Analysis of twenty years of categorical land transitions in the Lower Hunter of New South Wales, Australia

Agriculture, Ecosystems and Environment 135 (2010) 336–346

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Analysis of twenty years of categorical land transitions in the Lower Hunter of New South Wales, Australia Ramita Manandhar a,*, Inakwu O.A. Odeh a, Robert Gilmore Pontius Jr.b a b

Faculty of Agriculture, Food and Natural Resources, The University of Sydney, Sydney, NSW 2006, Australia School of Geography, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 May 2009 Received in revised form 27 October 2009 Accepted 30 October 2009 Available online 27 November 2009

Land use and land cover change (LUCC) studies are drawing increased attention due to their importance in ecosystem management. Post-classification change detection provides a ‘‘from-to’’ change matrix; however, traditional analysis of the change matrix is not sufficient to provide systematic signals of LUCC. This paper analyzes the details of the matrix to compute the quantity, allocation, and dominant signals of land use and land cover (LULC) transitions in a popular tourist destination, the Lower Hunter of New South Wales, Australia. We use classified maps that were derived from Landsat imageries of 1985 and 2005. We applied a change detection analysis based on an extended transition matrix of the two classified maps, and extracted systematic transitions. We then explored how changes are influenced by the resolution of the maps. The net quantity change less than 7% of the study area, while the total change is more than 28%, the latter due to considerable swap changes. Multiple-resolution analysis reveals that about half of the total change is attributable to spatial reallocation of the categories over distances less than 2.3 km. Vineyard has experienced substantial changes in terms of gross gains and gross losses, in spite of its small net change. The three transitions: Pasture/scrubland to Vineyard, Vineyard to Pasture/ scrubland and Vineyard to Built-up are the systematic transitions in the landscape. The transition of Vineyard to Built-up around the centre of the study area and the expansion of Vineyard away from centre is associated with tourism, which is also extending into the new outer vineyards and wineries. This indepth analysis has enabled us to quantify and to visualize the major signals of transitions of LULC categories in the study region. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Change detection Systematic transition Quantity and allocation change Swapping distance Lower Hunter

1. Introduction As land use and land cover change (LUCC) has been recognized as a key element of global environmental change, it is drawing increased attention of researchers concerned with environmental and socio-economic well-being. LUCC is an important process which is widespread and accelerating (Agarwal et al., 2002). The exploitation of natural resources to meet human needs such as food, fibre, shelter, etc. is the main driver of this accelerated land use change (Turner and Meyer, 1994; Lambin et al., 2001; Verburg et al., 2004a). When aggregated globally, LUCC substantially affects key aspects of earth system functioning (Lambin et al., 2001; Goldewijk and Ramankutty, 2004; Verburg et al., 2004b; Foley et al., 2005). Australia is no different, as large-scale destruction and fragmentation of natural woodland and grassland has occurred particularly within the last 200 years (Fensham and Fairfax, 1997; Lunt, 1999; Johnson et al., 2000; Barson et al., 2000; Gordon et al.,

* Corresponding author. Tel.: +61 2 9351 4843; fax: +61 2 9351 5108. E-mail address: [email protected] (R. Manandhar). 0167-8809/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2009.10.016

2003; Pitman et al., 2004; Ramankutty et al., 2006; Versace et al., 2008). The Department of Climate Change (DCC, 2008a) estimated that land use, land use change, forestry and agriculture together account for 23% of total greenhouse gas emission in Australia. More than 300 thousand hectares of forest land have been converted annually from 1990 to 2005, mainly to grassland and cropland (DCC, 2008b). The main contributing factor to this conversion is the rapid growth in population in the later part of 20th century. The national population has more than doubled in last five decades, 9.2 million in 1955 to 20.3 million in 2005 (Australian Bureau of Statistics, 2007). This has led to rapidly increasing urbanization, which is the primary threat to species in urban fringe areas which have been home to more than 50% of Australia’s nationally listed threatened species (Wintle et al., 2005). Similarly, Loughran et al. (2004) found 60% of the 206 sampled sites throughout Australia had net soil losses greater than 1 ton/ha per year. These high rates of soil loss have occurred since the mid 1950s despite there being substantial landholder awareness of soil erosion hazards. Narisma and Pitman (2003) conducted a study of 200-years of impact of land cover change on the Australian near-surface climate; they observed change on local air temperature and reductions in rainfall

