UID:
almahu_9949616271002882
Format:
1 online resource (1210 pages)
Edition:
1st ed.
ISBN:
9783031265884
Note:
Intro -- Foreword -- Introduction -- Fundamentals -- Part I: Programming and Remote Sensing Basics -- Part II: Interpreting Images -- Part III: Advanced Image Processing -- Part IV: Interpreting Image Series -- Part V: Vectors and Tables -- Part VI: Advanced Topics -- Applications -- Part VII: Human Applications -- Part VIII: Aquatic and Hydrological Applications -- Part IX: Terrestrial Applications -- Uses of This Book -- We Want Your Feedback -- Acknowledgements -- Other Sources -- Contents -- Part IProgramming and Remote Sensing Basics -- 1 JavaScript and the Earth Engine API -- 1.1 Introduction to Theory -- 1.2 Practicum -- 1.2.1 Section 1: Getting Started in the Code Editor -- 1.2.2 Section 2: JavaScript Basics -- 1.2.3 Section 3: Earth Engine API Basics -- 1.3 Synthesis -- 1.4 Conclusion -- 2 Exploring Images -- 2.1 Practicum -- 2.1.1 Section 1: Accessing an Image -- 2.1.2 Section 2: Visualizing an Image -- 2.1.3 Section 3: True-Color Composites -- 2.1.4 Section 4: False-Color Composites -- 2.1.5 Section 5: Additive Color System -- 2.1.6 Section 6: Attributes of Locations -- 2.1.7 Section 7: Abstract RGB Composites -- 2.2 Synthesis -- 2.3 Conclusion -- 3 Survey of Raster Datasets -- 3.1 Introduction to Theory -- 3.2 Practicum -- 3.2.1 Section 1: Image Collections: An Organized Set of Images -- 3.2.2 Section 2: Collections of Single Images -- 3.2.3 Section 3: Pre-made Composites -- 3.2.4 Section 4: Other Satellite Products -- 3.2.5 Section 5: Pre-classified Land Use and Land Cover -- 3.2.6 Section 6: Other Datasets -- 3.3 Synthesis -- 3.4 Conclusion -- References -- 4 The Remote Sensing Vocabulary -- 4.1 Introduction to Theory -- 4.2 Practicum -- 4.2.1 Section 1: Searching for and Viewing Image Collection Information -- 4.2.2 Section 2: Spatial Resolution -- 4.2.3 Section 3: Temporal Resolution -- 4.2.4 Section 4: Spectral Resolution.
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4.2.5 Section 5: Per-Pixel Quality -- 4.2.6 Section 6: Metadata -- 4.3 Synthesis -- 4.4 Conclusion -- Reference -- Part IIInterpreting Images -- 5 Image Manipulation: Bands, Arithmetic, Thresholds, and Masks -- 5.1 Introduction to Theory -- 5.2 Practicum -- 5.2.1 Section 1: Band Arithmetic in Earth Engine -- 5.2.2 Section 2: Thresholding, Masking, and Remapping Images -- 5.3 Synthesis -- 5.4 Conclusion -- References -- 6 Interpreting an Image: Classification -- 6.1 Introduction to Theory -- 6.2 Practicum -- 6.2.1 Section 1: Supervised Classification -- 6.2.2 Section 2: Unsupervised Classification -- 6.3 Synthesis -- 6.4 Conclusion -- References -- 7 Accuracy Assessment: Quantifying Classification Quality -- 7.1 Introduction to Theory -- 7.2 Practicum -- 7.2.1 Quantifying Classification Accuracy Through a Confusion Matrix -- 7.2.2 Hyperparameter Tuning -- 7.2.3 Spatial Autocorrelation -- 7.3 Synthesis -- 7.4 Conclusion -- References -- Part IIIAdvanced Image Processing -- 8 Interpreting an Image: Regression -- 8.1 Introduction to Theory -- 8.2 Practicum -- 8.2.1 Reducers -- 8.3 Section 1: Linear Fit -- 8.3.1 Section 2: Linear Regression -- 8.3.2 Section 3: Nonlinear Regression -- 8.3.3 Section 4: Assessing Regression Performance Through RMSE -- 8.4 Synthesis -- 8.5 Conclusion -- References -- 9 Advanced Pixel-Based Image Transformations -- 9.1 Introduction to Theory -- 9.2 Practicum -- 9.2.1 Section 1: Manipulating Images with Expressions -- 9.2.2 Section 2: Manipulating Images with Matrix Algebra -- 9.2.3 Section 3: Spectral Unmixing -- 9.2.4 Section 4: The Hue, Saturation, Value Transform -- 9.3 Synthesis -- 9.4 Conclusion -- References -- 10 Neighborhood-Based Image Transformation -- 10.1 Introduction to Theory -- 10.2 Practicum -- 10.2.1 Section 1: Linear Convolution -- 10.2.2 Section 2: Nonlinear Convolution.
