O crescimento acelerado das cidades e os reflexos desse aumento das populações urbanas é preocupação constante na atualidade. Nesse processo, o surgimento de ocupações precárias, especialmente nas regiões metropolitanas, torna-se uma das características mais explicitas, caracterizando a própria lógica de ocupação, uso e direito desigual ao território. O monitoramento dessas áreas, sua formação e expansão, são uma necessidade crescente, em diversos locais no mundo, visto que a inclusão dessas áreas à cidade formal é tido como gatilho para a melhoria das condições de vida de mais de 100 milhões de pessoas que vivem em favelas no mundo todo, como colocam as Metas de Desenvolvimento do Milênio propostas pela Organização das Nações Unidas. Contudo, para que os habitantes das favelas sejam atendidos em seu direito a uma vida digna, faz-se necessário seu conhecimento e principalmente quantas são e onde estão. Um importante instrumento, com relação benéfica entre tempo de aquisição, custo de aplicação e possibilidade de replicabilidade e transferência de conhecimento é o uso de dados de Sensoriamento Remoto. Estes possibilitam o estabelecimento de metodologias através de procedimentos de detecção de feições e classificação do uso do solo, para identificação dessas áreas. Não obstante, os métodos de classificação clássicos quando aplicados a imagens de altíssima resolução espacial não conseguem extrair de forma satisfatória, em determinados casos, informações para uso intraurbano. Nesse ínterim surgem novos paradigmas de classificação de imagens como a Análise Orientada a Objeto, onde o processo de classificação parte do objeto geográfico definido a partir da segmentação da imagem, aproximando o objeto de feições do mundo real. Sobre estes objetos é possível a aplicação de regras de pertinência e de contexto através de linguagens e softwares específicos que permitem a transposição do conhecimento humano de fotointerpretação relação contextual para o meio computacional. Este trabalho objetivou avaliar o uso desta técnica de classificação para a detecção e mapeamento de favelas no município de Taboão da Serra/SP, utilizando dados auxiliares para a caracterização destas áreas e seus graus e tipos de precariedade. Os resultados demonstram a validade da aplicação da técnica. / The accelerated growth of the cities and the reflections of the increase of the urban population has been a constant concern nowadays. In this process, the occurrence of precarious occupancies, mainly in the metropolitan regions, has become one of the most explicit characteristics, describing the logic of occupancy itself, unequal use and right to the territory. The monitoring of these areas, their lineup and expansion, are an increasing need in several places in the world, as the inclusion of these areas in the formal city is considered a trigger for the living conditions improvement of over 100 million people who live in slums all over the world, as the Developments Goals of the Millennium proposed by the United Nations Organization. However, in order to meet the rights to a dignified life of the slums inhabitants, it is necessary to know about them mainly their number and where they are. An important tool related to the beneficial relation among the acquisition time, application cost and possibility of applying again, and transference of knowledge is the use of data from Remote Sensing. These data make it possible to establish the methodologies through the detection of features procedures and classification of the land use for these areas identification. Nevertheless the classical methods of classification cannot obtain, in certain cases, information on the interurban use, in a satisfactory way. In the interim, new paradigms of images classification appear like the Object Based Image Analysis (OBIA) which goes from the defined geographic object to the image segmentation, approaching the object to features of the real world. The application of pertinent rules and context over these objects is possible through specific languages and softwares that allow the transference of human knowledge of photo interpretation and contextual relation to the computing environment. This work aimed at evaluating the use of this classification technique for detection and zoning of slums in Taboão da Serra/SP town using supporting data for the areas characterization, its grades and kinds of precarious conditions. The results show the validity of the technique application.

With the advent of very high spatial resolution (VHR) satellite, spatial details within the image scene have increased considerably. This led to the development of object-based image analysis (OBIA) for the analysis of VHR satellite images. Image segmentation is the fundamental step for OBIA. However, a large number of techniques exist for RS image segmentation. To identify the best ones for VHR imagery, a comprehensive literature review on image segmentation is performed. Based on that review, it is found that the multiresolution segmentation, as implemented in the commercial software eCognition, is the most widely-used technique and has been successfully applied for wide variety of VHR images. However, the multiresolution segmentation suffers from the parameter estimation problem. Therefore, this study proposes a solution to the problem of the parameter estimation for improving its efficiency in VHR image segmentation. The solution aims to identify the optimal parameters, which correspond to optimal segmentation. The solution to the parameter estimation is drawn from the Equations related to the merging of any two adjacent objects in multiresolution segmentation. The solution utilizes spectral, shape, size, and neighbourhood relationships for a supervised solution. In order to justify the results of the solution, a global segmentation accuracy evaluation technique is also proposed. The solution performs excellently with the VHR images of different sensors, scenes, and land cover classes. In order to justify the applicability of solution to a real life problem, a building detection application based on multiresolution segmentation from the estimated parameters, is carried out. The accuracy of the building detection is found nearly to be eighty percent. Finally, it can be concluded that the proposed solution is fast, easy to implement and effective for the intended applications.

Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt. / Graduate / 2019-03-21

The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 x 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic.

O crescimento acelerado das cidades e os reflexos desse aumento das populações urbanas é preocupação constante na atualidade. Nesse processo, o surgimento de ocupações precárias, especialmente nas regiões metropolitanas, torna-se uma das características mais explicitas, caracterizando a própria lógica de ocupação, uso e direito desigual ao território. O monitoramento dessas áreas, sua formação e expansão, são uma necessidade crescente, em diversos locais no mundo, visto que a inclusão dessas áreas à cidade formal é tido como gatilho para a melhoria das condições de vida de mais de 100 milhões de pessoas que vivem em favelas no mundo todo, como colocam as Metas de Desenvolvimento do Milênio propostas pela Organização das Nações Unidas. Contudo, para que os habitantes das favelas sejam atendidos em seu direito a uma vida digna, faz-se necessário seu conhecimento e principalmente quantas são e onde estão. Um importante instrumento, com relação benéfica entre tempo de aquisição, custo de aplicação e possibilidade de replicabilidade e transferência de conhecimento é o uso de dados de Sensoriamento Remoto. Estes possibilitam o estabelecimento de metodologias através de procedimentos de detecção de feições e classificação do uso do solo, para identificação dessas áreas. Não obstante, os métodos de classificação clássicos quando aplicados a imagens de altíssima resolução espacial não conseguem extrair de forma satisfatória, em determinados casos, informações para uso intraurbano. Nesse ínterim surgem novos paradigmas de classificação de imagens como a Análise Orientada a Objeto, onde o processo de classificação parte do objeto geográfico definido a partir da segmentação da imagem, aproximando o objeto de feições do mundo real. Sobre estes objetos é possível a aplicação de regras de pertinência e de contexto através de linguagens e softwares específicos que permitem a transposição do conhecimento humano de fotointerpretação relação contextual para o meio computacional. Este trabalho objetivou avaliar o uso desta técnica de classificação para a detecção e mapeamento de favelas no município de Taboão da Serra/SP, utilizando dados auxiliares para a caracterização destas áreas e seus graus e tipos de precariedade. Os resultados demonstram a validade da aplicação da técnica. / The accelerated growth of the cities and the reflections of the increase of the urban population has been a constant concern nowadays. In this process, the occurrence of precarious occupancies, mainly in the metropolitan regions, has become one of the most explicit characteristics, describing the logic of occupancy itself, unequal use and right to the territory. The monitoring of these areas, their lineup and expansion, are an increasing need in several places in the world, as the inclusion of these areas in the formal city is considered a trigger for the living conditions improvement of over 100 million people who live in slums all over the world, as the Developments Goals of the Millennium proposed by the United Nations Organization. However, in order to meet the rights to a dignified life of the slums inhabitants, it is necessary to know about them mainly their number and where they are. An important tool related to the beneficial relation among the acquisition time, application cost and possibility of applying again, and transference of knowledge is the use of data from Remote Sensing. These data make it possible to establish the methodologies through the detection of features procedures and classification of the land use for these areas identification. Nevertheless the classical methods of classification cannot obtain, in certain cases, information on the interurban use, in a satisfactory way. In the interim, new paradigms of images classification appear like the Object Based Image Analysis (OBIA) which goes from the defined geographic object to the image segmentation, approaching the object to features of the real world. The application of pertinent rules and context over these objects is possible through specific languages and softwares that allow the transference of human knowledge of photo interpretation and contextual relation to the computing environment. This work aimed at evaluating the use of this classification technique for detection and zoning of slums in Taboão da Serra/SP town using supporting data for the areas characterization, its grades and kinds of precarious conditions. The results show the validity of the technique application.

