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Record W6998670458

Are there common features in land cover and pattern changes in Mediterranean periurban areas ?

2013· article· en· W6998670458 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (University of Udine) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsLand coverMediterranean climateLand useMediterranean BasinCover (algebra)Spatial ecology
DOInot available

Abstract

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Agri-urban systems provide agro-environmental and feeding services to the cities (Zasada, 2011; De Bon et al., 2010). However, peri-urban areas are complex systems because of their geographical and productive characteristics (Van Veenhuizen, 2006; Moustier and Fall, 2004) and also for its stakeholder’s diversity (Allen, 2003; Dossa, et al., 2011). So, the dynamics of land cover features and patterns can vary according to stakeholder’s behaviour or local trends. Are there common features over scales in highly heterogeneous areas? Although case studies has been developed to identify the main landscape dynamics in the peri-urban areas (York et al., 2011; Hudalah et al., 2007), only a few of them concerned specifically the Mediterranean basin. In this context, how to quantify land cover and patterns changes in order to find common features?
\nWe propose a two steps method to analyse the land cover and pattern changes occurring in Mediterranean areas. We firstly analysed land cover in urban regions during the last 30 years using a semi-supervised classification of Landsat images. We quantified changes on urban, agricultural, natural/wood, bare soil and water land cover classes and calculated a transition matrix. Secondly, we analysed the patterns of land cover using some non-correlated landscape and class metrics, also analysing the shape diversity through the method of moving windows and the optimal window size (Zhang et al., 2012). The case studies are located in the north and south of the Mediterranean basin (Montpellier-France, Pisa-Italy, Lisbon-Portugal, Meknes-Morocco, and Constantine-Algeria) (Table 1).
\nThe analysis of the land cover changes suggests continuities over scales and local trends. In our case studies, the transition matrix suggested that the urban growth is associated to a decrease of the agricultural areas and an increase in the natural ones. Moreover, metrics suggests a general increase of the agricultural landscape fragmentation both for composition and configuration. Trend differences arise among case studies for the extent of urban growth, increasing of more than 10% in Montpellier and Constantine, and around 5% in the others cases. Decrease and fragmentation of agricultural spaces over long-distance area surrounding cities, coupled with an increase of natural areas, is typically a Mediterranean trend that must be compared with feeding and agro-environmental services in a currently climate change process. In addition, trends differences are not in contrast in north and south of Mediterranean basin but seem to depend on public choices about urban growth control.
\nOur work is part of a project (ANR-DAUME) on the agri-urban systems dynamics to understand the changes of farmers’ managements and practices and to analyze their impact on ecosystem services. Hence, these results are a first step in building scenarios of land use change in urban Mediterranean regions and can be useful to support policy-makers at local and regional levels.
\nReferences: 
\nAllen A., 2003. Environmental planning and management of the peri-urban interface: perspectives on an emerging field. Environment and Urbanization, 15 (1): 135-148.
\nDe Bon H., Parrot L., Moustier P., 2010. Sustainable urban agriculture in developing countries. A review. Agronomy for Sustainable Development 30: 21-32.
\nDossa L.H., Abdulkadir A., Amadou H. Sangare S., Schlecht E., 2011. Exploring the diversity of urban and peri-urban agricultural systems in Sudano-Sahelian West Africa: An attempt towards a regional typology. Landscape and Urban Planning, 102(3): 197-206.
\nHudalah D., Winarso H., Woltier J., 2007. Peri-urbanisation in East-Asia – A new challenge for planning? International Development Planning Review 26(4): 503-519.
\nMoustier P. and Fall S.A., 2004. Les dynamiques de l’agriculture urbaine: caractérisation et évaluation. In: Olanrewaju B. Smith, Moustier P., Mougeot A.J.L., Fall A., (Eds), CIRAD/CRDI, Montpellier, France, pp. 23-43.
\nVeenhuizen R. van, 2006. Cities farming for the future, in: van Veenhuisen René (Ed.), Cities farming for the future: Urban agriculture for green and productive cities, RUAF Foundation, IIRR, IDRC, Ottawa, Canada, pp. 1–17.
\nYork A.M., Shrestha M., Boone C.G., Zhang S.A., Harrington J.A., Prebyl T.J., Swann A., Agar M., Antolin M.F., Nolen B., Wright J.B., Skaggs R., 2011. Land fragmentation under rapid urbanization: A cross-site analysis of Southwestern cities. Urban Ecosystems 14(3): 429-455
\nZasada I., 2011. Multifunctional peri-urban agriculture – A review of societal demands and the provision of goods and services by farming. Land Use Policy 28: 639-648.
\nZhang S. A., Boone C. G., York A. M., Shrestha M., 2012. Methodological Advances in the Spatial Analysis of Land Fragmentation. The Professional Geographer.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.243
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2013
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