Wireless Sensor Network and Satellite Data Fusion Using a Deep Learning Approach for Spatio-temporal Land Surface Temperature Estimation and Forecasting PDF Download
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Author: Nicolás Esteban Cerna Araya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
La estimación y el monitoreo de la temperatura superficial del terreno (LST, por sussiglas en inglés) sobre un área es relevante en el estudio de una diversidad de procesos ambientales debido a que es una de las propiedades físicas fundamentales que gobiernan la interacción energética entre la superficie de la Tierra y la atmósfera tanto a escalas locales como globales. Actualmente las mediciones de LST sobre grandes áreas son obtenidas por satélites. Sin embargo, la LST medida de forma remota no posee la resolución temporal requerida para un adecuado seguimiento y análisis de cambios rápidos, cuyo monitoreo es especialmente necesario en la gestión de desastres y en sistemas de alerta temprana de procesos hidrometeorológicos. Por lo tanto, se propone una estrategia de fusión de datos para combinar mediciones de temperatura del aire provenientes de redes inalámbricas de sensores (WSN, por sus siglas en inglés) y mediciones satelitales de LST para realizar estimaciones en tiempo real y predicciones de imágenes de LST con una alta resolución temporal y espacial. El enfoque propuesto incluso puede ser utilizado para reconstruir datos faltantes o para suavizar imágenes de LST que puedan ser de baja resolución debido a efectos de reproyecciones y remuestreo de datos. El método propuesto para la estimación espacio-temporal puede proveer estimaciones de LST cada 15 minutos con un RMSE promedio de 2.21 °C. La estrategia propuesta para la fusión de datos entre WSN y satélites puede ser extendida a otras aplicaciones y no está limitada a la temperatura del aire y mediciones de LST. Considerando que el enfoque puede proporcionar estimaciones de LST entre pasos de satélites e incluso cuando existe cobertura de nubes, el enfoque puede probar ser una herramienta valiosa para futuras investigaciones de monitoreo ambiental e hidrometeorológico.
Author: Nicolás Esteban Cerna Araya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
La estimación y el monitoreo de la temperatura superficial del terreno (LST, por sussiglas en inglés) sobre un área es relevante en el estudio de una diversidad de procesos ambientales debido a que es una de las propiedades físicas fundamentales que gobiernan la interacción energética entre la superficie de la Tierra y la atmósfera tanto a escalas locales como globales. Actualmente las mediciones de LST sobre grandes áreas son obtenidas por satélites. Sin embargo, la LST medida de forma remota no posee la resolución temporal requerida para un adecuado seguimiento y análisis de cambios rápidos, cuyo monitoreo es especialmente necesario en la gestión de desastres y en sistemas de alerta temprana de procesos hidrometeorológicos. Por lo tanto, se propone una estrategia de fusión de datos para combinar mediciones de temperatura del aire provenientes de redes inalámbricas de sensores (WSN, por sus siglas en inglés) y mediciones satelitales de LST para realizar estimaciones en tiempo real y predicciones de imágenes de LST con una alta resolución temporal y espacial. El enfoque propuesto incluso puede ser utilizado para reconstruir datos faltantes o para suavizar imágenes de LST que puedan ser de baja resolución debido a efectos de reproyecciones y remuestreo de datos. El método propuesto para la estimación espacio-temporal puede proveer estimaciones de LST cada 15 minutos con un RMSE promedio de 2.21 °C. La estrategia propuesta para la fusión de datos entre WSN y satélites puede ser extendida a otras aplicaciones y no está limitada a la temperatura del aire y mediciones de LST. Considerando que el enfoque puede proporcionar estimaciones de LST entre pasos de satélites e incluso cuando existe cobertura de nubes, el enfoque puede probar ser una herramienta valiosa para futuras investigaciones de monitoreo ambiental e hidrometeorológico.
Author: Ni-Bin Chang
Publisher: CRC Press
ISBN: 1351650637
Category : Technology & Engineering
Languages : en
Pages : 627
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Book Description
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
Author: Shrutilipi Bhattacharjee
Publisher: Springer
ISBN: 9811386641
Category : Technology & Engineering
Languages : en
Pages : 144
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Book Description
This book identifies the need for modeling auxiliary knowledge of the terrain to enhance the prediction accuracy of meteorological parameters. The spatial and spatio-temporal prediction of these parameters are important for the scientific community, and the semantic kriging (SemK) and its variants facilitate different types of prediction and forecasting, such as spatial and spatio-temporal, a-priori and a-posterior, univariate and multivariate. As such, the book also covers the process of deriving the meteorological parameters from raw satellite remote sensing imagery, and helps understanding different prediction method categories and the relation between spatial interpolation methods and other prediction methods. The book is a valuable resource for researchers working in the area of prediction of meteorological parameters, semantic analysis (ontology-based reasoning) of the terrain, and improving predictions using auxiliary knowledge of the terrain.