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following land cover change. As land use is one of the primary determinants of ecosystem vulnerability, a timely and accurate assessment of land use change, the magnitude of the change and where the change has occurred, can provide useful information for policy decisions on managing resources efficiently (Lu et al., 2004; Verburg et al., 2005). However, there is hardly such a detailed analysis of the nature and spatial distribution of land use change in Australia, particularly at the local and regional scales. Because of the importance of LUCC phenomenon, scientists have developed various techniques for detecting the changes, relying heavily on advances in remote sensing and geographical information system, which have enhanced the efficiency of the techniques (Lu et al., 2004; Jensen, 2005; Berberoglu and Akin, 2009). Change detection involves the application of multitemporal datasets to analyze the temporal effects of the phenomenon quantitatively. Change detection from satellite imageries can be divided into two main categories: (i) preclassification (image-to-image comparison) and (ii) post-classification techniques (map-to-map comparison). The post-classification technique is widely adopted as it provides a matrix of land transitions among categories. A post-classification change detection technique generally involves a transition matrix comprising two-dimensional tables in which the rows show the categories of the map from initial time and the columns show the categories of the map from a subsequent time. On the margins of the transition matrix, the row totals indicate the land use and land cover (LULC) by category in the initial time and column totals indicate LULC by category in the subsequent time. It is a common practice to derive the net change by category from the transition matrices (Petit et al., 2001; Yang and Lo, 2002; Weng, 2002; Currit, 2005). However, reporting the net change alone is fraught with danger of dramatically underestimating the total change as it cancels a gross gain of a category in one location with a gross loss of the same category in another location. This type of land change in spatial allocation is termed as swap change (Pontius et al., 2004). Moreover, traditional transition matrices provide only limited information and fail to indicate the intensity of the LULC transitions. Extending the traditional transition matrices beyond the size of each transition can reveal information that is important for detecting important signals of LUCC. To achieve this, we need to analyze whether the observed transitions appear to have occurred due to a clearly systematic process or due to an apparently random process, according to the quantitative information in the transition matrix. In a statistical sense, a LULC category is said to have gained randomly from others if the gaining category replaces other categories in proportion to the availability of the other categories at the initial time. Similarly, the LULC category is said to have been randomly lost to others if the losing category is replaced by other categories in proportion to size of the other categories at the subsequent time. Large inter-categorical transitions might not constitute the dominant systematic intensities of change in a landscape, because large transitions with large LULC categories are expected under a random process of transitions. Therefore there is a need for a detailed approach to study LUCC transitions in order to detect systematic landscape changes based on deviations of observed patterns of change from random patterns of change. Pontius et al. (2004) was the first to introduce the methodology for determining systematic transitions and a few other studies have realized its importance, thus have adopted and extended the procedure (Braimoh, 2006; Alo and Pontius, 2008; Versace et al., 2008). Identification of the dominant signals of land transition in a landscape could assist with linking patterns to processes of LULC transitions. In following this procedure, there is also the need to explore the allocation of change by suburbs in order to observe where transitions are most prominent relative to socio-economic variation among the suburbs.

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Spatial scale is another issue that needs to be considered in LUCC detection studies. This is because the processes and parameters important at one scale may not be as important or predictive at another scale. Maps could reveal different types of spatial information as data are converted to coarser resolutions (Costanza, 1989; Turner et al., 1989a, 1989b; Luoto and Hjort, 2006). The outcomes regarding changes in landscape can be greatly influenced by the resolution of maps because the amount of swap diminishes and the proportion of agreement tends to rise with the coarsening of the grids. In this sense, multiple-resolution methods could be used to provide additional information that is helpful to evaluate the performance of complex ecological systems (Costanza, 1989; Pontius et al., 2004; Verburg and Veldkamp, 2004; Pontius et al., 2008). Thus multiple-resolution change detection could be used to find the distances over which the swap component of LUCC has occurred. LUCC patterns are the result of the complex interaction between the human and physical environment. Some social scientists hypothesize that humans optimize their well-being by allocating land use conversions at locations with the highest preferences for a specific type of land use conversion at a given time. Preference is an unobserved, dimensionless variable, defined by economic returns, market competition, socio-cultural context, arbitrary preferences and policy regulations. The in-depth analysis of LUCC patterns, such as presented here, would enable scientists to focus on the strongest signals of systematic landscape transitions, which ultimately should be linked to factors driving the transition, although this is out of scope of this paper. From the foregoing introduction, the aim of this paper is therefore to investigate two decades of LUCC in the Lower Hunter in terms of: (i) quantity of changes, (ii) spatial allocation of changes at the pixel level, (iii) spatial allocation of change at the suburb level, (iv) systematic transitions, and (v) distances over which swap changes have occurred. 2. Methods 2.1. Study area The study area is the Lower Hunter, specifically the ‘‘Hunter Wine Country Private Irrigation District’’ within the Lower Hunter. This is approximately 379 km2 and lies between 328370 2100 S to 328510 4500 S latitude and stretches between longitude 1518090 4300 E to 1518240 5800 E. It is located 160 km north of Sydney, within an undulating plain of the Lower Hunter valley, centered on little town of Pokolbin. The Hunter Wine Country Private Irrigation District (HWCPID) pipeline is a community-established irrigation water distribution system designed to ‘‘drought proof’’ the region so it can produce a good quality crop of grape vines. Mining was the principal industrial base and source of employment till the first half of 20th century. However, decline of mining industry by the second half of the century has been paralleled by the growth of the tourism industry due to fine wineries, beautiful views of stretching grape vineyards, golf courses and proximity to Sydney. The wine industry and tourism industry are the dominant contributors to the regional economy. The study area lies between Australia’s earliest European settlements, Sydney, the Howkesbury River and Newcastle, and thus had provided early European contact with indigenous people. The region is growing both economically and demographically. A bucolic rural landscape of the region with its varied mosaic of vineyards, pastures, scattered woodlands and wineries, has been threatened by overdevelopment (Holmes and Hartig, 2007). Some are calling for environmental protection of the region. Our knowledge of LUCC in the region is limited in spite of the importance of LUCC information required for environmental modelling and planning.

