In diesem Beitrag wird aufgezeigt, wie die Firma ILV durch langjährige Beteiligung an Forschungs- und Entwicklungsvorhaben sowie durch Industrieerprobung von innovativen Technologien erfolgreich im In- und Ausland arbeitet. Vorgestellt werden schwerpunkthaft Ergebnisse der Anwendung von Satellitenbilddaten im visuellen Bereich und von Radardaten sowie Erfahrungen bei der Einführung der digitalen Luftbildkameratechnik. Berichte aus der Praxis des digitalen Bildfluges im Ausland auch aus Sicht des Auftraggebers für geologische und geotechnische Fragestellungen und der Industrieerprobung von Multibeam-Sonar-Technik zeigen die innovativen Potentiale dieser Technologien. / In this paper, it is shown how the company ILV works successfully domestically and abroad by long standing involvement in research and development projects as well as by industrial testing of innovative technologies. The focus of the paper are results of application of satellite images in visual range and of radar data as experiences with the introduction of digital airborne camera technique. Field notes about digital photo flight abroad – also from the view of the customer for geological and geotechnical problems – and about industrial testing of Multi-beam Sonar Technique show the innovative potentials of these technologies.

Every year, the number of applications relying on information extracted from high-resolution satellite imagery increases. In particular, the combined use of different data sources is rising steadily, for example to create high-resolution maps, to detect changes over time or to conduct image classification. In order to correctly fuse information from multiple data sources, the utilized images have to be precisely geometrically registered and have to exhibit a high absolute geo-localization accuracy. Due to the image acquisition process, optical satellite images commonly have an absolute geo-localization accuracy in the order of meters or tens of meters only. On the other hand, images captured by the high-resolution synthetic aperture radar satellite TerraSAR-X can achieve an absolute geo-localization accuracy within a few decimeters and therefore represent a reliable source for absolute geo-localization accuracy improvement of optical data. The main objective of this thesis is to address the challenge of image matching between high resolution optical and synthetic aperture radar (SAR) satellite imagery in order to improve the absolute geo-localization accuracy of the optical images. The different imaging properties of optical and SAR data pose a substantial challenge for a precise and accurate image matching, in particular for the handcrafted feature extraction stage common for traditional optical and SAR image matching methods. Therefore, a concept is required which is carefully tailored to the characteristics of optical and SAR imagery and is able to learn the identification and extraction of relevant features. Inspired by recent breakthroughs in the training of neural networks through deep learning techniques and the subsequent developments for automatic feature extraction and matching methods of single sensor images, two novel optical and SAR image matching methods are developed. Both methods pursue the goal of generating accurate and precise tie points by matching optical and SAR image patches. The foundation of these frameworks is a semi-automatic matching area selection method creating an optimal initialization for the matching approaches, by limiting the geometric differences of optical and SAR image pairs. The idea of the first approach is to eliminate the radiometric differences between the images trough an image-to-image translation with the help of generative adversarial networks and to realize the subsequent image matching through traditional algorithms. The second approach is an end-to-end method in which a Siamese neural network learns to automatically create tie points between image pairs through a targeted training. The geo-localization accuracy improvement of optical images is ultimately achieved by adjusting the corresponding optical sensor model parameters through the generated set of tie points. The quality of the proposed methods is verified using an independent set of optical and SAR image pairs spread over Europe. Thereby, the focus is set on a quantitative and qualitative evaluation of the two tie point generation methods and their ability to generate reliable and accurate tie points. The results prove the potential of the developed concepts, but also reveal weaknesses such as the limited number of training and test data acquired by only one combination of optical and SAR sensor systems. Overall, the tie points generated by both deep learning-based concepts enable an absolute geo-localization improvement of optical images, outperforming state-of-the-art methods.

No description available.

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database. / Libyan Centre for Remote Sensing and Space Science / Appendix A was submitted with extra data files which are not available online.

