Planning for the rehabilitation of brownfield sites: a …...Planning for the rehabilitation of...

Planning for the rehabilitation of brownfield sites: a landscape ecological perspective

R. Lafortezza1, G. Sanesi1, B. Pace1, R. C. Corry2 & R. D. Brown2 1Dept. of Plant Production Science, University of Bari, Italy 2School of Environmental Design and Rural Development, University of Guelph, Ontario, Canada

Abstract

This paper is intended to present a model supporting the rehabilitation process of brownfield sites within a landscape ecological context. The model is based on the premise that brownfields can be seen as part of an overall landscape, thus having an influence on its ecological and social functions. If planned and restored properly brownfield sites with large areas of undisturbed habitat may complement parks and natural reserves in the effort to increase the survival of wildlife populations and the establishment of recreational activities. By using a GIS-based algorithm, the model attempts to define which brownfield sites have the potential to sustain wildlife populations in connection with other open spaces and green areas. The model has been used to simulate planning scenarios for a large brownfield area located at the edge of the city of Bari, in the Apulia Region (Southern Italy). Each scenario is characterised by different rehabilitation treatments, such as: site cleanup, planting of sparse vegetation, planting of large vegetation patches. Statistical analyses have been performed in order to discriminate the most valuable scenario among different treatments. Planners and decision makers can benefit from this landscape ecological model when implementing rehabilitation strategies in the redevelopment of brownfield sites. Keywords: landscape ecology, brownfields rehabilitation, GIS-spatial models.

1 Introduction

After a long period of theoretical development, landscape ecology has now achieved a stage of maturity so that principles and models can be applied with

Brownfield Sites II, A. Donati, C. Rossi & C. A. Brebbia (Editors)© 2004 WIT Press, www.witpress.com, ISBN 1-85312-719-1

confidence in real situations and case studies. Agencies and organization worldwide are now embracing integrated approaches to landscape planning and management in the hope to recover from a long period of ‘environmental consumption’. Many success stories have been mentioned in the fields of conservation of natural resources and enhancement of ecological connectivity of pristine habitat, Baker [1]. However, few contributions have explored the idea of applying landscape ecological insights in the planning and management of derelict lands (i.e., degraded ecosystems) at large. This is the case of brownfield sites, which represent an ideal situation for testing both the knowledge and the experience gained by landscape ecologists. In general, brownfields are associated with “abandoned, idled, or under-used industrial and commercial facilities where expansion or redevelopment is complicated by real or perceived environmental contamination”, USEPA [2]. From a landscape ecological perspective, brownfields can be seen as part of an overall landscape, thus having an influence on its spatial organization and ecological functionality. If planned and restored properly brownfield sites with large areas of undisturbed habitat may complement parks and natural reserves in the effort to increase the survival of wildlife populations and the establishment of recreational activities. On the basis of this premise, this paper is intended to present a model supporting the planning and the rehabilitation process of brownfield sites within a landscape ecological context. This modelling exercise seeks to answer the following questions: given a mosaic of brownfields and open spaces, which sites can better support viable population of species in connection with other open spaces and green areas?; and which kind of land-cover rehabilitation/re-vegetation scheme can better sustain the ecological functionality of the overall landscape mosaic? The modelling and simulation were performed through a case-study application of the model to the rehabilitation of brownfield site in the city of Bari, Apulia Region (Southern Italy).

2 Modelling brownfields rehabilitation

In general, brownfields and other open spaces appear in the periurban interface where industrial structures and infrastructures occur, Ferber & Grimski [3]. At larger scale, it is not unusual to encounter patches of natural and spontaneous vegetation, paddy fields, and other ‘greenfields’ such as periurban parks, floodplains, greenway systems, etc., De Sousa [4]. All these elements constitute a complex pattern of productive and unproductive lands whose arrangement is strongly influenced by the trend of the ‘local’ economy (i.e., market of industrial products) and demand for recreational areas at cities’ edges. In a similar context, landscape planners and ecologists can find a ‘fertile ground’ to apply their knowledge and findings. A common way to deal with complex pattern in landscape ecology is to develop models simulating landscape changes under different planning strategies. This helps to determine whether the landscape could be improved or not by the planners’ decision. If we think of brownfields as potential elements of ecological balance and wildlife connectivity in the periurban landscape, it is essential to discern which sites have the potential to

22 Brownfield Sites II


sustain viable population of species in conjunction with other greenfields and landscape elements. More critical is to define the magnitude of the rehabilitation strategy being required that can vary from a simple site cleanup to an intensive planting and/or construction scheme.

