Implementing Green Infrastructures beyond Protected Areas
Abstract
:1. Introduction
2. Materials and Methods
- P stands for priority; it accounts for the presence and significance, in a given land parcel, of habitats of community interest as listed in Annex I of the Habitats Directive, and can only take three values: P = 0 if no habitats of community interest are present (which also entails that in such cases VAL_C = 0); P = 1.5 in case of a priority habitat (these are defined as natural habitat types in danger of disappearance, calling for an early implementation of conservation measures, and marked with an asterisk in Annex I of the Habitats Directive); P = 1 otherwise;
- R stands for rarity and, for each habitat, it is quantified based on the habitat’s occurrences within the Sardinian Natura 2000 standard data forms, normalized in the (1–5) interval. The fewer the occurrences, the higher R is;
- T stands for threats and is quantified based on the number of threats listed in the regional Natura 2000 standard data forms, normalized in the (1–5) interval. The more the threats for a given site, the higher T;
- K stands for knowledge, which was assessed, for each habitat, by experts in the field of natural conservation within a regional monitoring program [40] by using qualitative judgments (good, acceptable, sufficient, poor). In this study, the qualitative score was next turned into a quantitative one, within the (1–4) interval. The maximum achievable score is lower than those of both R and T to take into account the subjectivity embedded in the experts’ judgments. Incorporating expert knowledge in assessing ecosystem values and services is a common approach that can be found, for instance, in Burkhard et al. [41,42], or in de Groot et al. [43]. The lower the level of knowledge on a given habitat, the higher K is, because, in line with the precautionary principle, low knowledge on a certain habitat’s spatial distribution and conservation status calls for greater attention and stricter conservation measures.
3. Results
3.1. Natural Protected Areas
3.2. Unprotected Areas
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Benedict, M.; McMahon, E. Green Infrastructure: Linking Landscapes and Communities; Sprawl Watch Clearinghouse Monograph Series; Island Press: Washington, DC, USA, 2006; Available online: http://www.sprawlwatch.org/greeninfrastructure.pdf (accessed on 13 September 2018).
- Landscape Institute. Green Infrastructure Connected and Multifunctional Landscapes: Position Statement; Landscape Institute: London, UK, 2009. [Google Scholar]
- Liquete, C.; Kleeschulte, S.; Dige, G.; Maes, J.; Grizzetti, B.; Olah, B.; Zulian, G. Mapping green infrastructure based on ecosystem services, and ecological networks: A pan-European case study. Environ. Sci. Policy 2015, 54, 268–280. [Google Scholar] [CrossRef]
- Lennon, M. Green infrastructure and planning policy: A critical assessment. Local Environ. 2015, 20, 957–980. [Google Scholar] [CrossRef]
- Benedict, M.; McMahon, E. Green Infrastructure: Smart Conservation for the 21st Century; Sprawl Watch Clearinghouse: Washington, DC, USA, 2002. [Google Scholar]
- Lafortezza, R.; Davies, C.; Sanesi, G.; Konijnendijk, C.C. Green Infrastructure as a tool to support spatial planning in European urban regions. iForest 2013, 6, 102–108. [Google Scholar] [CrossRef] [Green Version]
- Hansen, R.; Pauleit, S. From multifunctionality to multiple ecosystem services? A conceptual framework for multifunctionality in Green Infrastructure planning for urban areas. AMBIO 2014, 43, 516–529. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Banzhaf, E. Towards a better understanding of green infrastructure: A critical review. Ecol. Indic. 2018, 85, 758–772. [Google Scholar] [CrossRef]
- Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: A Framework for Assessment; Island Press: Washington, DC, USA, 2003. [Google Scholar]
- CICES Version 5.1 Now Available. Available online: https://cices.eu/ (accessed on 19 September 2018).
- Landers, D.; Nahlik, A. Final Ecosystem Goods and Services Classification System (FEGS-CS); U.S. Environmental Protection Agency: Washington, DC, USA, 2013.
