| Description |
xxxv, 705 pages, 40 unnumbered pages of color plates : illustrations ; 26 cm |
| Contents |
Contents note continued: 1.5.4.1.Soil-Adjusted Hyperspectral Two Band Vegetation Indices -- 1.5.4.2.Atmospherically Resistant Hyperspectral Two Band Vegetation Indices -- 1.5.4.3.Hyperspectral Vegetation Indices of SWIR and TIR Bands -- 1.6.Other Methods of Hyperspectral Data Analysis -- 1.7.Broadband Vegetation Index Models -- 1.8.Separating Vegetation Classes and Agricultural Crops Using Hyperspectral Narrowband Data -- 1.8.1.Class Separability Using Unique Hyperspectral Narrowbands -- 1.8.2.Class Separability Using Statistical Methods -- 1.8.3.Accuracy Assessments of Vegetation and Crop Classification Using Hyperspectral Narrowbands -- 1.9.Optimal Hyperspectral Narrowbands in Study of Vegetation and Agricultural Crops -- 1.10.Conclusions -- Acknowledgments -- References -- pt. II Hyperspectral Sensor Systems -- ch. 2 Hyperspectral Sensor Characteristics: Airborne, Spaceborne, Hand-Held, and Truck-Mounted; Integration of Hyperspectral Data with LIDAR / Fred Ortenberg -- |
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Contents note continued: 10.3.Effects of Water Content on Spectral Reflectance -- 10.4.Methods Used for Estimating Vegetation Water Content -- 10.4.1.Hyperspectral Vegetation Indices Sensitive to Water Content -- 10.4.1.1.NIR-Based Spectral Indices -- 10.4.1.2.SWIR-Based Spectral Indices -- 10.4.1.3.Greenness Indices -- 10.4.2.Absorption-Band-Depth Analysis and Continuum-Removed Spectral Indices -- 10.4.3.Derivative Techniques -- 10.4.4.Fitting of the Pure Water Absorption Spectra -- 10.4.5.Radiative Transfer Models -- 10.4.5.1.Sensitivity Analysis Studies on Reflectance and Spectral Indices -- 10.4.5.2.Retrieval of Vegetation Water Content from Model Inversion -- 10.4.6.Other Techniques -- 10.5.Summary and Conclusions -- References -- ch. 11 Estimation of Nitrogen Content in Crops and Pastures Using Hyperspectral Vegetation Indices / P. A. Brivio -- 11.1.Introduction -- 11.2.State of the Art -- 11.3.Physical and Physiological Basis -- 11.4.Retrieval Approaches -- |
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Contents note continued: 11.5.Review of Common Vegetation Indices -- 11.5.1.Individual/Multiple Waveband Reflectance -- 11.5.2.Simple Ratio and Normalized Difference Indices -- 11.5.3.Chlorophyll and Soil Sensitive Indices -- 11.5.4.The Red Edge -- 11.6.Conclusions -- References -- pt. V Vegetation Biophysical Properties -- ch. 12 Spectral Bioindicators of Photosynthetic Efficiency and Vegetation Stress / Hank A. Margolis -- 12.1.Introduction -- 12.2.Describing and Measuring Ecosystem Photosynthesis and Related Processes -- 12.2.1.Relevance of Ecosystem Photosynthetic Processes to Climate Change -- 12.2.2.Ecosystem Photosynthesis, Photosynthetic Efficiency, and Plant Stress -- 12.2.3.How Do We Measure Canopy/Ecosystem Photosynthesis? -- 12.2.4.The Photosynthetic Light Use Efficiency Model -- 12.3.Physiological Basis for Spectral Observations of Stress Responses -- 12.3.1.Xanthophyll Pigment Cycle -- 12.3.2.Chlorophyll Fluorescence -- |
