Urban Growth Patterns and Effectiveness of the Metropolitan Urban Service Areas in Woodbury, Minnesota

This study evaluates the effectiveness of a specific urban sprawl containment strategy called the Metropolitan Urban Service Areas (MUSA). MUSA was developed for Minneapolis/St. Paul in order to ensure organized and practical development in areas that already had pre-built roads and sewer system infrastructures. Currently, MUSA is not an urban boundary; its specific goal is to, synchronize urban growth with the provision of infrastructure needed to accommodate growth (Council, August 2006). To evaluate the rigidity of the MUSA boundaries, the sample years of 1990, 2000, 2010 and the projected 2020 boundaries were subjected to spatial analysis utilizing three different software programs. The following research questions were addressed: (1) Has MUSA been effective in limiting low-density development growth in Woodbury, MN? (2) Are the boundaries established by MUSA adaptable or more rigid? (3) How can the current strategy be improved to increase the effectiveness of urban growth control? The research found a redefinition of the MUSA boundaries is necessary to curb urban expansion in Woodbury. The city of Woodbury and the Metropolitan Council can use this research as a model for regulating urban sprawl in fast-growing suburbs within designated MUSA boundaries. By adjusting the rigidity and the resulting effectiveness of the MUSA boundaries, a projected 3.1 billion dollars needed in order to provide infrastructure to low-density developments would be reduced

Land change detection and effective factors on forest land use changes: application of land change modeler and multiple linear regression

Reducing forest covered areas and changing it to pasture, agricultural, urban and rural areas is performed every year and this causes great damages in natural resources in a wide range. In order to identify the effective factors on reducing the forest cover area, multiple regression was used from 1995 to 2015 in Mazandaran forests. A Multiple regressions can link the decline in forest cover (dependent variable) and its effective factors (independent variable) are well explained. In this study, Landsat TM data of 1995 and Landsat ETM+ data of 2015 were analyzed and classified in order to investigate the changes in the forest area. The images were classified in two classes of forest and non-forest areas and also forest map with spatial variables of physiography and human were analyzed by regression equation. Detection satellite images showed that during the studied period there was found a reduction of forest areas up to approximately 257331 ha. The results of regression analysis indicated that the linear combination of income per capita, rain and temperature with determined coefficient 0.4 as independent variables were capable of estimating the reduction of forest area. The results of this study can be used as an efficient tool to manage and improve forests regarding physiographical and human characteristics.Keywords: Land change Modeler, Multiple linear regression, remote sensing, Mazandaran forest

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Urbanization pattern of Greater Asmara Area for the last two decades (1989 to 2009) and a prediction for the coming ten years was studied. Satellite images and geospatial tools were employed to quantify and analyze the spatiotemporal urban land use changes during the study periods. The principal objective of this thesis was to utilize satellite data, with the application of geospatial and modeling tools for studying urban land use change. In order to achieve this, satellite data for three study periods (1989, 2000 and 2009) have been obtained from USGS. Object-Based Image Analysis (OBIA); and image classification with Nearest Neighbor algorithm in eCognition Developer 8 have been accomplished. In order to assess the validation of the classified LULC maps, Kappa measure of agreement has been used; results were above minimum and acceptable level. ArcGIS and IDRISI Andes have been employed for LUCC quantification; spatiotemporal analysis of the urban land use classes;to examine the land use transitions of the land classes and identify the gains and losses in relation to built up area; and to characterize impacts of the changes. Since, the major concern of the study was urban expansion, the LULC classes were reclassified in to built up and non-built up for further analysis. Urban sprawl has been measured using Shannon Entropy approach; results indicated the urban area has undergone a considerable sprawl. Finally, LCM has been used to develop a model, asses the prediction capacity of the developed model and predict future urban land use change of the GAA. Multi-layer perceptron Neural Network has been used to model the transition potential maps, results were successful to make ‘actual’ prediction with Markov Chain Analyst.Despite the GAA is center of development and its regional economic and social importance, its trend of growth remains the major factor for diminishing productive land and other valuable natural resources. The findings of the study indicated that, in the last twenty years the built up area has tripled in size and impacted the surrounding natural environment. Thus, the findings of this study might support decision making for sustainable urban development of GAA

Urban change monitoring using GIS and remote sensing tools in Kathmandu valley (Nepal)