2.1 Model development

In terms of model development, the basic structure of the model is reported in fig. 1. Starting from the current situation (i.e., land cover map), we analyse the potential connectivity of brownfield sites as they appear in the landscape. This step involves the use of a GIS-based algorithm that is commonly adopted in landscape ecological studies for visualizing the potential pathways between habitat patches, Knaapen et al. [5], Lafortezza & Brown [6]. The algorithm requires a source lattice that defines the source and destination patches (e.g., greenfields) and a resistance lattice containing ‘dispersal resistance’ value (inverse of ecological permeability) for each land cover type, Lafortezza et al. [7]. The cell value is a measure of the habitat suitability of a given land cover type for a focal species and/or a group of species: the higher the cell value the lower the habitat suitability of the cell. The algorithm calculates for each cell, the least ‘accumulative resistance distance’ to a source cell or set of source cells, Flower [8]. In terrestrial situations, this ‘least-resistance path algorithm’ (lrp-alg) can be used in an iterative manner to create a set of all potential paths between patches, Halpin & Bunn [9]. With the same scheme, the model simulates a series of planning scenarios, each one corresponding to a different rehabilitation treatment of brownfields (i.e., different cell values): site cleanup vs. planting of sparse/dense vegetation. Finally, the model makes use of statistical test (Student’s t-test) to compare the current situation with a series of planning scenarios, thus selecting the rehabilitation/re-vegetation scheme that has the potential to be implemented.

Current situation(land cover map) Resistance

Lattice (0)

SourceLattice

Assignvalue

Scenario (1)

Criteria

Accumulativeresistance

distance map (0)lrp alg

ResistanceLattice (1)

SourceLattice

Assignvalue

lrp alg

Test Statistic(t-test)

Scenario (n)

Criteria

RejectHo?

Accumulativeresistance

distance map (1)NO

Consider forimplementation

YES

Figure 1: Basic structure of the model.

Brownfield Sites II 23


2.2 Model application

2.2.1 Defining the current situation The model has been analysed and tested through a case-study application represented by a large brownfield area located at the edge of the city of Bari, in the Apulia Region, Southern Italy. The area of study extends to approximately 1411 hectares. This area was mapped using a technical map, as vector layer, and a colour aerial photograph (dated 1999) with 1m of resolution, as raster layer. The integration of this data set with field validation and other ancillary data allowed the development of an up-to-date land cover map (year 2003) with 15 land cover classes, fig.2. Land cover types have been classified using a hierarchical classification system based on the CORINE Land Cover (CLC) nomenclature (three levels of classification). To augment the spatial resolution of the land cover map, CLC classes have been subdivided in a fourth level of classification adding an extra-code to the standard CLC code, table 1. In the study area more than 420 hectares (30%) are covered by industrial (229.9ha), residential (56.7ha) and transport (137.4ha) units. Apart from these land cover types, it can be noticed that there is a significant presence of brownfield sites and open spaces covering respectively areas of almost 300ha (21.4%) and 160ha (11.6%). These sites include areas with sparse vegetation, which result from abandonment or in same cases from being used as dump sites, fig.3. The study area also contains extensive olive plantations (12.4%), recreational areas and green spaces (3%), and two major patches of natural vegetation represented by the so called ‘lame’ or ‘canyons’, which cover a total area of approximately 85 hectares (6%). Due to their high floristic and structural diversity (presence of Mediterranean-type vegetation), the two ‘lame’ can be considered suitable habitat for terrestrial fauna moving across this landscape. Indeed, following a landscape ecological perspective (i.e., source/sink model), these patches can be seen as ‘source areas’ meaning that they can act as source of biodiversity (species dispersal) in their own landscape. The higher the level of connectivity among these patches, the higher the probability that wildlife species can successfully disperse to these patches. A landscape is connected from an ecological point a view if there are green corridors and/or stepping-stones between source areas and the lack of resistance in the landscape matrix, Forman [10]. In this sense, an appropriate rehabilitation strategy of brownfields and other derelict sites may enhance the ecological functionality of the landscape at city’s edges, thus providing opportunities for species to move from one habitat patch to another. On the basis of the general scheme already illustrated in fig.1, we applied the ‘least-resistance path algorithm’ (lrp-alg) to the current situation as shown in the land cover map. As a first step, polygonal coverages (vector format) were converted into grid format (cell size: 1x1m) and then reclassified into five major classes by assigning a ‘dispersal resistance’ value to each land cover type: (a) cell value=0 (high habitat suitability, e.g., ‘lame’); (b) cell value=4 (low habitat suitability, e.g., industrial sites) (see table 1). Secondly, a ‘source lattice’ and a ‘resistance lattice’ were produced from those pixels having respectively: cell value=0 and cell value>0. Finally, we generated a first