Les activités anthropiques dans le bassin méditerranéen sont en forte évolution. Dans les zones agricoles, cette croissance entraîne des évolutions considérables de l'occupation du sol. Cette activité agricole exerce un impact majeur sur le fonctionnement hydrologique des paysages qui n'est identifiable qu'à une échelle bien plus large, sur plusieurs dizaines de km². Cette thèse se concentre sur la classification de l'occupation du sol sur une large étendue à partir d'une image monodate à haute résolution spatiale (SPOT6/7).Dans ce contexte, les données d'apprentissage sont collectées par des enquêtes terrain, par conséquent, elles sont très limitées. Les méthodes d'apprentissage supervisées sont généralement utilisées, en supposant que la distribution des classes est stable sur toute l'image. Cependant, en pratique, on constate une distorsion des distributions des classes (apparition de nouvelles classes, disparition de classes). Ce problème, intitulé "datashift", se produit souvent sur des larges étendues. Ainsi le modèle construit sur les données d'apprentissage initiales s'avère sous optimal pour la classification de l'image entière. Pour atténuer ce problème, les techniques d'apprentissage actif définissent un ensemble d'apprentissage efficace, en l'adaptant itérativement par l'ajout des données non labellisées les plus informatives. Ces techniques permettent d'améliorer le modèle de classification tout en conservant un petit ensemble d'apprentissage initial. L'échantillonnage se base généralement sur deux métriques : l'incertitude et la diversité.Dans cette thèse, nous montrons l'apport des techniques d'apprentissage actif pour la cartographie de l'occupation du sol en milieu agricole, en proposant un échantillonnage adapté par parcelle.L'apport des méthodes d'apprentissage actif est validé par rapport à une sélection aléatoire des parcelles. Une métrique de diversité basée sur l'algorithme Meanshift a été proposée.Dans un deuxième temps, nous avons traité le sous-problème du "datashift" qui est l'apparition de nouvelles classes. Nous avons proposé de nouvelles métriques de diversité basées sur l'algorithme Meanshift et les Fuzzy k-means ainsi qu'une nouvelle stratégie de sélection des données adaptées à la détection de nouvelles classes.Dans la dernière partie, nous nous sommes intéressés aux contraintes spatiales induites par les observations sur terrain et nous avons proposé une stratégie de labellisation par points de vue qui permet de diminuer largement les coûts humains d'observations terrain tout en gardant de bonnes précisions de classification ainsi que la découverte des nouvelles classes.Les méthodes proposées ont été testées et validées avec une image multispectrale SPOT6 à 6m de résolution sur le bassin versant de Lebna, Cap-Bon, Tunisie. / Anthropogenic activities in the Mediterranean are in strong evolution. In agricultural areas, this growth leads to considerable changes in land cover. This agricultural activity has a major impact on the hydrological functioning of the landscapes which can be only identified on a wide scale, over several tens of km². This thesis focuses on the land cover classification on wide area from a high spatial resolution monodate image (SPOT6/7).In this context, the learning data are collected by field surveys, therefore they are very limited. Supervised learning methods are generally used, assuming that the class distribution is stable over all the image. However, in practice, there is a class distributions distortion (new classes appear, classes disappear). This problem, called "datashift", always occurs over wide areas. Thus, the model constructed on the initial learning data is sub-optimal for the classification of the entire image. To lessen this problem, active learning techniques define an effective learning set, by iteratively adapting it by adding the most informative unlabeled data. These techniques improve the classification model while retaining a small initial learning set. Sampling is generally based on two metrics: uncertainty and diversity.In this thesis, we show the contribution of active learning techniques for the land cover mapping in agricultural environment, proposing a suitable sampling per parcel.The active learning methods contribution is validated respectively to a random selection of parcels. A diversity metric based on the Meanshift algorithm has been proposed.Secondly, we treated the sub-problem of the "datashift" which is the appearance of new classes. We proposed new metrics of diversity based on the Meanshift algorithm and Fuzzy k-means as well as a new data selection strategy adapted to the detection of new classes.Finally we were interested in the spatial constraints induced by the field observations and we proposed a strategy of labeling by stand points which make it possible to greatly reduce the human costs for field observations while maintaining good classification precisions as well as the discovery of new classes.The proposed methodologies were tested and validated on a multispectral SPOT6 image with 6m resolution on the Lebna watershed, Cap-Bon, Tunisia.

