Monitoring farmland loss and projecting the future

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Monitoring farmland loss and projecting the future land use of an urbanized watershed in Yogyakarta, Indonesia a

Partoyo & Rajendra Prasad Shrestha

a

a

Natural Resources Management Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, Pathumthani, Thailand Available online: 05 Oct 2011

To cite this article: Partoyo & Rajendra Prasad Shrestha (2011): Monitoring farmland loss and projecting the future land use of an urbanized watershed in Yogyakarta, Indonesia, Journal of Land Use Science, DOI:10.1080/1747423X.2011.620993 To link to this article: http://dx.doi.org/10.1080/1747423X.2011.620993

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Journal of Land Use Science iFirst, 2011, 1–26

Downloaded by [Asian Institute of Technology], [Rajendra Prasad Shrestha] at 06:36 05 October 2011

Monitoring farmland loss and projecting the future land use of an urbanized watershed in Yogyakarta, Indonesia Partoyo and Rajendra Prasad Shrestha* Natural Resources Management Field of Study, School of Environment, Resources and Development, Asian Institute of Technology, Pathumthani, Thailand (Received 24 January 2011; final version received 2 September 2011) This study analyzes land use changes in Yogyakarta, Indonesia, specifically farmland loss, which has occurred as a result of rapid urbanization by employing remote sensing, GIS, and land use modeling techniques. Landsat images from 1992 and 2004 and ASTER Terralook images from 2009 were classified using a supervised classification to generate land use maps. Land use change was detected using a post-classification method. During 1992–2009, high-density built-up areas increased by a factor of 3.7, and 80.47 km2 of farmland was lost due to land conversion to built-up areas during the same period. Based on these findings, land uses for the year 2029 were spatially simulated using Dyna-CLUE model for three scenarios: business as usual, farmland protection, and minimum required farmland. The results reveal that the ongoing trend of land use conversion combined with the lack of implementation of proper spatial policies will lead to a large loss of farmland and that it is desirable to protect prime farmland from urban sprawl. Keywords: farmland loss; land use simulation; GIS; remote sensing; Dyna-CLUE; Yogyakarta

1. Introduction Global land use change increased markedly in the twentieth century, although agricultural practice shifted away from expansion toward intensification late in the century (FAO 2002). Although agricultural intensification can contribute to increased food production, there is widespread concern regarding global food security due to increased scarcity. The trend toward scarcity is associated with population growth and is aggravated by farmland loss (Tan, Li, Xie, and Lu 2005; Mariyono, Harini, and Agustin 2007). Rapid loss of farmland occurs due to the combined effect of rapid economic development, population growth, urbanization, agricultural restructuring, natural hazards, and land degradation (Heilig 1999; Yang and Li 2000; Seto et al. 2002; Ding 2003; Mariyono et al. 2007). Farmland preservation is therefore recognized as an important way to address this issue. In response, a number of measures have been introduced aimed at protecting farmland, especially farmland with the greatest production potential (Daniels and Nelson 1986; Coughlin 1991; Daniels 1991; Nelson 1992; Alterman 2001; Lichtenberg and Ding 2008). In terms of integrated land use planning, agricultural lands in urban fringe areas are attracting increasing concern. Farmland should be protected in such a way that agricultural *Corresponding author. Email: [email protected] ISSN 1747-423X print/ISSN 1747-4248 online © 2011 Taylor & Francis http://dx.doi.org/10.1080/1747423X.2011.620993 http://www.tandfonline.com


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Partoyo and R.P. Shrestha

practices and urban environmental functions do not disturb one another (Braimoh and Onishi 2007). The conversion of agricultural land to urban areas in Indonesia is largely uncontrolled, including the conversion of thousands of hectares of prime irrigated paddy fields to other uses (Firman 2004). At the national level, agricultural land expansion was 1.78% per year from 1980 to 1989, but it decreased to 0.17% per year from 2000 to 2005. The area under irrigated paddy fields increased by 1.6 million hectares from 1981 to 1989 but was reduced by 0.4 million hectares from 1999 to 2002 (PSE-KP 2007). During that period, the conversion of land use from irrigated paddy fields to non-agricultural purposes reached 110,000 hectares per year, particularly in the most populated islands, such as Java and Bali. At the national average productivity rate of 4.61 tons of dried unhulled rice (Gabah Kering Giling (GKG)) per hectare, within a year, the national paddy production decreased by 507,100 tons of GKG, which is equivalent to 329,615 tons of rice, due to the farmland conversion (Deptan-RI 2008). If this condition is allowed to continue, it is estimated that countrywide rice production will be greatly reduced. Moreover, statistics from the Ministry of Agriculture suggest that the increase of rice productivity has reached the saturation point. Additionally, farmland conversion has been considered to have reached an alarming state for a decade or so. Therefore, to produce enough rice, it is imperative for Indonesia to prevent farmland conversion (Apriyantono 2007; Deptan-RI 2008). Indonesia promulgated Law No. 41/2009 regarding sustainable crop land in October 2009 (RI 2009). Different from its predecessor, this law is promising in its potential to preserve prime farmland, as its new paradigm encourages agricultural policies and approaches to deal with farmland conversion rather than just enact formal legal punishment for defaulters. Article 62 of the law deals with the government protection and empowerment of farmers, farmer groups, farmer cooperatives, and farmer associations to sustain their farming activity. Protection includes the provision of guarantees, such as the price of basic food commodities to remain profitable, the acquisition of production facilities and infrastructure agriculture, the marketing of staple food crop production, and the compensation due to crop failure. Empowerment includes the institutional strengthening of farmers, counseling and training to improve the quality of human resources, the provision of facilities for financing sources/capital, and the provision of facilities for accessing knowledge, technology, and information (Article 63) (RI 2009). Farmland preservation is also important from the viewpoint of large investments in irrigation infrastructure, protecting agricultural jobs (Firman 1997) and providing certain public goods, such as air cleansing and water filtration (Nelson 1992). Most of the agricultural land encroached upon by urban growth in Indonesia has occurred in Java Island, which is the most populated island, with 58% of the total 237.7 million population of the country in 2010 (BPS-RI 2010). In Java and Bali together, 1.7 million hectares have been converted during the last decade, particularly in West, East, and Central Java, which contain the most fertile land in Java Island. Because Java Island has the most fertile soil and the highest level of agricultural infrastructure among other islands, the loss of farmland in urban fringe areas may jeopardize the local, regional, or even national agricultural base of the economy (Yunus 1990). Yogyakarta Province is located in the southern part of central Java Island. Land use change analysis is of high interest in this region due to the high dynamics of the landscape, especially land use change related to rapid urbanization and loss of prime agriculture area. Land use change monitored from 2002 to 2006 by the provincial board of planning and development of Yogyakarta shows a significant increase in urban area. This urban development has increasingly encroached upon farmland (BAPPEDA-DIY 2007). Yogyakarta has

