O transporte ferroviário de passageiros ganha importância estratégica na melhoria do trânsito e qualidade de vida dos grandes centros urbanos, mas no Brasil enfrenta sérios problemas operacionais, como obsolescência da infra-estrutura e invasão da faixa de domínio, comprometendo sua segurança e operacionalidade. O objetivo central deste trabalho é o mapeamento da cobertura do solo ao longo da faixa de domínio de uma linha de trem metropolitano. A área de estudo concentra-se nas adjacências da Linha F de trem metropolitano de São Paulo, operada pela CPTM Companhia Paulista de Trens Metropolitanos, com 38,8 km de extensão, cruzando as zonas central e leste da capital paulista, estendendo-se até outros municípios da Grande São Paulo. A metodologia adotada compreende o uso de imagem de satélite de alta resolução espacial IKONOS II, e técnica de classificação baseada em regiões. Os classificadores por regiões utilizam, além de informação espectral de cada pixel, a informação espacial que envolve a relação entre os pixels e seus vizinhos. Através da interpretação visual, foram identificadas doze classes de coberturas do solo, cujas feições nortearam os parâmetros da segmentação. A imagem segmentada foi então classificada usando o algoritmo de Bhattacharyya e pós-classificada utilizando regras e critérios definidos para cada classe, de acordo com seus atributos e contextualização. O desempenho da classificação final foi avaliado com base no coeficiente Kappa tendo atingido nível aceitável de correspondência. O mapa de cobertura do solo gerado a partir da classificação, demonstra que o entorno da linha é composto, predominantemente, por materiais impermeáveis como asfalto e concreto mas possui presença significativa de elementos naturais como vegetação e água. Desta forma, comprova-se a aplicabilidade de imagem de satélite de alta resolução e técnicas de processamento de imagem como suportes ao planejamento e gerenciamento do entorno da ferrovia. / The rail transport of passengers has strategic importance in the traffic and quality of life improvement in the downtowns, but in Brazil it faces serious operational problems such as obsolete infrastructure and invasion of the right of way, compromising its safety and operability. The principal aim of this paper is the mapping of land cover area of metropolitan railway right of way. The study focuses on the outskirts of the Line F metropolitan train in Sao Paulo, operated by CPTM - Companhia Paulista de Trens Metropolitanos, with 38,8 km long, crossing the central and eastern areas of São Paulo, extending to other cities in the Metropolitan Region of Sao Paulo. The methodology includes the use of the high spatial resolution satellite image - IKONOS II, and regions-based classification. The regions-based classifiers use, besides spectral information of each pixel, the spatial information that involves the relationship between the pixels and its neighbours. Through visual interpretation, were identified twelve classes of land covers, which features guided the segmentation parameters. The segmented image was classified using Bhattacharyya distance and post-classified using rules and criteria established for each class, according to their attributes and contextualization. The performance of the final classification was evaluated based on Kappa coefficient, resulting acceptable level of correspondence. The mapping of land cover generated from the classification, shows that the outskirts of the metropolitan railway`s right way is predominantly composed by impermeable materials - such as asphalt and concrete - but has significant presence of natural elements - such as vegetation and water. Thus, is evident the applicability of high spatial resolution satellite image added to classification methods for remotely sensed data for planning and management of the railway right of way.

A dinâmica das paisagens e as consequentes alterações no uso e cobertura da terra podem ser analisadas a partir de modelagens espaciais, que auxiliam na interpretação das variáveis responsáveis pelas alterações na paisagem, bem como viabilizam a criação de cenários distintos visando ao conhecimento do impacto produzido pela inserção ou supressão de variáveis na simulação de mudanças da paisagem. Em Guarulhos, a instalação de grandes rodovias conectando o município de São Paulo ao Vale do Paraíba e, posteriormente, de um terminal aeroportuário, em consonância com diferentes períodos de crescimento econômico e expansão urbana, impactaram fortemente na formação da paisagem encontrada neste município, principalmente com a instalação dos grandes pátios industriais e bairros residenciais de média e baixa renda. Com isso, a presente pesquisa buscou avaliar as mudanças no uso e cobertura da terra a partir de produtos de sensoriamento remoto para identificar a influência advinda da instalação dos grandes pontos de conexão do município, por meio de modelagem com autômatos celulares (cellular automata - CA). Dentre os principais resultados obtidos está a influência positiva nas transições para a classe residencial regular exercida pela instalação do aeroporto no primeiro período de análise (1986 a 1993), e a repelência a essa transição nos anos seguintes. Resultados similares foram obtidos na avaliação da influência das grandes vias de acesso na implantação dos parques industriais e galpões logísticos. A modelagem dinâmica, por meio do software Dinamica-EGO, mostrou-se uma ferramenta bastante versátil para avaliação da influência das variáveis espaciais na dinâmica das paisagens urbanas. / The dynamics of landscapes and the consequent changes in land use and land cover can be analyzed according to spatial models, which helps the interpretation of the variables responsible for territorial changes, as well as enables the creation of distinct scenarios in order to know the impact produced by the insertion or suppression of variables in the simulation of landscape changes. In the municipal area of Guarulhos, the installation of large highways linking the municipality of São Paulo to the Paraíba Valley and an airport terminal afterwards, in accordance with different periods of economic growth and urban expansion, had a strong impact on the landscape found in this municipality, mostly because of the installation of large industrial yards and residential neighborhoods of medium and low income. Hence, this research aimed to evaluate the changes in land use and land cover according to remote sensing products, and it also aimed to try to identify the influence of the installation of big connection points of the municipality through modeling with cellular automata (CA). Among the main results obtained are the positive influence on the transitions to the regular residential class exercised by the airport installation in the first period of analysis (1986 to 1993), and the repellency of this transition in the following years. A similar result was obtained in the evaluation of the influence of the great access roads in the implantation of industrial parks and logistic sheds. Thus, the dynamic modeling through the Dynamica-EGO software proved to be a very versatile tool to evaluate the influence of spatial variables on the dynamics of urban landscapes.

