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|a 960494981
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|a (OCoLC)960719028
|z (OCoLC)960494981
|z (OCoLC)965414718
|z (OCoLC)974034275
|z (OCoLC)974331339
|z (OCoLC)1021272506
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|a 550.285
|2 23
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|a Sensitivity analysis in Earth observation modelling /
|c edited by George F. Petropoulous, Prashant K. Srivastava.
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| 260 |
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|a Amsterdam, Netherlands :
|b Elsevier,
|c 2016.
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| 300 |
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|a 1 online resource (448)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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|a Print version record.
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|a Front Cover; Sensitivity Analysis in Earth Observation Modelling; Sensitivity Analysis in Earth Observation Modelling; Copyright; Dedication; Contents; List of Contributors; Preface; ABOUT THE COVER; 1 -- INTRODUCTION TO SA IN EARTH OBSERVATION (EO); 1 -- OVERVIEW OF SENSITIVITY ANALYSIS METHODS IN EARTH OBSERVATION MODELING; 1. INTRODUCTION; 1.1 DEFINING THE MODEL OUTPUTS AND INPUTS FOR SENSITIVITY ANALYSIS; 1.1.1 Defining Factor (or Parametric) Uncertainty; 2. LOCAL SENSITIVITY ANALYSIS; 2.1 CORRELATION ANALYSIS; 2.2 REGRESSION ANALYSIS; 3. GLOBAL SENSITIVITY ANALYSIS.
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| 505 |
8 |
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|a 3.1 ONE-AT-A-TIME SENSITIVITY ANALYSIS METHODS3.2 THE MORRIS METHOD FOR FACTOR SCREENING; 3.3 VARIANCE-BASED SENSITIVITY ANALYSIS; 3.4 SAMPLING METHODS FOR GLOBAL SENSITIVITY ANALYSIS; 3.4.1 Random Sampling; 3.4.2 Stratified Sampling and the Latin Hypercube; 3.4.3 Sampling for Sensitivity Indices; 3.5 SURROGATE MODELS FOR GLOBAL SENSITIVITY ANALYSIS; 3.5.1 Generalized Linear Modeling; 3.5.2 Neural Networks; 3.5.3 Direct Sensitivity Analysis of Surrogate Models; 3.6 POLYNOMIAL CHAOS; 3.7 GAUSSIAN PROCESS AND BAYES LINEAR EMULATION; 4. GRAPHICAL METHODS FOR GLOBAL SENSITIVITY ANALYSIS.
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8 |
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|a 4.1 SCATTER PLOTS4.2 PLOTTING THE RESPONSE SURFACE; 4.3 PLOTTING THE SENSITIVITY INDICES; 5. CONCLUSIONS; REFERENCES; 2 -- MODEL INPUT DATA UNCERTAINTY AND ITS POTENTIAL IMPACT ON SOIL PROPERTIES; 1. INTRODUCTION; 2. A WORLD OF MODELS -- HOW CAN THEY BE CLASSIFIED?; 3. CAN WE TRUST MODELS? -- MODEL ACCURACY AND THEIR SENSITIVITY TO INPUT DATA UNCERTAINTY; 4. SELECTING THE MOST APPROPRIATE MODEL; 5. WHY AND HOW TO ACCOUNT FOR MODELING UNCERTAINTIES CAUSED BY DIFFERENT INPUT DATA SOURCES; 6. ASSESSING SENSITIVITY OF ENVIRONMENTAL MODELS.
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|a 7. HOW SOIL TEXTURE MEASURED WITH VISIBLE-NEAR-INFRARED SPECTROSCOPY AFFECTS HYDROLOGICAL MODELING: A CASE STUDY7.1 STUDY SITES AND INSTRUMENTS; 7.2 SOIL SAMPLES; 7.3 CHEMOMETRICS; 7.4 IMPACT OF CHEMOMETRIC METHOD ON SOIL PREDICTION; 7.5 DIFFERENT INSTRUMENTS, DIFFERENT SOIL PREDICTIONS? WHAT WAS FINALLY THE BEST SOIL PREDICTION ACCURACY?; 7.6 WHAT DOES THIS FINALLY MEAN FOR OUR ENVIRONMENTAL MODELING?; 8. WHAT DID WE LEARN?; REFERENCES; 2 -- LOCAL SA METHODS: CASE STUDIES; 3 -- LOCAL SENSITIVITY ANALYSIS OF THE LANDSOIL EROSION MODEL APPLIED TO A VIRTUAL CATCHMENT; 1. INTRODUCTION.
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|a 2. MATERIALS AND METHODS2.1 MODEL DESCRIPTION; 2.2 SENSITIVITY ANALYSIS; 2.2.1 Parameters; 2.2.2 Virtual Catchment; 2.2.3 Sensitivity Calculation; 3. RESULTS AND DISCUSSION; 3.1 LINEAR HILLSLOPE; 3.1.1 Aggregated Parameters; 3.2 COMPLEX HILLSLOPES; 4. CONCLUDING REMARKS; Acknowledgments; REFERENCES; 4 -- SENSITIVITY OF VEGETATION PHENOLOGICAL PARAMETERS: FROM SATELLITE SENSORS TO SPATIAL RESOLUTION AND TEMPORAL CO ... ; 1. INTRODUCTION; 2. MONITORING VEGETATION PHENOLOGY; 3. SENSITIVITY ANALYSIS; 4. SENSITIVITY OF REMOTELY SENSED PHENOLOGICAL PARAMETERS; 4.1 SATELLITE SENSOR.
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| 650 |
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0 |
|a Earth sciences
|x Remote sensing.
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| 650 |
|
6 |
|a Sciences de la terre
|x T�el�ed�etection.
|0 (CaQQLa)201-0118208
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| 650 |
|
7 |
|a Earth sciences
|x Remote sensing
|2 fast
|0 (OCoLC)fst00900758
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| 700 |
1 |
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|a Petropoulos, George P.
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| 700 |
1 |
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|a Srivastava, Prashant K.
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| 776 |
0 |
8 |
|i Print version:
|t Sensitivity analysis in Earth observation modelling.
|d Amsterdam, Netherlands : Elsevier, 2016
|z 0128030119
|z 9780128030110
|w (OCoLC)949749831
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128030110
|z Texto completo
|
