Remote Sensing of Water-Related Hazards

Learn basic Python programming to create functional and effective visualizations from earth observation satellite data sets Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks. Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to process satellite data sets for Earth science research. Gain Python fluency using real data and case studies Read and write common scientific data formats, like netCDF, HDF, and GRIB2 Create 3-dimensional maps of dust, fire, vegetation indices and more Learn to adjust satellite imagery resolution, apply quality control, and handle big files Develop useful workflows and learn to share code using version control Acquire skills using online interactive code available for all examples in the book The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Modelling of flow in naturally fractured reservoirs is quickly becoming mandatory in all phases of oil and gas exploration and production. Creation of a Static Conceptual Fracture Model (SCFM) is needed as input to create flow simulations for today and for prediction of flow into the future. Unfortunately, the computer modelers tasked with constructing the gridded fracture model are often not well versed in natural fracture characterization and are often forced to make quick decisions as to the input required by the software used to create these models. Static Conceptual Fracture Modelling: Preparing for Simulation and Development describes all the fracture and reservoir parameters needed to create the fracture database for effective modelling and how to generate the data and parameter distributions. The material covered in this volume highlights not only natural fracture system quantification and formatting, but also describes best practices for managing technical teams charged with creating the SCFM. This book will become a must on the shelf for all reservoir modelers.

The scientific expedition of H. M. S. Challenger in the 1870s marks the starting point of physical oceanography. This ship traveled the seas of the globe pursuing a dual objective: to conduct an in-depth study of animal life and to observe the physical properties of ocean waters. Volume 3 focuses on measurements and modeling of liquid compressibility. Based on the work initiated by the physicist Peter Tait, a detailed presentation of liquid equations-of-state is proposed. The physical interpretation of the parameters of these equations is discussed, leading to a description of the «structure» of liquid media. From Deep Sea to Laboratory is available in three volumes for curious readers drawn to travel, history and science. Students, researchers and teachers of physics, fluid mechanics and oceanography will find material to deepen their knowledge.

A digital elevation model (DEM) is a digital representation of ground surface topography or terrain. It is also widely known as a digital terrain model (DTM). A DEM can be represented as a raster (a grid of squares) or as a vector based triangular irregular network (TIN). DEMs are commonly built using remote sensing techniques, but they may also be built from land surveying. DEMs are used often in geographic information systems, and are the most common basis for digitally-produced relief maps. The terrain surface can be described as compromising of two different elements; random and systematic. The random (stochastic) elements are the continuous surfaces with continuously varying relief. It would take an endless number of points to describe exactly the random terrain shapes, but these can be described in practice with a network of point. It is usual to use a network that creates sloping triangles or regular quadrants. This book examines how the methods and data sources used to generate DEMs and calculate land surface parameters have changed over the past 25 years. The primary goal is to describe the state-of-the-art for a typical digital terrain modeling workflow that starts with data capture, continues with data preprocessing and DEM generation, and concludes with the calculation of one or more primary and secondary land surface parameters. Taken as a whole, this book covers the basic theory behind the methods, the instrumentation, analysis and interpretation that are embedded in the modern digital terrain modeling workflow, the strengths and weaknesses of the various methods that the terrain analyst must choose among, typical applications of the results emanating from these terrain modeling workflows, and future directions. This book is intended for researchers and practitioners who wish to use DEMs, land surface parameters, land surface objects and landforms in environmental projects. The book will also be valuable as a reference text for environmental scientists who are specialists in related fields and wish to integrate these kinds of digital terrain workflows and outputs into their own specialized work environments.