In this dissertation, we focus on two related parts of a 3D face recognition system with wireless transportation. In the first part, the core components of the system, namely, the feature extraction and classification component, are introduced. In the feature extraction component, range images are taken as inputs and processed in order to extract features. The classification component uses the extracted features as inputs and makes classification decisions based on trained classifiers. In the second part, we consider the wireless transportation problem of range images, which are captured by scattered sensor nodes from target objects and are forwarded to the core components (i.e., feature extraction and classification components) of the face recognition system. Contrary to the conventional definition of being a transducer, a sensor node can be a person, a vehicle, etc. The wireless transportation component not only brings flexibility to the system but also makes the “proactive” face recognition possible. For the feature extraction component, we first introduce the 3D Morphable Model. Then a 3D feature extraction algorithm based on the 3D Morphable Model is presented. The algorithm is insensitive to facial expression. Experimental results show that it can accurately extract features. Following that, we discuss the generic face warping algorithm that can quickly extract features with high accuracy. The proposed algorithm is robust to holes, facial expressions and hair. Furthermore, our experimental results show that the generated features can highly differentiate facial images. For the classification component, a classifier based on Mahalanobis distance is introduced. Based on the classifier, recognition performances of the extracted features are given. The classification results demonstrate the advantage of the features from the generic face warping algorithm. For the wireless transportation of the captured images, we consider the location-based wireless sensor networks (WSN). In order to achieve efficient routing perfor¬mance, a set of distributed stateless routing protocols (PAGER) are proposed for wireless sensor networks. The loop-free and delivery-guaranty properties of the static version (PAGER-S) are proved. Then the performance of PAGER protocols are compared with other well-known routing schemes using network simulator 2 (NS2). Simulation results demonstrate the advantages of PAGER.

Agriculture has an important role in Turkey / hence automated approaches are crucial to maintain sustainability of agricultural activities. The objective of this research is to classify eight crop types cultivated in Karacabey Plain located in the north-west of Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this objective, first, the fused Kompsat-2 images were segmented separately to define homogenous agricultural patches. The segmentation results were evaluated using multiple goodness measures to find the optimum segments. Next, multispectral single-date Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs algorithms. To combine the thematic information of the multi-temporal data set, probability maps were generated for each classification result and the accuracies of the thematic maps were then evaluated using segment-based manner. The results indicated that the segment-based approach based on the SVMs method using the multispectral Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The combined thematic maps of June-August and June-July-August provided the highest overall accuracy and kappa value around 92% and 0.90, respectively, which was 4% better than the highest result computed with the MLC method. The produced thematic maps were also evaluated based on field-based manner and the analysis revealed that the classification performances are directly proportional to the size of the agricultural fields.

This thesis deals with multimodal image annotation in the context of social media. We seek to take advantage of textual (tags) and visual information in order to enhance the image annotation performances. However, these tags are often noisy, overly personalized and only a few of them are related to the semantic visual content of the image. In addition, when combining prediction scores from different classifiers learned on different modalities, multimodal image annotation faces their imperfections (uncertainty, imprecision and incompleteness). Consequently, we consider that multimodal image annotation is subject to imperfections at two levels: the representation and the decision. Inspired from the information fusion theory, we focus in this thesis on defining, identifying and handling imperfection aspects in order to improve image annotation.