Climate change has repeatedly been framed as the defining issue of the Anthropocene and with the Arctic changing at unpreceded speed need is high for a profound understanding of the Northern Swedish landscape. Northern Swedish aeolian sand dunes have been impacted by climatic changes throughout time. Their location, orientation and geomorphometry can therefore be used to explore past wind patterns and dune activity. By systematically and spatially mapping the dunes, patterns in location can be illustrated, dune orientations investigated, the dunes’ geomorphometry characterised and sediment sources determined. Based on this knowledge, insight in landscape development along with a better understanding of long-term landscape (in)stability in Northern Sweden can be gained. This M. Sc. thesis sets out to summarize useful concepts to understand the formation of Northern Swedish aeolian sand dunes and to derive its implications for understanding landscape development. Based thereon, it deduces the strong need to systematically and spatially analyse aeolian sand dunes in Northern Sweden. The use of geographic object-based image analysis (GEOBIA) allows for the detection of potential dune locations over a large area and provides defined and reproducible mapping boundaries. Polygons are created by segmenting a residual-relief separated digital elevation model (DEM) as well as slope and curvature data. The multi-resolution segmentation provides best results with a scale parameter of 15 and a homogeneity criterion of 0.1 for the shape criterion, as well as 0.5 for the compactness criterion. A rule-based classification with empirically derived parameters accepts on average 2.5 % of the segmented image objects as potential dune sites. Subsequent expert-decision confirms on average 25 % of the classified image objects as identified dune locations. The rule-based classification provides best results when targeting a smaller area as this allows for less variability within the dune characteristics. The investigation of expert-accepted dune locations confirms a prevalence of parabolic dune forms, reveals the coexistence of simple dunes with large coalesced systems, exemplifies variation in dune orientation and highlights that the majority of dunes are supplied by glaciofluvial deposits. By mapping Northern Swedish aeolian sand dunes and investigating their meaning for landscape development, this thesis furthermore contributes to closing the gap identified for research on Northern Swedish aeolian sand dunes. / Den första associationen till sanddyner är säkert Sahara snarare än norra Sverige. Ändå är dessa fossila sanddyner också mycket relevanta och intressanta att studera. De kan analyseras i samband med det omgivande landskapet och dess orientering. Dessa egenskaper hjälper till att identifiera mönster i landskapsutveckling. Detta och på grund av dynarnas relativt gamla ålder kan slutsatser om landskapets (in)stabilitet på geologiska tidsskalor dras. Detta är mycket användbart eftersom det kan ge insikter om hur klimatet såg ut under tiden som sanddynerna bildades - perioder där människor ännu inte har bevittnat klimatet. Kunskap som till exempel hur klimatet som rådde för länge sedan såg ut kan användas bland annat för att uppskatta hur landskapet kommer förändras i framtiden till följd av klimatförändringar. Trots dessa användbara egenskaper hos sanddynerna har lite forskning gjorts hittills. Det här examensarbet försöker motverka detta kunskapsgap och kartlägger sanddyner i norra Sverige med hjälp av geografisk objektbaserad bildanalys (geographic object-based image analysis, GEOBIA). Det innebär att bildmaterial och digitala höjdmodeller frigjorda från vegetation automatiskt analyseras med hjälp av algoritmer. Fokus här är inte på att analysera enskilda pixlar. Snarare grupperas pixlar med liknande egenskaper så som lutning (slope), krökning (curvature) och spektralegenskaper. Dessa blir sedan grunden för analysen. Möjliga sanddyner upptäcks semi-automatiskt så att deras position och orientering sedan kan analyseras. Den kunskap som erhållits på detta sätt utgör grunden för vidare forskning. Ett annat mål är att bidra till en djupare förståelse kring landskapsutvecklingen i norra Sverige. Det är viktigt att komma ihåg att detta är ett område som särskilt påverkas av klimatförändringar. En ökad kunskap om landskapets tidigare klimatrespons kan därmed bidra till att förutsäga framtiden för denna region. Förutom att öka kunskapen kring sanddyner i norra Sverige hjälper det här mastersarbetet även till att utvidga användningen av GEOBIA inom geomorfologiska studier.

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point) on 309 subplots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was > 45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point method (r = 0.85 for ENVI, 0.83 for Feature Analyst, and 0.83 for eCognition). Results from this study suggest that OBIA techniques may be used to extract P-J tree canopy cover accurately and inexpensively. All software packages accurately evaluated accurately extracted P-J canopy cover from NAIP imagery when imagery was not blurred and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which are shrubs with similar spectral values as P-J.

Wetlands are vital ecosystems providing essential services to both humans and the environment, yet they face threats from human activities leading to loss and disturbance. This study utilizes remote sensing (RS) methods, including object-based image analysis (OBIA), to map and assess wetland health in Kristianstad’s Vattenrike in the southernmost part of Sweden between 2015 and 2023. Objectives include exploring RS capabilities in detecting wetlands and changes, deriving wetland health indicators, and assessing classification accuracy. The study uses Sentinel-2 imagery, elevation data, and high-resolution aerial images to focus on wetlands along the river Helge å. Detection and classifications were based on Sentinel-2 imagery and elevation data, and the eCognition software was employed. The health assessment was based on the spectral indices Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (mNDWI). Validation was conducted through aerial photo interpretation. The derived classifications demonstrate acceptable accuracy levels and the analysis reveals relatively stable wetland conditions, with an increase in wetland area attributed to the construction of new wetlands. Changes in wetland composition, such as an increase in open meadows and swamp forests, were observed. However, an overall decline in NDVI values across the study area indicates potential degradation, attributed to factors like bare soil exposure and water presence. These findings provide insights into the local changes in wetland extent, composition, and health between the study years.

