quantified the driver of CLUC inSelangor, Malaysia between 1990 and 2006. Findings indicated thatthe driving factors of coastal land use change are dynamic over timeranging from agricultural expansion, accessibility, soil factors,urbanization and climatic factors. Agricultural land use are themajor coastal land use driver in the 1990whereas, urbanization andclimatic factor are the drivers of coastal land use change in 2006.

Scientific investigations on how coastal vegetation providesa measure of shoreline protection have been conducted (French,2001; Moller, 2006). Coastal forests such as mangroves and salt-marshes have been found to serve as a coastal defence systemagainst the tidal storms, tsunamis and ocean waves. And theconversion of these protective forests has increased the vulnera-bility of coasts to erosion such as in Vietnam (Cat et al., 2006),Malaysia (Othman, 1994), Indonesia (Tjardana, 1995), Sri Lanka(Samarayanke, 2003), India (Gopinath and Seralathan, 2005) andThailand (Thampanya et al., 2006).

In relation to the 2004 Indian Ocean tsunami, anecdotal feed-back and scientific studies indicated that mangroves had savedlives and resources (NST, 2005). In Malaysia and other countriesmangrove forest has been identified as one of the most importantnatural barrier to the tsunami. “For thousands of years, mangroveforests have provided a natural buffer against cyclones and otherstorms that often hit the shores of southern India (Kremmer, 2005).Also in Malaysia, fishermen in Penang and parts of Kedah weresaved from death and serious damage to their homes during theIndian Ocean Tsunamis in 2004. This is because, mangrove swamps,along the Pulau Betong, Balik Pulau, Batu Kawan and Kuala SungaiPinang coastlines in Penang, shielded the shores from the killerwaves, (NST, 2005). According to Othman (1994), nearly 30 percentof the Malaysian coastline is undergoing erosion.

The implications of the coastal land use change on Malaysia arefar reaching thus putting the country at a crossroad of criticaldecision of whether to conserve or convert a coastal area foreconomic development. Therefore, the decision to convert a coastalarea cannot just be taken for mere economic reason given thesignificant environmental importance of the coast and itsresources.

The attention given to the environmental protection in Malay-sian economic policies i.e., Outline Perspective Plan (OPP2)(Mahathir, 1991, p 21) is appreciated an example is the StrategicEnvironmental Assessment (SEA) has been put in place to ensure

environmental plans are mainstreamed into development pro-grammes (NinthMalaysian Plan). However, the achievement of thisobjective particularly, in the coastal areas, has been hampered bythe constitutional devolution of powers between the federal andthe state governments (Vincent and Rasali, 2005). This constitu-tional provision saddles the authority of land administration on thestate governments (McMorrow and Abdul Talip, 2001) thus limitingthe ability of the federal government to enforce environmentalconservation laws on the states.

However, the hope of Malaysian coastal restoration andconservation is not lost, given the available national constitutionalprovisions, but there is a need for environmental education of allstakeholders in order to harmonize of economic development goalswith environmental conservation objectives (Macintosh andAshton, 2002). The achievements of these (Malaysian coastalrestoration and conservation) objectives in the long term, however,require strong political will (Chong, 2006) by the government at alllevel particularly at the states.

Acknowledgement

The authors wish to appreciate the supports received fromvarious Malaysian government agencies particularly the Depart-ments of Agriculture and Statistics for the datasets supplied. Alsowe appreciate the LandeOcean Interaction in the Coastal ZoneInternational Project Office (LOICZ e IPO) based in Germany forgranting full scholarship for the presentation of part of this reportat the Open Science Conference in Yantai, China between 12e15September, 2011.

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