Análise de inundações e classificação da cobertura vegetal no bioma amazônico usando séries temporais sentinel-1 SAR e técnicas de deep learning

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Os recursos hídricos e os estudos fenológicos florestais são extremamente importantes para a compreensão de diversos fenômenos naturais como as mudanças climáticas, dinâmica hidrogeomorfológica, condicionamento ambiental e gestão dos recursos. Inserida na dinâmica hídrica, estão presentes as áres inundáveis que estão intrinsecamente ligadas à manuntenção da biota e da fauna nos biomas brasileiros. Nesse contexto, os produtos derivados de sensoriamento remoto têm sido bastante utilizados para a análise e monitoramento de áreas inundáveis, mapeamento de uso e ocupação da terra e dinâmica fenológica devido à sua importância ambiental. As imagens de radar de abertura sintética (SAR) são produtos potenciais por não apresentar interferências atmosféricas, entretanto, necessitam de diversos tratamentos iniciais, definidos de pré-processamento para assim ser possível obter uma melhor extração de informações de uma determinada área. Nesse sentido, essa pesquisa teve como objetivo aplicar as técnicas de deep learning utilizando algoritmos de processamento de séries temporais de imagens de satélite baseados em redes neurais para extração e identificação de áreas inundáveis, corpos hídricos e fenologias florestais em áreas de cerrado, floresta amazônica, mangues, cultivos agrícolas e várzea. O presente estudo foi dividido em três capítulos principais: (a) análises métricas e estatísticas de filtragens espaciais em imagem Sentinel-1 SAR da Amazônia Central, Brasil; (b) análise de série temporal Sentinel-1 SAR em inundações na Amazônia Central; e (c) classificação fenológica de floresta, mangues, cerrado e vegetação alagada do bioma Amazônia por meio de comparação dos modelos LSTM, Bi-LSTM, GRU, Bi-GRU e modelos de aprendizagem de máquina baseados em séries temporais do satélite Sentinel-1. As etapas metodológicas foram distintas para cada capítulo e todos apresentaram precisão e altos valores métricos para mensuração e análise dos corpos hídricos, inundação e fenologias florestais. Dentre os métodos de filtragem analisados na imagem SAR, o filtro Lee com janela 3 × 3 apresentou os melhores desempenhos na redução do ruído speckle (MSE igual a 1,88 e MAE igual a 1,638) e baixo valor de distorção de contraste na polarização VH. Entretanto, para a polarização VV, mensuraram-se diferentes resultados para análise da redução do ruído speckle, onde o filtro Frost com janela 3 × 3 apresentou o melhor desempenho, com baixo valor para as métricas em geral (MSE igual a 1,2 e MAE igual a 6,28) e também um baixo valor de distorção de contraste. Por apresentar os melhores valores estatísticos, o filtro de mediana com janela 11 × 11 nas polarizações VH e VV pode ser utilizado como uma técnica de filtragem alternativa na imagem Sentinel-1 nas duas polarizações. As áreas de inundação mensuradas nas polarizações VH e VV apresentaram uma forte correlação e sem significância estatística entre as amostras, presumindo que se pode utilizar as duas polarizações para obtenção do pulso de inundação e mapeamento da dinâmica das áreas inundáveis na região. Pelo fato de não haver imagens Sentinel-1 anteriores ao ano de 2016, quando os eventos extremos de LMEO foram superiores a 100%, não foi possível delimitar a LMEO por meio de dados SAR. Algumas áreas ao longo da costa e rios apresentam assinaturas temporais de retroespalhamento que evidenciam transições entre ambientes terrestres e áreas cobertas por água. A variação temporal do retroespalhamento de valores mais altos para mais baixos indica erosão e inundação progressiva, enquanto o inverso indica aumento terrestre. O modelo Bi-GRU apresentou a maior acurácia geral, precisão, recall e F-score tanto na polarização individual como na polarização combinada VV+VH. A combinação entre as polarizações forneceu os melhores resultados na classificação e a polarização VH obteve melhores resultados quando comparado à polarização VV. O