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Table 1 Land use and land cover classes delineated for the classification. Land use and land cover categories

Description

Woodland Pasture/scrubland Vineyard Built-up Other

Forest covers including tree cover along the creeks Natural and cultivated pastures, and scrubs with partial grassland Irrigated and non irrigated vineyards Commercial, and residential areas, and other areas with man made structure; roads, railway lines Farm dams, sewage ponds, mining areas, and olive groves

The study area is not a formal political unit, but rather a community-established locale, with the participating members of HWCPID owning farms within the unit. The area is situated astride on two local government areas (LGAs): Cessnock LGA and Singleton LGA. It has a number of suburbs within the LGAs. Some suburbs are almost wholly contained within the study area, while others are partially so. Rothbury-Bal, Pokolbin and Nulkaba are the three suburbs that are almost wholly contained within the study area, while just over 50% of Singleton military areas (SMA), Lower Belford, Keinbah-Bal are within the study area. The remaining suburbs, including Cessnock-Bal which is the only major high density suburb, have less than 50% within the study area. Pokolbin suburb constitutes the largest part of the study area (40%), followed by Singleton military area (19%), Lower Belford (11%), Rothbury-Bal (10%), Keinbah-Bal (8%), North Rothbury (5%) and others: Mount View, Warkworth, Nulkaba, Cessnock-Bal, Branxton, Bellbird, Broke, and Glendon.

Mines/quarries and Olive Groves (Table 1 and Fig. 1). The 2005 LULC map indicates about 4147 ha of vineyards, which is only a few percent different from the Australian Bureau of Statistics (2006), which reported 4390 ha of planted vineyards in 2005– 2006. 2.3. Quantity of change

2.2. Data

The quantity of LUCC by category was analyzed in terms of gross gains, gross losses and persistence as well as net and swap changes. The 1985 and 2005 maps were overlain to produce a matrix that provides the LULC areas by categorical transition between the two points in time. The off-diagonal entries comprise the proportions of the landscape that experienced transition from one category to a different category while the on-diagonal entries indicate persistence of categories. The row totals at the right denote the proportion of the landscape by LULC category in 1985 and the column totals at the bottom denote the proportion of landscape by category in time 2005.

This study uses LULC maps produced for 1985 and 2005 (Manandhar et al., 2009). The Landsat MSS image of January 8, 1985 and Landsat TM image of June 8, 2005 were re-sampled to a common spatial grid of 25 m resolution. The LULC maps were initially produced based on a maximum likelihood classification algorithm and finalized after post-classification correction using ancillary data. The overall classification accuracies of final LULC maps were 91.3% for 1985 and 86.6% for 2005. The LULC categories used for this study are: Woodland, Pasture/scrubland, Vineyard, Built-up and Others, which includes Water-bodies,

2.3.1. Gross gains, gross losses and persistence The cross tabulation matrix of 1985 and 2005 is extended to derive the gross gains and gross losses by categories. The gross gain for each category is derived by subtracting the persistence from the column total, while the gross loss is computed by subtracting the persistence from the row total. Loss-to-persistence ratio (i.e. loss/ persistence) and gain-to-persistence ratio (i.e. gain/persistence) were also derived to assess the tendency of each LULC category to lose to and to gain from other categories, using an approach developed by Braimoh (2006).

Fig. 1. Classified maps of the study area.

R. Manandhar et al. / Agriculture, Ecosystems and Environment 135 (2010) 336–346

2.3.2. Net change and swap change LUCC in terms of the net changes and swap changes are derived from the extended cross tabulation matrix. The total change for a category is the sum of its gross gain and its gross loss. The net change for a category is the difference between the gross gain and gross loss, i.e. difference between the row total and the column total for a given category in the matrix. The swap change for a category is the total change minus the net change for the category.

