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Operationalization of Remote Sensing Solutions for Sustainable Forest Management
The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry.
Remote Sensing Time Series
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 Analys...
Theory and Applications of Time Series Analysis
This book presents selected peer-reviewed contributions from the International Conference on Time Series and Forecasting, ITISE 2018, held in Granada, Spain, on September 19-21, 2018. The first three parts of the book focus on the theory of time series analysis and forecasting, and discuss statistical methods, modern computational intelligence methodologies, econometric models, financial forecasting, and risk analysis. In turn, the last three parts are dedicated to applied topics and include papers on time series analysis in the earth sciences, energy time series forecasting, and time series analysis and prediction in other real-world problems. The book offers readers valuable insights into ...
Remote Sensing of Vegetation
How is the vegetation distribution influencing the erosion and surface formation in the different eco zones of Chile? To answer this question, it is mandatory to possess fundamental knowledge about plant species habitats, occurrence and their dynamics. In his study Christian Bödinger utilizes satellite imagery in combination with machine learning to derive maps of land use and land cover (LULC) in four study sites along a climatic gradient and to monitor vegetation using monthly Normalized Difference Vegetation Index (NDVI) time series. The findings contribute to a better understanding of climate impacts on Chilean vegetation and serve as a basis of landscape evolution models. About the Author: Christian Bödinger holds a M.Sc. in Physical Geography from the University of Tübingen, Germany. His focus in research lies on remote sensing and image analysis for environmental applications. He is currently working for a company focusing on aquatic remote sensing.
Multitemporal Earth Observation Image Analysis
Earth observation has witnessed a unique paradigm change in the last decade with a diverse and ever-growing number of data sources. Among them, time series of remote sensing images has proven to be invaluable for numerous environmental and climate studies. Multitemporal Earth Observation Image Analysis provides illustrations of recent methodological advances in data processing and information extraction from imagery, with an emphasis on the temporal dimension uncovered either by recent satellite constellations (in particular the Sentinels from the European Copernicus programme) or archival aerial images available in national archives. The book shows how complementary data sources can be efficiently used, how spatial and temporal information can be leveraged for biophysical parameter estimation, classification of land surfaces and object tracking, as well as how standard machine learning and state-of-the-art deep learning solutions can solve complex problems with real-world applications.
Climate Variability and Change in the 21th Century
- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.
Pattern Recognition and Artificial Intelligence
This book constitutes the proceedings of the Second International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020, which took place in Zhongshan, China, in October 2020. The 49 full and 14 short papers presented were carefully reviewed and selected for inclusion in the book. The papers were organized in topical sections as follows: handwriting and text processing; features and classifiers; deep learning; computer vision and image processing; medical imaging and applications; and forensic studies and medical diagnosis.