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undergone a transition period toward full implementation of Law No. 41/2009 on farmland preservation, which requires data inventory and supporting regulation to be prepared during the transition period. On that premise, this paper aims to do the following: (1) to detect land use changes from 1992 to 2009 and quantify farmland loss during this period and (2) to assess the impact of the farmland protection policy on future land use. 2. Study area The study area, the Progo-Opak sub-watershed of Progo-Opak-Oyo watershed, lies between the latitudes 07◦ 32" and 08◦ 02" S and stretches between longitudes 110◦ 09" and 110◦ 31" E in the Province of the Yogyakarta Special Region (Figure 1). This region contains the urban area of Yogyakarta municipality in the center and the Sleman and Bantul regencies in the northern and the southern parts, respectively. The study area covers approximately 1502 km2 of land between an altitude of 0 m in the Bantul alluvial plain bordering the Indian Ocean in the south and 2968 m at the summit of Mount Merapi, an active volcanic mountain, in the north. Yogyakarta has a tropical monsoon climate with two distinct seasons. The rainy season spans from October to March, and the dry season is from April to September. The average temperature varies between a minimum of 19.0◦ C

Figure 1. Location of the study area.


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(July) and a maximum of 36.2◦ C (October), with a relative humidity between a minimum of 73.8% (August) and a maximum of 88.7% (January). The average annual rainfall is 1893.6 mm/year, and the monthly average rainfall varies between none in August to 709.2 mm in January (BPS-DIY 2008). This sub-watershed represents the most urbanized region in the southern part of the central Java Island. The population density of Yogyakarta municipality was 12,679 people/km2 in 1990, increasing to 12,905 people/km2 by 2005; the surrounding areas were inhabited by 566 people/km2 and 1104 people/km2 in 1990 and 2005, respectively (BPS-RI 2005). The Gross Regional Domestic Product (GRDP) of the Province of the Yogyakarta Special Region during 2003–2009 shows a trend of the largest contribution from sectors such as commerce, hotels, and restaurants; however, the contribution from the agriculture sector tends to decrease (BAPPEDA-DIY 2009). This trend indicates that urbanization is increasingly dominating the area. The development characteristics in the Yogyakarta Special Region directly affected the land use pattern. Land conversion to a built-up environment from 1996 to 2007 was continuously increasing. The statistical records show that paddy fields covered 19.86% of the total province area in 1996 and reduced to 17.97% in 2007. Meanwhile, residential and industrial areas increased from 15.74% in 1996 to 16.42% in 2007. The development of residential areas was concentrated at the Sleman and Bantul regencies around the Yogyakarta municipality (BAPPEDA-DIY 2008). Rice demand has been increasing at a rate of 1.0% per year due to population growth (BPS-RI 2005), and the rate of rice consumption has increased from 69 to 87.5 kg/capita/year (Distan-DIY 2009) during the last three decades. Hence, there is a need to increase rice production and availability. With the current rice yield of 6 tons/hectare/season with three crops per season, approximately 400 km2 of wet agricultural land for rice area will be needed in the next two decades to meet the demand of the inhabitants of Yogyakarta. Although rice import is another source of rice availability in the area, securing production is a priority policy for maintaining self-sufficiency of rice in Indonesia, especially for rice production regions such as Yogyakarta and its surroundings. 3. Methodology 3.1. Detection of land use/land cover change This study analyzed land use/land cover changes using Landsat and ASTER Terralook satellite images acquired for 1992, 2004, and 2009. Change detection was performed by using post-classification technique. The Landsat satellite was selected because of its high temporal frequency, thus providing more choices for the most cloud-free images, and there is good familiarity of this satellite data by local planning and governing agencies in Indonesia. Moreover, the data are available at no charge from the Global Land Cover Facility (GLCF) at ftp://ftp.glcf.umd.edu/glcf/Landsat/. However, the 30 m spatial resolution of Landsat is at the lower end of the suitable range for urban land use/land cover classification (Jensen and Cowen 1999), though it has proved to be appropriate for monitoring land use change and farmland conversion in urban fringe areas (Li, Zhou, and Chen 2006; Mundia and Aniya 2006; Lin 2007). For more recent data and because good quality Landsat data are not available due to a slice-off defect, we used ASTER Terralook data with a 15 m spatial resolution to prepare the land use map of 2009. The ASTER Terralook data were downloaded from the USGS Global Visualization site at http://glovis.usgs.gov/.



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One scene from each of Landsat data was acquired on 16 July 1992 (TM sensor) and 7 June 2004 (ETM+) for Path/Row 120/65. Two scenes of ASTER Terralook images were acquired on 7 July 2009 to cover the study area. All scenes were the most cloud-free and were acquired in the same season for comparable climatic conditions. The images were georectified and georeferenced into the Universal Transverse Mercator (UTM) coordinate system. The ASTER Terralook images were resampled into a 30 m grid and registered to georeferenced Landsat images. We classified the images using a supervised classification technique. The land use/land cover signatures were selected based on the spectral reflectance of the images, with the guidance of the topographic map scale at 1:25,000 (published in 1994 by the National Coordinating Agency for Surveys and Mapping of Indonesia (Badan Koordinasi Sunai dan Permetaan Nasional, BAKOSURTANAL)), QuickBird images (from the Agency for Regional Development Planning (Badan Perencannaan Pembangunan Daerah, BAPPEDA) used for some parts of the study area, acquired in 2006), Google Earth captures (acquired in 2003 and 2007), and our familiarity with the study area. To achieve a highly distinctive classification, we developed highly separable land signatures. The land signatures were evaluated based on accepted measures of transformed divergence (TD) and Jefferies– Matusita (JM) distance. The separability value had to range between 1.70 and 2.00 for each training site; otherwise, it would be discarded or refined if it failed to meet the criteria. Image analysis was accomplished using the ENVI® 4.7 remote sensing image analysis software (ITT Visual Information Solutions, Colorado, USA). Twenty-seven final signatures were used in the maximum likelihood algorithm to produce land use/land cover maps. These signatures represented seven land use classes: (1) wet agricultural land, (2) dry agricultural land, (3) mixed garden, (4) low density builtup land, (5) high density built-up land, (6) forest, and (7) miscellaneous (water body, lava flow, and others). The first three classes were identified as the most important land use classes by the National Land Agency (Badan Pertanahan Nasional, BPN) of Indonesia (Setiawan, Mathieu, and Thompson-Fawcett 2006). Low- and high-density built-up land classes represented urban development (Xiao et al. 2008; Yili, Xuezhi, Pengfeng, Chenglei, and Jia 2009). The characteristics of land use type considered for image classification are shown in Table 1. Table 1. Description of land use/land cover type for the image classification. Land use/land cover type Wet agricultural land (sawah irigasi) Dry agricultural land (tegalan) Mixed garden (kebun campur) Low-density built-up land High-density built-up land Forest Miscellaneous

Description Agricultural land which is utilized as rice field supported with irrigation facilities. Agricultural land which is not supported with irrigation facilities, usually utilized to cultivate secondary food crops other than rice, for example, cassava, corn, and soybean. Land covered with a mixture of trees and perennial crops, for example, coconut, banana, and bamboo. Land covered by either housing or buildings with surrounding home gardens. Land covered by high density of housing or buildings, usually in a developed area, for example, urbanized area. Land covered by forest, including national park and shrubs. Land covered by water body, lava, and others.