Ainda que o conhecimento sobre a flora e a ecologia do cerrado tenha sido consideravelmente ampliado nas últimas décadas, persistem dificuldades relacionadas com a caracterização estrutural das fitofisionomias e pouco se sabe sobre as transformações fisionômicas que ocorrem nesta vegetação ao longo do tempo em áreas protegidas. Adicionalmente, mediante as mudanças climáticas, surgiu a demanda de quantificação de fitomassa e estoque de carbono em formações vegetais, entre os quais o cerrado. Os objetivos deste estudo foram: a) verificar se a classificação fisionômica de três fisionomias de cerrado reflete diferenças florísticas e estruturais; b) caracterizar a dinâmica espaço-temporal das fisionomias de cerrado; c) quantificar a fitomassa dessa vegetação e sua contribuição para estoque de carbono; d) avaliar a aplicabilidade de dados de sensoriamento remoto para estimar a fitomassa do cerrado. A pesquisa foi desenvolvida na Estação Ecológica de Assis (EEcA) localizada no estado de São Paulo. O cerrado típico, cerrado denso e cerradão foram caracterizados florística e estruturalmente e comparados para verificar se podem ser considerados distintos. Foram alocadas 30 parcelas de 20 x 50 m, sendo 10 parcelas para cada um dos tipos fisionômicos. Os indivíduos de espécies lenhosas com DAP \'> OU =\' 5 cm foram identificados e medidos. As fisionomias mostraram-se estruturalmente distintas, em classes de densidade, área basal e altura média das árvores e o melhor descritor para classificá-las, por ser pouco variável com o critério de inclusão, é a área basal (\'M POT.2\'/ha). Floristicamente, há diferenças apenas entre o cerradão e as fisionomias savânicas. Analisou-se a dinâmica espaço-temporal das fisionomias de cerrado, ao longo de 44 anos, com base em aerofotos (1962, 1984 e 1994) e imagens QuickBird (2006). Após a criação da unidade de conservação e devido à suspensão das atividades antrópicas (fogo e agropecuária), tem ocorrido um adensamento da vegetação, em que áreas de campo foram ocupadas por fisionomias de maior fitomassa, o cerradão correspondendo, em 2006, 91,43% da EEcA. Analisou-se, em particular, a oscilação na área ocupada por uma espécie invasora de samambaia (Pteridium arachnoideum). As imagens de 1994 e 2006 mostram que espécies arbóreas estão aumentando em densidade e cobertura em meio às manchas de samambaias e, em campo, constatou-se que a fitomassa das samambaias está diminuindo consideravelmente sob as copas das árvores. A fitomassa do cerrado stricto sensu e cerradão da EEcA foi estimada por meio de equações alométricas. Utilizaram-se parcelas de 20 x 40 m, sendo 20 parcelas para cada fitofisionomia. Utilizou-se regressão robusta com reamostragem Bootstrap para explorar as relações entre a fitomassa aérea do cerrado e as imagens do QuickBird e TM/Landsat, índices espectrais de vegetação (IV), componentes principais (CP), modelo linear de mistura espectral (MLME). Na EEcA, os valores médios de fitomassa aérea (23,22 Mg/ha) e total (28,88 Mg/ha) do cerrado stricto sensu foram próximos aos descritos na literatura para o cerrado do Brasil Central e os valores médios de fitomassa aérea (98,18 Mg/ha) e total (118,36 Mg/ha) do cerradão aproximaramse aos descritos para florestas estacionais. As bandas espectrais dos sensores QuickBird e TM apresentaram correlações fracas a moderadas com a fitomassa aérea de cerrado. As transformações espectrais (IV e CP) melhoram, em geral, a predição da fitomassa aérea de cerrado, contudo as correlações se mantiveram entre fracas e moderadas. / Even though knowledge on the ecology