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difference between the observed and the expected transition under random process of loss is positive, then the category in that column gained more from the category in the row than would be expected by a random process of loss in that category of the row. If the value is negative, then the category in that column gained less from the category in row than would be expected by a random process of loss in that category of the row. 2.6. Swapping distances

2.4. Allocation of change When LULC maps of two points in time are overlain, the spatial distribution of change can be visualized. The gain, loss and persistence for each category are derived to see where the changes have taken place. We based our analysis on suburban divisions provided by the Australian Bureau of Statistics (ABS), since the study area is not an administrative unit, but rather split between two political units. The change maps with the gains, losses and persistence were laid over the suburbs map of the region in order to compute the gains, losses and persistence within each suburb. 2.5. Systematic process of transitions in the landscape One must interpret the transitions relative to the sizes of the categories in order to identify systematic transitions within the matrix (Pontius et al., 2004). A non-random gain and a non-random loss for a particular transition imply a systematic process of change (Alo and Pontius, 2008). Eq. (1) gives expected transitions under a random process of gain where all variables are expressed as a percent (Pontius et al., 2004). ! piþ Gi j ¼ ð pþ j  p j j Þ ; whereby i 6¼ j: (1) 100  p jþ where Gij is the expected transition from category i to j under a random process of gain, p+j is the column total of category j, and pjj is the persistence for category j; thus, (p+j  pjj) is the observed gain for category j, pi+ is the row total for category i, and pj+ is the row total for category j; thus, (100  pj+) indicate sum of row totals of all the categories except the category j. Eq. (1) assumes that the gross gain of each category is fixed, and thus distributes the gross gain across the other categories according to the relative proportion of the other categories at the initial time. Thus the observed persistence is retained on the diagonals in order to examine the off-diagonal transitions. If the difference between the observed and the expected transition under a random process of gain is positive, the category in that row lost more to the category in column than would be expected by random process of gain in that category of the column. If the value is negative, the category in that row lost less to the category in column than that would be expected by random process of gain in that category of the column. Similarly, Eq. (2) gives the expected transition due to a random process of loss.   pþ j Li j ¼ ð piþ  pii Þ ; whereby i 6¼ j: (2) 100  pþi where Lij is the expected transition from category i to j under random process of loss, pi+ is the row total of category i, and pii is the persistence for category i; thus, the (pi+  pii) is the observed loss for category i, p+j is the column total for category j, and p+i is the column total for category i; thus, (100  p+i) indicates the sum of the column totals of all categories except category i. Eq. (2) assumes that the gross loss of each category is fixed, and then distributes the loss across the other categories according to the relative proportion of the other categories in time 2. Again, the observed persistence is retained in order to examine the off-diagonal transitions. If the

The total amount LULC change is influenced by the resolution at which the LUCC studies are conducted. The multiple-resolution aggregation procedure maintains the net quantity of each category because the concept of net quantity is independent of resolution since we use an averaging rule to coarsen the resolution (Pontius et al., 2004; Kuzera and Pontius, 2008). However, a change in resolution can have a dramatic influence on swap because swap is a measurement of spatial reallocation. In this study, a multipleresolution aggregation procedure was applied to compute swap and net change, which could lead to an understanding of the geographic distances over which LUCC transitions have occurred as suggested by Pontius et al. (2004). The multiple-resolution aggregation analysis was performed using the Validate module of IDRISI Andes. Swap, persistence and net changes over the multiples of the base resolution in powers of two were computed. 3. Results and discussion 3.1. Quantity of change 3.1.1. Gross gain, gross loss, and persistence The transition matrix of the 1985 and 2005 LULC maps is shown in Table 2, wherein the rows display the results of the LULC categories of 1985 and the columns display those of the categories of 2005. The traditional transition matrix would have had only the bold digits without the last column and the last row, while this extended transitional matrix has the last column indicating gross loss by category and the last row indicating gross gain by category in the landscape during the 20-year accounted period. The largest category is Pasture/scrubland followed by Woodland and then Vineyard at both points in time. Pasture/scrubland, Woodland and Vineyard respectively constituted 53%, 35% and 10% of the study area in 1985 and 46%, 38% and 11% in 2005. Built-up comprised only 2% in 1985 but expanded to 4% in 2005. The cross tabulation matrix (Table 2) is extended to show the gross gains and gross losses by category. The gain is highest for Pasture/scrubland followed by Woodland and Vineyard (9%, 9% and 7%, respectively). Loss is highest for Pasture/scrubland followed by Vineyard and Woodland (16%, 6% and 6%, respectively). Hence Pasture/scrubland has the highest area of gain and loss as this is the largest category, however loss is much higher in comparison to gain in this category while for all others the gain is more than the loss. There are substantial exchanges of areas between Woodland and Pasture/ scrubland and also between Pasture/scrubland and Vineyard (Table 2). The prominent transitions are from Pasture/scrubland to both Woodland (9%) and Vineyard (6%). These are followed by both Vineyard and Woodland converting to Pasture/scrubland (5% and 4%, respectively) (see Table 2). As persistence dominates most landscapes, it is important that statistical methods account for persistence when examining LUCC (Pontius et al., 2004). The persistence is 72% of the landscape. In other words, about 28% of the study area exhibited transition from one category to a different category during the 20-year period. Loss-to-persistence ratio is greater than 1 for Vineyard indicating its higher tendency to lose than to persist (Table 3). Gain-topersistence ratio is greater than 1 for Vineyard, Built-Up and Other,