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10.2.3 Section 3: Morphological Processing -- 10.2.4 Section 4: Texture -- 10.3 Synthesis -- 10.4 Conclusion -- References -- 11 Object-Based Image Analysis -- 11.1 Introduction to Theory -- 11.2 Practicum -- 11.2.1 Section 1: Unsupervised Classification -- 11.2.2 Section 2: Detecting Objects in Imagery with the SNIC Algorithm -- 11.2.3 Section 3: Object-Based Unsupervised Classification -- 11.2.4 Section 4: Classifications with More or Less Categorical Detail -- 11.2.5 Section 5: Effects of SNIC Parameters -- 11.3 Synthesis -- 11.4 Conclusion -- References -- Part IVInterpreting Image Series -- 12 Filter, Map, Reduce -- 12.1 Introduction to Theory -- 12.2 Practicum -- 12.2.1 Section 1: Filtering Image Collections in Earth Engine -- 12.2.2 Section 2: Mapping over Image Collections in Earth Engine -- 12.2.3 Section 3: Reducing an Image Collection -- 12.3 Synthesis -- 12.4 Conclusion -- 13 Exploring Image Collections -- 13.1 Practicum -- 13.1.1 Section 1: Filtering and Inspecting an Image Collection -- 13.1.2 Section 2: How Many Images Are There, Everywhere on Earth? -- 13.1.3 Section 3: Reducing Image Collections to Understand Band Values -- 13.1.4 Section 4: Compute Multiple Percentile Images for an Image Collection -- 13.2 Synthesis -- 13.3 Conclusion -- Reference -- 14 Aggregating Images for Time Series -- 14.1 Introduction to Theory -- 14.2 Practicum -- 14.2.1 Section 1: Filtering an Image Collection -- 14.2.2 Section 2: Working with Dates -- 14.2.3 Section 3: Aggregating Images -- 14.2.4 Section 4: Plotting Time Series -- 14.3 Synthesis -- 14.4 Conclusion -- References -- 15 Clouds and Image Compositing -- 15.1 Introduction to Theory -- 15.2 Practicum -- 15.2.1 Section 1: Cloud Filter and Cloud Mask -- 15.2.2 Section 2: Incorporating Data from Other Satellites -- 15.2.3 Section 3: Best-Available-Pixel Compositing Earth Engine Application.