A forced landing for an Unmanned Aerial Vehicle (UAV) is required if there is an emergency on board that requires the aircraft to land immediately. Piloted aircraft in the same scenario have a human on board that is able to engage in the complex decision making process involved in the choice of a suitable landing location. If UAVs are to ever fly routinely in civilian airspace, then it is argued that the problem of finding a safe landing location for a forced landing is an important unresolved problem that must be addressed. This thesis presents the results of an investigation into the feasibility of using machine vision techniques to locate candidate landing sites for an autonomous UAV forced landing. The approach taken involves the segmentation of the image into areas that are large enough and free of obstacles; classification of the surface types of these areas; incorporating slope information from readily available digital terrain databases; and finally fusing these maps together using a high level set of simple linguistic fuzzy rules to create a final candidate landing site map. All techniques were evaluated on actual flight data collected from a Cessna 172 flying in South East Queensland. It was shown that the use of existing segmentation approaches from the literature did not provide the outputs required for this problem in the airborne images encountered in the gathered dataset. A simple method was then developed and tested that provided suitably sized landing areas that were free of obstacles and large enough to land. The advantage of this novel approach was that these areas could be extracted from the image directly without solving the difficult task of segmenting the entire image into the individual homogenous objects. A number of neural network classification approaches were tested with the surface types of candidate landing site regions extracted from the aerial images. A number of novel techniques were developed through experimentation with the classifiers that greatly improved upon the classification accuracy of the standard approaches considered. These novel techniques included: automatic generation of suitable output subclasses based on generic output classes of the classifier; an optimisation process for generating the best set of input features for the classifier based on an automated analysis of the feature space; the use of a multi-stage classification approach; and the generation of confidence measures based on the outputs of the neural network classifiers. The final classification result of the system performs significantly better than a human test pilot's classification interpretation of the dataset samples. In summary, the algorithms were able to locate candidate landing site areas that were free of obstacles 92.3 ±2.6% (99% confidence in the result) of the time, with free obstacle candidate landing site areas that were large enough to land in missed only 5.3 ±2.2% (99% confidence in the result) of the time. The neural network classification networks developed were able to classify the surface type of the candidate landing site areas to an accuracy of 93.9 ±3.7% (99% confidence in the result) for areas labelled as Very Certain. The overall surface type classification accuracy for the system (includes all candidate landing sites) was 91.95 ±4.2% (99% confidence in the result). These results were considered to be an excellent result as a human test pilot subject was only able to classify the same data set to an accuracy of 77.24 %. The thesis concludes that the techniques developed showed considerable promise and could be used immediately to enhance the safety of UAV operations. Recommendations include the testing of algorithms over a wider range of datasets and improvements to the surface type classification approach that incorporates contextual information in the image to further improve the classification accuracy.

Η παρούσα διατριβή είχε σαν κύριο στόχο την ανάλυση και επεξεργασία των δεδομένων SAR υπό το πρίσμα του περιεχομένου υφής για την ανίχνευση περιοχών με περιβαλλοντικές αλλοιώσεις όπως είναι οι παράνομες εναποθέσεις απορριμμάτων. Τα δεδομένα που χρησιμοποιήθηκαν προέρχονταν από τον δορυφόρο ENVISAT και το όργανο ASAR του Ευρωπαϊκού Οργανισμού Διαστήματος με διακριτική ικανότητα 12.5m και 30m για τις λειτουργίες μονής και διπλής πολικότητας αντίστοιχα καθώς και από τον δορυφόρο Terra-SAR με διακριτική ικανότητα 3m και HH πολικότητα. Χρησιμοποιήθηκαν κλασσικές τεχνικές ανάλυσης και ταξινόμησης υφής όπως GLCM, Markov Random Fields, Gabor Filters και Neural Networks. Η μελέτη προσανατολίστηκε στην ανάπτυξη νέων μεθόδων ταξινόμησης υφής για αυξημένη αποτελεσματικότητα. Χρησιμοποιήθηκαν δεδομένα πολυφασματικά και SAR. Για τα πολυφασματικά δεδομένα προτάθηκε η χρήση της spectral co-occurrence ως χαρακτηριστικό υφής που χρησιμοποιεί πληροφορία