Sensitivity Analysis in Earth Observation Modelling

AvGeorge P. Petropoulos,Prashant K. Srivastava

Häftad, Engelska, 2016

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Beskrivning

Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling.

Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement.

Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling
Provides readers with a roadmap for directing future efforts
Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling
Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow

Produktinformation

Utgivningsdatum:2016-10-17
Mått:191 x 235 x 30 mm
Vikt:910 g
Format:Häftad
Språk:Engelska
Antal sidor:448
Förlag:Elsevier Science
ISBN:9780128030110

Utforska kategorier

Astronomi inom Naturvetenskap och teknik
Geologi inom Naturvetenskap och teknik
Tillämpad matematik inom Naturvetenskap och teknik

Mer om författaren

Dr. George P. Petropoulos is Assistant Professor in Geoinformatics at the Department of Geography, Harokopio University of Athens Greece. His research focuses on the exploitation of geoinformation technology & geospatial data analysis techniques in geographical and environmental applications. He is author/co-author of over 110 articles, 40 book chapters, and has edited 6 books. He has developed collaborations with key scientists in his area of specialisation globally and his research & teaching work has received international recognition via several significant awards and research funding. Prashant K. Srivastava obtained his PhD from the Department of Civil Engineering at the University of Bristol in Bristol, UK,and currently serves on the faculty at the Institute of Environment and Sustainable Development at Banaras Hindu University inVaranasi, India. He formerly worked in the Hydrological Sciences Department at the NASA Goddard Space Flight Center andis currently an investigator for several national and international projects. He has published 100+ papers, many books, andseveral book chapters. He is also acting as an editorial board member of several reputed journals.

Innehållsförteckning

Section I: Introduction to SA in Earth Observation (EO)1. Overview of Sensitivity Analysis Methods in Earth Observation ModelingL. Lee, P.K. Srivastava, G.P. Petropoulos2. Model Input Data Uncertainty and its Potential Impact on Soil PropertiesT. Mannschatz, P. DietrichSection II : Local SA Methods: Case Studies3. Local Sensitivity Analysis of the LandSoil Erosion Model Applied to a Virtual CatchmentR. Caimpalini, S. Follain, B. Cheviron, Y. Le Bissonnais, A. Couturier4. Sensitivity of Vegetation Phenological Parameters from Satellite Sensors to Spatial Resolution and Temporal Compositing PeriodG.L. Mountford, P.M. Atkinson, J. Dash, T. Lankester, S. Hubbard5. Radar Rainfall Sensitivity Analysis Using Multivariate Distributed Ensemble GeneratorQ. Dai, D. Han, P.K. Srivastava6. Field-Scale Sensitivity of Vegetation Discrimination to Hyperspectral Reflectance and Coupled StatisticsK. Manevski, M. Jabloun, M. Gupta, C. KalaitzidisSection III: Global (or Variance)-Based SA Methods: Case Studies7. A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface ModelsF. Pianosi, J. Iwema, R. Rosolem, T. Wagener8. Global Sensitivity Analysis for Supporting History Matching of Geomechanical Reservoir Models Using Satellite InSAR Data: A Case Study at the CO2 Storage Site of In Salah, AlgeriaJ. Rohmer, A. Loschetter, D. Raucoules9. Artificial Neural Networks for Spectral Sensitivity Analysis to Optimize Inversion Algorithms for Satellite-Based Earth Observation: Sulfate Aerosol Observations with High-Resolution Thermal Infrared SoundersP. Sellitto10. Global Sensitivity Analysis for Uncertain Parameters, Models, and ScenariosM. Ye, M.C. HillSection IV: Other SA Methods: Case Studies11. Sensitivity and Uncertainty Analyses for Stochastic Flood Hazard SimulationZ. Micovic, M.G. Schaefer, B.L. Barker12. Sensitivity of Wells in a Large Groundwater Monitoring Newtork and Its Evaluation Using GRACE Satellite Derived InformationV. Uddameri, A. Karim, E.A. Hernandez, P.K. Srivastava13. Making the Most of the Earth Observation Data Using Effective Sampling TechniquesJ. Indu, D. Nagesh Kumar14. Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula ModelS. Moazami, S. GolianSection V: Software Tools in SA for EO15. Efficient Tools for Global Sensitivity Analysis Based on High-Dimensional Model RepresentationT. Ziehn, A.S. Tomlin16. A Global Sensitivity Analysis Toolbox to Quantify Drivers of Vegetation Radiative Transfer ModelsJ. Verrelst, J.P. Rivera17. GEM-SA: The Gaussian Emulation Machine for Sensitivity AnalysisM.C. Kennedy, G.P. Petropoulos18. An Introduction to The SAFE Matlab Toolbox with Practical Examples and GuidelinesF. Sarrazin, F. Pianosi, T. WagenerSection VI: Challenges and Future Outlook19. Sensitivity in Ecological Modeling: From Local to Regional ScalesX. Song, B.A. Bryan, L. Gao, G. Zhao, M. Dong20. Challenges and Future Outlook of Sensitivity AnalysisH. Gupta, S. Razavi