Author: Qihao Weng
Publisher: CRC Press
ISBN: 1351680560
Category : Science
Languages : en
Pages : 244
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Book Description
Today, remote sensing technology is an essential tool for understanding the Earth and managing human-Earth interactions. There is a rapidly growing need for remote sensing and Earth observation technology that enables monitoring of world’s natural resources and environments, managing exposure to natural and man-made risks and more frequently occurring disasters, and helping the sustainability and productivity of natural and human ecosystems. The improvement in temporal resolution/revisit allows for the large accumulation of images for a specific location, creating a possibility for time series image analysis and eventual real-time assessments of scene dynamics. As an authoritative text, Remote Sensing Time Series Image Processing brings together active and recognized authors in the field of time series image analysis and presents to the readers the current state of knowledge and its future directions. Divided into three parts, the first addresses methods and techniques for generating time series image datasets. In particular, it provides guidance on the selection of cloud and cloud shadow detection algorithms for various applications. Part II examines feature development and information extraction methods for time series imagery. It presents some key remote sensing-based metrics, and their major applications in ecosystems and climate change studies. Part III illustrates various applications of time series image processing in land cover change, disturbance attribution, vegetation dynamics, and urbanization. This book is intended for researchers, practitioners, and students in both remote sensing and imaging science. It can be used as a textbook by undergraduate and graduate students majoring in remote sensing, imaging science, civil and electrical engineering, geography, geosciences, planning, environmental science, land use, energy, and GIS, and as a reference book by practitioners and professionals in the government, commercial, and industrial sectors.
Author: Salim Lamine
Publisher: Elsevier
ISBN: 0323914640
Category : Technology & Engineering
Languages : en
Pages : 555
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Book Description
Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones' geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. - Presents a well-integrated collection of chapters, with quality, consistency and continuity - Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts - Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented - Covers geospatial data, with codes available through shared links
Author: Abdourrahmane M. Atto
Publisher: John Wiley & Sons
ISBN: 1119882281
Category : Computers
Languages : en
Pages : 274
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Book Description
Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.
Author: Rohan Zanje
Publisher:
ISBN:
Category : Multisensor data fusion
Languages : en
Pages : 134
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Book Description
Author: John A
Publisher: Springer Nature
ISBN: 9819996511
Category :
Languages : en
Pages : 253
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Book Description
Author: Claudia Kuenzer
Publisher: Springer
ISBN: 9783319352435
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This volume comprises an outstanding variety of chapters on Earth Observation based time series analyses, undertaken to reveal past and current land surface dynamics for large areas. What exactly are time series of Earth Observation data? Which sensors are available to generate real time series? How can they be processed to reveal their valuable hidden information? Which challenges are encountered on the way and which pre-processing is needed? And last but not least: which processes can be observed? How are large regions of our planet changing over time and which dynamics and trends are visible? These and many other questions are answered within this book “Remote Sensing Time Series Analyses – Revealing Land Surface Dynamics”. Internationally renowned experts from Europe, the USA and China present their exciting findings based on the exploitation of satellite data archives from well-known sensors such as AVHRR, MODIS, Landsat, ENVISAT, ERS and METOP amongst others. Selected review and methods chapters provide a good overview over time series processing and the recent advances in the optical and radar domain. A fine selection of application chapters addresses multi-class land cover and land use change at national to continental scale, the derivation of patterns of vegetation phenology, biomass assessments, investigations on snow cover duration and recent dynamics, as well as urban sprawl observed over time.
Author: John F. Roddick
Publisher: Springer
ISBN: 3540452443
Category : Computers
Languages : en
Pages : 184
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Book Description
This volume contains updated versions of the ten papers presented at the First International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (TSDM 2000) held in conjunction with the 4th European Conference on Prin- ples and Practice of Knowledge Discovery in Databases (PKDD 2000) in Lyons, France in September, 2000. The aim of the workshop was to bring together experts in the analysis of temporal and spatial data mining and knowledge discovery in temporal, spatial or spatio-temporal database systems as well as knowledge engineers and domain experts from allied disciplines. The workshop focused on research and practice of knowledge discovery from datasets containing explicit or implicit temporal, spatial or spatio-temporal information. The ten original papers in this volume represent those accepted by peer review following an international call for papers. All papers submitted were refereed by an international team of data mining researchers listed below. We would like to thank the team for their expert and useful help with this process. Following the workshop, authors were invited to amend their papers to enable the feedback received from the conference to be included in the ?nal papers appearing in this volume. A workshop report was compiled by Kathleen Hornsby which also discusses the panel session that was held.
