LEADER 08710nam 2200505 450 001 9910624384403321 005 20230315082957.0 010 $a3-031-14937-8 035 $a(MiAaPQ)EBC7127027 035 $a(Au-PeEL)EBL7127027 035 $a(CKB)25207989900041 035 $a(PPN)265863252 035 $a(EXLCZ)9925207989900041 100 $a20230315d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTowards tree-level evapotranspiration estimation with small UAVs in precision agriculture /$fHaoyu Niu and YangQuan Chen 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (176 pages) 311 08$aPrint version: Niu, Haoyu Towards Tree-Level Evapotranspiration Estimation with Small UAVs in Precision Agriculture Cham : Springer International Publishing AG,c2022 9783031149368 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Acknowledgments -- Contents -- Acronyms -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 What Is Evapotranspiration Estimation? -- 1.2 Challenges and Opportunities -- 1.3 Smart Big Data in Precision Agriculture: Acquisition and Advanced Analytics -- 1.3.1 What Is Smart Big Data in Precision Agriculture? -- 1.3.2 Plant Physiology-Informed Machine Learning: A New Frontier for Precision Agriculture -- 1.3.3 Big Data Acquisition and Advanced Analytics -- 1.3.4 Fractional Calculus (FC) and Fractional-Order Thinking (FOT) -- 1.3.5 Complexity and Inverse Power Laws (IPLs) -- 1.3.6 Heavy-Tailed Distributions -- 1.3.6.1 The Lévy Distribution -- 1.3.6.2 The Mittag-Leffler PDF -- 1.3.6.3 The Weibull Distribution -- 1.3.6.4 The Cauchy Distribution -- 1.3.6.5 The Pareto Distribution -- 1.3.6.6 The ?-Stable Distribution -- 1.3.6.7 Mixture Distributions -- 1.3.6.8 The Gaussian Distribution -- 1.3.6.9 The Laplace Distribution -- 1.3.7 Big Data, Variability, and FC -- 1.3.7.1 The Hurst Parameter, fGn, and fBm -- 1.3.7.2 Fractional Lower-Order Moments (FLOMs) -- 1.3.7.3 Fractional Autoregressive Integrated Moving Average (FARIMA) and Gegenbauer Autoregressive Moving Average (GARMA) -- 1.3.7.4 Continuous-Time Random Walk (CTRW) -- 1.3.7.5 Unmanned Aerial Vehicles (UAVs) and Precision Agriculture -- 1.3.8 Optimal Machine Learning and Optimal Randomness -- 1.3.8.1 Derivative-Free Methods -- 1.3.8.2 Gradient-Based Methods -- 1.3.8.3 The Nesterov Accelerated Gradient Descent (NAGD) -- 1.4 Main Contributions -- 1.5 Book Organization -- 1.6 Results Reproducibility -- References -- 2 Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 2.1 The UAV Platform -- 2.2 Lightweight Sensors -- 2.2.1 RGB Camera -- 2.2.2 Multispectral Camera -- 2.2.3 Shortwave Infrared Camera -- 2.2.4 Thermal Camera. 327 $a2.3 UAV Image Acquisition and Processing -- 2.3.1 Flight Mission Design -- 2.3.2 UAV Image Processing -- 2.4 Challenges and Opportunities -- 2.4.1 UAVs -- 2.4.2 UAV Path Planning and Image Processing -- 2.4.3 Preflight Path Planning -- 2.4.4 Multispectral Image Calibration -- 2.4.5 Thermal Camera Calibration and Image Processing -- 2.4.6 Image Stitching and Orthomosaick Image Generation -- 2.5 Case Study I: A UAV Resolution and Waveband Aware Path Planning for Irrigation -- 2.5.1 Introduction -- 2.5.2 Material and Methods -- 2.5.2.1 Onion Study Area -- 2.5.2.2 A UAV Platform and Sensors -- 2.5.2.3 UAV Image Collection and Preprocessing -- 2.5.2.4 Principal Component Analysis -- 2.5.2.5 Linear Discriminant Analysis -- 2.5.3 Results and Discussion -- 2.5.3.1 UAV Flight Height or Resolution's Effect -- 2.5.3.2 Wavebands Configuration's Effect -- 2.5.4 Conclusions -- 2.6 Case Study II: A Detailed Study on Accuracy of Uncooled Thermal Cameras -- 2.6.1 Introduction -- 2.6.2 Material and Methods -- 2.6.2.1 Study Site -- 2.6.2.2 Image Collection -- 2.6.2.3 Groundtruth Data Collection -- 2.6.3 Results and Discussion -- 2.6.3.1 Experiment Setup -- 2.6.3.2 Thermal Camera Warm-Up Time -- 2.6.3.3 Calibration Experiment -- 2.6.3.4 The View Angle of Thermal Camera -- 2.6.3.5 The Effect of Stitching -- 2.6.4 Conclusions and Future Work -- 2.7 Case Study III: High Spatial Resolution Has Little Impact on NDVI Mean Value -- 2.7.1 Introduction -- 2.7.2 Material and Methods -- 2.7.2.1 The Study Site -- 2.7.2.2 The UAV and the