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.

Comparing global land cover datasets through the Eagle matrix land cover components for continental Portugal

Global land cover maps play an important role in the understanding of the Earth's ecosystem dynamic. Several global land cover maps have been produced recently namely, Global Land Cover Share (GLC-Share) and GlobeLand30. These datasets are very useful sources of land cover information and potential users and producers are many times interested in comparing these datasets. However these global land cover maps are produced based on different techniques and using different classification schemes making their interoperability in a standardized way a challenge. The Environmental Information and Observation Network (EIONET) Action Group on Land Monitoring in Europe (EAGLE) concept was developed in order to translate the differences in the classification schemes into a standardized format which allows a comparison between class definitions. This is done by elaborating an EAGLE matrix for each classification scheme, where a bar code is assigned to each class definition that compose a certain land cover class. Ahlqvist (2005) developed an overlap metric to cope with semantic uncertainty of geographical concepts, providing this way a measure of how geographical concepts are more related to each other. In this paper, the comparison of global land cover datasets is done by translating each land cover legend into the EAGLE bar coding for the Land Cover Components of the EAGLE matrix. The bar coding values assigned to each class definition are transformed in a fuzzy function that is used to compute the overlap metric proposed by Ahlqvist (2005) and overlap matrices between land cover legends are elaborated. The overlap matrices allow the semantic comparison between the classification schemes of each global land cover map. The proposed methodology is tested on a case study where the overlap metric proposed by Ahlqvist (2005) is computed in the comparison of two global land cover maps for Continental Portugal. The study resulted with the overlap spatial distribution among the two global land cover maps, Globeland30 and GLC-Share. These results shows that Globeland30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal.

Gas emissions on rice culture and its importance on air quality

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente Perfil Gestão de Sistemas Ambientais

Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

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".

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.