Note: Italicized texts are local names.


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To validate the image classification result, we conducted field checks during December 2009 and April 2010. Due to the time lapse between the satellite images and the field survey, the field data were mainly collected to provide a secondary source of information and to resolve any confusion identified during image analysis. A field check is crucial for generating data that is retrospective in nature for land use map development in this study. Confirmation from local inhabitants and our familiarity of the study area were the basis of refining the preliminary classified land use map. An accuracy assessment of the classified images was performed by developing a set of sample areas using land use maps produced by the government offices, such as those of 1998 and 2002 by BAPPEDA and of 2008 by BPN. Because no ground-truthing was possible for the abovementioned years, those maps were the best available information. Although assuming that these official maps were true, as they have been widely used by planners and stakeholders, we performed an additional verification of the selected sample areas during the second field visit on April 2010. We checked the agreement between the land use of the selected sample areas and the field reality through observations and through collecting information from local inhabitants and from the authors’ own familiarity and judgment as a local inhabitant. Overall accuracies of 90.49% (kappa coefficient 0.89), 92.29% (kappa coefficient 0.91), and 89.51% (kappa coefficient 0.88) were obtained for the 1992, 2004, and 2009 images, respectively. All classified images were then used to detect farmland conversion to nonagricultural use. Attention was given to the conversion of wet agricultural land into built-up areas. 3.2. Evaluation of land potential for rice cultivation To assess the significance of farmland loss due to the land use change, we evaluated the land potential class of the converted land for rice cultivation. Rice is predominantly planted in prime farmland, as it is the staple food in the study area. We developed a base map of land potential for the whole study area and then superimposed the map with a map of converted farmland during 1992–2009. It would be a significant loss if the converted land had a high potential for rice cultivation. Land potential was evaluated based on the land suitability class for rice cultivation and irrigation support availability. Land suitability is considered an important factor because it describes land characteristics that succeed rice cultivation. Higher land suitability and adequate irrigation water availability can be considered as having the higher potential. Two separate maps, namely, rice suitability and irrigation support, were prepared to represent these two factors. The land suitability map was developed based on a soil map with a 1:50,000 scale and respective soil series data (Puslittanak 1994). We classified the suitability of the respected soil series for rice cultivation into ‘suitable (S)’ and ‘not suitable (NS),’ according to the FAO Framework of Land Suitability (FAO 1976) and the criteria published by the Ministry of Agriculture of the Republic of Indonesia (AARD 2003). A map of irrigation support availability was developed from the irrigation coverage map of 2004 at a scale of 1:50,000 collected from the Agency of Public Works of Province of the Yogyakarta Special Region. Irrigation availability was classified into ‘available’ and ‘not available’ classes. Both maps were prepared as raster maps with the same extent and grid size (30 m × 30 m) as the land use map derived from the satellite images. Analysis was performed by cross-tabulation of both criteria, which resulted in four classes of land potential for rice cultivation, as shown in Table 2. A nonsuitable land without irrigation support was considered as ‘no potential,’ while in contrast any suitable land with irrigation support was classified as ‘high potential.’ Suitable land but without

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Journal of Land Use Science Table 2. Classification of land potential for rice cultivation. Irrigation network availability Land suitability for rice


Not suitable Suitable

Not available

Available

No potential Moderate potential

Low potential High potential

irrigation support was marked as ‘moderate potential,’ whereas land with irrigation facility but not suitable for rice was considered as ‘low potential.’ Moderate potential land is intrinsically more upgradable than low potential land. The land suitability analysis was achieved using the Map Calculator procedure of ArcView GIS. 3.3. Projecting future land use We simulated the future land use change for the year 2029 using the Dyna-CLUE model (Dynamic Conversion of Land Use and its Effects) (Verburg and Overmars 2009), as the current regulation and spatial policy of the government are planned to last until 2029. Hence, we assume that the generated information will be valuable policy inputs. The DynaCLUE model is based on the spatial allocation of demands for different land use types to individual grid cells. The model is an adapted version of the CLUE’s model (Verburg et al. 2002; Castella, Pheng-Kam, Dinh-Quang, Verburg, and Thai-Hoanh 2007). The predecessor CLUE has been used and validated in multiple case studies of land use change in many regions, including Costa Rica (Veldkamp and Fresco 1996), Java-Indonesia (Verburg, Veldkamp, and Bouma 1999), Sibuyan Island (Philippines) and Malaysia (Verburg et al. 2002), and Vietnam (Castella and Verburg 2007). Dyna-CLUE has been successfully used in regions such as Thailand, where Trisurat, Alkemade, and Verburg (2010) used the model to project land use change from 2002 to 2050 due to deforestation. An improvement over the former version of CLUE, DynaCLUE, combines more dynamic modeling and empirical quantification of the relations between land use and its driving factors. For each location, the possibilities for change are evaluated based on the actual land use and the competitive strength of the different land uses. Scenarios can be put forth to evaluate different land use change situations caused by the differences in land use requirements and spatial policies (Verburg and Overmars 2009). Four inputs were prepared to run Dyna-CLUE: (1) land demand, (2) location characteristics, (3) spatial policies and restriction, and (4) land use conversion settings. These are described in the following sections. 3.3.1. Land demand Land demand sets the area of each type of land use, which will be allocated by the model. Dyna-CLUE simulates the pattern of land use change, not the quantity of change. The area of change was calculated separately by the land demand module under a specific scenario on a yearly basis for the whole period of simulation. Three land demand scenarios were developed for this study (Table 3). The first scenario was business as usual, meaning that land use change follows the existing trend. We calibrated the linear trends of each land use type derived from the land use change analysis during 1992–2009. The second scenario was protecting high potential land for rice cultivation. We delineated the zone of high potential class land as a restricted

Spatial policy

Existing wet agriculture land (km2 ) (2004) Projected wet agriculture land (2029)

Follows the trend of existing conversion rate (high-density built-up, +2.75%; low-density built-up, +1.8%; wet agriculture, −0.41%; dry agriculture, −1.2%; forest, −1.0%) No land conversion allowed in Merapi National Park

438.81

Scenario 1: business as usual

Table 3. Characteristics of land demand scenarios for 2029.