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Table 2 Transitions in percentage of the total landscape under observed (in bold), random process of gain (in italics) and random process of loss (in normal font), with the three systematic transitions highlighted in gray. Year 1985

2005 Woodland

Total 1985 (pi+) Pasture/scrubland

Vineyard

Built-up

Other

Loss

Woodland

28.8

4.2 6.9 4.4

0.9 2.7 1.0

0.7 0.9 0.4

0.1 0.1 0.1

34.7 39.3 34.7

5.9 10.5 5 .9

Pasture/scrubland

8.6 7.5 11.3

36.9

5.9 4.1 3.3

1.3 1.3 1.2

0.3 0.2 0.2

53.0 50.0 53.0

16.0 13.1 16.0

Vineyard

0.6 1.4 2.5

4.8 2.0 3.1

4.0

0.4 0.2 0.3

0.1 0.0 0.0

9.9 7.7 9.9

5.9 3.6 5.9

Built-up

0.0 0.3 0.1

0.1 0.4 0.1

0.1 0.2 0.0

1.7

0.0 0.0 0.0

2.0 2.6 2.0

0.3 0.8 0.3

Other

0.0 0.1 0.1

0.1 0.1 0.1

0.0 0.0 0.0

0.0 0.0 0.0

0.3

0.4 0.4 0.4

0.2 0.2 0.2

Total 2005 (p+j)

38.0 38.0 42.8

46.2 46.2 44.6

10.9 10.9 8.4

4.2 4.2 3.7

0.7 0.7 0.6

100.0 100.0 100.0

28.3 28.3 28.3

Gain

9.2 9.2 14.0

9.3 9.3 7.7

6.9 6.9 4.3

2.4 2.4 1.9

0.4 0.4 0.3

28.3 28.3 28.3

indicating their tendency to expand relative to their initial size. Loss-to-persistence and gain-to-persistence ratios are greater than 1 for Vineyard, indicating that this category has a tendency to experience both gross losses and gross gains. 3.1.2. Net change and swap change A gross gain of one category is always accompanied by a gross loss of another category, so the total gross gain is equivalent to the total gross loss in a landscape, which is 28% of our study area. Pasture/scrubland is the most dynamic category in terms of both gross gains and gross losses, since it accounts for 9 percentage points of the total gross gain and for 16 percentage points of the total gross losses (Table 4). While the sum of gross gain and gross loss indicates the total change, the difference between the gross gain and gross loss for a category is the net change for the given category. The difference between the total change and net change is the amount of swap change. Thus Pasture/scrubland exhibits net change on 7% of the study area and swapping change on about 19% of the study area. There is a high proportion of swapping components of change for Pasture/scrubland, Vineyard and Woodland, while Built-up is the only category that has minimal swap change in comparison to net change, since loss of Built-up is negligible. Vineyard experiences only 1.0 percentage point of net change in the landscape; thus, had we considered only the net change, the bulk of change in Vineyard would have been overlooked, which could have led to the wrong conclusion that Vineyard is one of the more stable categories. The overall net Table 3 Ratios of loss- and gain-to-persistence. Land use/land cover class