φασματικού περιεχομένου. Για τα δεδομένα SAR αναπτύχθηκε μία νέα μέθοδος ταξινόμησης η οποία βασίζεται σε συνήθεις περιγραφείς υφής (GLCM, Gabor, MRF) οι οποίοι μελετώνται για την ικανότητά τους να διαχωρίζουν ζεύγη μεταξύ τάξεων. Για κάθε ζεύγος τάξεων προκύπτουν χαρακτηριστικά υφής που βασίζονται στις στατιστικές ιδιότητες της cumulative καθώς και της πρώτης και δεύτερης τάξης αυτής. Η μέθοδος leave one out χρησιμοποιείται για τον εντοπισμό των χαρακτηριστικών που μπορούν να διαχωρίσουν τα δείγματα ανά ζεύγη τάξεων στα οποία αντιστοιχίζεται και ένας ξεχωριστός και ανεξάρτητος γραμμικός ταξινομητής. Η τελική ταξινόμηση γίνεται με τη μέθοδο της πλειοψηφίας η οποία εφαρμόζεται στο πρόβλημα των δύο τάξεων και τριών τάξεων αλλά επεκτείνεται και στο πρόβλημα των N-τάξεων δεδομένης της ύπαρξης κατάλληλων χαρακτηριστικών. / Texture characteristics of MERIS data based on the Gray-Level Co-occurrence Matrices (GLCM) are explored as far as their classification capabilities are concerned. Classification is employed in order to reveal four different land cover types, namely: water, forest, field and urban areas. The classification performance for each cover type is studied separately on each spectral band, while the combined performance of the most promising spectral bands is explored. In addition to GLCM, spectral co-occurrence matrices (SCM) formed by measuring the transition from band-to-band are employed for improving classification results. Conventional classifiers and voting techniques are used for the classification stage. Furthermore, the properties of texture characteristics are explored on various types of grayscale or RGB representations of the multispectral data, obtained by means of principal components analysis (PCA), non-negative matrix factorization (NMF) and information theory. Finally, the accuracy of the proposed classification approach is compared with that of the minimum distance classifier. A simple and effective classification method is furthermore proposed for remote sensed data that is based on a majority voting schema. We propose a feature selection procedure for exhaustive search of occurrence measures resulting from fundamental textural descriptors such as Co-occurrence matrices, Gabor filters and Markov Random Fields. In the proposed method occurrence measures, that are named texture densities, are reduced to the local cumulative function of the texture representation and only those that can linearly separate pairs of classes are used in the classification stage, thus ensuring high classification accuracy and reliability. Experiments performed on SAR data of high resolution and on a Brodatz texture database have given more than 90% classification accuracy with reliability above 95%.

Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2005. / Salinisation is a major environmental hazard that reduces agricultural yields and degrades arable land. Two main categories of salinisation are: primary and secondary soil salinisation. While primary soil salinisation is caused by natural processes, secondary soil salinisation is caused by human factors. Incorrect irrigation practices are the major contributor to secondary soil salinisation. Because of low costs and less time that is associated with the use of remote sensing techniques, remote sensing data is used in this study to identify and map salinised irrigated land between Upington and Keimoes, Northern Cape Province, in South Africa. The aim of this study is to evaluate the potential of digital aerial imagery in identifying salinised cultivated land. Two methods were used to realize this aim. The first method involved visually identifying salinised areas on NIR, and NDVI images and then digitizing them onscreen. In the second method, digital RGB mosaicked, stacked, and NDVI images were subjected to unsupervised image classification to identify salinised land. Soil samples randomly selected and analyzed for salinity were used to validate the results obtained from the analysis of aerial photographs. Both techniques had difficulties in identifying salinised land because of their inability to differentiate salt induced stress from other forms of stress. Visual image analysis was relatively successful in identifying salinised land than unsupervised image classification. Visual image analysis correctly identified about 55% of salinised land while only about 25% was identified by unsupervised classification. The two techniques predict that an average of about 10% of irrigated land is affected by salinisation in the study area. This study found that although visual analysis was time consuming and cannot differentiate salt induced stress from other forms; it is fairly possible to identify areas of crop stress using digital aerial imagery. Unsupervised classification was not successful in identifying areas of crop stress.