No land conversion allowed in Merapi National Park and high potential land for rice

Follows the trend of existing conversion rate (same as scenario 1)

438.81

Scenario 2: farmland protection

At minimum 40.000 ha (high-density built-up, +2.63%; low-density built-up, +1.8%; wet agriculture, −0.39%; dry agriculture, −1.2%; forest, −1.0%) No land conversion allowed in Merapi National Park

438.81

Scenario 3: minimum required farmland


8 Partoyo and R.P. Shrestha


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area for land use other than wet agricultural land. This scenario supports the implementation of agricultural land protection policy, which is currently still in the stage of initiation. The third scenario was setting 400 km2 as a minimum area of wet agricultural land that should be available by 2029. This value is based on a prediction by a document from the Regional Long-Term Development Plan (Rencana Pembangunan Jangka Panjang Daerah, RPJPD) year 2009–2029 of Yogyakarta Province (BAPPEDA-DIY 2009). 3.3.2. Location characteristics The location characteristics included biophysical and socioeconomic factors that affect the land use allocation. These inputs are required to develop the binary logistic regression model, as given in the equation below, which is used by Dyna-CLUE to calculate the probabilities of each land use type to be assigned to the preferred pixel. The regression models relate to the occurrence of a land use type and the location characteristics. !

Pi Log (1 − Pi )

"

= β0 + β1 X1i + β2 X2i + · · · + βn Xni

(1)

where Pi is the probability of a grid cell for the occurrence of the considered land use type on location i and the X’s are the location factors. The coefficients (βn ) are estimated through the logistic regression using the land use pattern in 2004 as the dependent variable. We used the land use map for 2004 considering the most available ancillary data for location factors. The regression models were developed for all land use/land cover types except for the miscellaneous type described in Table 1. The literature suggests that a number of factors can play an important determining role in land use allocation or change, as summarized in Table 4. We selected 14 factors as determinants of land use allocation or change based on local conditions. These factors were considered as independent variables in this study, including physical factors (e.g., elevation, slope, distance to road and capital city, land suitability, and irrigation support availability) and socioeconomic factors (e.g., population density and land ownership). Each factor was prepared as a raster map with a 30 m × 30 m grid size to match the resolution of the land use map of 2004. It is better to use high-resolution data for representing detailed land feature; however, it is not clear whether finer scale information leads to more accurate modeling (Chen and Pontius 2011). In our study, we maintained the most possible finest grid size considering reality of land use/land cover change in the study area, data availability, and software limitation for simulation procedure. We developed land use maps from Landsat TM (30 m) and ASTER Terralook (15 m) images. The ASTER Terralook images were converted to 30 m as all other images were of 30 m resolution. It would have been better to maintain a 30 m resolution, but the resolution of other coarser GIS data did not allow the 30 m resolution to be maintained. Although observed conversion of wet agricultural land to nonagricultural land was at subhectare size, the limitation of Dyna-CLUE in handling the maximum number of grids constrained creating grid size smaller than 60 m × 60 m resolution to cover the study area. To prepare input maps with required resolution to use in Dyna-CLUE, we had to downscale land suitability map from the scale of 1:50.000 due to unavailability of large scale map. Considering the area coverage, land use parcel size, and landscape and biophysical characteristics of the study area, the used map scale was found to be satisfactory, specifically in the lack of large resolution maps.

Note: GRDP, gross regional domestic product.

Residential and industrial/commercial land development in Lagos, Nigeria (Braimoh and Onishi 2007)

Land use change patterns in the Netherlands (Verburg, Ritsema van Eck, de Nijs, Dijst, and Schot, 2004)

Urban growth by a case study of Wuhan City in PR China (Cheng and Masser 2003)

Conversion of prime agricultural land into residential and industrial area in the case of Java (Verburg et al. 1999)

Land use change

Table 4. Determinants of land use change.

Elevation, slope, geological unit; soil fertility, soil drainage, soil permeability, soil texture; precipitation, temperature, sunshine, agro-climatic zone, number of wet months; distance to nearest city, to nearest river, and to nearest road; population density, fraction rural population, labor force density, fraction agricultural labor force, GRDP. Distance to roads, to railway lines, to industrial centers, to city centers, and to rivers; density of neighboring waters, industrial areas, developable areas, developed area; population density, spatial master plan. Elevation, groundwater depth and fluctuation; soil organic matter content, soil calcium content, soil texture, soil pH; distance and travel time to nearest railway station, airport, and harbor, distance and travel time to nearest jobs, distance to nearest motorway intersection, travel time to nearest highway entrance, distance and travel time to nearest town/city, to nearest forest/water area/coast/main river; noise level; enrichment factors for neighborhood residential land use, industrial/commercial land use, recreational land use, forest/nature; designated municipalities, delineation of central open space. Elevation, slope; distance from water, distance from protected forest, distance from water works, travel time to targets (major roads, Lagos Island, the Central Business district, industrial centers, airport, and Harbor); population potential, neighborhood indices, spatial policies, income potential.

Major determinants


10 Partoyo and R.P. Shrestha



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Elevation and slope were derived from the ASTER GDEM provided by METI and NASA at the website: http://www.gdem.aster.ersdac.or.jp/search.jsp. Accessibility was calculated as the distance to infrastructure and location centers, such as main road, district capital, regency capital, and provincial capital, using the ‘Find Distance’ function of ArcView based on a topographic raster map converted from a vector map of 1:25,000 scale published in 1994 by BAKOSURTANAL and an administrative map published in 2009 by BAPPEDA. The population density map was generated from the aggregated data based on a district (kecamatan) level for 2004 published by BPS (Central Bureau of StatisticsBiro Pusat Statistik). A land ownership map was compiled from land tenure maps of 2008 provided by BPN. One of the specific land ownership types in Yogyakarta is Sultan Ground/Pakualam Ground (SG/PAG), which requires a specific permit from the authority of the Yogyakarta/Pakualaman kingdom to utilize such land as provisioned by the local culture. A binary logistic regression analysis was carried out for each land use type, which resulted in six regression models. To avoid the effect of spatial autocorrelation, the regressions were calculated based on selected random samples of grids. For each model, we evaluated the goodness-of-fit using the relative operating characteristics (ROC). The ROC compares the observed values, that is, the binary data over the whole range of predicted probabilities. An ROC value of 1.0 indicates a perfectly fit model and a value of 0.5 indicates a completely random model (Pontius and Schneider 2001; Braimoh and Onishi 2007). 3.3.3. Spatial policies and restrictions These inputs delineate areas where land use changes are restricted because of policy measures, such as protected areas. We put the existing Merapi Mountain National Park as a restricted area in all scenarios. The park consists of a forest and shrub area surrounding Merapi volcano at the topmost altitude of the study area. In farmland protection scenarios, we put the national park and farmland preserved areas as restricted areas, and hence, no further land encroachment was allowed in these areas. 3.3.4. Land use conversion settings These land use factors directed the dynamics of the simulations by two sets of parameters, namely, conversion elasticity and land use transition settings. The elasticity was estimated by expert judgment based on capital investment, time, and energy costs needed in converting the land use, ranging from 0 (easy conversion) to 1 (irreversible change), as explained by Verburg et al. (2002). High values for this parameter were assigned to the built-up area and forest because these land use types are not likely to be displaced. Medium values were given to agricultural land and mixed garden. The Dyna-CLUE model uses all inputs to calculate the total probability for each grid cell of each land use type based on the local suitability of the location derived from the logistic regression model, the conversion elasticity, and the competitive strength of the land use type (Verburg and Overmars 2009). Where no constraints to a specific conversion were specified, the location was allocated to the land use with the highest total suitability. Using an iterative process, the competitive strength of the different land use types was adapted until the total allocated area of each land use equaled the total land requirements specified in the scenario.