Model projections of future climate change require that coupled climate-vegetation models are developed and validated, i.e. these models should be able to reproduce past climate and vegetation change. Records of pollen deposited in lake bottoms and peat bogs can provide the information needed to validate these models. The aim of this thesis was i) to explore the modern relationships between pollen and vegetation in northern and temperate China and estimate pollen productivity of major plant taxa, and ii) to use the results of i) to produce the first reconstruction of plant cover in China over the last 10 000 years for the purpose of climate modelling. A study of the modern pollen-vegetation-climate relationships was performed in northwestern China (Paper I). Pollen productivity for 18 major plants of cultural landscapes in central-eastern China was estimated (Paper II). Based on a synthesis and evaluation of all existing estimates of pollen productivity in the study region, a standard dataset of pollen productivity for 31 plant taxa is proposed (Paper III). This dataset was used to achieve pollen-based REVEALS reconstructions of plant cover over the last 10 000 years in 35 regions of northern and temperate China (Paper IV). The major findings can be summarized as follows. Paper I: Annual precipitation (Pann) is the major climatic factor influencing pollen assemblages, followed by July precipitation (PJul). The shared effect of combinations of two climatic factors explains a larger portion of the variation in pollen data than individual variables. Paper II: Of the 16 reliable pollen productivities estimated, the estimates for 8 taxa are new, Castanea, Cupressaceae, Robinia/Sophora, Anthemis type/Aster type, Cannabis/Humulus, Caryophyllaceae, Cruciferae, and Galium type. Trees have in general larger pollen productivity than herbs. Paper III: Of the total 31 taxa for which estimates of pollen productivity are available in China, 13 taxa have more than 1 value. All or most of these values are similar for Artemisia, Cyperaceae, Larix, Quercus and Pinus. Eight taxa have very variable estimates. Paper IV: The REVEALS plant percentage-cover strongly differs from the pollen percentages, and they provide new important insights on past changes in plant composition and vegetation dynamics.

Land Use Land Cover (LULC) changes can take place at the expense of degrading environmental conditions and undermining ecosystem’s capacity to deliver benefits to people. In the Appalachian region, surface mining for coal is a major driver of LULC change. The Surface Mining Control and Reclamation Act (SMCRA) of 1977 requires mine site reclamation but typical reclamation practices often result in land cover dominated by grass and shrubs. The Forestry Reclamation Approach (FRA) is a promising reclamation strategy but not in widespread use by industry. Assessing ecosystem services that can be obtained from a forest landscape may help policy-makers and other stakeholders fully understand the benefits of forestry based reclamation. The objectives of this study are to 1) identify how surface mining and reclamation changed the LULC of a watershed encompassing the north fork of the Kentucky River 2) assess the biophysical value of four major ecosystem services under the contemporary LULC condition and 3) assess the benefits of the FRA scenario in the provision of ecosystem services. Geographic Information System (GIS) was used to study the LULC change and InVEST software models for ecosystem services assessment. The results indicate that watershed’s forest area has decreased by 7,751 hectares from 2001 to 2011 and mining activity may have contributed 75% of the change in LULC. Barren and grassland land covers provide less carbon storage, yield more water, and export more sediments and nutrients than forests. At the watershed level, the FRA modeled scenario increased carbon storage (13%) and reduced water yield (5%), sediment export (40%) and nutrient export (7%). This study provides critical information regarding the ecological benefits of Forestry Reclamation Approach to assist policy and decision making in this region even considering the modeling and data limitations.