- Taylor-Lovell, S.; Taylor, J.R. Supplying urban ecosystem services through multifunctional green infrastructure in the United States. Landsc. Ecol. 2013, 28, 1447–1463. [Google Scholar] [CrossRef]
- Cannas, I.; Lai, S.; Leone, F.; Zoppi, C. Green infrastructure and ecological corridors: A regional study concerning Sardinia. Sustainability 2018, 10, 1265. [Google Scholar] [CrossRef]
- Hansen, A.J.; DeFries, R. Ecological mechanisms linking protected areas to surrounding lands. Ecol. Appl. 2007, 17, 974–988. [Google Scholar] [CrossRef] [PubMed]
- Gaston, K.J.; Charman, K.; Jackson, S.F.; Armsworth, P.R.; Bonn, A.; Briers, R.A.; Callaghan, C.S.Q.; Catchpole, R.; Hopkins, J.; Kunin, W.E.; et al. The ecological effectiveness of protected areas: The United Kingdom. Biol. Conserv. 2006, 132, 76–87. [Google Scholar] [CrossRef]
- Ruiz Benito, P.; Cuevas, J.A.; Bravo de la Parra, R.; Prieto, F.; García del Barrio, J.M.; Zavala, M.A. Land use change in a Mediterranean metropolitan region and its periphery: Assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Syst. 2010, 19, 315–328. [Google Scholar] [CrossRef]
- Wickham, J.D.; Riitters, K.H.; Wade, T.G.; Vogt, P. A national assessment of green infrastructure and change for the conterminous united states using morphological image processing. Landsc. Urban Plan. 2010, 94, 186–195. [Google Scholar] [CrossRef]
- Spanò, M.; Gentile, F.; Davies, C.; Lafortezza, R. The DPSIR framework in support of green infrastructure planning: A case study in Southern Italy. Land Use Policy 2017, 61, 242–250. [Google Scholar] [CrossRef]
- Arcidiacono, A.; Ronchi, S.; Salata, S. Managing multiple ecosystem services for landscape conservation: A green infrastructure in Lombardy Region. Procedia Eng. 2016, 161, 2297–2303. [Google Scholar] [CrossRef]
- Lai, S.; Leone, F. A methodological approach to identify a multifunctional green infrastructure at the regional scale. A case study from Sardinia, Italy. Urban. Inf. 2017, 272, 836–840. [Google Scholar]
- Salomaa, A.; Paloniemi, R.; Kotiaho, J.S.; Kettunen, M.; Apostolopoulou, E.; Cent, J. Can green infrastructure help to conserve biodiversity? Environ. Plan. C 2017, 35, 265–288. [Google Scholar] [CrossRef]
- Weber, T.; Sloan, A.; Wolf, J. Maryland’s Green Infrastructure Assessment: Development of a comprehensive approach to land conservation. Landsc. Urban Plan. 2006, 77, 94–110. [Google Scholar] [CrossRef]
- Sharma, R.; Nehren, U.; Rahman, S.A.; Meyer, M.; Rimal, B.; Seta, G.A.; Baral, B. Modeling land use and land cover changes and their effects on biodiversity in Central Kalimantan, Indonesia. Land 2018, 7, 57. [Google Scholar] [CrossRef]
- Polasky, S.; Nelson, E.; Pennington, D.; Johnson, K.A. The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the state of Minnesota. Environ. Resour. Econ. 2011, 48, 219–242. [Google Scholar] [CrossRef]
- Yang, S.; Zhao, W.; Liu, Y.; Wang, S.; Wang, J.; Zhai, R. Influence of land use change on the ecosystem service trade-offs in the ecological restoration area: Dynamics and scenarios in the Yanhe watershed, China. Sci. Total Environ. 2018, 644, 556–566. [Google Scholar] [CrossRef] [PubMed]
- Serkan, G.; Rehber, E. A travel cost study to estimate recreational value for a bird refuge at Lake Manyas, Turkey. J. Environ. Manag. 2008, 88, 1350–1360. [Google Scholar] [CrossRef]
- Martín-López, B.; Gómez-Baggethun, E.; Lomas, P.L.; Montes, C. Effects of spatial and temporal scales on cultural services valuation. J. Environ. Manag. 2009, 90, 1050–1059. [Google Scholar] [CrossRef] [PubMed]
- Lankia, T.; Kopperoinen, L.; Pouta, E.; Neuvonen, M. Valuing recreational ecosystem service flow in Finland. J. Outdoor Recreat. Tour. 2015, 10, 14–28. [Google Scholar] [CrossRef] [Green Version]
- Mayer, M.; Woltering, M. Assessing and valuing the recreational ecosystem services of Germany’s national parks using travel cost models. Ecosyst. Serv. 2018, 31, 371–386. [Google Scholar] [CrossRef]
- Kenter, J.O. Deliberative and non-monetary valuation: A review of methods. In Handbook of Ecosystem Services; Potschin, M., Haines-Young, R., Fish, R., Turner, R.K., Eds.; Routledge: London, UK, 2016; pp. 271–288. ISBN 9781317687047. [Google Scholar]
- Kelemen, E.; García-Llorente, M.; Pataki, G.; Martín-López, B.; Gómez-Baggethun, E. Non-monetary techniques for the valuation of ecosystem service. In OpenNESS Reference Book EC FP7 Grant Agreement No. 308428; Potschin, M., Jax, K., Eds.; EU Publication Office: Luxembourg, 2014; Available online: http://www.openness-project.eu/sites/default/files/SP-Non-monetary-valuation.pdf (accessed on 19 September 2018).
