Environmental quality of life index incorporationg MCA & GIS : case study Lisbon Metropolitan Area, Portugal

The study of Quality of Life (Qol) has been conducted on various scales throughout the years with focus on assessing overall quality of living amongst citizens. The main focus in these studies have been on economic factors, with the purpose of creating a Quality of Life Index (QLI).When it comes down to narrowing the focus to the environment and factors like Urban Green Spaces (UGS) and air quality the topic gets more focused on pointing out how each alternative meets this certain criteria. With the benefits of UGS and a healthy environment in focus a new Environmental Quality of Life Index (EQLI) will be proposed by incorporating Multi Criteria Analysis (MCA) and Geographical Information Systems (GIS). Working with MCA on complex environmental problems and incorporating it with GIS is a challenging but rewarding task, and has proven to be an efficient approach among environmental scientists. Background information on three MCA methods will be shown: Analytical Hierarchy Process (AHP), Regime Analysis and PROMETHEE. A survey based on a previous study conducted on the status of UGS within European cities was sent to 18 municipalities in the study area. The survey consists of evaluating the current status of UGS as well as planning and management of UGS with in municipalities for the purpose of getting criteria material for the selected MCA method. The current situation of UGS is assessed with use of GIS software and change detection is done on a 10 year period using NDVI index for comparison purposes to one of the criteria in the MCA. To add to the criteria, interpolation of nitrogen dioxide levels was performed with ordinary kriging and the results transformed into indicator values. The final outcome is an EQLI map with indicators of environmentally attractive municipalities with ranking based on predefinedMCA criteria using PROMETHEE I pairwise comparison and PROMETHEE II complete ranking of alternatives. The proposed methodology is applied to Lisbon’s Metropolitan Area, Portugal.

Modelação do risco de incêndio florestal à escala municipal com recurso a dados de detecção remota

Neste projecto é testada uma metodologia para actualização de mapas de ocupação do solo já existentes, derivados de fotografia aérea, usando uma imagem satélite para posterior modelação com vista à obtenção da cartografia do risco de incêndio actualizada. Os diferentes passos metodológicos na fase da actualização dos mapas de ocupação de solo são: Classificação digital das novas ocupações, Produção do mapa de alterações, Integração de informação auxiliar, Actualização da Cartografia Temática. Para a produção do mapa de alterações a detecção de alterações foi efectuada através de expressões de Álgebra de Mapas. A classificação digital foi realizada com um classificador assistido - Classificador da Máxima Verosimilhança. A integração de informação auxiliar serviu para melhorar os resultados da classificação digital, nomeadamente em termos das áreas ardidas permitindo uma resolução temática mais detalhada. A actualização resultou da sobreposição do mapa das áreas alteradas classificadas com o mapa desactualizado. Como produto obteve-se a Carta de Alterações da Ocupação do Solo com Potencial Influência no Risco de Incêndio actualizada para 2008, base para a fase da Modelação do Risco. A metodologia foi testada no concelho de Viseu, Centro de Portugal. A Carta de Uso e Ocupação do Solo de Portugal Continental para 2007 (COS2007) foi utilizada como carta de referência. A nova carta actualizada para 2008, no concelho de Viseu, apresenta 103 classes temáticas, 1ha de unidade mínima e 90% de precisão global. A modelação do risco de incêndio geralmente é feita através de índices que variam, de forma geral, numa escala qualitativa, tendo como fim possibilitar a definição de acções de planeamento e ordenamento florestal no âmbito da defesa da floresta contra incêndios. Desta forma, as cartas de risco são indicadas para acções de prevenção, devendo ser utilizadas em conjunto com a carta da perigosidade que juntas podem ser utilizadas em acções de planeamento, em acções de combate e supressão. A metodologia testada, neste projecto, para elaboração de cartografia de risco foi, a proposta por Verde (2008) e adoptada pela AFN (2012). Os resultados apresentados vão precisamente ao encontro do que diz respeito no Guia Técnico para Plano Municipal de Defesa da Floresta Contra Incêndios, "O mapa de Risco combina as componentes do mapa de perigosidade com as componentes do dano potencial (vulnerabilidade e valor) para indicar qual o potencial de perda face ao incêndio florestal".

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Stops and bonanzas in the EU: Does being part of the Euro Area make a difference?

In this paper, we investigate whether being part of the euro area influences the conditional probability of going through a sudden stop or a bonanza of capital flows. Our sample period is from 1995 until 2014. We identify these two phenomena and we evaluate which push and pull factors help predict the conditional probability of experiencing one of them. We find that most countries had significant capital inflows until 2008 and that there were more sudden stops during the recent financial crisis than in any other moment in our sample. The factors that better help forecast the conditional probability of a sudden stop are global uncertainty (represented by the push factor “Volatility Index”), and the domestic economic activity (pull factors “GDP growth” and “consumer confidence”). An indicator of country risk (pull factor “change in credit rating”) is the most significant one for predicting bonanzas. Ultimately, we find no evidence that being part of the euro area influences the conditional probability of going through a sudden stop or a bonanza.

How can packaging change children's eating habits from un-healthy food towards healthier options?

Field Lab: Children consumer behaviour