presente estudo atestou o procedimento metodológico adequado para mensurar as áreas de corpos hídricos e seu pulso de inundação como também obteve a classificação de fenologias com alta precisão na Amazônia Central por meio de deep learning advindas de série temporal de imagens Sentinel-1 SAR.Water resources and forest phenological studies are extremely important for the understanding of various natural phenomena, such as climate variation, hydrogeomorphological dynamics, environmental conditioning, and resource management. In this context, products derived from remote sensing have been widely used for the analysis and monitoring of flooding areas, land use and occupation mapping, and phenological dynamics due to their environmental importance. Synthetic aperture radar (SAR) images are potential products as they do not present atmospheric interference, however, they require several initial treatments, defined as pre-processing, so that it is possible to obtain a better extraction of information from a certain area. In this sense, this research aimed to apply deep learning techniques using algorithms based on neural networks for the extraction and identification of flooding areas, water bodies, and forest phenologies such as cerrado, Amazon forest, mangroves, agricultural crops, and floodplain through time series of remote sensing images. This study was divided into three main chapters: (a) metric and statistical analysis of spatial filtering in Sentinel-1 SAR images of Central Amazonia, Brazil; (b) Sentinel-1 SAR time series analysis in flooding areas of Central Amazon; and (c) phenological classification of forest, mangroves, savannas, and two flooded vegetation of the Amazon biome by comparing LSTM, Bi-LSTM, GRU, Bi-GRU, and machine learning models from Sentinel-1 time series. The methodological steps were different for each chapter and all presented precision and high metric values for measurement and analysis of water bodies, flooding and forest phenologies. Among the filtering methods analyzed in the SAR image, the Lee filter with 3 × 3 window presented the best performance in reducing speckle noise (MSE of 1.88 and MAE of 1.638) and low value of contrast distortion in the VH polarization. However, for the VV polarization, different results were measured for the analysis of the sepeckle noise reduction, where the Frost filter with 3 × 3 window presented the best performance, with a low value for the metrics in general (MSE of 1.2 and MAE of 6.28) and also a low contrast distortion value. Statistical values derived from the median filter with 11 × 11 window in the VH and VV polarizations can be used as an alternative filtering technique in the Sentinel-1 SAR image in both polarizations. The flooding areas measured in the VH and VV polarizations showed a strong correlation and no statistical significance between the samples, assuming that both polarizations can be used to obtain the flood pulse and mapping the dynamics of the flooded areas in the region. Because there are no Sentinel1 SAR images prior to 2016 when extreme LMEO events were greater than 100%, it was not possible to delimit the LMEO through SAR data. Some areas along the coast and rivers show temporal backscatter signatures with transitions between terrestrial environments and areas covered by water. The temporal variation of backscatter from higher to lower values indicates erosion and progressive flooding, while the inverse indicates terrestrial increase. The Bi-GRU model showed the highest overall accuracy, precision, recall, and F-score in both separate polarization and combined VV+VH polarization. The combination between the polarizations provided the best results in the classification and the VH polarization obtained better results when compared to the VV polarization. This study attested an adequate methodological procedure to measure the areas of water bodies and their flood pulse, as well as obtaining the classification of phenologies with high precision in the Central Amazon by means of deep learning applied to the time series of Sentinel-1 SAR images