Made available in DSpace on 2014-06-11T19:23:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2005Bitstream added on 2014-06-13T18:09:43Z : No. of bitstreams: 1 cruz_nc_me_prud.pdf: 1504734 bytes, checksum: 24dad2fab48cdca8018cdd5f1df08e04 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Nos últimos anos, infestações de plantas aquáticas em reservatórios estão sendo estudadas como um efeito do desequilíbrio causado pela poluição e represamento dos rios. A quantidade excessiva de plantas, conseqüente desse desequilíbrio, dificulta tanto a navegação como a produção de energia elétrica. Esse tipo de ocorrência, assim como a presença de algumas substâncias na água, provocam mudanças na radiância da mesma, registradas por sensores orbitais. Nesse sentido, técnicas de processamento e análise de dados de sensoriamento remoto podem se constituir em uma fonte complementar de dados e fornecer informações relacionadas ao grau de infestação de reservatórios. Nesse contexto, o presente trabalho teve como objetivo verificar a influência da resolução espacial de imagens multiespectrais na detecção e mapeamento de áreas infestadas por plantas aquáticas emersas em um reservatório de pequeno porte, através de utilização de procedimentos de análise multiescala e classificação supervisionada usando redes neurais artificiais. Para isso foram utilizadas imagens IKONOS multiespectrais (4 metros de resolução espacial) do reservatório de Salto Grande localizado na cidade de Americana- SP. Assim, foram geradas imagens multiescala, resultando em imagens de 8, 16 e 32 metros de resolução espacial. Na classificação das imagens, utilizando Redes Neurais Artificiais, os dados de entrada constituíram-se de imagens multiespectrais IKONOS (bandas 1, 2, 3 e 4), imagem de textura (banda do IVP), e uma imagem de índice de vegetação (NDVI). O procedimento metodológico adotado mostrou-se adequado para o mapeamento das variações espectrais da água e detecção das infestações por plantas aquáticas, nos vários níveis de resolução da imagem. Os resultados obtidos mostraram que a classificação pela rede neural, com os parâmetros... / In past few years, great infestations of aquatic plants in reservoirs have been studied as an effect of the environmental unbalance caused by pollution and damming of rivers. The excessive amount of plants, deriving from this unbalance, makes navigation and the production of electricity difficult. This kind of occurrence, as well as the appearance of some substances in the water, cause changes in the water radiance detected by satellite sensors. Thus, processing techniques and data analysis may be used as a complementary data source to give information related to the degree of infestation of these plants in reservoirs. So, the present dissertation aimed at verifying the influence of the spatial resolution of multispectral images in the detection and mapping of areas infested by aquatic plants in a small reservoir , through the use of multiscale analysis procedures and supervised classification using artificial neural networks. Multiespectral imagens IKONOS (spatial resolution of 4 meters) of the reservoir of Salto Grande, in the city of Americana-SP were used. So, multiscale images were generated, resulting in images of 8, 16 and 32 meters of spatial resolution. In the classification of these images, using Artificial Neural Networks, the input data was constituted of multispectral images IKONOS (bands 1, 2, 3 and 4), image of texture (band of NIR), and one image of vegetation index (NDVI). The method used was adequate to map the spectral variation of the water and to detect infested areas of aquatic plants in the various levels of resolution of the image. The results obtained showed that the classification by the parameters defined for the original image and applied in the training of the scheme adopted for the different resolution levels was satisfactory. Furthermore, an analysis was made comparing multiscale images classified through crossed comparison, which permits comparing...(Complete abstract click electronic access below)

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No. of bitstreams: 1 leaojunior_e_me_prud.pdf: 4186679 bytes, checksum: ee186b23411343c3e2d782d622226699 (MD5) Previous issue date: 2017-04-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Considerando o cenário durante a crise hídrica de 2014 e a situação crítica dos reservatórios do sistema Cantareira no estado de São Paulo, este estudo realizado no reservatório Jaguari-Jacareí, consistiu na extração de informações a partir de imagens multiespectrais e análise da qualidade da informação relacionada com a acurácia no cálculo do volume de água do reservatório. Inicialmente, a superfície do espelho d’água foi obtida pela classificação da cobertura da terra a partir de imagens multiespectrais RapidEye tomadas antes e durante a crise hídrica (2013 e 2014, respectivamente), utilizando duas abordagens distintas: classificação orientada a objeto (Object-based Image Analysis - OBIA) e classificação baseada em pixel (Support Vector Machine – SVM). A acurácia do usuário por classe permitiu expressar o erro para detectar a superfície do espelho d’água para cada abordagem de classificação de 2013 e 2014. O segundo componente da estimação do volume foi a representação do relevo submerso, que considerou duas fontes de dados na construção do modelo numérico do terreno (MNT): dados topográficos provenientes de levantamento batimétrico disponibilizado pela Sabesp e o modelo de superfície AW3D30 (ALOS World 3D 30m mesh), para complementar a informação não disponível além da cota 830,13 metros. A comparação entre as duas abordagens de classificação dos tipos de cobertura da terra do entorno do reservatório Jaguari-Jacareí mostrou que SVM resultou em indicadores de acurácia ligeiramente superiores à OBIA, para os anos de 2013 e 2014. Em relação à estimação de volume do reservatório, incorporando a informação do nível de água divulgado pela Sabesp, a abordagem SVM apresentou menor discrepância relativa do que OBIA. Apesar disso, a qualidade da informação produzida na estimação de volume, resultante da propagação da variância associada aos dados envolvidos no processo, ambas as abordagens produziram valores similares de incerteza, mas com uma sutil superioridade de OBIA, para alguns dos cenários avaliados. No geral, os métodos de classificação utilizados nesta dissertação produziram informação acurada e adequada para o monitoramento de recursos hídricos e indicou que a abordagem SVM teve um desempenho sutilmente superior na classificação dos tipos de cobertura da terra, na estimação do volume e em alguns dos cenários considerados na propagação da incerteza. / This study aims to extract information from multispectral images and to analyse the information quality in the water volume estimation of Jaguari-Jacareí reservoir. The presented study of changes in the volume of the Jaguari-Jacareí reservoir was motivated by the critical situation of the reservoirs from Cantareira System in São Paulo State caused by water crisis in 2014. Reservoir area was extracted from RapidEye multispectral images acquired before and during the water crisis (2013 and 2014, respectively) through land cover classification. Firstly, the image classification was carried out in two distinct approaches: object-based (Object-based Image Analysis - OBIA) and pixel-based (Support Vector Machine - SVM) method. The classifications quality was evaluated through thematic accuracy, in which for every technique the user accuracy allowed to express the error for the class representing the water in 2013 and 2014. Secondly, we estimated the volume of the reservoir’s water body, using the numerical terrain model generated from two additional data sources: topographic data from a bathymetric survey, available from Sabesp, and the elevation model AW3D30 (to complement the information in the area where data from Sabesp was not available). When compare the two classification techniques, it was found that in the image classification, SVM performance slightly overcame the OBIA classification technique for 2013 and 2014. In the volume calculation considering the water level estimated from the generated DTM, the result obtained by SVM approach was better in 2013, whereas OBIA approach was more accurate in 2014. Considering the quality of the information produced in the volume estimation, both approaches presented similar values of uncertainty, with the OBIA method slightly less uncertain than SVM. In conclusion, the classification methods used in this dissertation produced accurate information to monitor water resource, but SVM had a subtly superior performance in the classification of land cover types, volume estimation and some of the scenarios considered in the propagation of uncertainty.

A detecção de mudanças no uso e na cobertura do solo pode ser considerada a função do sensoriamento remoto que agrega uma dimensão temporal à análise das informações contidas nas imagens. Ao confrontá-las duas a duas, para identificar, localizar e qualificar as transformações que ocorreram na cobertura e no uso do solo em determinados espaço e tempo, através das respostas espectrais registradas nos pares de pixels homólogos quando aplicados limiares que permitam distinguir entre a mudança e a não mudança. A análise ponto a ponto, ou instante a instante permite inferir sobre a quantidade e qualidade das mudanças detectadas em uma região durante um determinado período. Já a análise comparativa entre dois ou mais destes resultados, utilizando uma série temporal de imagens, permite inferir acerca da dinâmica das transformações em vários outros aspectos; como topologia, intensidade, tipo de mudança (substituição ou conversão) e taxa de mudança. São várias as técnicas disponíveis para a detecção de mudanças no uso e cobertura do solo a partir de imagens digitais, obtidas