- Eagles, P.; McLean, D.; Stabler, M. Estimating the tourism volume and value in protected areas in Canada and the USA. George Wright Forum 2000, 17, 62–76. [Google Scholar]
- Wood, S.A.; Guerry, A.D.; Silver, J.M.; Lacayo, M. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 2013, 3, 2976. [Google Scholar] [CrossRef] [PubMed]
- Sonter, L.J.; Watson, K.B.; Wood, S.A.; Ricketts, T.H. Spatial and temporal dynamics and value of nature-based recreation, estimated via social media. PLoS ONE 2016, 11, e0162372. [Google Scholar] [CrossRef] [PubMed]
- Hausmann, A.; Toivonen, T.; Slotow, R.; Tenkanen, H.; Moilanen, A.; Heikinheimo, V.; Di Minin, E. Social media data can be used to understand tourists’ preferences for nature-based experiences in protected areas. Conserv. Lett. 2018, 11, e12343. [Google Scholar] [CrossRef]
- Cunha, J.; Elliott, M.; Ramos, S. Linking modelling and empirical data to assess recreation services provided by coastal habitats: The case of NW Portugal. Ocean Coast. Manag. 2018, 162, 60–70. [Google Scholar] [CrossRef]
- Zoppi, C.; Lai, S. Assessment of the Regional Landscape Plan of Sardinia (Italy): A participatory-action-research case study type. Land Use Policy 2010, 27, 690–705. [Google Scholar] [CrossRef]
- Orantes, M.J.C.; Kim, J.; Kim, J. Socio-cultural asset integration for a green infrastructure network plan in Yesan County, Korea. Sustainability 2017, 9, 191. [Google Scholar] [CrossRef]
- CRITERIA; TEMI. Monitoraggio dello Stato di Conservazione degli Habitat e delle Specie di Importanza Comunitaria Presenti nei Siti della Rete Natura 2000 in Sardegna. Definizione della Rete di Monitoraggio. Volume 2: Piano di Monitoraggio degli Habitat e delle Specie Vegetali; MIMEO: New York, NY, USA, 2014. [Google Scholar]
- CRITERIA; TEMI. Monitoraggio dello Stato di Conservazione degli Habitat e delle Specie di Importanza Comunitaria Presenti nei Siti della Rete Natura 2000 in Sardegna. Elaborazione Rapporto di Sintesi sullo Stato di Conservazione di Habitat e Specie (Linea 4, 4.C.1); MIMEO: New York, NY, USA, 2014. [Google Scholar]
- Burkhard, B.; Kroll, F.; Müller, F.; Windhorst, W. Landscapes’ capacities to provide ecosystem services—A concept for land-cover based assessments. Landsc. Online 2009, 15, 1–22. [Google Scholar] [CrossRef]
- Burkhard, B.; Kroll, F.; Nedkov, S.; Müller, F. Mapping ecosystem service supply, demand and budgets. Ecol. Indic. 2012, 21, 17–29. [Google Scholar] [CrossRef]
- De Groot, R.S.; Alkemade, R.; Braat, L.; Hein, L.; Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 2010, 3, 260–272. [Google Scholar] [CrossRef]
- De Montis, A.; Caschili, S. Nuraghes and landscape planning: Coupling viewshed with complex network analysis. Landsc. Urban Plan. 2012, 105, 315–324. [Google Scholar] [CrossRef]
- Blake, E. Sardinia’s nuraghi: Four millennia of becoming. World Archaeol. 1998, 30, 50–71. [Google Scholar] [CrossRef]
- Minchilli, M.; Tedeschi, L.F. The geographical distribution of nuraghi in north-western Sardinia: Analysis and evaluation of the influence of anthropic and natural factors. In Computational Science and Its Applications—ICCSA 2017, Proceedings of the 17th International Conference on Computational Science and Its Applications (ICCSA 2017), Trieste, Italy, 3–6 July 2017; Lecture Notes in Computer Sciences Series; Springer: Cham, Switzerland, 2017; Volume 10408, pp. 701–712. ISBN 978-3-319-62403-7. [Google Scholar]