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Contents note continued: 12.3.3.Influence of PAR Levels on Vegetation Responses -- 12.4.Remote Sensing Observations of Photosynthesis and LUE -- 12.4.1.Remote Sensing Estimates of APAR -- 12.4.2.Hyperspectral Bioindicators of Vegetation Stress -- 12.4.3.Advantages of Remote Sensing Observations -- 12.5.Photochemical Reflectance Index and LUE -- 12.5.1.Leaf, Whole Plant, and Canopy PRI Studies -- 12.5.2.Canopy Airborne PRI Observations -- 12.5.3.PRI from Satellite -- 12.5.4.Confounding Effects on PRI -- 12.6.Future Remote Sensing Opportunities -- 12.7.Conclusions -- Acknowledgments -- References -- ch. 13 Spectral and Spatial Methods of Hyperspectral Image Analysis for Estimation of Biophysical and Biochemical Properties of Agricultural Crops / Yafit Cohen -- 13.1.Introduction -- 13.2.Spectral Methods -- 13.2.1.Spectral Band Selection -- 13.2.2.Spectral Indices -- 13.2.3.Multivariate Statistics -- 13.3.Sensing of Agricultural Crop Properties -- |
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Contents note continued: 13.3.1.Prediction of Biophysical Properties -- 13.3.1.1.LAI -- 13.3.1.2.Biomass -- 13.3.1.3.Water Status -- 13.3.2.Prediction of Biochemical Properties -- 13.3.2.1.Chlorophyll Content -- 13.3.2.2.Nitrogen Content -- 13.4.Spatial Methods -- 13.4.1.Hyperspectral Data Set -- 13.4.2.Spatial Information as a Preprocessing Tool -- 13.4.3.Spatial Information to Improve Spectral Classification -- 13.4.4.Fusion of Spectral and Spatial Information -- 13.5.Discussion and Future Directions -- References -- ch. 14 Hyperspectral Vegetation Indices / Ryan L. Perroy -- 14.1.Introduction -- 14.2.Applications of Hyperspectral Vegetation Indices -- 14.2.1.Vegetation Structure (i.e., LAI, FPAR) -- 14.2.2.Canopy Biochemistry -- 14.2.2.1.Plant Pigments -- 14.2.2.2.Canopy Moisture -- 14.2.2.3.Lignin and Cellulose/Plant Residues -- 14.2.3.Plant Physiology -- 14.3.Applications -- 14.3.1.Estimating LAI Using Hyperspectral Vegetation Indices -- 14.3.2.Soil Moisture and AET -- |
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Contents note continued: 14.4.Discussion -- References -- ch. 15 Remote Sensing Estimation of Crop Biophysical Characteristics at Various Scales / Anatoly A. Gitelson -- 15.1.Introduction -- 15.2.Vegetation Fraction -- 15.3.Fraction of Absorbed Photosynthetically Active Radiation -- 15.4.Chlorophyll Content -- 15.5.Green Leaf Area Index -- 15.6.Gross Primary Production -- 15.7.Conclusions -- Acknowledgments -- References -- pt. VI Vegetation Processes and Function (ET, Water Use, GPP, LUE, Phenology) -- ch. 16 Hyperspectral Remote Sensing Tools for Quantifying Plant Litter and Invasive Species in Arid Ecosystems / Edward P. Glenn -- 16.1.Introduction---Hyperspectral Remote Sensing of Landscape Components -- 16.1.1.Distinguishing between Green Vegetation, Soil, and Litter Using the CAI in Agricultural Systems -- 16.1.1.1.Plant Litter -- 16.1.1.2.Importance of Litter to the Soil System -- 16.1.1.3.Benefits of Litter Left in Agriculture Systems -- |