For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photography, in combination with light detection and ranging (LIDAR) and independent component analysis (ICA). A critical analysis of each approach will be discussed in detail, as well as the ability of each methodology to generate landscape metrics that can accurately characterize the quality of the landscape. This will be done through the comparison of various landscape metrics derived from the different classifications approaches, with a goal of enhancing the literature concerning how these metrics vary across methodologies and across scales. This is a familiar problem encountered when analyzing land cover datasets over time, which are often at different scales or generated using different methodologies. The diversity of remotely sensed imagery, including varying spatial resolutions, landscapes, and extents, as well as the wide range of spatial metrics that can be created, has generated concern about the integrity of these metrics when used to make inferences about landscape quality. Finally, inferences will be made about land cover and land cover change dynamics for the state of Iowa based on insight gained throughout the process.

East Iowa experienced large flooding during June of 2008. This study used Cuencas hydrological model to simulate the discharges of June 2008 at Eldorado and Elkader, in the Turkey River Watershed, in North East Iowa. The results of this study were used to test the performance of Cuencas modeling this flood event and to explore the role of land cover change in the floods of 2008 at Elkader, Iowa. Cuencas was found to be a suitable tool to predict this event, that requires relatively low resources. The total time to run each simulation was around two hours which is reasonable for such large watershed (900 mi2), but a computer cluster was needed to run these simulations. The results from this study suggest that the role of land cover change from pre-settlement to current conditions was significant when using the rainfall conditions of 2008. The discharges simulated at Elkader, Iowa were almost twice as large when using the 2001 land cover, than when using the land cover found during 1832-1859, recorded during the General Land Office (GLO) survey. These results need to be taken only as preliminary results, since there is no data to validate the model at the time of the GLO survey, and since it is the first time that Cuencas is used to model the effects of land cover in Iowa's hydrology. However, the potential large reduction on discharge of the pre-settlement land cover is an incentive to investigate this issue further and continue developing Cuencas to capture the effects of less drastic land cover changes.

Currently, the U.S. and China are the two largest national economic entities in the world. However, it is noticeable that the two countries have considerably different strategies for economic development, environmental protection and land supply in coastal zones. In order to understand the coastline dynamics, land use land cover (LULC) changes and land management policies in the U.S. and China, a case study of the Tampa Bay (TB) watershed, Florida, U.S., and Xiangshan Harbor (XH), Zhejiang Province, China was conducted. The two areas possess similar humid subtropical climate and dense population, but experienced different anthropogenic impacts. TB sat at a developed stage with sound environmental laws, regulations and projects to preserve natural landscapes. XH was at a developing stage and focused more on an economic development in the last 30 years. Comparing the LULC change patterns and the major driving forces for the changes between the two study areas, governments and public could know what factors cause the land use conversion and how to preserve the natural landscapes. A new water index called the weighted normalized water index (WNDWI) was proposed to extract coastlines in TB and XH since current water indices could not classify turbid water bodies and shadow areas well. Two threshold methods (i.e., Otsu threshold method and multiple thresholds method) were implemented to find an optimal threshold to segment the water from the land. The experiments demonstrate that the WNDWI algorithm can achieve high accuracies to classify water from land with an optimal threshold in the two study sites. Coastlines in 1985, 1995, 2005 and 2015 in TB and XH were extracted and the changes were detected and highlighted. The results indicate that coastlines in TB were mostly stable, while those in XH had been undergoing intensive human interferences, indicating that XH was at a developing stage. Major anthropogenic impacts on XH coastlines are land reclamation and aquaculture, resulting in an impacted area of approximately 20.3 km2. The land cover maps of TB and XH in 1985 (1986), 1995, 2005 and 2015 were produced by classifying Landsat images using the random forest algorithm. The reflectance distributions of the land cover types indicate that it is difficult to classify agricultural land, rangeland, upland forest and wetland if using the optical bands only from a single Landsat image. Multi-seasonal image composites and the land surface temperature (LST) band were involved in image classification to achieve higher accuracies. The overall accuracies (OAs) of the land cover map of TB in 2015 and that of XH in 2005 were increased by 5.14% and 4.33% after adding the LST band. The OAs of the four years’ land cover maps of TB range from 81.14% to 83.43%, whereas those of XH vary from 84.67% to 87.67%. According to the experimental results, the total urban area increased by 11.8% in TB, while that in XH increased 138.9% during the last 30 years. Wetland in TB reduced by 8.3% while that in XH reduced 49.0%. The results of logistic regression analysis indicated that the density of wetland is a major driver for urban growth in TB with a strong negative impact while the relationship is opposite in XH. It is worth noting that XH has been undergoing a rapid urbanization and industrialization process with a vast amount of natural landscapes converted to urban areas, whereas TB has already passed the developing stage and issued environmental laws and programs to preserve natural landscapes from human exploitation. In terms of preserving natural landscapes and protecting the vulnerable coastal environment for our next generation, the coastal planning decision makers in XH should not only consider economic values and short-term benefits but also integrate values of ecological, social, and cultural and long-term benefits when making coastal management decisions.

