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This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
The development of a procedure to calculate the effect of certain environmental factors on the rate of photo-synthesis imposed mainly geometrical problems, which were solved in such a way that the actual calculation could be carried out by means of a computer. The calculation procedures have been used to study the. relative importance of the variables under various conditions. The results for a standard set of conditions, have been summarized in order to make it possible to estimate the daily photosynthesis at any time and place for a wide range of photosynthesis functions without a computer.
The last 30 years has seen the development of increasingly sophisticated models that quantify canopy carbon exchange. These models are now essential parts of larger models for prediction and simulation of crop production, climate change, and regional and global carbon dynamics. There is thus an urgent need for increasing expertise in developing, use and understanding of these models. This in turn calls for an advanced, yet easily accessible textbook that summarizes the “canopy science” and introduces the present and the future scientists to the theoretical background of the current canopy models. This book presents current knowledge of functioning of plant canopies, models and strategies employed to simulate canopy function, and the significance of canopy architecture, physiology and dynamics in ecosystems, landscape and biosphere.
This report examines the scientific basis for the use of remotely sensed data, particularly Normalized Difference Vegetation Index (NDVI), primarily for the assessment of land degradation at different scales and for a range of applications, including resilience of agro-ecosystems. Evidence is drawn from a wide range of investigations, primarily from the scientific peer-reviewed literature but also non-journal sources. The literature review has been corroborated by interviews with leading specialists in the field. The report reviews the use of NDVI for a range of themes related to land degradation, including land cover change, drought monitoring and early warning systems, desertification processes, greening trends, soil erosion and salinization, vegetation burning and recovery after fire, biodiversity loss, and soil carbon. This SpringerBrief also discusses the limits of the use of NDVI for land degradation assessment and potential for future directions of use. A substantial body of peer-reviewed research lends unequivocal support for the use of coarse-resolution time series of NDVI data for studying vegetation dynamics at global, continental and sub-continental levels. There is compelling evidence that these data are highly correlated with biophysically meaningful vegetation characteristics such as photosynthetic capacity and primary production that are closely related to land degradation and to agroecosystem resilience.
We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet presents prioritized science, applications, and observations, along with related strategic and programmatic guidance, to support the U.S. civil space Earth observation program over the coming decade.
Abiotic environment and ecosystem processes; The plant community: Composition, dynamics, and life-history processes; The animal community; Plant-animal interactions; La selva's human environment.
In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Remote Sensing of Drought: Innovative Monitoring Approaches presents emerging remote sensing-based tools and techniques that can be applied to operational drought monitoring and early warning around the world. The first book to focus on remote sensing and drought monitoring, it brings together a wealth of information that has been scattered throughout the literature and across many disciplines. Featuring contributions by leading scientists, it assembles a cross-section of globally applicable techniques that are currently operational or have potential to be operational in the near future. The book explores a range of applications for monitoring four critical components of the hydrological cycle related to drought: vegetation health, evapotranspiration, soil moisture and groundwater, and precipitation. These applications use remotely sensed optical, thermal, microwave, radar, and gravity data from instruments such as AMSR-E, GOES, GRACE, MERIS, MODIS, and Landsat and implement several advanced modeling and data assimilation techniques. Examples show how to integrate this information into routine drought products. The book also examines the role of satellite remote sensing within traditional drought monitoring, as well as current challenges and future prospects. Improving drought monitoring is becoming increasingly important in addressing a wide range of societal issues, from food security and water scarcity to human health, ecosystem services, and energy production. This unique book surveys innovative remote sensing approaches to provide you with new perspectives on large-area drought monitoring and early warning.
Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for
The pioneering research of Hirotugu Akaike has an international reputation for profoundly affecting how data and time series are analyzed and modelled and is highly regarded by the statistical and technological communities of Japan and the world. His 1974 paper "A new look at the statistical model identification" (IEEE Trans Automatic Control, AC-19, 716-723) is one of the most frequently cited papers in the area of engineering, technology, and applied sciences (according to a 1981 Citation Classic of the Institute of Scientific Information). It introduced the broad scientific community to model identification using the methods of Akaike's criterion AIC. The AIC method is cited and applied in almost every area of physical and social science. The best way to learn about the seminal ideas of pioneering researchers is to read their original papers. This book reprints 29 papers of Akaike's more than 140 papers. This book of papers by Akaike is a tribute to his outstanding career and a service to provide students and researchers with access to Akaike's innovative and influential ideas and applications. To provide a commentary on the career of Akaike, the motivations of his ideas, and his many remarkable honors and prizes, this book reprints "A Conversation with Hirotugu Akaike" by David F. Findley and Emanuel Parzen, published in 1995 in the journal Statistical Science. This survey of Akaike's career provides each of us with a role model for how to have an impact on society by stimulating applied researchers to implement new statistical methods.