Determinação do teor de umidade em função da constante dielétrica de seis espécies de madeira

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Florestal, 2014.O conteúdo de água na madeira afeta grande parte de suas características mais importantes, tais como suas propriedades físicas e mecânicas, o que ressalta a necessidade da utilização de métodos eficientes e confiáveis de medição do seu teor de umidade. O objetivo do presente estudo foi o desenvolvimento de equações matemáticas para estimar teores de umidade em função da constante dielétrica das espécies Nectandra dioica, Terminalia glabrescens, Virola michelii, Tachigali myrmecophila, Eucaliptus saligna e Sclerolobium paniculatum var rubiginosum. Para isto foi medida a constante dielétrica das espécies em diferentes teores de umidade, a fim de criar modelos de regressão para sua estimativa. O modelo que melhor se aplicou a todas as espécies foi o modelo linear “y=a+Bx”, onde “y” é o teor de umidade em porcentagem, “a” e “B” os coeficientes da equação, e “x” a constante dielétrica do material. As equações das espécies N. dioica, T. glabrescens, V. michelii e T. myrmecophila apresentaram acurácia adequada, com erros médios inferiores a 1% dentro do intervalo de 0% e 30% de umidade, onde se encontram madeiras destinadas à comercialização. As equações das espécies E. saligna e S. paniculatum mostraram maior dispersão de seus dados, com erros superiores a 1% dentro do mesmo intervalo. Observou-se também que a massa específica exerceu certa influência sobre a relação entre a constante dielétrica e o teor de umidade da madeira, e espécies com massas específicas similares mostraram comportamentos análogos. _____________________________________________________________________________ ABSTRACTThe moisture content of wood exerts great influence over its most important characteristics, like its physical and mechanical properties. This fact highlights the necessity of the utilization of efficient and trustable moisture content measurement methods. The objective of this study was the creation of mathematical equations in order to estimate wood moisture content based on the dielectric constant of the species: Nectandra dioica, Terminalia glabrescens, Virola michelii, Tachigali myrmecophila, Eucaliptus saligna and Sclerolobium paniculatum var rubiginosum. For that, the dielectric constant of the species was measured along with different moisture contents, in order to elaborate the regression models. The model with the best fits for the species was the linear model “y=a+Bx”, where “y” is the moisture content in percentage, “a” and “B” the equation coefficients, and “x” the dielectric constant of the material. The equations for the species N. dioica, T. glabrescens, V. michelii and T. myrmecophila showed adequate precision, with average error under 1% in moisture contents between 0% and 30%, where most commercial woods are found. The equations for the species E. saligna and S. paniculatum showed greater dispersion in its values, with average errors over 1% in the same interval. It was also observed that the density of the wood pieces exerted certain influence over the relation between their moisture content and dielectric constant, and species with similar densities showed analogous behavior

Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente superiores em processos como a classificação de uso e cobertura da terra e detecção de mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando estes algoritmos com um enfoque no monitoramento de alvos críticos no Brasil por via de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos: (a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e (c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos, procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou imagens de radar Sentinel-1 (SAR) em uma série temporal contínua para a delimitação dos plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have been gaining popularity recently, showing superior results when compared to traditional classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes such as land use classification and change detection. This thesis had as its objective the development of methodologies using these algorithms with a focus on monitoring critical targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles were produced evaluating their use for the detection of three distinct targets: (a) burnt areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles, the methodologies in each of them was made sufficiently distinct in order to expand the methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of binary Landsat time series to detect new deforested areas between the years of 2017, 2018 and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice fields in the state of Rio Grande do Sul. Similar models were used in all articles, however certain models were exclusive to each one. In general, the results show that not only are the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and change detection of the targets evaluated

Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks

Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

Performance analysis of deep convolutional autoencoders with different patch sizes for change detection from burnt areas

Fire is one of the primary sources of damages to natural environments globally. Estimates show that approximately 4 million km2 of land burns yearly. Studies have shown that such estimates often underestimate the real extent of burnt land, which highlights the need to find better, state-of-the-art methods to detect and classify these areas. This study aimed to analyze the use of deep convolutional Autoencoders in the classification of burnt areas, considering di erent sample patch sizes. A simple Autoencoder and the U-Net and ResUnet architectures were evaluated. We collected Landsat 8 OLI+ data from three scenes in four consecutive dates to detect the changes specifically in the form of burnt land. The data were sampled according to four di erent sampling strategies to evaluate possible performance changes related to sampling window sizes. The training stage used two scenes, while the validation stage used the remaining scene. The ground truth change mask was created using the Normalized Burn Ratio (NBR) spectral index through a thresholding approach. The classifications were evaluated according to the F1 index, Kappa index, and mean Intersection over Union (mIoU) value. Results have shown that the U-Net and ResUnet architectures offered the best classifications with average F1, Kappa, and mIoU values of approximately 0.96, representing excellent classification results. We have also verified that a sampling window size of 256 by 256 pixels offered the best results

Performance Analysis of Deep Convolutional Autoencoders with Different Patch Sizes for Change Detection from Burnt Areas

Fire is one of the primary sources of damages to natural environments globally. Estimates show that approximately 4 million km2 of land burns yearly. Studies have shown that such estimates often underestimate the real extent of burnt land, which highlights the need to find better, state-of-the-art methods to detect and classify these areas. This study aimed to analyze the use of deep convolutional Autoencoders in the classification of burnt areas, considering different sample patch sizes. A simple Autoencoder and the U-Net and ResUnet architectures were evaluated. We collected Landsat 8 OLI+ data from three scenes in four consecutive dates to detect the changes specifically in the form of burnt land. The data were sampled according to four different sampling strategies to evaluate possible performance changes related to sampling window sizes. The training stage used two scenes, while the validation stage used the remaining scene. The ground truth change mask was created using the Normalized Burn Ratio (NBR) spectral index through a thresholding approach. The classifications were evaluated according to the F1 index, Kappa index, and mean Intersection over Union (mIoU) value. Results have shown that the U-Net and ResUnet architectures offered the best classifications with average F1, Kappa, and mIoU values of approximately 0.96, representing excellent classification results. We have also verified that a sampling window size of 256 by 256 pixels offered the best results

Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery

Misappropriation of public lands is an ongoing government concern. In Brazil, the beach zone is public property, but many private establishments use it for economic purposes, requiring constant inspection. Among the undue targets, the individual mapping of straw beach umbrellas (SBUs) attached to the sand is a great challenge due to their small size, high presence, and agglutinated appearance. This study aims to automatically detect and count SBUs on public beaches using high-resolution images and instance segmentation, obtaining pixel-wise semantic information and individual object detection. This study is the first instance segmentation application on coastal areas and the first using WorldView-3 (WV-3) images. We used the Mask-RCNN with some modifications: (a) multispectral input for the WorldView3 imagery (eight channels), (b) improved the sliding window algorithm for large image classification, and (c) comparison of different image resizing ratios to improve small object detection since the SBUs are small objects (<322 pixels) even using high-resolution images (31 cm). The accuracy analysis used standard COCO metrics considering the original image and three scale ratios (2×, 4×, and 8× resolution increase). The average precision (AP) results increased proportionally to the image resolution: 30.49% (original image), 48.24% (2×), 53.45% (4×), and 58.11% (8×). The 8× model presented 94% AP50, classifying nearly all SBUs correctly. Moreover, the improved sliding window approach enables the classification of large areas providing automatic counting and estimating the size of the objects, proving to be effective for inspecting large coastal areas and providing insightful information for public managers. This remote sensing application impacts the inspection cost, tribute, and environmental conditions