‘accumulative resistance distance’ map for the area of study, fig.4a. This map gives a visual indication of the spatial relationship (i.e., ecological connectivity) of habitat patches in the landscape. Patches corresponding to lighter zones are those with a high potential to support species in their movements. From the model’s outcome, the current situation in the landscape can be summarised as follow: (1) high incidence of zones of ecological connectivity in the NW and SE portions; (2) poor presence of zones of connectivity in the central portion between the two major source areas (i.e., ‘lame’). Industrial sites, degraded brownfields and open spaces create a sort of disconnection in this landscape acting like a barrier between two habitat patches in opposite portions, Forman [10]. A possible strategy to recover from this situation is the planning for the rehabilitation of brownfields and open spaces within a landscape ecological context.

Table 1: Land cover classes and their extension in the area of study.

Land cover class (class code) Area (ha) % Value

Residential continuous urban fabric (1.1.1_0) 49.4 3.6 4

Residential discontinuous urban fabric (1.1.2_0) 7.3 0.5 3

Industrial, commercial sites and structures (1.2.1_1) 229.9 16.3 4

Paved areas annexed to industrial sites (1.2.1_2) 138.8 9.8 4

Green areas annexed to industrial sites (1.2.1_3) 48.4 3.4 3

Brownfield sites (1.2.1_4) 302.0 21.4 4

Road and rail networks and assoc. land (1.2.2_0) 137.4 9.7 4

Mineral extraction sites (1.3.1_0) 23.7 1.7 4

Green urban areas with dense vegetation (1.4.1_1) 10.1 0.7 1

Green urban areas with sparse vegetation (1.4.1_2) 28.9 2.0 2

Sport and leisure facilities (1.4.2_0) 4.0 0.3 3

Extensive olive plantation (2.2.3_1) 174.8 12.4 2

Bare rock (3.3.2_0) 8.8 0.6 2

Sparely vegetated areas and open spaces (3.3.3_1) 163.5 11.6 2

‘Lame’ (5.1.1_1) 84.5 6.0 0

Total 1411.5 100



Figure 2: Land cover map of the study area.

2.2.2 Simulating planning scenarios The model simulation involved applying the model scheme, fig.1, to three different planning scenarios of increasing landscape ecological functionality: Scenario (1) cleanup of brownfields; Scenario (2) cleanup plus planting of sparse vegetation; Scenario (3) cleanup plus planting of sparse vegetation plus planting nucleus of dense vegetation. With the term cleanup we intended all the practices required to decontaminate brownfields to tolerable limits. This process commonly involves the technique of excavating and removal of contaminated soil and sludge, and pumping and treating near-surface water. On the other hand, with planting of vegetation (sparse/dense) we meant the whole process of soil preparation (e.g., soil aeration, soil biochemical activity, percolation of water, pH regulation, nutrient application, etc.) and vegetation establishment (e.g., plant selection, planting materials, plant maintenance, etc.). Each scenario has been generated by assigning decreasing values of ‘dispersal resistance’ to land cover types labelled as brownfields: cell value=3 for Scenario (1); cell value=2 for Scenario (2); cell value=1 for Scenario (3). In terms of model computation, it is worth noting that new cell values led only to different resistance lattices without modifying the source lattice (see fig.1). Newly produced resistance lattices were then used to calculate ‘accumulative resistance distance’ maps by implementing the ‘lrp’ algorithm, fig.4. Although visual differences were evident comparing new maps with the former map (current situation), we performed the Student’s t-test to estimate whether a significant difference existed between two sample means taken from the current situation and a given planning scenario. As a null hypothesis (H0), we stated that the mean ‘accumulative resistance distance’ value of the existing landscape was not significantly different from the one generated by the model simulation. A total of 50 randomly generated points were used to extract sample means with 4 iterations. The significance level and critical region used for testing is a one-tailed test at the P<0.05 level. The critical region for