A superfície da terra foi modificada nos últimos 50 anos mais do que em qualquer outro período da História, mais intensa e rápida nos trópicos pela expansão das frentes de ocupação humana sobre floresta madura. A Amazônia brasileira, caracterizada pela alternância de ciclos econômicos extrativistas, exemplifica esse processo. Entre o abandono de áreas degradadas e a abertura de novas frentes de ocupação, ocorre a regeneração florestal. A floresta secundária tem uma reconhecida importância para o restabelecimento das funções dos ecossistemas e dos estoques de nutrientes perdidos da floresta madura, mas ignorados por muitos anos de taxas oficiais de desmatamento na Amazônia brasileira. Este estudo apresenta uma abordagem utilizando Análise de Imagens Baseada em Objetos Geográficos (GEOBIA) para classificar os estágios de sucessão secundária numa área com cerca de 11.124 km² na região de Santarém (Pará, Brasil). Dentre os resultados, foram produzidas 19 diferentes classificações cobrindo o período 1984 a 2016, que permitiu identificar a redução da floresta madura e da floresta secundária devido à expansão da fronteira agrícola. Outro resultado relevante foi a modelagem de uma árvore de decisão aplicável às imagens de refletância de superfície coletadas pelos satélites LANDSAT, processando esses atributos de classificação em um aplicativo de mineração de dados / The earth surface was modified in the last 50 years more than in any other period of the History, more intense and fast in the tropics by the expansion of human occupation frontiers on the mature forest. The Brazilian Amazon, characterized by alternating extractive economic cycles, exemplifies this process. Between the degraded areas abandonment and the new occupation fronts, forest regeneration takes place. The secondary forest has a recognized importance for the restoration of ecosystem functions and the nutrient stocks lost from the mature forest but ignored for many years of official deforestation rates in the Brazilian Amazon. In this study, an approach using Geographic Object-Based Imaging Analysis (GEOBIA) is presented to classify the stages of secondary succession in an area with near 11,124 km² on Santarém region (Pará State, Brazil). Among the results, 19 different classifications were produced covering the period 1984 to 2016, which allowed identify the reduction of mature forest and secondary forest due to agricultural frontier expansion. Another relevant result was the modeling of a decision tree applicable to surface reflectance images collected by the LANDSAT satellites, processing these classifications attributes in a data mining software

O estudo das cidades requer um olhar amplo, capaz de identificar e relacionar os inúmeros processos que atuam na produção do espaço urbano. Geotecnologias comumente são utilizadas para adquirir informação detalhada da cobertura da terra do espaço urbano. Neste contexto, o objetivo desta pesquisa é a proposição de metodologia para geração de informações da ocupação urbana nas periferias, definindo procedimentos para análise urbana que gere informações sobre as características da ocupação urbana, a partir de imagens de satélite de alta resolução espacial. Para tanto, foi escolhida como área de estudo o distrito do Jardim São Luís compreendido na bacia do reservatório Guarapiranga, manancial que fornece água para a Região Metropolitana de São Paulo (RMSP) e cuja bacia é área de proteção e recuperação de mananciais, de acordo com a legislação estadual. Foram realizadas discussões de como se organiza o espaço urbano e dos processos que refletiram na ocupação urbana da periferia da RMSP. A metodologia desenvolvida nesta pesquisa articulou o uso de técnicas de Sensoriamento Remoto e Sistemas de Informação Geográfica com dados socioeconômicos do censo demográfico. Os resultados foram apresentados e discutidos e a metodologia proposta demonstrou-se promissora para ser aplicada na atualização de informação do espaço urbano para subsidiar o planejamento urbano e a gestão territorial e consequentemente, para a melhoria da qualidade de vida da população. / Studies from cities require a wide look to identify the amount of processes occurring in the production of the urban space. Geo-technologies are commonly used to acquire detailed information of land cover from the urban space. In this context, the objective of this study is to propose methodology for the generation of information from the occupation at the urban peripheries, defining procedures for the analysis of urban areas, to obtain information of the characteristics of this occupation, from high resolution satellite images. The area under study was the district Jardim São Luis, located at the Guarapiranga Reservoir basin, an important water supplier for the São Paulo Metropolitan Region (RMSP), an area of environmental protection and recuperation, according to State legislation. Discussions were made on how the urban space is organized and on the processes of urban occupation in the periphery of RMSP. The methodology developed in this study used remote sensing and GIS techniques and socio-economic data from the last demographic census. The results were presented and the methodology proposed is very promising to be used to update information of the urban space and land management and consequently to improve the quality of life from the population.