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Novel methods for recording and reconstructing images in digital holographic microscopy

The difficulty in visualizing unstained biological cells using brightfield microscopy has resulted in the development of several specialized imaging techniques that can enhance the contrast of subcellular features without the need for labeling. Examples include phase contrast, differential interference microscopy, dark field microscopy, and Rheinberg illumination. However, these techniques are qualitative in nature and do not provide any direct measurement of cellular morphology in terms of thickness or refractive index. Quantitative phase imaging refers to a set of emerging methods with the potential to provide quantitative real-time measurement of the phase delay introduced by the specimen with nanometric accuracy and with the same spatial resolution afforded by brightfield microscopy. Quantitative phase imaging, therefore, provides a powerful means to study cellular dynamics. Several methods exist for implementing quantitative phase imaging, which include coherent approaches based on interferometry known as digital holographic microscopy. Digital holographic microscopy is an optic-electronic technique that enables the numerical reconstruction of the complex wave-field reflected from, or transmitted through, a target with a single capture. Together with phase unwrapping, this method permits a height profile, a thickness profile, and/or a refractive index profile, to be extracted, in addition to the reconstruction of the image intensity. Digital holographic microscopy is unlike classical imaging systems in that one can obtain the focused image without situating the camera in the focal plane; indeed, it is possible to recover the complex wave-field at any distance from the camera plane. Therefore, the focus distance from the image plane to the camera plane can be estimated automatically by using a focus metric. The aim of the work presented in this thesis is to develop novel methods for digital holographic microscopy in order to improve the quantitative analysis of cellular morphology and detect the nucleus in vivo, together with a number of numerical process techniques both in amplitude and phase profile. This thesis includes a number of separate contributions, some relating to novel optical systems that can be used to record the holograms, and some relating to method of processing the recorded holograms in order to generate meaningful images. A low-cost compact portable module is proposed that can be easily integrated with a brightfield microscope in order to record quantitative phase images. This is the first of two contributions on novel methods to optically record digital holograms. The second optical system that is proposed is a novel optical architecture for off-axis digital holographic microscopy, which allows for continuous change in magnification and numerical aperture by simply moving the sample. There are also three separate contributions that deal with numerical methods for the reconstruction of images recorded using digital holographic microscopy. The first relates to a thorough examination of the potential for sparsity metrics to be used for autofocusing in digital holographic microscopy. The last two contributions both relate to new image processing techniques for label-free color staining of subcellular features using the quantitative phase image as input. The first method is based on simulated Rheinberg illumination, while the second method is purely digital and can be related to the concept of local spatial frequency in the image. Both are shown to provide high quality color images of diatom cells

Using Radio Frequency and Motion Sensing to Improve Camera Sensor Systems

Camera-based sensor systems have advanced significantly in recent years. This advancement is a combination of camera CMOS (complementary metal-oxide-semiconductor) hardware technology improvement and new computer vision (CV) algorithms that can better process the rich information captured. As the world becoming more connected and digitized through increased deployment of various sensors, cameras have become a cost-effective solution with the advantages of small sensor size, intuitive sensing results, rich visual information, and neural network-friendly. The increased deployment and advantages of camera-based sensor systems have fueled applications such as surveillance, object detection, person re-identification, scene reconstruction, visual tracking, pose estimation, and localization. However, camera-based sensor systems have fundamental limitations such as extreme power consumption, privacy-intrusive, and inability to see-through obstacles and other non-ideal visual conditions such as darkness, smoke, and fog. In this dissertation, we aim to improve the capability and performance of camera-based sensor systems by utilizing additional sensing modalities such as commodity WiFi and mmWave (millimeter wave) radios, and ultra-low-power and low-cost sensors such as inertial measurement units (IMU). In particular, we set out to study three problems: (1) power and storage consumption of continuous-vision wearable cameras, (2) human presence detection, localization, and re-identification in both indoor and outdoor spaces, and (3) augmenting the sensing capability of camera-based systems in non-ideal situations. We propose to use an ultra-low-power, low-cost IMU sensor, along with readily available camera information, to solve the first problem. WiFi devices will be utilized in the second problem, where our goal is to reduce the hardware deployment cost and leverage existing WiFi infrastructure as much as possible. Finally, we will use a low-cost, off-the-shelf mmWave radar to extend the sensing capability of a camera in non-ideal visual sensing situations.Doctor of Philosoph

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing