MINERAÇÃO DE DADOS APLICADA NA IDENTIFICAÇÃO E CAUSAS DE DESFLORESTAMENTO NA AMAZÔNIA: ESTUDO DE CASO, LESTE DA FLORESTA NACIONAL DO TAPAJÓS

O monitoramento ambiental gradativamente tem sido utilizado para acompanhar as ações antrópicas, como também tem auxiliado no gerenciamento e planejamento do uso e ocupação do espaço geográfico. Neste sentido, este trabalho tem por objetivo utilizar técnicas de Mineração de Dados para identificar a distribuição espacial e a dinâmica temporal do desflorestamento em uma área na Amazônia Legal. Os materiais utilizados contemplam uma série temporal (1984 a 2007) de imagens LANDSAT-5/TM. Os procedimentos envolvem Processamento Digital de Imagens para geração de uma Matriz de Mudanças, que permite não só representar alterações na cobertura do solo ao longo da série temporal, como também identificar mudanças no padrão de desflorestamento. Ou seja, se o padrão de desflorestamento permaneceu com a extração de recursos naturais ou migrou para realização de atividades econômicas. Os resultados revelam alternâncias entre as classes analisadas (Floresta Primária, Regeneração e Solo Exposto) no intervalo do estudo. As principais dinâmicas verificadas foram o contínuo processo de desflorestamento e o aumento das áreas de solo exposto. Este último comumente associado a atividades econômicas ligadas à agropecuária. As áreas identificadas como de extração de recursos naturais encontram-se estabilizadas, mas de modo geral, tornam-se áreas de solo exposto, destinadas a agropecuária.&nbsp

Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

Algal blooms are a frequent subject in scientific discussions and are the focus of many recent studies, mainly due to their adverse effect on society. Given the lack of ground truth data and the need to develop tools for their detection and monitoring, this research proposes a novel method to automate detection. Concepts derived from multi-temporal image series processing, spectral indices and classification with One-class Support Vector Machine (OC-SVM) are used in this proposal. Imagery from multi-spectral sensors on Landsat-8 and MODIS were acquired through the Google Earth Engine API (GEE API). In order to evaluate our method, two bloom detection case studies (Lake Erie (USA) and Lake Taihu (China)) were performed. Comparisons were made with methods based on spectral index thresholds. Also, to demonstrate the performance of the OC-SVM classifier compared to other machine learning methods, the proposal was adapted to be used with a Random Forest (RF) classifier, having its results added to the analysis. In situ measurements show that the proposed method delivers highly accurate results compared to spectral index thresholding approaches. However, a drawback of the proposal refers to its higher computational cost. The application of the new method to a real-world bloom case is demonstrated

Construção de um plug-in TerraView para extração de geotags de fotos digitais

Geosciences studies frequently require to conduct field works. When a huge amount information is collected during such surveys, its manipulation, organization and interpretation is, generally, an arduous task. A tool resulting from advances in Geotechnology are GPS cameras. They are capable to record the geographical position of the locations photographed, which are extremely useful when performing field surveys. The present work had aimed to build a plug-in for GIS TerraView able to use information captured by GPS cameras to generate spatial databases, making manipulation, organization and visualization of collected data easy and fast.Pages: 8912-891

Aplicação de Modelos de Aprendizado Semissupervisionado na Classificação de Imagens de Sensoriamento Remoto

Nas mais diversas aplicações, a escassez de informação para o devido treinamento e utilização de métodos de Aprendizado de Máquina supervisionado é um problema persistente. Este fato motivou o desenvolvimento do paradigma de aprendizado semissupervisionado, que pode ser entendido como uma combinação de conceitos dos paradigmas supervisionado e não supervisionado. A maneira como o aprendizado é conduzido permite organizar os métodos semissupervisonados em diferentes modelos. Este trabalho apresenta um estudo comparativo entre diferentes modelos de aprendizado semissupervisionado. É também proposta uma versão semissupervisonada do método SVM, o qual alcançou melhor desempenho nas comparações realizadas

Desafios para uma agenda de prevenção de desastres em sítios históricos: o caso de São Luiz do Paraitinga, SP

Disasters have been affecting several municipalities listed as cultural heritage sites, as highlighted by the media stories of the 2010 January floods in São Luiz do Paraitinga city, São Paulo state. This paper shared some findings related to two phases of research. The first phase occurred from January 2010 to June 2013, when of the authors studied the long term disaster recovery process. The second phase was developed from October 2014 to October 2016, when an educational project was run to create intergenerational capacities of disaster risk prevention, articulating the early warning system and educational sectors. The findings denote the need of strengthening the linkages between cultural heritage sector and disaster risk prevention.Desastres têm ocorrido em municípios com bens tombados como patrimônio histórico, com destaque para a ocorrência amplamente noticiada das inundações ocorridas em São Luiz do Paraitinga, em janeiro de 2010. A presente pesquisa compartilha alguns resultados de dois momentos de análise, quais sejam: de janeiro de 2010 a junho de 2013, em que se estudou o processo de reconstrução e recuperação do município; e de outubro de 2014 a outubro de 2016, por meio de  projetos de extensão voltados à articulação entre sistemas de alerta e setor educativo, com vistas a fortalecer as capacidades intergeracionais de prevenção de riscos de desastres. Os resultados demonstram a necessidade de fortalecer a articulação entre a agenda do patrimônio histórico e a prevenção de riscos de desastres

A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning

A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing data from these two spaces have found that, generally, any study related to anomaly detection can achieve the same or similar results when applied to both dimensional spaces. However, there have been no studies that compare differences in these spaces related to anomaly detection strategy based on Kittler’s Taxonomy (ADS-KT). This study aims to investigate the differences between both spaces when dimensionality reduction is associated with ADS-KT while analyzing a satellite image. Our methodology starts applying the pre-processing phase of the ADS-KT to create the high-dimensional space. Next, a dimensionality reduction technique generates the low-dimensional space. Then, we analyze extracted features from both spaces based on visualizations. Finally, machine-learning approaches, in accordance with the ADS-KT, produce results for both spaces. In the results section, metrics assessing transformed data present values close to zero contrasting with the high-dimensional space. Therefore, we conclude that dimensionality reduction directly impacts the application of the ADS-KT. Future work should investigate whether dimensionality reduction impacts the ADS-KT for any set of attributes

Implementação da Com-VidAção nas escolas de ensino médio por meio da educação a distância

In partnership with the project Cemaden Educação, this paper presents the initial stage of implementation of the Commission of Disaster Prevention and Protection of Life (Com-VidAção) involving graduate students in Environmental Engineering at the São Paulo State University, São José dos Campos. Students performed the implementation of Com-VidAção in a high school of São Luiz do Paraitinga, and with the experience and knowledge acquired they prepared materials in video form to be inserted into the Moodle virtual learning environment, allowing to create and to implement the Com-VidAção in schools in cities monitored by the National Center for Monitoring and Alerts Natural Disasters (Cemaden) by way of distance learning. The implementation of ComVidAção in schools is a way to generate awareness of the school community for a culture of perception and prevention of risk through discussions and reflections on disasters, aiming at the organization of the school community focused on self-protection, so that adolescents and young people can to exercise co-responsibility for the environment they live.Em parceria com o projeto Cemaden Educação, o presente trabalho apresenta a etapa inicial de implementação da Comissão de Prevenção de Desastres e Proteção da Vida (Com-VidAção), envolvendo estudantes do Curso de Engenharia Ambiental da Unesp de São José dos Campos. Os estudantes realizaram a implementação da ComVidAção, de modo presencial, em uma escola de ensino médio de São Luiz do Paraitinga - SP, e com a experiência e o conhecimento adquiridos prepararam material em forma de vídeo para serem inseridos no ambiente virtual Moodle, possibilitando que a Com-VidAção seja criada e implementada nas escolas em municípios monitorados pelo Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden) por meio da educação a distância. A implementação da Com-VidAção nas escolas é uma forma de gerar a conscientização da comunidade escolar para uma cultura de percepção e prevenção de riscos por meio de discussões e reflexões sobre desastres, visando à organização da comunidade escolar voltada para a sua autoproteção, de modo que os adolescentes e jovens possam exercitar a co-responsabilidade pelo meio em que vivem