and flora of cerrado vegetation has considerably improved in recent years, gaps are still remaining on structural differentiation of the cerrado physiognomies and few is known about cerrado vegetation dynamics after protection from human pressure. In addition, before the climate changes, phytomass and carbon storage quantification has been a new challenge for different vegetation types, including the cerrado physiognomies. The present study was carried out with the aim of a) to characterize three cerrado physiognomies to verify if they are structurally and floristically distinct; b) to analyze the vegetation dynamics in time and space, to verify if the vegetation is undergoing a sucessional process, whose structural climax will be a forest physiognomy; c) to quantify phytomass in different physiognomies and their contribution to carbon stock; and d) to assess the application of remote sensing techniques to estimate the cerrado phytomass in large scale. This study was carried out at Assis Ecological Station (EEcA), located in the southwestern São Paulo state, Brazil. This protected area preserves one of the few brazilian cerrado (savanna) biome remnants in the State. Three distinct physiognomic types of cerrado (typical cerrado, dense cerrado and woodland cerrado) were floristically and structurally characterized and submitted to comparative analyses to verify if they can, or not, be considered as separate vegetation types within the cerrado gradient. Thirty permanent plots (20 x 50 m each) were set, ten in every cerrado type, and all woody individuals with DBH \'> OR =\' 5 cm were measured and identified. The three types of cerrado under comparison are structurally distinct in terms of density, medium height and basal area (\'M POT.2/ha), the last being considered as the best and more precise descriptor to classify the physiognomies of the cerrado vegetation. The woodland cerrado is also distinct by its flora, but the two open physiognomies (dense cerrado and cerrado stricto sensu) are floristically very similar. The dynamics of the vegetation types along 44 years in the studied area was analyzed by using aerial photographs (1962, 1984 and 1994) and QuickBird images (2006). After protection from human pressures (fire and agriculture), the woody vegetation density and phytomass has continuously increased, with open physiognomies tending to disappear and woodland cerrado replacing them. Surprisingly, the area covered by the invasive fern Pteridium arachnoideum has also decreased, replaced by sparse or clustered trees. The cerrado phytomass was estimated by allometric equations. Forty plots (20 x 40 m each) were used, twenty in every cerrado type - cerrado stricto sensu and woodland cerrado. Robust Regression and Bootstrap methods were used to explore relationships between aboveground cerrado phytomass and remote sensing data of QuickBird and Landsat Thematic Mapper (TM) sensors (spectral bands, vegetation index - VI, principal components - PC, linear spectral mixture model LSMM). Valors of medium phytomass obtained for the cerrado stricto sensu at Assis Ecological Station were close to those described in the literature for Central Brazilian cerrado. The valors of medium phytomass of the woodland cerrado were close to those described for seasonal forests. Spectral bands of QuickBird and TM sensors presented weak to moderate correlations with the aboveground cerrado phytomass. In general, spectral transformations (VI and PC) improved the prediction of the cerrado phytomass, however the correlation remained from weak to moderate.