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4. Result and discussion 4.1. Land use change Land use change was detected from land use maps derived from satellite images of the years 1992, 2004, and 2009. Table 5 summarizes the area coverage of each land use type based on the satellite image interpretation. In 1992, wet agricultural land was the predominant land use type in the study area, covering more than 35% of the area, compared with low-density built-up land, mixed garden, and dry agricultural land, which covered 19.5%, 21.7%, and 16.3%, respectively. These four land use types were also the major land uses in 2004 and 2009. Meanwhile, forest remained constant at approximately 5% of the study area during 1992–2009. High-density built-up land, which covered only 1.5% of the study area in 1992, significantly expanded to 4.1% and 5.4% in 2004 and 2009, respectively. Figure 2 shows a clear expansion of built-up land in 1992, 2004, and 2009. The highdensity built-up land expanded beyond the municipality boundary toward the northeast and southeast. Comparing the series of land use map, it can be clearly seen that the highdensity built-up land has occupied formerly low-density built-up land and wet agricultural land surrounding the municipality. In 2009, it was found that some high-density built-up lands developed along the main roads (Figure 2c). Meanwhile, low-density built-up land has sprawled into the formerly massive wet agricultural land. This trend has increased land fragmentation, which in turn may induce greater threats to farmland susceptibility against conversion. By calculating the area, we found that the major changes in the study area were a decrease in wet and dry agricultural land and an increase in high- and low-density builtup land (Table 5). The prime farmland indicated by wet agricultural land was reduced by almost 94 km2 in 2004 from 527.39 km2 in 1992, although it slightly increased by 2009. The high-density built-up land was increased by a factor of 2.8 in 2004 from 21.87 km2 in 1992, and it continuously increased until 2009. Meanwhile, low-density built-up land slightly decreased during 2004–2009 after significantly increasing from 293.45 km2 in 1992 to more than 355 km2 in 2004. There are two possible reasons for this development. First, it may be due to the implementation of policies prohibiting land conversion launched by the local governments of the Sleman and Bantul regencies. Despite the BPN being in the regency level, the Sleman regency has also established a new institution called the Board of Regional Land Control

Table 5. Coverage area of the land use/land cover categories based on satellite images interpretation. 1992 LC type Wet agricultural land Dry agricultural land Mixed garden High-density built-up Low-density built-up Forest Miscellaneous (river, lava, etc.) Total

2004

Change (km2 )

2009

Area (km2 )

%

Area (km2 )

%

Area (km2 )

%

527.39 244.45 325.42 21.87 293.45 75.83 13.99

35.1 16.3 21.7 1.5 19.5 5.0 0.9

433.85 291.09 273.68 61.06 355.17 71.71 15.70

28.9 19.4 18.2 4.1 23.6 4.8 1.0

438.81 220.78 323.02 81.32 348.02 71.48 18.83

29.2 14.7 21.5 5.4 23.2 4.8 1.3

1502

100

1502

100

1502

100

1992–2004 2004–2009 −93.54 46.64 −51.74 39.19 61.72 −4.12 1.71

4.96 −70.31 49.34 20.26 −7.15 −0.23 3.13



13

Figure 2. Land use/land cover maps interpreted from satellite images for the years: (a) 1992, (b) 2004, and (c) 2009.

(Badan Pengendalian Pertanahan Daerah, BPPD). Both institutions collaborate in land administration and land conversion permit issuance. In the Bantul regency, the government has established tax exemptions for irrigated paddy fields as an incentive for farmers to sustain their farmland. Second, the upgrading of the existing irrigation infrastructure has extended the coverage of irrigated command areas and prolonged the period of irrigation water availability, thus making it possible to cultivate three crops of rice per year. We detected a third planting season of paddy fields in the July 2009 ASTER Terralook image, which was otherwise not found in the image of 2004. For a detailed understanding of the changes at the spatial level, a pixel-by-pixel land use/land cover transition analysis was performed by superimposing a pair of digitally classified images and producing a matrix of change. Table 6 shows that during 1992–2004, 6.96 km2 and 43.11 km2 of wet agricultural lands in 1992 were converted to high- and lowdensity built-up areas, respectively, in 2004. Wet agricultural areas were also converted to dry land and mixed garden land use, amounting to 77.71 km2 and 78.93 km2 , respectively.

14


Table 6. Land use/land cover conversion between 1992, 2004, and 2009.


2004 (km2 ) WetA

DryA

MixG

HiBu

LoBu

Forest

Misc

1992

1992 (km2 )

WetA DryA MixG HiBu LoBu Forest Misc 2004

314.51 45.98 66.08 1.96 1.22 1.26 2.84 433.85

77.71 88.66 81.16 1.02 33.51 7.41 1.62 291.09

78.93 51.93 111.31 0.58 22.46 7.86 0.61 273.68

6.96 43.11 2.33 49.46 2.82 56.56 15.38 2.44 33.14 200.36 0.15 2.01 0.28 1.23 61.06 355.17 2009 (km2 )

3.15 5.19 6.99 0.26 0.61 54.57 0.94 71.71

3.02 0.9 0.5 0.23 2.15 2.57 6.07 15.44

527.39 244.45 325.42 21.87 293.45 75.83 13.59 1502.00

2004 (km2 )

WetA DryA MixG HiBu LoBu Forest Misc 2009

309.8 57.95 51.99 6.86 6.85 3.01 2.35 438.81

40.41 82.04 44.25 3.3 46.1 3.51 1.17 220.78

48.73 75.11 117.1 2.15 67.26 10.97 1.70 323.02

4.71 4.01 2.06 44.32 26.12 0 0.10 81.32

1.5 7.61 8.9 0.1 2.12 49.95 1.30 71.48

3.01 1.9 0.57 0.11 2.12 3.14 7.72 18.57

433.85 291.09 273.68 61.06 355.17 71.71 15.44 1502.00

25.69 62.47 48.81 4.22 204.6 1.13 1.10 348.02

Note: WetA, wet agricultural land; DryA, dry agricultural land; MixG, mixed garden; HiBu, high-density built-up land; LoBu, low-density built-up land; Misc, miscellaneous.

