Are there common features in land cover and pattern changes in Mediterranean periurban areas ?
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".