- Rendeli, M. Sardinia. In Etruscology; De Naso, A., Ed.; Walter de Gruyter GmbH: Berlin, Germany, 2017; pp. 1669–1678. [Google Scholar]
- Lai, S.; Leone, F.; Zoppi, C. Land cover changes and environmental protection: A study based on transition matrices concerning Sardinia (Italy). Land Use Policy 2017, 67, 126–150. [Google Scholar] [CrossRef]
- Lai, S.; Leone, F.; Zoppi, C. Anthropization processes and protection of the environment: An assessment of land cover changes in Sardinia, Italy. Sustainability 2017, 9, 2174. [Google Scholar] [CrossRef]
- He, J.; Huanga, J.; Li, C. The evaluation for the impact of land use change on habitat quality: A joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecol. Model. 2017, 366, 58–67. [Google Scholar] [CrossRef]
- Sallustio, L.; De Toni, A.; Strollo, A.; Di Febbraro, M.; Gissi, E.; Casella, L.; Geneletti, D.; Munafò, M.; Vizzarri, M.; Marchetti, M. Assessing habitat quality in relation to the spatial distribution of protected areas in Italy. J. Environ. Manag. 2017, 201, 129–137. [Google Scholar] [CrossRef] [PubMed]
- Maiorano, L.; Falcucci, A.; Garton, E.O.; Boitani, L. Contribution of the Natura 2000 network to biodiversity conservation in Italy. Conserv. Biol. 2007, 21, 1433–1444. [Google Scholar] [CrossRef] [PubMed]
- Terrado, M.; Sabater, S.; Chaplin-Kramer, B.; Mandle, L.; Ziv, G.; Acuña, V. Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning. Sci. Total Environ. 2016, 540, 63–70. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ball, I.R.; Possingham, H.P.; Watts, M. Marxan and relatives: Software for spatial conservation prioritization. In Spatial Conservation Prioritization: Quantitative Methods and Computational Tools; Moilanen, A., Wilson, K.A., Possingham, H.P., Eds.; Oxford University Press: New York, NY, USA, 2009; pp. 185–195. ISBN 9780199547777. [Google Scholar]
- Pressey, R.L.; Watts, M.E.; Barrett, T.W.; Ridges, M.J. The C-Plan conservation planning system: Origins, applications, and possible futures. In Spatial Conservation Prioritization: Quantitative Methods and Computational Tools; Moilanen, A., Wilson, K.A., Possingham, H.P., Eds.; Oxford University Press: New York, NY, USA, 2009; pp. 211–234. ISBN 9780199547777. [Google Scholar]
- Kuemmerlen, M.; Schmalz, B.; Guse, B.; Cai, Q.; Fohrer, N.; Jähnig, S.C. Integrating catchment properties in small scale species distribution models of stream macroinvertebrates. Ecol. Model. 2014, 277, 77–86. [Google Scholar] [CrossRef] [Green Version]
- Sharp, R.; Tallis, H.T.; Ricketts, T.; Guerry, A.D.; Wood, S.A.; Chaplin-Kramer, R.; Nelson, E.; Ennaanay, D.; Wolny, S.; Olwero, N.; et al. InVEST 3.5.0 User’s Guide; The Natural Capital Project; Stanford University: Stanford, CA, USA; University of Minnesota: Minneapolis, MN, USA; The Nature Conservancy: Arlington, VA, USA; World Wildlife Fund: Morges, Switzerland, 2018. [Google Scholar]
- Alkemade, R.; van Oorschot, M.; Miles, L.; Nellemann, C.; Bakkenes, M.; ten Brink, B. GLOBIO3: A framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 2009, 12, 374–390. [Google Scholar] [CrossRef]