The latter was caused by either a permanent disruption of the irrigation water facility or temporary use as dry agricultural land or mixed garden before a formal permit for building construction was issued by the government authorities. A similar trend of increasing built-up land at the cost of wet agricultural land was observed during 2004–2009. Within a short period of 5 years, 4.71 km2 of wet agricultural land was converted to high-density built-up land. The wet agricultural land that remained unchanged from 2004 to 2009 was just 309.8 km2 , even less than the area of 314.51 km2 during 1992–2004. Within both periods, high-density built-up areas were predominantly converted from low-density built-up areas, which were converted from formerly mixed garden, dry agricultural land, or wet agricultural land. To a small extent, high density builtup areas were converted directly from wet agricultural land, dry agricultural land, or mixed garden. The transition of land use change is summarized in Figure 3. Figure 3 shows the pathways of change observed among the different types of land use/land cover, particularly the most important change, that is, the conversion to builtup land. We excluded forest and miscellaneous types because both remained relatively unchanged. The lines indicate pathways, while arrowheads indicate the direction of change. The thickness of the line indicates the intensity of change, while the numbers show the percent area of change based on the initial land use/land cover. In Figure 3, it is apparent that the wet agricultural land category appears as source of built-up land, either directly or indirectly. The built-up land that was formerly wet agricultural land has been converted to meet the demand of land for housing and industry due to urban development. This conversion has occurred in various ways. Some rice field plots were directly converted to settlements. Because the conversion occurred either sporadically or massively in a block of plot, it resulted in a low-density and eventually a high-density built-up land. The growth of such

15



High-density built-up land

8 0.8

0.9

1 Low-density built-up land

13

18 Dry agricultural land

Mixed garden 7

13

12 Wet agricultural land

Figure 3. Pathways of land use/land cover change in the study area. The numbers show percent area of change based on original land use/land cover.

new settlements has induced more pressure on the surrounding farmland plots for additional conversion. The construction of the built-up land affected irrigation services due to the disruption of irrigation facilities and the adverse impacts on irrigation, such as water pollution. Some plots became less productive because of a lack of irrigation water availability or poor irrigation water quality. In addition, the newly built-up land gradually raised the land price of the surrounding area, and this trend of increasing land price was a stimulus for farmers to sell their plots rather than maintain them for cultivation. 4.2. Land potential for rice cultivation We assessed the land potential for rice cultivation in the study area. This assessment indicated that 34.1% of the study area is suitable for rice cultivation, and 35.2% of the study area is supported by irrigation facilities (Table 7). In fact, the irrigation facilities also cover existing rice fields at some locations of nonsuitable land for rice. The nonsuitable land was mostly constrained by soil permeability that was too high, caused by coarse soil texture and low fertility due to low organic matter and clay content (Puslittanak 1994). It is possible

16


Table 7. Distribution of land potential in the study area. Irrigation support availability


Land suitability for rice Not suitable Suitable Total

Not available

Available

Total (%)

No potential (52.8%) Moderate potential (12.0%) 64.8%

Low potential (13.1%) High potential (22.1%) 35.2%

65.9 34.1 100.0

Table 8. Area of existing wet agricultural land according to land potential classification. Land potential class High potential Moderate potential Low potential No potential Total

Area in 1992

Area in 2009

Change

km2

%

km2

%

km2

km2 /year

189.70 83.33 84.75 169.61 527.39

35.97 15.80 16.07 32.16 100

170.26 65.29 68.81 134.45 438.81

38.8 14.88 15.68 30.64 100

−19.44 −18.04 −15.94 −35.16 −88.58

1.14 1.06 0.94 2.07 5.21

to upgrade the potential of some land for rice cultivation by improving the soil quality. Currently, only 22.1% of the land in the study area has a high potential for rice cultivation. A cross-tabulation of the maps on land potential and existing farmland in 1992 and 2009 indicated an uneven distribution of farmland. Table 8 shows that rice has been cultivated not only on high potential land but also on areas of low potential. Not more than 39% of wet agricultural land was situated on high potential land during 1992–2009. If we take into account the moderate potential land, which is suitable for rice cultivation but currently without irrigation facilities, it accounts to approximately 15% of the total wetland area. The rest of the lands (46%) are of low or nonexistent potential. Land use change analysis revealed that the wet agricultural land situated on the high potential land suffered a 1.14 km2 loss per annum (Table 8). This result was confirmed with statistics released by the Agency of Agriculture and Forestry of the Regency of Sleman and Bantul located in the study area. The statistics show that during 2000–2008, the average prime farmland converted to urban use was 1.09 km2 per annum (Dispertahanan 2009). As prime farmland, the conversion of this high potential land is considered a significant loss. In relation to rice cultivation, this negative trend might threaten rice-harvesting areas, which in turn will reduce rice production. 4.3. Farmland preservation The Indonesian Law No. 41/2009 requires the government to develop a program of farmland preservation and farmland extension (RI 2009). The main cause of farmland loss is the encroachment upon prime farmland by urban development. In the study area, high-density built-up land has been multiplied by a factor of 3.7 during 17 years, from 21.87 km2 in 1992 to 81.32 km2 in 2009 (Table 9). Low-density built-up land, which consists of less dense settlements, also increased though at a lower rate. That both conversions occurred in high potential land is a serious concern for sustaining farmland preservation.

17

Journal of Land Use Science Table 9. Land potential area coverage according to land use types in 1992 and 2009.


Land use 1992 (km2 ) Wet agricultural land High-density built-up Low-density built–up 2009 (km2 ) Wet agricultural land High-density built-up Low-density built-up