rejection of H0 is t>t0.05=1.984. The results of the statistical analysis are indicated in table 2.

Table 2: Statistical significance of model results for the study area.

Statistical significance Scenario Standard

deviation Statistical test

(t-test) Reject H0 Fail to Reject H0

Scenario (1)

Test i 291.78 t = 0.37 * Test ii 281.70 t = 0.67 * Test iii 271.14 t = 0.60 * Test iv 291.60 t = 0.58 *

Scenario (2)


Scenario (3)


Results in table 2 indicate that there was not a significant difference between model scores of Scenario (1) and the mean ‘accumulative resistance distance’ for the existing landscape (fail to reject H0): a strategy based on the mere brownfields cleanup could not led to a more functional landscape. In the case of

Figure 3: Brownfields in the study area.



Scenario (2), only two of the four tests were statistically significant (reject H0), meaning that there was a significant difference between the two sample means 50% of the time. A rehabilitation treatment based on sites cleanup and ‘light’ vegetation planting (e.g., herbs, small shrubs, etc.) could ameliorate the ecological functionality of the landscape reducing the overall resistance, but only in some cases. All four tests in Scenario (3) were statistically significant (reject H0), indicating that there was a significant difference between the scenario generated by the combination of cleanup, planting of sparse vegetation and planting of dense vegetation, and the existing landscape. If we think of brownfields’ rehabilitation as a multi-stage process, the strategy of cleaning up the soil and planting sparse vegetation with pioneer species represents an essential priority: it provides better soil conditions and basic needs for the establishment of wildlife species (i.e., generalist species) and trees. This stage determines the transition from ‘brown-’ to ‘green-’ fields. Once these needs have been fulfilled, the process can drive the planting of large patches of vegetation made up by trees and shrubs arranged to ensure an appropriate vertical structure and habitat heterogeneity. These patches typically support more suitable habitats for a wide range of species (i.e. specialist species), and are more likely to be intercepted and colonized by dispersing species, Lindenmayer et al [11].

Figure 4: Accumulative resistance distance maps for the area of study; a=current situation; b=Scenario (1); c=Scenario (2); d=Scenario (3).