Loss-to-persistence

Gain-to-persistence

Woodland Pasture/scrubland Vineyard Built-up Other

0.2 0.4 1.5 0.1 0.6

0.3 0.3 1.7 1.4 1.5

Total

0.4

0.4

change is less than 7% of the study area in the 20-year accounting period, while the observed total change is more than fourfold of net change. Thus both swap and net changes are important to understand the total change in a landscape. This is in agreement with the finding of Pontius et al. (2004) who stated that accounting for only net change could lead to a bias of dramatically underestimating the total change. 3.2. Change by suburb Fig. 2 shows the loss of Vineyard is prominent in the Lower west of Rothbury-Bal and Centre of Pokolbin Suburbs; however, Vineyard increases in total area in Pokolbin suburb as well as in the whole of the study area due to expansion of Vineyard away from the centre of the suburb as well as in other surrounding suburbs. Expansion of Woodland occurs mainly in the SMA, which is the northwestern part of study area where private access is denied, and in Keinbah-Bal, which is the eastern part of study area. There is not much difference of gains and losses in other suburbs. The Builtup expansion is mainly in the outskirts of the 1985 Built-up, i.e. expansion of Cessnock city, and also at the lower east of RothburyBal, and in Nulkaba and at the centre of Pokolbin suburbs (Fig. 2). Pokolbin is the suburb covering a substantial part of the study area, and it has the largest proportion of Woodland, Pasture/ scrubland, Vineyard, and Built-up (Fig. 3). Pokolbin is the main suburb known for vineyard and wine tourism. The gain in Built-up is more than the loss in all the suburbs (Fig. 3d). Though Built-up area is only 4% of the total area in 2005, this is more than double that of 1985. Built-up area constitutes a small percentage of the total landscape, but it contributes a substantial ecological footprint (Douglas, 1994; Lambin et al., 2003; Ode and Fry, 2006), and thus increase in Built-up areas needs to be considered in the realm of environmental monitoring and sustainability. 3.3. Systematic process of transitions in the landscape The relatively high percentage of transitions from the Pasture/ scrubland to Woodland and Vineyard and vice versa is not enough

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Table 4 Budget of landscape persistence and components of change in terms of percent of study area. Land use/land cover class

Persistence

Gain

Loss

Total change

Swap

Absolute value of net change

Woodland Pasture/scrubland Vineyard Built-up Other

28.8 36.9 4.0 1.7 0.3

9.2 9.3 6.9 2.4 0.4

5.9 16.0 5.9 0.3 0.2

15.1 25.3 12.8 2.7 0.6

11.8 18.6 11.8 0.5 0.4

3.3 6.7 1.0 2.2 0.2

Total

71.8

28.3

28.3

28.3

21.5

6.7

evidence to conclude that these transitions are systematic, since these are the largest categories in the study region. In order to identify systematic transitions within the transition matrix, one can interpret the transitions with respect to a random process, i.e. relative to the sizes of the categories. Though, analysis of

persistence, gains and losses is instructive, it fails to inform whether the transitions among categories are systematic or random (Pontius et al., 2004). In comparing the observed transitions to the expected transitions based on a random process of gain, we find that the

Fig. 2. Gain, loss and persistence by category from 1985 to 2005 for (a) Woodland, (b) Pasture/scrubland, (c) Vineyard, and (d) Built-up. Thick black line is the study area boundary while thinner black lines are the suburb boundaries.

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Fig. 3. Gain, loss and persistence from 1985 to 2005 for (a) Woodland, (b) Pasture/scrubland, (c) Vineyard, and (d) Built-up in different suburbs in the study area.

observed gains are more than one percentage point greater than the expected gains for: Pasture/scrubland to Woodland, Pasture/ scrubland to Vineyard, and Vineyard to Pasture/scrubland. Observed gains are 0.8–2.7 percentage points less than expected gains for: Woodland to Pasture/scrubland, Woodland to Vineyard and Vineyard to Woodland (Table 2). This means that the gains in Pasture/scrubland led to systematic replacement of Vineyard but avoided replacement of Woodland. Similarly, the gains in Woodland led to systematic replacement of Pasture/scrubland but avoided replacing Vineyard. When Vineyard gains, it systematically replaces Pasture/scrubland but avoids replacing Woodland. Likewise, the gains in Built-up led to a systematic replacement of Vineyard. The same comparison of the observed transitions with the expected transitions based on a random process of loss shows that the observed transitions are higher than random transitions for:

Pasture/scrubland to Vineyard, Vineyard to Pasture/scrubland, and Vineyard to Built-up, but lower for: Pasture/scrubland to Woodland and Vineyard to Woodland (Table 2). This means that the losses in Pasture/scrubland systematically attracted replacement by Vineyard, but avoided replacement by Woodland. Similarly, losses in Vineyard systematically attracted replacement by Pasture/scrubland and Built-up, but avoided replacement by Woodland. Woodland systematically avoided replacing other categories in comparison to random process, while Built-up tended to replace other categories. According to Braimoh (2006) and Alo and Pontius (2008), if category A loses systematically to category B, and category B gains systematically from category A, then we can conclude that there is systematic process of transition from A to B. In this study we can conclude that there is a systematic transition from Pasture/ scrubland to Vineyard as Vineyard was systematically gaining