No potential

Low potential

Moderate potential

High potential

Total

169.61

84.75

83.33

189.70

527.39

17.70

1.39

0.97

1.82

21.87

150.42

49.11

36.26

57.66

293.45

134.45

68.81

65.29

170.26

438.81

60.74

7.49

6.87

6.20

81.32

193.76

58.48

40.73

62.20

355.17

In summary, both high- and low-density built-up lands increasingly occupied high potential land from approximately 59 km2 in 1992 to more than 68 km2 in 2009. This change was approximately 14% of the total increase of built-up area during the same period. At an average rice productivity of 6 tons GKG/hectare/season, as reported by the Agency of Agriculture of Yogyakarta Province (Distan-DIY 2009), approximately 1661 tons GKG per season have been lost solely due to the loss of high potential land as a result of land conversion during the study period. The cumulative effect is enormous and is of serious concern. According to the Board of Food Security and Agricultural Extension of Yogyakarta, at least 400 km2 of paddy field should be preserved to fulfill the rice demand of the Yogyakarta population for the next 20 years (2029) (BAPPEDA-DIY 2009). Increasing cropping intensity can be an alternative to increase total rice production, but its contribution will not be comparable to rice yield loss due to farmland conversion. Besides, increase in cropping intensity is not possible everywhere due to biophysical and irrigation limitations as high potential area suitable for rice is very limited (Table 7). Indeed, in those highly suitable areas, growing three crops per year is already in practice. 4.4. Future land use simulation As discussed earlier, 14 location factors were selected for the land use simulation. Among them, seven factors were continuous variables and the rest were binary variables (Table 10). With respect to altitudinal variation in the study area, the southern beach area has the lowest elevation, and the summit of Mount Merapi has the highest. The Bantul alluvial plain is flat, while the hilly area in the western and southeastern parts of the study area has a steep slope (Figure 1). The binary logistic regression analysis for each land use type, using each type as a binary dependent variable and the 14 location factors as independent variables, resulted in the regression estimates shown in Table 11. It can be seen from the table that not all of the location factors were included in the regression models, and each factor contributed differently depending on the land use type. For high-density built-up land, all factors, except land property rights, the land owned by villages, and the lands of SG/PAG, were found to have

18 Table 10.

Partoyo and R.P. Shrestha Location factors for future land use simulation.

Variable name Elevation (m)

Land suitability for rice Irrigation support Population density (people/km2 ) Land property right Land utilization right

Continuous variable Continuous variable Continuous variable Continuous variable Continuous variable Continuous variable Binary variable Binary variable Continuous variable Binary variable Binary variable

Land owned by village Land owned by state Land of SG/PAG

Binary variable Binary variable Binary variable

Slope (%)


Variable scale

Distance to main road (m) Distance to district (m) Distance to regency (m) Distance to province (m)

Minimum

Maximum

0

2,960

0

72

0

4,141

0

5,250

0

18,632

0

34,363

0 = not suitable 0 = not available 359

1 = suitable 1 = available 12,085

0 = nonproperty right 0 = nonutilization right 0 = nonvillage land 0 = nonstate land 0 = non-SG/PAG

1 = property right 1 = utilization right 1 = village land 1 = state land 1 = SG/PAG

Note: SG/PAG, Sultan Ground/Pakualam Ground.

significant relations. This result means that the development of high-density built-up land was not done in consideration of private land tenure, although it was not preferable to build on the land owned by the state or land with utilization rights. The latter was evidenced by a negative relation between high-density built-up and land utilization rights or land owned by the state. It should be specifically noted that the land suitable for rice cultivation is highly correlated with high-density built-up areas, which implies that high-density built-up land was developed at locations with a high potential for wet agriculture land. However, dry agricultural land was not a competitor of wet agricultural land because it does not need land suitability for rice and irrigation facilities. Accessibility-related factors (distance to road, district, and province) showed a negative relationship with high-density built-up land because of more evidence that this type of land is located close to roads, districts, and provincial capitals. In contrast, a positive relationship between the distance to the regency and high-density built-up land could be due to this type of land usually being away from the regency. Housing and building developers prefer to build in district areas because of cheaper land prices and the greater convenience of the natural environment. As commonly found, provincial capital was surrounded by a denser built-up area than regency capital. Table 11 shows that low-density built-up land had a negative relationship with distance to regency but a positive relationship with distance to province, meaning the low-density built-up land was developed close to the regency and away from the provincial capital. Low-density built-up land was also related to land with utilization rights, land owned by the state, and the land of the SG/PAG. Currently, low-density built-up land is widespread for these types of land ownership. The spatial distributions of the seven land use types were explained moderately to well by the selected location factors, as indicated by the ROC values that ranged from

19

Journal of Land Use Science Table 11. β-Values of location factors for binary logistic regressions of each land use type.


Variable Elevation (m) Slope (%) Distance to main road (m) Distance to district (m) Distance to regency (m) Distance to province (m) Land suitability for rice Irrigation support Population density (people/km2 ) Land property right Land utilization right Land owned by village Land owned by state Land of SG/PAG Constant ROC

High-density built-up land

Low-density built-up land

Wet agricultural land

Dry agricultural land

Mixed garden

−0.003 −0.04 0.00009

−0.002 0.09 0.0002

n.s. 0.04 0.0001

0.006 −0.22 −0.0003

0.0001

n.s.

−0.00006

n.s.

n.s.

0.0002

0.004 −0.13 −0.0005

−0.002 n.s. n.s.

−0.0001

−0.00008

0.0001

0.0001

0.0001

−0.00005

−0.00005

−0.00004

0.0001

0.0001

−0.0003

0.00008

Forest

0.56

−0.23

−0.93

n.s.

n.s.

n.s.

−1.10

0.27

1.21

n.s.

n.s.

−2.49

n.s.

n.s.

−0.00001

n.s.

−0.0001

n.s.

n.s.

n.s.

n.s.

−0.46

−1.95

2.81

2.73

n.s.

n.s.

−2.70

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

−1.29

1.45

n.s.

1.05

2.36

n.s.

n.s.

1.75

1.44

n.s.

−1.11

n.s.

5.26 0.94

−8.36 0.76

−6.14 0.83

−4.21 0.81

−5.08 0.82

−0.75 0.99

0.00001

n.s.

Note: n.s. = not significant at 0.05 level; SG/PAG, Sultan Ground/Pakualam Ground; ROC, relative operating characteristic.

0.76 to 0.99 (Table 11). The lowest ROC of low-density built-up land (0.76) was due to the widespread location of this land use type in the study area. Considering the long-term existing trend from 1992 to 2009, the farmland loss is expected to continue. Based on the logistic regression estimates summarized in Table 11 and scenarios described in Table 3, the simulation of land use change from 2004 to 2029 using Dyna-CLUE resulted in different patterns of land use (Figure 5). For validation purposes, we ran Dyna-CLUE to project a land use map from 2004 to 2009 under the existing trend scenario. We compared this projected map (Figure 5a) to the actual land use map of 2009 derived from the satellite image interpretation (Figure 2c). Under visual examination, both maps looked similar. The high-density built-up land clearly showed a similar extent and pattern. We can track that some high-density built-up areas were developed along the main roads. Wet agricultural land, mixed garden, and forest were also spread out in a similar pattern. This good conformity implies the validity of the


20


Dyna-CLUE model for simulating future land use change in the study area. In addition, a statistical approach was performed to validate the model. We calculated the ROC for each land use type between the projected map and the reference map. The ROC demonstrated how well the projected map matched the location shown on the reference map (Pontius and Schneider 2001). An ROC of 0.5 indicates a conformity equivalent to random chance when the grid cells are difficult to classify. An ROC of 1 indicates perfect conformity (Pontius and Chen 2006). Table 12 shows that the ROC of each land use type ranged between 0.54 and 0.83. The lowest ROC showed slightly better conformity of low-density builtup land with random locations, which means that the location of land use projected by the model conformed to the reference map. The higher ROC of all other land use types implied that, as a whole, the model projected a valid future land use map. Based on the ROC value, the prediction was better for the high-density built-up, forest, mixed garden, wet agricultural land, dry agricultural land, and low-density built-up land use types. Aside from the above validation for the location of change (i.e., pattern), the model was also validated for the quantity of change. By design, Dyna-CLUE simulates the quantity of change with a land demand module using various scenarios. The predictive accuracy of the model for the quantity of change was validated based on a three-map comparison technique (Pontius et al. 2008), that is, the reference map of 2004, the reference map of 2009, and the predicted map of 2009. Figure 4 presents the results of the overlay of the three maps. The black pixels in Figure 4 show where the model predicted change correctly. The dark gray pixels show where the change was observed and the model predicted change; however, the model predicted conversion in the wrong category. The medium gray pixels show the errors where change was observed at locations where the model predicted persistence. The light gray pixels show errors where persistence was observed at locations where the model predicts change. The white pixels show locations where the model predicted persistence correctly. Figure 4 allows a visual assessment of the nature of the prediction errors, which are various shades of gray. The model is less accurate than its corresponding null model, as there are more light gray pixels than black pixels, which is common for models using fine pixel resolution and for land use change, which includes a large portion of persistence areas. This result was also indicated by the calculation of the accuracy of the model’s prediction for the quantity of change using values of figures of merit, producer’s accuracy, and user’s accuracy (Pontius et al. 2008) based on the following equations:

Table 12. ROC between projected map and reference map of year 2009 for each land use type. Land use type

ROC

High-density built-up Low-density built-up Wet agricultural land Dry agricultural land Mixed garden Forest

0.83 0.54 0.60 0.57 0.62 0.80

Note: ROC, relative operating characteristic.



21

Figure 4. Validation map obtained by overlaying the reference map of year 2004, reference map of year 2009, and prediction map for year 2009.

Figure of merit =

B (A + B + C + D)

B (A + B + C) B User" s accuracy = (B + C + D)

Producer saccuracy =

(2)

(3) (4)


22


where A is the area of error due to the observed change predicted as persistence, B the correct areas due to the observed change predicted as change, C the area of error due to the observed change predicted as the wrong-gaining category, and D the area of error due to the observed persistence predicted as change. The calculation of the accuracy of the model prediction resulted in 21%, 24%, and 47% for figure of merit, producer’s accuracy, and user’s accuracy, respectively. To maintain the fine pixel resolution, we used the model to simulate future land use. Figure 5 presents the simulation of land use for the year 2029. Under scenario 1 (business as usual), the simulation resulted in an expansion of urban area. Both high- and low-density built-up lands expanded to the west instead to the northeast and southeast, as observed between 2009 and 2029 (Figure 5a and 5b). It is also clearly shown that some mixed garden and dry agricultural lands have been changed to low-density built-up land in the southwest part. Scenario 2 was developed to assess the impact of farmland protection on future land use. It was proved that, although land demand was set similar for scenarios 1 and 2 (Table 13), the simulation results are different (Figure 5b and 5c), particularly due to the varying policies of the scenarios. Where no constraints to a specific conversion were specified, the location was allocated to the land use with the highest total probability, which means that spatial policy will effectively affect the land allocation, preventing the conversion of high potential land. For scenario 3, we projected 400 km2 for wet agricultural land. It is shown that the urban area of scenario 3 will look similar in shape as scenario 1, but the former will have a less substantial distribution of high-density built-up land (Figure 5d). The National Park of Merapi Mountain will be preserved from land use conversion, but not for the surrounding forest and shrubs, which are converted into low-density built-up land or dry agricultural land. As we intend to assess the impact of farmland protection policy on future land use, scenario 2 proved that the policy will preserve wet agricultural land from urban sprawl. In fact, both scenarios 1 and 2 allocated similar areas for each land use type (Table 13), as both assume the same land demand. However, the implementation of farmland protection in scenario 2 clearly resulted in a spatially different pattern of land use. Regarding scenario 3, the conversion rate of wet agricultural land to built-up land should be suppressed to provide adequate farmland area in 2029. 5. Conclusion This study, based on the best available input data and assumptions, demonstrates that the ongoing farmland loss in the study area, which is alarming, is predominately due to the expansion of built-up areas. Urban development has expanded with low- and high-density built-up areas combined by nearly fourfold in last two decades. Urban sprawl clearly occupied the former low-density built-up areas and agricultural lands around the municipality boundary. At least 14% of the high-density built-up land in 2009 was converted from formerly high potential land for rice cultivation. It is also evident that the typical pathway of farmland conversion gradually evolved from low-density urban sprawl to high-density area. As shown by land use simulations, the trend of farmland conversion will continue in the lack of strong policies for farmland protection. In relation to the allocation of adequate farmland area, scenarios 1 and 2 failed to preserve at least 400 km2 of wet agricultural land, which suggests that the current rate of farmland conversion is high and should not



23

Figure 5. Land use map predicted by the Dyna-CLUE: (a) year 2009, scenario 1 – business as usual; (b) year 2029, scenario 1 – business as usual; (c) year 2029, scenario 2 – farmland protection; and (d) year 2029, scenario 3 – minimum required farmland.

be allowed to continue. In fact, no future built-up land should be allowed to occupy wet agricultural land. This conservation goal needs robust spatial policies to regulate zoning for urban development, including high-density built-up areas.

24 Table 13.

Partoyo and R.P. Shrestha Coverage area of each land use type projected by 2029.


Area (km2 ) High-density built-up Low-density built-up Wet agricultural land Dry agricultural land Mixed garden Forest Miscellaneous

Scenario 1: business as usual

Scenario 2: farmland protection

Scenario 3: minimum required farmland

105.99

105.99

95.18

526.63

526.63

515.77

389.19

389.19

400.00

201.41

201.41

223.04

206.92 58.24 13.62

206.92 58.24 13.62

223.14 31.26 13.62

To secure food production, farmland preservation should be given priority against the increasing trend of agricultural land conversion to nonagricultural use. Land potential evaluation based on land suitability for rice and the availability of irrigation services to delineate the potential areas can be useful in this regard. This should be supported by designing and implementing a proper spatial policy for the preservation of such important land. Acknowledgments This research was financially supported by the DIKTI Scholarship of the Government of the Republic of Indonesia. The authors gratefully acknowledge Prof. Peter H. Verburg for allowing us to use the full version of Dyna-CLUE and for the necessary guidance on the software. They also thank Prof. Junun Sartohadi and his colleagues for providing digital maps and field assistance. They are grateful to the anonymous reviewers for their valuable comments and suggestions that enhanced the manuscript. Thanks are also due to GLCF (Global Land Cover Facility), USGS Glovis (United States Geological Survey-Global Visualization, METI (the Ministry of Economy, Trade and Industry) of Japan and US NASA for the free download of remote sensing data.

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