- Amoako-Tuffour, J.; Martinez-Espineira, R. Leisure and the net opportunity cost of travel time in recreation demand analysis: An application to Gros Morne national park. J. Appl. Econ. 2012, 15, 25–49. [Google Scholar] [CrossRef]
- Heagney, E.C.; Rose, J.M.; Ardeshiri, A.; Kovac, M. Optimising recreation services from protected areas—Understanding the role of natural values, built infrastructure and contextual factors. Ecosyst. Serv. 2018, 31, 358–370. [Google Scholar] [CrossRef]
- Font, A.R. Mass tourism and the demand for protected natural areas: A travel cost approach. J. Environ. Econ. Manag. 2000, 39, 97–116. [Google Scholar] [CrossRef]
- Vallecillo, S.; Polce, C.; Barbosa, A.; Castillo, C.P.; Vandecasteele, I.; Rusch, G.M.; Maes, J. Spatial alternatives for Green Infrastructure planning across the EU: An ecosystem service perspective. Landsc. Urban Plan. 2018, 174, 41–54. [Google Scholar] [CrossRef]
- De la Fuente, B.; Mateo-Sánchez, M.; Rodríguez, G.; Gastón, A.; de Ayala, R.P.; Colomina-Pérez, D.; Melero, M.; Saura, S. Natura 2000 sites, public forests and riparian corridors: The connectivity backbone of forest green infrastructure. Land Use Policy 2018, 75, 429–441. [Google Scholar] [CrossRef]
- Pelorosso, R.; Gobattoni, F.; Geri, F.; Leone, A. PANDORA 3.0 plugin: A new biodiversity ecosystem service assessment tool for urban green infrastructure connectivity planning. Ecosyst. Serv. 2017, 26, 476–482. [Google Scholar] [CrossRef]
- Rall, E.; Hansen, R.; Pauleit, S. The added value of public participation GIS (PPGIS) for urban green infrastructure planning. Urban For. Urban Green. 2018. [Google Scholar] [CrossRef]
Required Input | Data Used |
---|---|
Land use/land cover map | Regional vector land cover map produced by the Regional Administration of Sardinia in 2008, third level of the CORINE nomenclature, rasterized. |
Threats to habitats | A list of threats to biodiversity, identified on the basis of Natura 2000 standard data forms pertaining to Sardinian Natura 2000 sites. To each threat, a weight and a distance decay were assigned based on the outcomes of a survey involving five selected experts in the field of biodiversity and environmental assessments. |
Impact sources | A raster map showing the spatial distribution of each of the 10 above-mentioned threats. |
Accessibility to sources of degradation | A vector map describing accessibility to sources of degradation, under the assumption that the stricter the legal/institutional protection, the lower the accessibility. On this basis, the following three protection levels were mapped: regional and natural parks, together with public woods managed by the Regional Forestry Agency (highest protection level: accessibility set to 0.2); Natura 2000 sites (intermediate level: accessibility set to 0.5); and the remaining part of the island (lowest level: accessibility set to 1). |
Habitat types and sensitivity of each habitat to each threat | A matrix listing land cover types (third level of the CORINE taxonomy) in the first column; for each land cover, a (0–1) score of its suitability to be regarded as habitat (second column), and a (0–1) score of its sensitivity to each of the 10 above-mentioned threats (3rd to 12th column) is given; sensitivity levels were assigned through an expert-based approach. |