<p>This project analyzes satellite images from 1976, 1985 and 2000 of Dutse, Jigawa state, in north-western Nigeria. The analyzed satellite images were used to determine land-use and vegetation changes that have occurred in the land-use from 1976 to 2000 will help recommend possible planning measures in order to protect the vegetation from further deterioration.</p><p>Studying land-use change in north-western Nigeria is essential for analyzing various ecological and developmental consequences over time. The north-western region of Nigeria is of great environmental and economic importance having land cover rich in agricultural production and livestock grazing. The increase of population over time has affected the land-use and hence agricultural and livestock production.</p><p>On completion of this project, the possible land use changes that have taken place in Dutse will be analyzed for future recommendation. The use of supervised classification and change detection of satellite images have produced an economic way to quantify different types of landuse and changes that has occurred over time.</p><p>The percentage difference in land-use between 1976 and 2000 was 37%, which is considered to be high land-use change within the period of study. The result in this project is being used to propose planning strategies that could help in planning sustainable land-use and diversity in Dutse.</p>

The Houston-Galveston-Brazoria Consolidated Metropolitan Statistical Area (Houston CMSA) has experienced rapid population growth during the past decades and is the only major US metropolitan area with no zoning regulations. We use SLEUTH, a spatially explicit cellular automata model, to simulate future (2002-2030) urban growth in the Houston metropolitan area, one of the fastest growing metropolises in the United States during the past decades. The model is calibrated with historical data for the period 1974-2002 that are extracted from a time series of satellite images. The dataset consists of four historical urban extents (1974, 1984, 1992, 2002), two land use layers (1992, 2002), five transportation layers (1974, 1984, 1990, 2002, 2025), slope layer, hillshade layer, and excluded layer. Future growth patterns are predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth of the Houston CMSA for the period from 2002 to 2030 is predicted. Within SLEUTH, growth in the Houston CMSA is predominately "organic" with most growth occurring along the urban/rural fringe. Projected increases in urban area from 2002 to 2030 parallel projected increases in population growth within the Houston CMSA. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the unmanaged growth with no restrictions. The second scenario is to project the moderate growth trend by taking into consideration environmental protection, specifically for agricultural areas, forests and wetlands. The last scenario is to simulate the managed growth with maximum environmental protection. Adjusting the level of protection for different land cover types was found to markedly affect the land use changes in the Houston CMSA. Without any protection on resource lands, Houston CMSA is estimated to lose 2,000 km2 of forest land by 2030, about 600 km2 of agricultural land, and approximately 400 km2 of wetland. Approximately half of all resource land could be saved by the third scenario, managed growth with maximum protection.

Rapid urban growth and unprecedented rural to urban transition, along with a huge population growth are new phenomena for both high and low income countries, which started in the mid-20th century. However, urban growth rates and patterns are different in developed countries and developing ones. In less developed countries, urbanization and rural to urban transition usually takes place in an unmanaged way and they are associated with a series of socioeconomical and environmental issues and problems. Identification of the city growth trends in past decades can help urban planners and managers to minimize these negative impacts. In this research, urban growth in the city of Isfahan, Iran, is the subject of study. Isfahan the third largest city in Iran has experienced a huge urban growth and population boom during the last three decades. This transition led to the destruction of natural and agricultural lands and environmental pollutions. Historical and recent remotely sensed data, along with different remote sensing techniques and methods have been used by researchers for urban land use and land cover change detection. In this study three Landsat TM and ETM+ images of the study site, acquired in 1985, 2000 and 2009 are used. Before starting processing, radiometric normalization is done to minimize the atmospheric effects. Then, processing methods including principal component analysis (PCA), vegetation indices and supervised classification are implemented on the images. Accuracy assessment of the PCA method showed that the first PC was responsible for more than 81% of the total variance, and therefore used for analysis of PCA differencing. ΔPC1t1-t2 shows the amount of changes in land use and land cover during the period of study. In this study ten vegetation indices were selected to be applied to the 1985 image. Accuracy assessments showed that Transformed Differencing Vegetation Index (TDVI) is the most sensitive and accurate index for mapping vegetation in arid and semi-arid urban areas. Hence, TDVI was applied to the 2000 and 2009 images. ΔTDVIt1-t2 showed the changes in land use and land cover especially the land use transformation from vegetation cover into the urban class. Supervised classification is the last method applied to the images. Training sites were assigned for the selected classes and accuracy was monitored during the process of training site selection. The results of classification show the expansion of urban class and diminishment in natural and agricultural lands.