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15.3 Synthesis -- 15.4 Conclusion -- References -- 16 Change Detection -- 16.1 Introduction to Theory -- 16.2 Practicum -- 16.2.1 Section 1: Preparing Imagery -- 16.2.2 Section 2: Creating False-Color Composites -- 16.2.3 Section 3: Calculating NBR -- 16.2.4 Section 4: Single Date Transformation -- 16.2.5 Section 5: Classifying Change -- 16.3 Synthesis -- 16.4 Conclusion -- References -- 17 Interpreting Annual Time Series with LandTrendr -- 17.1 Introduction to Theory -- 17.2 Practicum -- 17.2.1 Section 1: Pixel Time Series -- 17.2.2 Section 2: Translating Pixels to Maps -- 17.3 Synthesis -- 17.4 Conclusion -- References -- 18 Fitting Functions to Time Series -- 18.1 Introduction to Theory -- 18.2 Practicum -- 18.2.1 Section 1: Multi-temporal Data in Earth Engine -- 18.2.2 Section 2: Data Preparation and Preprocessing -- 18.2.3 Section 3: Estimating Linear Trend Over Time -- 18.2.4 Section 4: Estimating Seasonality with a Harmonic Model -- 18.2.5 Section 5: An Application of Curve Fitting -- 18.2.6 Section 6: Higher-Order Harmonic Models -- 18.3 Synthesis -- 18.4 Conclusion -- References -- 19 Interpreting Time Series with CCDC -- 19.1 Introduction to Theory -- 19.2 Practicum -- 19.2.1 Section 1: Understanding Temporal Segmentation with CCDC -- 19.2.2 Section 2: Running CCDC -- 19.2.3 Section 3: Extracting Break Information -- 19.2.4 Section 4: Extracting Coefficients Manually -- 19.2.5 Section 5: Extracting Coefficients Using External Functions -- 19.3 Synthesis -- 19.4 Conclusion -- References -- 20 Data Fusion: Merging Classification Streams -- 20.1 Introduction to Theory -- 20.2 Practicum -- 20.2.1 Section 1: Imagery and Classifications of the Roosevelt River -- 20.2.2 Section 2: Basics of the BULC Interface -- 20.2.3 Section 3: Detailed LULC Inspection with BULC -- 20.2.4 Section 4: Change Detection with BULC-D.
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20.2.5 Section 5: Change Detection with BULC and Dynamic World -- 20.3 Synthesis -- 20.4 Conclusion -- References -- 21 Exploring Lagged Effects in Time Series -- 21.1 Introduction to Theory -- 21.2 Practicum -- 21.2.1 Section 1: Autocovariance and Autocorrelation -- 21.2.2 Section 2: Cross-Covariance and Cross-Correlation -- 21.2.3 Section 3: Auto-Regressive Models -- 21.3 Synthesis -- 21.4 Conclusion -- References -- Part VVectors and Tables -- 22 Exploring Vectors -- 22.1 Introduction to Theory -- 22.2 Practicum -- 22.2.1 Section 1: Using Geometry Tools to Create Features in Earth Engine -- 22.2.2 Section 2: Loading Existing Features and Feature Collections in Earth Engine -- 22.2.3 Section 3: Importing Features into Earth Engine -- 22.2.4 Section 4: Filtering Feature Collections by Attributes -- 22.2.5 Section 5: Reducing Images Using Feature Geometry -- 22.2.6 Section 6: Identifying the Block in the Neighborhood Surrounding USF with the Highest NDVI -- 22.3 Synthesis -- 22.4 Conclusion -- 23 Raster/Vector Conversions -- 23.1 Introduction to Theory -- 23.2 Practicum -- 23.2.1 Section 1: Raster to Vector Conversion -- 23.2.2 Section 2: Vector-To-Raster Conversion -- 23.3 Synthesis -- 23.4 Conclusion -- 24 Zonal Statistics -- 24.1 Introduction to Theory -- 24.2 Practicum -- 24.2.1 Section 1: Functions -- 24.2.2 Section 2: Point Collection Creation -- 24.2.3 Section 3: Neighborhood Statistic Examples -- 24.2.4 Section 4: Additional Notes -- 24.3 Synthesis -- 24.4 Conclusion -- References -- 25 Advanced Vector Operations -- 25.1 Practicum -- 25.1.1 Section 1: Visualizing Feature Collections -- 25.1.2 Section 2: Joins with Feature Collections -- 25.2 Synthesis -- 25.3 Conclusion -- 26 GEEDiT-Digitizing from Satellite Imagery -- 26.1 Introduction to Theory -- 26.2 Practicum.
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26.2.1 Section 1: Loading GEEDiT and Selecting Imagery Options and a Location.
Additional Edition:
Print version: Cardille, Jeffrey A. Cloud-Based Remote Sensing with Google Earth Engine Cham : Springer International Publishing AG,c2023 ISBN 9783031265877
Language:
English
Keywords:
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