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe urbanization pattern during the period of 1989 to 2006 of Kathmandu valley was studied using Landsat data. The main aims of the study were to apply Geographic Information Systems (GIS) and Remote Sensing tools for the study of land use and land cover classification, change analysis and urban growth model for 2019 of the Kathmandu valley. The study also reviewed population growth and urbanization trends in connection with increasing built up areas leading to the environmental degradation. The population growth and urbanization trend of Kathmandu valley was the highest among other cities in Nepal. Principal component analysis was applied to spectrally enhance images to get the better image classification results. Images were classified in six land use and land cover classes using supervised classification and maximum likelihood algorithm which were then re-classed into built up and non-built up to focus on urbanization. The analysis showed that the built up area had grown up to 134% in 2006 since 1989. The assessed overall accuracies for the classification of three images were between 86 to 89 percentages. Cellular Automata Markov (CA_MARKOV) and GEOMOD modeling programs were used to project the 2006 and then 2019 land use and land cover classes. The 2019 land use and land covers was projected after satisfactory validation of projected 2006 land classes resulting with Kappa more than 0.55 up to 0.75. The future projection of land classes did not show that the urban growth will have significant effects to the designated areas. However, there will be some effects in water bodies. The Landsat images along with other ancillary data proved to be useful for the overall study

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe high rate of urbanization coupled with population growth has caused changes in land use and land cover in Bahir Dar, Ethiopia. Therefore, understanding and quantifying the spatio- temporal dynamics of urban land use and land cover changes and its driving factors is essential to put forward the right policies and monitoring mechanisms on urban growth for decision making. Thus, the objective of this study was to analyze land use and land cover changes in Bahir Dar area, Ethiopia by applying geospatial and land use change modeling tools. In order to achieve this, satellite data of Landsat TM for 1986 and ETM for 2001 and 2010 have been obtained and preprocessed using ArcGIS. The Maximum Liklihood Algorithm of Supervised Classification has been used to generate land use and land cover maps. For the accuracy of classified land use and land cover maps, a confusion matrix was used to derive overall accuracy and results were above the minimum and acceptable threshold level. The generated land cover maps have been run with Land Change Modeler for quantifying land use and land cover changes, to examine land use transitions between land cover classes, to identify gain and losses of built up areas in relation to other land cover classes and to asses spatial trend of built up areas. Finally, Land Change Modeler has been run to model land use and land cover changes in Bahir Dar area and to predict future urban land use changes. To achieve this, four model variables that explain urban growth and six land cover transitions were incorporated in the modeling process. Multi-layer perceptron neural network was used to model the transition potential maps and achieved an accuracy of 61%. This result was acceptable to make actual prediction using Markov chain analysis for year 2010. Validation results showed that the model (Land Change Modeler) had a lower accuracy in simulating changes for the year 2010. Generally, the results of this study have shown that there was an increased expansion of built up areas in the last 25 years from 1.5% in 1986 to 4.1 % in 2001 and 9.4% in 2010 at the expense of agricultural areas. The spatial trend of built up areas also showed that there was a growing trend in the western part of Bahir Dar relative to other directions. Therefore, the findings of this study could provide as decision making for urban planning

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km2 . We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of − 75.58 km2. The tidal flat will experience a stunning net gain of 80.55 km2 between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of − 1 m and a slope of less than 2◦have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.info:eu-repo/semantics/publishedVersio

The handwriting of society on the landscape: modeling of the Environmental Changes on the Borders of Protected Areas located in the Espinhaço Mountain Range, state of Minas Gerais, Brazil

Este artigo analisa os contextos da criação de duas áreas protegidas localizadas na Serra do Espinhaço, no estado de Minas Gerais (Brasil): Parque Estadual do Rio Preto e Parque Estadual de Serra Negra. O trabalho compara os contextos sociais e naturais do processo de criação das duas áreas protegidas entre os anos de 1986 e 2009. Para tanto, buscamos entender os padrões de uso dos recursos naturais pelas populações locais, relacionando-os com os resultados da dinâmica do uso da terra ao longo do tempo, identificados através do monitoramento com imagens orbitais e através da modelagem ambiental. Quanto à análise do uso do solo, as mudanças na paisagem ao redor do Parque Estadual do Rio Preto corroboraram os levantamentos de campo, que registraram um aumento na percepção da população local sobre os problemas ambientais e na fiscalização após a criação do parque. Nenhuma relação relevante foi encontrada para o Parque Estadual de Serra Negra. A modelagem ambiental do Parque Estadual do Rio Preto registrou um resultado positivo em termos de proteção ambiental, pois uma tendência de degradação dos recursos naturais foi contida após a criação do parque. Para o Parque Estadual de Serra Negra, embora a proteção da área não tenha influenciado as mudanças na paisagem, a dinâmica de uso dos recursos pela população local não degradou o ambiente natural