O inventário florestal é uma das principais ferramentas na gestão dos recursos florestais, uma vez que as informações geradas por ele são utilizadas ao longo de toda a cadeia produtiva do setor. Desta forma, erros nas estimativas volumétricas dos inventários florestais devem ser controlados. Inúmeras informações podem ser obtidas a partir de imagens orbitais ou aerotransportadas, uma vez que podem cobrir facilmente toda a área de interesse, e estão comumente disponíveis em empresas florestais ou ao usuário final. A utilização de preditores derivados das imagens pode trazer benefícios para as estimativas do inventário florestal. Desta forma, a aplicação de técnicas de regressão linear múltipla (RLM) ganhou espaço no setor devido a sua facilidade de aplicação. Porém, a RLM não leva em consideração a dependência espacial entre as unidades amostrais, sendo que a geoestatística pode ser utilizada para predizer a distribuição espacial do estoque de madeira (VTCC) para uma dada região. A modelagem geoestatística mais simples como a krigagem ordinária (KO), por considerar apenas a dependência espacial entre os pontos não amostrados, pode apresentar erros de predição nestes locais. Tais erros podem ser reduzidos com a aplicação de técnicas mais robustas como a Krigagem com Deriva Externa (KDE), pois esta agrega as informações obtidas das imagens com a distribuição espacial do volume. Buscando-se avaliar as vantagens da integração do Sensoriamento Remoto (SR) ao inventário florestal foram testados 4 tipos diferentes de imagens; as oriundas dos satélites LANDSAT8, RAPIDEYE e GEOEYE, e as provenientes de aeronaves (Imagens Aerotransportadas). Avaliou-se também diferentes tipos de estimativas para a predição volumétrica sendo estas RLM, KDE e KO. A melhor estimativa serviu de variável auxiliar para o estimador de regressão (ER), sendo os resultados comparados com a abordagem tradicional da amostragem aleatória simples (AAS). Os resultados demonstraram por meio da validação cruzada que as estimativas da KDE foram mais eficientes que as estimativas da KO e da RLM. Os melhores preditores (variáveis auxiliares) foram aqueles derivados do satélite LANDSAT8 e do satélite RAPIDEYE. Obteve-se como produto das estimativas de KDE e RLM mapas capazes de detectar áreas com mortalidade ou anomalias em meio a formação florestal. A utilização de uma estimativa de KDE utilizando imagens LANDSAT8 como medida auxiliar para o ER permitiu reduzir o erro amostral da AAS de 3,87% para 2,34%. Da maneira tradicional, tal redução de erro apenas seria possível com um aumento de mais 99 unidades amostrais. / Forest Inventory (FI) is one of the main tools for managing forest resources, once the information derived from FI is used along the sector production chain. When estimating volume, errors resulting from FI are common, therefore these errors must be controlled. Once orbital or airborne imaging data are easily acquired for an entire area, and are commonly available in forest companies or for the end user, much information can be obtained from these products. The use of predictor derived from images can be of significant benefits to forest inventory estimates. For that reason, the application of linear multiple regression (LMR) techniques have taken place in the forest sector, due to the facilities of its application. However, the LMR technique does not take the spatial dependence among sample units in consideration, the geostatistics utilized to predict the spatial distribution of the wood stock (VTCC) for a specific region. Simpler geostatistical modeling as the ordinary kriging (OK), just takes in consideration the spatial dependence among non-sampled points, because of that, prediction errors can be found. Such errors can be reduced when techniques that are more robust are applied, such as the kriging with external drift (KED) approach. This technique aggregates the information obtained from the images with the spatial distribution of the volume. In order to evaluate the advantages of Remote Sensing and Forest Inventory integration, we considered 4 different types of images, from the satellites LANSAT 8, RAPIDEYE, GEOEYE and from airborne images. When predicting volume, three different approaches were evaluated: LMR, EDK, OK. The best model among those evaluated, served as auxiliary variable for the regression estimator (RE). The result were then compared to the traditional approach, simple random sampling (SRS).This approach showed, through a cross-validation, that the KDE estimates were more efficiently than the OK and the LMR. The best predictor model (auxiliary variables) were derived from LADNSAT 8 and RAPIDEYE satellites. There is a significant advantage to using the KDE and LMR approaches, as it allows for a spatial representation of areas with mortality or anomalies, in a forest environment. The combination of KDE approach and LANDSAT 8 images as an auxiliary method for the RE, abled the decrease of the sampling error of SRS from 3.87% to 2.34%.The traditional approaches to conduct plantation inventories would allow for this error reduction, only if there were an increase of 99 more sampling units.

A classificação de imagens de satélite é muito utilizada para elaborar mapas de cobertura do solo. O objetivo principal deste trabalho consistiu no mapeamento automático da cobertura do solo no entorno da Usina de Lajeado (TO) utilizando-se o classificador SVM. Buscou-se avaliar a dimensão de áreas antropizadas presentes na represa e a acurácia da classificação gerada pelo algoritmo, que foi comparada com a acurácia da classificação obtida pelo tradicional classificador MAXVER. Esta dissertação apresentou sugestões de calibração do algoritmo SVM para a otimização do seu resultado. Verificou-se uma alta acurácia na classificação SVM, que mostrou o entorno da represa hidrelétrica em uma situação ambientalmente favorável. Os resultados obtidos pela classificação SVM foram similares aos obtidos pelo MAXVER, porém este último contextualizou espacialmente as classes de cobertura do solo com uma acurácia considerada um pouco menor. Apesar do bom estado de preservação ambiental apresentado, a represa deve ter seu entorno devidamente monitorado, pois foi diagnosticada uma grande quantidade de incêndios gerados pela população local, sendo que as ferramentas discutidas nesta dissertação auxiliam esta atividade de monitoramento. / Satellite Image Classification are very useful for building land cover maps. The aim of this study consists on an automatic land cover mapping in the domain area of Lajeados dam, at Tocantins state, using the SVM classifier. The aim of this work was to evaluate anthropic dimension areas near the dam and also to verify the algorithms classification accuracy, which was compared to the results of the standard ML (Maximum Likelihood) classifier. This work presents calibration suggestions to the SVM algorithm for optimizing its results. SVM classification presented high accuracy, suggesting a good environmental situation along Lajeados dam region. Classification results comparison between SVM and ML were quite similar, but SVMs spatial contextual mapping areas were slightly better. Although environmental situation of the study area was considered good, monitoring ecosystem is important because a significant quantity of burnt areas was noticed due to local communities activities. This fact emphasized the importance of the tools discussed in this work, which helps environmental monitoring.

Made available in DSpace on 2017-07-21T18:15:15Z (GMT). No. of bitstreams: 1 Dulcina Aquino.pdf: 3843065 bytes, checksum: 42372506ad20f20325f1f7146ca4bfde (MD5) Previous issue date: 2014-01-27 / Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná / The evaluation of vegetal coverage, open spaces and green areas play a fundamental role for the planning and development of cities like Ponta Grossa. It enables both to highlight the effectiveness and identify the weakness related to those elements on the urban space. In this sense, this thesis proposes to understand the open spaces, green areas and spatial dynamics of the vegetal coverage of the Ponta Grossa's urban area.To accomplish this, it recurs to bibliographical review, data generation and to identify both tipology and spatialization of green areas as well as to evaluate its availability to the public. The thesis classifies Ponta Grossa's vegetation and analysis its distribution along 2004. In this context, it quantifies open spaces and green areas per borough by means of geotechnologies. In the aforementioned year, Ponta Grossa had 172,59 km² of urban area and 266,683 habitants. In turn, vegetal coverage had 49% of the entire urban area and presented an index of 319,3 m²/habitant. Finally, open space had an area of 2,5% composed by 230 spaces, they are: 132 sport fields, 63 public squares, 4 parks, 6 cemeteries and 8 clubs; with an index of 16,4 m²/habitant. In summary, in spite of the fact the numeric results for each category can be classified as satisfactory, their distribution can not and might impairing their effectiveness. / A avaliação da distribuição da cobertura vegetal, espaços livres e áreas verdes em cidades médias como Ponta Grossa-PR, constitui uma parte importante a ser considerada no planejamento nas cidades em desenvolvimento. Essa avaliação permite identificar as fragilidades e apontar a eficácia das funções que eles desempenham no espaço urbano. Dentro deste contexto, a presente dissertação se propõe a compreender a dinâmica espacial da cobertura vegetal, espaços livres e áreas verdes na área urbana de Ponta Grossa por meio da identificação da tipologia das áreas verdes, espacialização das mesmas e avaliação da disponibilidade à população, a partir de revisão bibliográfica, levantamento de dados e utilização de geotecnologias. Foi analisada a distribuição da cobertura vegetal, espaços livres e áreas verdes para o ano de 2004 na área urbana de Ponta Grossa bem como, realizada a classificação dessa cobertura vegetal. Foram realizadas quantificações dos espaços livres e áreas verdes por bairro, além de apontadas as disponibilidades dos mesmos, a partir de produtos gerados pelas geotecnologias. Neste período, a área urbana correspondia a 172,59 km² e contava com 266.683 habitantes. Verificou-se que a cobertura vegetal ocupava 49% do total da área urbana, com um índice de 319,3 m²/habitante. Os espaços livres ocupavam 2,5% de área composto por 230 espaços dos quais: 132 campos de esportes, 63 praças, 4 parques, 6 cemitérios e 8 clubes de lazer, com um índice de 16,4 m²/habitante. As áreas verdes ocupavam 2% distribuídos em 102 espaços com índice de 13m²/habitante. Embora os resultados numéricos para cada categoria sejam indicadores positivos, a distribuição compromete a sua eficácia.