por sensores orbitais, dois grandes grupos podem ser propostos: técnicas de pré-classificação e de pós-classificação, diferindo fundamentalmente sobre quais produtos são aplicados os limiares da detecção das mudanças, se sobre produtos temáticos da classificação de imagens, ou se sobre imagens diretamente. Este trabalho utiliza técnicas de detecção baseadas em subtração de imagens de ambos os grupos, especificamente, a pós-classificação por máxima verossimilhança e da pré-classificação, por Índice de Vegetação por Diferença Normalizada (NDVI) e por Transformada Kauth-Thomas (KT), nesse caso o componente de verdor. Visa avaliar a sensibilidade e adequação destas técnicas para a análise das transformações ocorridas no uso e da cobertura do solo durante os dois períodos de comparação: 1988 a 2001 e 2001 a 2006 e no conjunto dos 18 anos, na captação das tendências das transformações deste ambiente da Campanha Sul do Estado do Rio Grande do Sul, que é majoritariamente rural, muito dinâmico e bastante heterogêneo. Considerando que durante o período abrangido neste trabalho, grandes fazendas tradicionais de pecuária, em um primeiro momento, foram convertidas para agricultura familiar através da criação intensiva de assentamentos da reforma agrária, ocorrida entre a metade dos anos 80 e a metade dos anos 90, mais recentemente, substituídas por plantios florestais da indústria do papel. Os resultados permitiram confrontar os tratamentos e verificar as acurácias das detecções e identificar as principais dificuldades, em especial, o efeito da fenologia nas diversas fases em que se apresentam nas substituições florestais de ciclos longos. A dificuldade da técnica KT, em lidar com plantios semi-perenes e perenes, a impossibilidade de se considerar áreas cobertas em algum momento por nuvens. Por fim, corrobora com a inviabilidade do estabelecimento a priori da melhor técnica, ou mesmo, daquela mais acurada, sem que sejam considerados plenamente os objetivos, a escala, a natureza do ambiente analisado e as classes de mudança estabelecidas para o trabalho, além da qualidade das imagens disponíveis. / The detection of alterations in land use and cover can be considered as being an operation in Remote Sensing which adds a time dimension to the analysis of information in images. This is done when images are compared, by groups of two, at certain space and time looking for spectral responses stored in pairs of homologous pixels, through the application of thresholds which lead to the differentiation between change and non-change. A point-to-point, or instant-to-instant analysis, permits to infer on the amount and quality of alterations detected in a region, during a certain period. The comparative analysis between two or more of these results, via a time series of images, informs on the dynamics of transformations in other aspects, as topology, intensity, kind of change (substitution or conversion), and change rate. Several techniques are available to detect alterations in land use and cover, from digital images collected by orbital sensors. Two larger groups can be highlighted: preclassification techniques, and post-classification techniques. They differ basically on over which products the thresholds defining changes are applied, these products being either thematic ones for image classification, or the image itself. This work uses detection techniques based on image subtraction of both groups. Pre-classification uses the Normalized Difference Vegetation Index (NDVI) and the Kauth-Thomas Transform (KT), the green index in this last case. Post-classification uses the Maximum Likelihood. The objective is to estimate the sensitivity and adequacy of these techniques for the detection and analysis of changes in land use and cover during two comparison periods: the first one is from 1988 to 2001; the second, from 2001 to 2006. Besides, the whole 18-years period is studied to detect tendencies of the transformation of the region. The study area is at the Campanha Sul region, at Rio Grande do Sul State Brazil. It is largely rural, heterogeneous and dynamic, since during the period covered (1988-2006) large estates where either converted into smaller properties, family-managed; though an intensive policy of agrarian reform (from the mid-eighties to the mid-nineties), or, more recently, by industrial-style cultures of forests to serve the paper industry. The results allowed comparing the different treatments and to verify the accuracy of detections. The main difficulties were the phonological cycles, the various phases of long-cycle artificial forests, the limitations of the KT technique to handle semi-perennial cultures, and cloud-covered areas. It was not possible to clearly define the better or more accurate technique; this definition depends of specific objectives, of the scale and nature of the study region, and of the classes of change being analyzed, besides of the quality of available images.