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Fig. 4. Dominant signals of land use and land cover changes in the study area (a) Pasture/scrubland to Vineyard, (b) Vineyard to Pasture/scrubland, and (c) Vineyard to Built-up.

from Pasture/scrubland and at the same time Pasture/scrubland was also systematically losing to Vineyard (Table 2). The same was the case with the transition of Vineyard to Pasture/scrubland. In other words, there are tendencies for systematic exchanges between Vineyard and Pasture/scrubland. In the case of the Built-up category, Vineyard was systematically losing to Built-up and Built-up was systematically gaining from Vineyard. However, we cannot conclude that there was a systematic transition from Pasture/scrubland to Woodland even though Woodland is systematically gaining from Pasture/scrubland, as Pasture/scrubland rather systematically avoided losing to Woodland. Hence, the dominant signals of changes are (Fig. 4):

converting to Built-up. This is because the vineyards located at or close to Centre of Pokolbin and the southwestern part of RothburyBal are being developed for golf courses, hotels and resorts. These transitions and expansion away from the centre indicate increased touristic activity as tourism in the area is closely linked with viticulture and wine production. In contrast, the Woodland category did not attract replacements because most of forest area is stateowned with limited private/public access for development. The Built-up, comprising mainly residential and commercial, is usually

1. Pasture/scrubland to Vineyard 2. Vineyard to Pasture/scrubland 3. Vineyard to Built-up As the study area is gaining importance as a tourist destination, the land value and location seems to determine the transformation from the Vineyard to Pasture/scrubland and vice versa. We found that Vineyard was expanding away from the centre towards the southern and northern parts of Pokolbin and the southern part of Belford. The results also indicated that Vineyard is systematically

Fig. 5. Percent of total land use/land cover change (net and swap) and persistence with respect to coarsening of resolution.

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Fig. 6. Percent of persistence, net and swap changes as affected by resolution of pixels for (a) Woodland, (b) Pasture/scrubland, (c) Vineyard, and (d) Built-up.

characterised by persistence as it is investment-oriented and back transformation to non-Built-up is least attractive. Thus this study has shown that if we had analysed LULC based only on the traditional transitional matrix, then the results would have focused on only the transition of the larger categories, and thus would have missed the systematic signals of change in the landscape. We note that systematic transitions could provide some additional clues to the understanding of the drivers of change. Though Built-up has gained more from Pasture/scrubland (1.3% point) than from Woodland (0.7% point), these are not systematic transitions. However, the change of Vineyard to Built-up is a systematic transition, probably due to socio-economic factors, such as tourism. As the region’s economy is largely dependent on tourism, the transition from Vineyards to Built-up can lead to unsustainable tourism, which is an important issue to be addressed by policy makers. 3.4. Swapping distances As the representation of LULC categories is inherently linked to the scale of the analysis, a variety of questions involving spatial analysis now require understanding of spatial scales of landscape patterns (Turner, 1989; Luoto and Hjort, 2006). The resolution at which the LUCC studies are conducted can influence the conclusions as the amount of swap diminishes as grid cells become coarser and the proportion agreement tends to rise (Turner, 1989; Kok et al., 2001; Petit and Lambin, 2002; Pontius, 2002; Luoto and Hjort, 2006) (also see Fig. 5). Verburg and Veldkamp (2004) noted that the scale of any analysis is an important determinant of the model configuration, the interpretation of the results and the potential use by stakeholders. There is not necessarily a single, optimal scale for land use change assessments. Different scales enable different types of analysis and assessment; applications at multiple scales therefore give complementary information useful for environmental management. Observing how measurement changes as the function of the resolution of the pixels is important and interesting as it tells us about the overall pattern in the landscape (Pontius et al., 2008). In this study, a multiple-resolution analysis procedure was used to determine the distances over which swapping changes have occurred. The net change remains the same at various resolutions while the swap component of change can shrink with coarsening the pixel size. The total change at the base resolution (25 m by