CENÁRIOS AMBIENTAIS E O INCENTIVO À PROTEÇÃO DE ÁREAS DE PRESERVAÇÃO PERMANENTE NA MICROBACIA DO RIO FAGUNDES, PARAÍBA DO SUL/RJ

To encourage farmers to preserve and/or restore native vegetation as envisaged in Law 12,651/2012, a sustainable rural development program in Rio de Janeiro invests in the protection and recovery of permanent preservation areas, especially in the spring. Related to the program, the objective of this work is to construct environmental scenarios in the Fagundes River Basin in Paraíba do Sul/RJ, which will subsidize the planning and management of PPAs. The methodology is the quantification of land cover classes, "Forest", "Non Forest", "Water" and "Unclassified" using geotechnologies in two comparative scenarios, 2011 and 2016 and a future scenario, 2046. The program was used ArcGis 10.3 for digital image processing, the Landsat 5TM and Landsat 8 OLI sensors and the Idrisi Selva 17.0/Land Change Modeler program for the generation of predictive models and comparative analysis. The results indicate that, between 2011 and 2016, there was an increase of 160.4 hectares of the "No Forest" class on the "Forest" class: the "Forest" class decreased by 7.4%, while the "No Forest" class increased 5.33%. For the future scenario 2046, the Markovian probabilistic model presents a 52% chance of converting from the "Forest" class to "No Forest", therefore, there is a prediction of a pessimistic future trend of deforestation. It is recommended the monitoring of the watershed related to changes in the land cover and actions of preservation of "Forest"

Changes in the socio-ecological system of a protected area in the yucatan peninsula: A case study on land-use, vegetation cover, and household management strategies

Protected areas (PA) are effective means for protecting biodiversity, but less is known about their effect on the social-ecological system (SES). Using a semi-experimental approach and a descriptive case study based evaluation, we analyzed the effect of a PA in the Yucatan Peninsula on land-cover and household resource management strategies in time and space (before and after the PA establishment; inside and outside its limits). To assess the changes of land-use practices in the areas surrounding the communities inside and outside the PA, and their change over time (from 2003 to 2015), we used remote sensing analysis and semi-structured interviews. Our results show that after the PA was established, the forest increased and agricultural plots decreased inside and to a lesser extent outside the PA. However, fires reduced the area of old-growth forest and increased young secondary forest, highlighting the system’s vulnerability to uncommon events. Resource management strategies were also affected: while inside the PA households tended toward specializing on tourism, outside the PA household strategies implied a diversification of productive activities. Overall, the establishment of the PA proved to be an effective tool to promote forest recovery and prevent deforestation in the regions surrounding the communities both inside and outside the PA

Measuring and Predicting Long-Term Land Cover Changes in the Functional Urban Area of Budapest

The loss of farmland to urban use in peri-urban areas is a global phenomenon. Urban sprawl generates a decline in the availability of productive agricultural land around cities, causing versatile conflicts between nature and society and threatening the sustainability of urban agglomerations. This study aimed to uncover the spatial pattern of long-term (80 years) land cover changes in the functional urban area of Budapest, with special attention to the conversion of agricultural land. The paper is based on a unique methodology utilizing various data sources such as military-surveyed topographic maps from the 1950s, the CLC 90 from 1990, and the Urban Atlas from 2012. In addition, the multilayer perceptron (MLP) method was used to model land cover changes through 2040. The research findings showed that land conversion and the shrinkage of productive agricultural land around Budapest significantly intensified after the collapse of communism. The conversion of arable land to artificial surfaces increased, and by now, the traditional metropolitan food supply area around Budapest has nearly disappeared. The extent of forests and grasslands increased in the postsocialist period due to national afforestation programs and the demand of new suburbanites for recreational space. Urban sprawl and the conversion of agricultural land should be an essential issue during the upcoming E.U. Common Agricultural Policy (CAP) reforms