3 Conclusion

The theory in landscape ecology suggests that large, heterogeneous habitat patches and networks of habitat connections support more biologically diverse plant and animal populations by facilitating the movement of species. When applied to human-dominated landscape (i.e., brownfield landscape), this basic principle requires an ecological analysis of the landscape context to identify larger habitat patches and corridors that may be locally important. In the urban and periurban context, former industrial sites are often the largest patches undergoing change. With this paper we attempted to introduce a landscape ecological perspective to brownfield sites rehabilitation. Planners and decision makers can benefit from this approach when implementing rehabilitation strategies in the redevelopment of brownfield sites and abandoned urban properties. Through a case-study application, we explained how to simulate planning scenarios for decreasing values of ‘landscape resistance’ (more ecological connectivity), therefore selecting the rehabilitation/re-vegetation scheme that has the potential to be implemented. The results of the modelling and simulation exercise support the idea that a strategy based on brownfields cleanup and ‘light’ vegetation planting could benefit wildlife population partially reducing the resistance of the landscape matrix. This can be explained by the large distribution of brownfield sites in the study area (300ha): a small step towards the amelioration of brownfield sites may augment the ecological functionality of the surrounding landscape. The more complex strategy of creating large patches of vegetation exhibited promising results in the study area: it may enhance the ecological role of brownfields that can act as stepping-stones in connection with other open spaces and greenfields. It is worth noting that where brownfield sites (especially soils and water) have been contaminated, connecting habitat patches or corridors could bring wildlife in contact with water or plants that contain contaminants. In this situation maintaining separation will reduce the risk of transport of contaminants by wildlife. Thus there may be cases where reducing the connectedness (i.e., increasing the dispersal resistance) among potentially contaminated and ‘clean’ sites is advisable, Nassauer et al. [12]. The model application presented here considered brownfields to have been remediated to eliminate any remnant contamination. The model could equally well imply which brownfield sites should be cleaned to the highest standards and re-vegetated to approach ‘lame’-like conditions. Brownfield sites that can significantly contribute to ecological functionality could be targeted for intensive and complete remediation. Brownfield sites that can not contribute significantly to ecological permeability could be cleaned to industrial standards and re-used as new industrial sites or could be re-vegetated with species that are less attractive to wildlife (e.g., some ornamental species, or turf), Nassauer et al. [13], Chiesa [14]. Remediated brownfield properties can be explored for their contribution to a more permeable urban matrix that improves the ecological functions of some of our most highly-fragmented landscapes. In conclusion, in order to know which brownfield sites offer the greatest benefit to ecological function, a broad-scale assessment is needed, the results of which could target



the most comprehensive and extensive cleanup efforts. This effectively combines expensive and time-consuming site-specific activities and the landscape scale, and avoids expensive site cleanup where the contributions to broad patterns would be less effective.

References

[1] Baker, W.L., The landscape ecology of large disturbances in the design and management of nature reserves. Landscape Ecology, 3, pp. 181-194, 1992.

[2] United States Environmental Protection Agency (USEPA). Brownfields definition, www.epa.gov/swerosps/bf/glossary.htm.

[3] Ferber, U. & Grimski, D. Brownfields and redevelopment of urban areas. Austrian Federal Environment Agency. CLARINET project, 2002.

[4] De Sousa, C.A., Turning brownfields into green space in the City of Toronto. Landscape and Urban Planning 62, pp. 181-198, 2003.

[5] Knaapen, J.P., Scheffer, M., & Harms, B., Estimating habitat isolation in landscape planning. Landscape and Urban Planning, 23, pp. 1-16, 1992.

[6] Lafortezza, R. & Brown, R.D., A framework for landscape ecological design of new patches in the rural landscape. Environmental Management, 32, 2003, in press.

[7] Lafortezza, R., Sanesi, G.& Brown, R.D., Un approccio metodologico alla progettazione di nuove aree verdi ad uso ricreativo e sportivo. Genio Rurale, Estimo e Territorio, 3, pp. 20-29, 2003.

[8] Flower, S., The distribution and conservation significance of cerrado remnants in Sao Paolo State, Brazil. University of Edinburgh, MSc. thesis, Department of Geography, 1997.

[9] Halpin, P.N. & Bunn A.G., Using GIS to compute a Least-Cost Distance matrix: A comparison of terrestrial and marine ecological Applications. Proceeding of 20th Annual ESRI User Conference, 2000.

[10] Forman, R.T.T., Land mosaic: the ecology of landscapes and regions, Cambridge University Press, 1995.

[11] Lindenmayer, D., McCarthy, M.A., Parris, K.M. & Pope, M.L., Habitat fragmentation, landscape context, and mammalian assemblages in Southern Australia. Journal of Mammalogy, 81, pp. 787-797, 2000.

[12] Nassauer, J.I., Chiesa, A. & Corry, R.C, Principles for ecological landscape design in brownfield business parks, Draft report, 2001. www.snre.umich.edu/nassauer.

[13] Nassauer, J.I., Chiesa, A. & Corry, R.C, Landscape pattern: an ecological issue for brownfield redevelopment design and planning, Landscape Journal, in prep.

[14] Chiesa, A., Industrial landscape ecology: design of an industrial park on a brownfield site in Calumet, Chicago, Illinois. Master of Landscape Architecture thesis. University of Michigan, pp. 106, 2001.



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