As zonas costeiras estão em constante processo de pressão, tanto pela ação humana, que acaba rompendo o equilíbrio dominante, como pela ação da natureza, que está ligada principalmente a fatores geológicos, fatores climáticos e à dinâmica costeira. Neste trabalho buscou-se analisar a evolução do processo de ocupação urbana na faixa oceânica no município de Santa Vitória do Palmar, nos balneários do Hermenegildo e da Barra do Chuí, devido ao fato de esse local apresentar problemas em seu processo de urbanização, que se configuram pela falta de planejamento e de ordenamento territorial. Para essa análise, utilizou-se produtos de sensoriamento remoto em escala multitemporal, com perspectivas temporais em que se pode trabalhar e entender as rupturas de paradigmas em diferentes momentos históricos. Para isso, foram feitas análises em um levantamento aerofotogramétrico, adquirido pelo Exército Brasileiro, na Escala 1:75.000 do ano de 1964, análises em Imagens de Satélite Landsat TM7, do ano de 1996 e Imagens de Satélite QuickBird do ano de 2010. Portanto, esse trabalho propôs uma análise em escala multitemporal no processo de urbanização dos balneários do Hermenegildo e da Barra do Chuí, para um melhor entendimento do porquê dos problemas com as construções residenciais na faixa frontal ao Oceano Atlântico, que tem levando muitos moradores a perda total de suas residências. Verificou-se que a evolução dos percentuais de ocupação urbana nos balneários do Hermenegildo e da Barra do Chuí foi bastante significativa, sendo o que os dois balneários apresentaram crescimento mais elevado nas três primeiras décadas analisadas e ainda concluiu-se que no último intervalo da análise os índices de crescimento urbano foram menores para os dois balneários, recomendando-se maiores estudos e monitoramento dos vetores de crescimento urbano para ambos os balneários, com maior atenção para o balneário do Hermenegildo, devido ao grave problema de erosão costeira. / Coastal zones are constantly affected by the pressure process, caused by the human action, which ends up breaking the dominant balance, as well as by the action of the nature, which is mainly related to geologic and climatic factors and to the coastal dynamic. This study aims to analyze the urban occupation evolution process along Santa Vitória do Palmar coastline, especially Balneário do Hermenegildo and Balneário da Barra do Chuí, considering the fact that this specific territory presents lots of problems concerning its urbanization process. For this analysis, images captured by remote sensing were used in a multitemporal scale, trough time perspectives that enable this study to develop and understand the paradigmatic ruptures in different historical periods. In order to do so, different types of images were analyzed, such as the aerial photogrammetric survey, taken by the Brazilian Army, in the 1:75.000 scale of 1964, TM7 Landsat Satellite Images, taken in 1996, and QuickBird Satellite Images, taken in 2010. Therefore, this study promoted an analysis in a multitemporal scale of the urbanization process regarding the territory already mentioned, in order to discover the causes of the problems involving residential constructions located on the frontal area of the Atlantic Ocean, which might be the reason why the residents are totally losing their residences. It was found that the development of the urban occupation percentage in Balneário do Hermenegildo and Balneário da Barra do Chuí was very significant, based upon the fact that both beaches present a notorious increase on the first three analyzed decades and, beyond that, it was concluded that during the last interval of the analysis, the urban growth indices were lower for both, suggesting that this field demands more studies and monitoring of the urban growth vectors for both beaches, attaching particular attention to Balneário do Hermenegildo because of its severe coastal erosion problem.