As Unidades de Conservação (UC) são espaços territoriais com características naturais relevantes, que têm a função de assegurar a representatividade de amostras significativas e ecologicamente viáveis das diferentes populações, habitats e ecossistemas. A Legislação Brasileira instituiu no ano de 2000 o Sistema Nacional de Unidades de Conservação da Natureza (SNUC). Dentre os diversos ambientes encontrados em território nacional, o Pampa tem uma representatividade de apenas 0,4% de sua área protegida, conforme consta no SNUC. O Refúgio da Vida Silvestre Banhado dos Pachecos (RVSBP) é uma UC de proteção integral estadual, localizada no Rio Grande do Sul e no Bioma Pampa, com uma área de 2.560 ha e representa cerca de 3,5% de todas as UC’s de proteção integral desse bioma. O RVSBP criada no ano de 2002, e ainda não possui plano de manejo, bem como carece de maiores investimentos e atenção. O uso de imagens de satélites como subsídio aos estudos ambientais já está consolidado e a interpretação destas imagens, a partir de diversos métodos, para classificar o uso e cobertura da terra, tem se tornado uma constante, munindo os pesquisadores de informações dos diversos processos que possam estar ocorrendo em uma determinada área de estudo, inclusive monitorando as mudanças ao longo do tempo. Os objetivos desta dissertação são os de verificar as mudanças no uso e cobertura do solo ocorridas entre 2001 e 2017 no RVSBP e em seu entorno direto de 10km, baseando-se na análise de imagens de satélite. Para tanto serão mapeadas as classes de uso e cobertura do solo, a partir de imagens dos satélites LANDSAT 5 – Sensor TM, LANDSAT 7 – Sensor ETM+ e LANDSAT 8 – Sensor OLI, para os anos de 2001, 2009 e 2017. O método de detecção das mudanças no uso e cobertura do solo aplicada foi a técnica de comparação pós-classificação para uma melhor compreensão das interações entre os fenômenos naturais e as atividades humanas. Essa técnica foi aplicada para os períodos de 2001 a 2009, de 2009 a 2017 e de 2001 a 2017. Para o período de 2001 a 2009 as mudanças ocorreram em 17,5% da área de estudo e em 19,9% do RVSBP. Para o período de 2009 a 2017 as mudanças ocorreram em 22,8% da área de estudo e em 23,9% do RVSBP. Para o período de 2001 a 2017 as mudanças ocorreram em 24% da área de estudo e em 32% do RVSBP. Dentre esses 32% a classe que apresentou os maiores acréscimos de área foram as classes de Agricultura – Arroz e de Associação de Sítio e produtores rurais, que respectivamente compreendem áreas de 410 hectares e de 135 hectares. As classes que foram mais impactadas com perda de área foram as classes Banhado e Vegetação Arbórea, que respectivamente compreendem áreas de 435 hectares e de 173 hectares. A análise de detecção de mudanças se mostrou efetiva como uma forma de monitoramento sistemático do uso e cobertura do solo do RVSBP e entorno, trazendo elementos importantes para a gestão da UC. / Conservation Units (UC) are territorial spaces with relevant natural characteristics, which have a role of ensuring the representativeness of significant and ecologically viable samples of different populations, habitats and ecosystems. Brazilian legistlation established in 2000 the National System of Nature Conservation Units (SNUC). Among the several environments found in the national territory, the Pampa has a representation of only 0,4% of its own protected area, according to SNUC. The Wildlife Refuge Banhado dos Pachecos (RVSBP) is a state UC of integral protection, located in Rio Grande do Sul and Bioma Pampa, with 2.560 ha and comprises about 3,5% of all integral protection UC of this biome. RVSBP was created in 2002, still does not have a management plan, and lacks greater investments and attention. The use of satellite images to suppott environmental studies is already consolidate and the interpretation of these images, using different methods, to classify land and use cover, has become a constant, providing researchers with information on the various processes that may be occurring in a particular study area, including monitoring changes over time. The objective of this dissertation is to verify the changes in the land use and cover occurred between 2001 and 2017 in RVSBP and in its surrounds of 10km, based on the analysis of satellite images. Therefore, the land and use coverage classes were mapped using images from the LANDSAT 5 - Sensor TM, LANDSAT 7 - ETM + and LANDSAT 8 - OLI Sensor, for the years 2001, 2009 and 2017. The method of detecting changes in land use and cover was the post-classification comparison technique for a better understanding of the interactions between natural phenomena and human activities. This technique was applied for the periods from 2001 to 2009, from 2009 to 2017, and from 2001 to 2017. For the period 2001 to 2009 the changes occurred in 17,5% of the whole study area and in 19,9% of RVSBP. For the period from 2009 to 2017, changes occurred in 22,8% of the whole study area and 23,9% of RVSBP. For the period from 2001 to 2017, changes occurred in 24% of the whole study area and 32% of RVSBP. Among these 32%, the class with the greatest increases in area were Agriculture – Rice crops and Site Association of Rural Producers, which respectively comprises areas of 410 hectares and 135 hectares. The classes that were most impacted with loss of area were the class Weands and Arboreal Vegetation, which respectively comprise areas of 435 and 173 hectares. The change detection analysis was effective as a way of systematically monitoring the land use and coverage of RVSBP and surroundings, bringing important elements to the management of the UC.