Multispectral Sensor -- 2.7.2.3 Details of the UAV Imagery Dataset -- 2.7.3 Results and Discussion -- 2.7.3.1 The Relationship Between NDVI and UAV Flight Height -- 2.7.3.2 Individual Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 2.7.3.3 Entropy of Individual Tree-Level NDVI Image -- 2.7.4 Conclusions and Future Work. 327 $a2.8 Chapter Summary -- References -- 3 ET Estimation Methods with Small UAVs: A Literature Review -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 One-Source Energy Balance (OSEB) -- 3.2.2 High-Resolution Mapping of ET (HRMET) -- 3.2.3 Machine Learning (ML) and Artificial Neural Networks (ANN) -- 3.2.4 Two-Source Energy Balance (TSEB) Models -- 3.2.5 Dual-Temperature-Difference (DTD) Model -- 3.2.6 Surface Energy Balance Algorithm for Land (SEBAL) -- 3.2.7 Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) -- 3.3 Existing ET Estimation Methods with UAVs: Results and Discussion -- 3.3.1 OSEB and TSEB Models -- 3.3.2 HARMET Model -- 3.3.3 Machine Learning and Neural Networks -- 3.3.4 TSEB and DTD Models -- 3.3.5 TSEB and DATTUTDUT Models -- 3.3.6 SEBAL Model -- 3.3.7 METRIC and METRIC-HR Models -- 3.4 Chapter Summary -- References -- 4 Estimating ET Using Stochastic Configuration Network and UAV-Based Crop Coefficients -- 4.1 Introduction -- 4.2 Material and Methods -- 4.2.1 Pomegranate Study Area -- 4.2.2 The UAV Platform and Multispectral Camera -- 4.2.3 UAV Image Collection and Preprocessing -- 4.2.4 Deep Stochastic Configuration Networks (DeepSCNs) -- 4.3 Results and Discussion -- 4.3.1 Seasonal Kc and NDVI -- 4.3.2 Regression Models for Kc and NDVI -- 4.4 Conclusions -- 4.5 Case Study: Optimal Randomness for SCN with Heavy-Tailed Distributions -- 4.5.1 Introduction -- 4.5.2 SCN with Heavy-Tailed PDFs -- 4.5.3 A Regression Model and Parameter Tuning -- 4.5.3.1 Performance Comparison Among SCNs with Heavy-Tailed PDFs -- 4.5.4 MNIST Handwritten Digit Classification -- 4.5.4.1 Performance Comparison Among SCNs on MNIST -- 4.6 Chapter Summary -- References -- 5 Reliable Tree-Level ET Estimation Using Lysimeter and UAV Multispectral Imagery -- 5.1 Introduction -- 5.2 Material and Methods -- 5.2.1 Study Site Description. 327 $a5.2.2 UAV Image Collection and Processing -- 5.3 Results and Discussion -- 5.3.1 Determination of Individual Tree Kc from NDVI -- 5.3.2 The Spatial Variability Mapping of Kc and ETc -- 5.3.3 Performance of the Individual Tree-Level ET Estimation -- 5.3.4 Conclusion -- 5.4 Chapter Summary -- References -- 6 Tree-Level Water Status Inference Using UAV Thermal Imagery and Machine Learning -- 6.1 Introduction -- 6.2 Material and Methods -- 6.2.1 Experimental Site and Irrigation Management -- 6.2.2 Ground Truth: Infrared Canopy and Air Temperature -- 6.2.3 Thermal Infrared Remote Sensing Data -- 6.2.3.1 UAV Thermal Image Collection and Processing -- 6.2.3.2 Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 6.2.4 Complexity-Informed Machine Learning (CIML) -- 6.2.5 Principle of Tail Matching -- 6.2.5.1 Pareto Distribution -- 6.2.6 Machine Learning Classification Algorithms -- 6.2.7 Image Preprocessing for the CNN Model -- 6.3 Results and Discussion -- 6.3.1 Comparison of Canopy Temperature Per Tree Based on Ground Truth and UAV Thermal Imagery -- 6.3.2 The Relationship Between ?T and Irrigation Treatment -- 6.3.3 The Classification Performance of CIML on Irrigation Treatment Levels -- 6.3.4 The Performance of the CNN Model -- 6.4 Conclusion and Future Research -- 6.5 Chapter Summary -- References -- 7 Conclusion and Future Research -- 7.1 Conclusions -- 7.2 Future Research -- References -- Index. 606 $aPrecision farming 606 $aEvapotranspiration$xMeasurement 606 $aDrone aircraft in remote sensing 615 0$aPrecision farming. 615 0$aEvapotranspiration$xMeasurement. 615 0$aDrone aircraft in remote sensing. 676 $a631 700 $aNiu$b Haoyu$01263980 702 $aChen$b YangQuan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910624384403321 996 $aTowards tree-level evapotranspiration estimation with small UAVs in precision agriculture$93064927 997 $aUNINA