Half-saturation constant | Default value (i.e., 0.5). |
VAL_C | VAL_N | VAL_R | VAL_L | ||
---|---|---|---|---|---|
Natural protected areas (467,635.13 ha) | |||||
1st tertile: VAL_T = [0–1] (94,150.93 ha) | mean | 0.13 | 0.55 | 0.002 | 0.11 |
median | 0.00 | 0.50 | 0.00 | 0.00 | |
standard deviation | 0.17 | 0.33 | 0.01 | 0.31 | |
no. patches VAL_x = 0 | 17,366 | 4526 | 23,944 | 24,401 | |
% patches VAL_x = 0 1 | 63.34 | 16.51 | 87.34 | 89.01 | |
total area VAL_x = 0 [ha] | 87,332.79 | 2063.00 | 89,766.74 | 93,097.17 | |
% area VAL_x = 0 2 | 92.76 | 2.19 | 95.34 | 98.88 | |
2nd tertile: VAL_T = [1–1.57] (148,548.90 ha) | mean | 0.25 | 0.47 | 0.017 | 0.57 |
median | 0.29 | 0.50 | 0.01 | 1.00 | |
standard deviation | 0.21 | 0.44 | 0.04 | 0.49 | |
no. patches VAL_x = 0 | 16,661 | 20,482 | 24,064 | 20,825 | |
% patches VAL_x = 0 1 | 33.99 | 41.78 | 49.09 | 42.48 | |
total area VAL_x = 0 [ha] | 30,961.17 | 3244.88 | 123,262.52 | 120,388.14 | |
% area VAL_x = 0 2 | 20.84 | 2.18 | 82.98 | 81.04 | |
3rd tertile: VAL_T = [1.57–3.53] (224,935.30 ha) | mean | 0.34 | 0.89 | 0.024 | 0.95 |
median | 0.36 | 1.00 | 0.00 | 1.00 | |
standard deviation | 0.24 | 0.23 | 0.07 | 0.18 | |
no. patches VAL_x = 0 | 21,452 | 2102 | 44,796 | 2741 | |
% patches VAL_x = 0 1 | 24.41 | 2.39 | 50.98 | 3.12 | |
total area VAL_x = 0 [ha] | 45,805.20 | 509.24 | 160,293.70 | 9970.11 | |
% area VAL_x = 0 2 | 20.36 | 0.23 | 71.26 | 4.43 | |
Unprotected areas (1,940,665.60 ha) | |||||
1st tertile: VAL_T = [0–1] (1,015,579.00 ha) | mean | 0.08 | 0.53 | 0.005 | 0.05 |
median | 0.00 | 0.50 | 0.00 | 0.00 | |
standard deviation | 0.15 | 0.29 | 0.02 | 0.21 | |
no. patches VAL_x = 0 | 132,144 | 22,824 | 129,865 | 157,793 | |
% patches VAL_x = 0 1 | 79.62 | 13.75 | 78.25 | 95.08 | |
total area VAL_x = 0 [ha] | 963,608.81 | 50,825.56 | 940,178.56 | 999,186.97 | |
% area VAL_x = 0 2 | 94.88 | 5.00 | 92.58 | 98.39 | |
2nd tertile: VAL_T = [1–1.57] (563,063.30 ha) | mean | 0.15 | 0.60 | 0.012 | 0.61 |
median | 0.00 | 0.50 | 0.00 | 1.00 | |
standard deviation | 0.21 | 0.33 | 0.04 | 0.48 | |
no. patches VAL_x = 0 | 73,136 | 15,193 | 67,584 | 43,224 | |
% patches VAL_x = 0 1 | 64.27 | 13.35 | 59.39 | 37.98 | |
total area VAL_x = 0 [ha] | 271,474.10 | 21,355.85 | 448,720.03 | 328,386.09 | |
% area VAL_x = 0 2 | 48.21 | 3.79 | 79.69 | 58.32 | |
3rd tertile: VAL_T = [1.57–3.53] (362,023.30 ha) | mean | 0.24 | 0.86 | 0.019 | 0.96 |
median | 0.29 | 1.00 | 0.00 | 1.00 | |
standard deviation | 0.23 | 0.23 | 0.08 | 0.16 | |
no. patches VAL_x = 0 | 40,890 | 868 | 64,398 | 2338 | |
% patches VAL_x = 0 1 | 43.43 | 0.92 | 68.41 | 2.48 | |
total area VAL_x = 0 [ha] | 198,254.15 | 1134.46 | 305,528.38 | 17,732.95 | |
% area VAL_x = 0 2 | 54.76 | 0.31 | 84.39 | 4.90 |
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Lai, S.; Leone, F.; Zoppi, C. Implementing Green Infrastructures beyond Protected Areas. Sustainability 2018, 10, 3544. https://doi.org/10.3390/su10103544
Lai S, Leone F, Zoppi C. Implementing Green Infrastructures beyond Protected Areas. Sustainability. 2018; 10(10):3544. https://doi.org/10.3390/su10103544
Chicago/Turabian StyleLai, Sabrina, Federica Leone, and Corrado Zoppi. 2018. "Implementing Green Infrastructures beyond Protected Areas" Sustainability 10, no. 10: 3544. https://doi.org/10.3390/su10103544