25 m) is 28%; however, it shrinks to 13% at 1.6 km resolution, due to the decrease in the swapping component (Fig. 5). This means that more than half of the total change is attributable to a spatial reallocation of the categories over a distance of less than 2.26 km, which is the diagonal distanceffi across a square that is 1.6 km per pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi side, calculated as 1:62 þ 1:62 . Similarly, about half of the change in Woodland is a swapping type of change over distances less than 2.26 km. For comparison, the coarsest resolution diagonal distance examined was 36 km, which extends diagonally across the study area. Fig. 2(a) shows that the loss of Woodland occurs on the edge of large patches, while the gain of Woodland tends to extend from existing patches. Almost half of the change in Vineyard and Pasture/Scrubland is attributable to swapping at distances of 1.13 km (Fig. 6). Fig. 2(c) shows gross loss of Vineyard occurred at the centre of the study area while gross gain occurred in a pattern that is more widely distributed across the landscape; consequently, a substantial amount of the gross loss of Vineyard is within about a kilometer of gross gain of Vineyard. As mentioned in Section 3.1.2, the swap change in Built-up category is far lower than the net change. Fig. 6(d) further illustrates this by showing how the Built-up swaps were negligible and swap virtually disappears at the resolution of 1 km, thus indicating swap in Built-up occurring within 1.41 km, since the small gross loss of Built-up is near some gross gain. 4. Conclusions This paper has provided an analysis of LUCC in the Lower Hunter of New South Wales. The salient findings are: 1. About 28% of the study area experienced a transition from one category to a different category during the 20-year accounting period, when measured with 25 m resolution pixels. Out of the 28%, about one-fourth (7%) of the changed area is due to a net change while more than three-fourths (about 21%) is attributable to swap change. Pasture/scrubland is a net losing category while all others are net gaining categories. The net changes are much less than the swap changes for most categories especially in the case of Vineyard; however, in the case of Built-up, the net change is larger than the swap change. 2. Woodland gain occurs mainly in the SMA, which is in the northwest of the study area, where public access is limited. Vineyard loss is occurring mainly at the centre of the study area where there are large investments in tourism; and its gain is

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occurring away from the centre. The Built-up gain is also taking place near the 1985 Built-up, probably to support the expanding tourism activity. 3. Vineyard is the land category that has tendency to gain from other categories and also lose to other categories. As the study region is well known for grape vineyards and wine tourism, the tendency of the Vineyard category to transition has economic and environmental implications. 4. The dominant systematic transitions are: Pasture/scrubland to Vineyard; Vineyard to Pasture/scrubland; and Vineyard to Builtup. These transitions are probably due to increased land values caused by the growing tourism activities in the region. 5. Slightly more than half of the overall change is attributable to a swapping type of spatial reallocation over distances of less than 2.26 km. Similar swapping distances exist at the categoryspecific level for all categories, except Built-up, which is characterized by net change and not by swapped change. The overarching conclusion of this study is that had we used only the net changes, we would have missed the bulk of changes accruing from swap changes. Additionally, had we done the analysis based on the traditional transitional matrix, we would have focussed only on the larger categories and would have missed the systematic transitions in the landscape. Thus this in-depth analysis has enabled the visualization of the major transitions of LULC categories, which in turn have provided some insights to the nature and processes (either random or systematic) of LULC transitions. Deeper explanation of the driving factors of LULC dynamics will be the subject of another study. Finally, we suggest that the transformation from Vineyard to Pasture/scrubland, the dominant land cover, and to Built-up especially at the centre of tourism activity area are important signals that need to be addressed in managing the bucolic environment of the landscape. Acknowledgement The first author would like to acknowledge the support of the Australian Government for providing Endeavour International Postgraduate Research Scholarship to pursue her study at the University of Sydney. References Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P., Schweik, C.M., 2002. A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice, GTR NE-297. USDA Forest Service, Northeastern Research Station, Newton Square, PA. Alo, C.A., Pontius, R.G., 2008. Identifying systematic land-cover transitions using remote sensing and GIS: the fate of forests inside and outside protected areas of Southwestern Ghana. Environment and Planning: Planning and Design 35 (2), 280–295. Australian Bureau of Statistics, 2006. Australian Wine and Grape Industry. Australian Bureau of Statistics, 2007. 2007 Year Book. Barson, M.M., Randall, L.A., Bordas, V., 2000. Land Cover Change in Australia— Executive Summery. Results of the Collaborative Bureau of Rural Sciences–State Agencies’ Project on Remote Sensing of Agricultural Land Cover Change. Bureau of Rural Sciences, Canberra. Berberoglu, S., Akin, A., 2009. Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean. International Journal of Applied Earth Observation and Geoinformation 11, 46–53. Braimoh, A.K., 2006. Random and systematic land-cover transitions in northern Ghana. Agriculture, Ecosystems and Environment 113, 254–263. Costanza, R., 1989. Model goodness of fit: a multiple resolution procedure. Ecological Modelling 47, 199–215. Currit, N., 2005. Development of remotely sensed, historical land cover change database for Rural Chihuahua, Mexico. International Journal of Applied Earth Observation and Geoinformation 7, 232–247. DCC, 2008a. National Greenhouse Gas Inventory 2006—Accounting for the Kyoto Target. Department of Climate Change, Australian Government. DCC, 2008b. National Inventory Report 2006, Volume 2, Part A—The Australian Government Submission to the UN Framework Convention on Climate Change June 2008. Department of Climate Change, Australian Government.

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