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Contents note continued: 16.1.1.4.Importance of Quantifying Litter -- 16.1.1.5.Senescent Leaves in the Life Cycle of Invasive Species -- 16.1.2.Reflectance Spectra (400-2400nm) Used in a Laboratory to Distinguish Green Vegetation, Soil, and Litter in the Landscape -- 16.1.2.1.Distinguishing between Pure Scenes of Plant Litter and Green Vegetation -- 16.1.2.2.Distinguishing between Pure Scenes of Plant Litter and Soils -- 16.1.2.3.Remote Sensing Techniques (and Their Limitations) to Discriminate Litter from Soils and Green Vegetation -- 16.1.2.4.A Diagnostic Feature---Cellulose Absorption Index -- 16.1.2.5.Effects of Water on CAI -- 16.1.2.6.Benefits (and Limitations) of CAI -- 16.1.3.Summary of Pure Scenes of Soils and Litter -- 16.1.4.Mixed Scenes of Plant Litter and Soils -- 16.1.5.Conclusions for Mixed and Pure Scenes of Soils and Litter -- |
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Contents note continued: 16.2.4.5.Spectral Separability of Mixed Fractional Cover of Buffelgrass -- 16.2.4.6.Results -- 16.2.4.7.Conclusions -- References -- pt. VII Species Identification -- ch. 17 Crop Type Discrimination Using Hyperspectral Data / Antonio Roberto Formaggio -- 17.1.Introduction -- 17.2.Factors Affecting Crop Type Discrimination by Remote Sensing -- 17.3.Crop Type Discrimination Using Hyperion Data -- 17.3.1.Selected Crops -- 17.3.2.Hyperion Datasets and Preprocessing -- 17.3.3.Hyperion Color Composites -- 17.3.4.Reflectance and Band Ratio Differences between the Crops -- 17.3.5.Narrowband Vegetation Indices -- 17.3.6.Spectral Features -- 17.3.7.Cultivar Discrimination -- 17.3.8.Multispectral versus Hyperspectral Discrimination -- 17.4.Conclusions -- Acknowledgments -- References -- ch. 18 Identification of Canopy Species in Tropical Forests Using Hyperspectral Data / Matthew L. Clark -- 18.1.Introduction -- |
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Contents note continued: 16.2.Applications of Hyperspectral Remote Sensing to Invasive Species---Research Approach and Case Studies with Tamarisk and Buffelgrass -- 16.2.1.Ecological Importance of Discriminating Invasive Plant Species in the Landscape -- 16.2.1.1.Hyperspectral Reflectance Data as a Monitoring Tool for Invasive Plants -- 16.2.2.Reflectance Spectra (0.4-2.4um) Used Outdoors in Natural Settings -- 16.2.2.1.Remote Sensing -- 16.2.3.Tamarisk Study -- 16.2.3.1.Description of Study Area and Vegetation -- 16.2.3.2.Spectral Reflectance and Image Analysis -- 16.2.3.3.Spectral Characteristics of Riparian and Other Vegetation -- 16.2.3.4.Landsat TM Spectral Ratios and Image Interpretation -- 16.2.3.5.Discussion and Future Directions -- 16.2.4.Buffelgrass Study -- 16.2.4.1.Study Area -- 16.2.4.2.Measurements of Community Composition -- 16.2.4.3.Field Spectroscopy of Dominant Cover -- 16.2.4.4.Spectral Analysis of Sonorcn Desert Vegetation -- |
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Contents note continued: 18.2.Drivers of Spectral Variation in Tropical Forest Canopies -- 18.2.1.Leaves, Bark, and Other Fine-Scale Canopy Components -- 18.2.2.Pixel to Canopy Scales -- 18.3.Mapping Canopy Species over Broad Spatial Scales -- 18.3.1.Automated Delineation of Individual Tree Crowns or Crown Clusters -- 18.3.2.Classification Schemes -- 18.4.Conclusions and Future Challenges -- References -- ch. 19 Detecting and Mapping Invasive Plant Species by Using Hyperspectral Data / Ruiliang Pu -- 19.1.Introduction -- 19.2.Potential of Detecting and Mapping Invasive Plant Species -- 19.2.1.Physiological and Phenological Characteristics of IPS -- 19.2.2.Canopy Structure and Biochemistry of IPS -- 19.3.Techniques and Methods -- 19.3.1.Derivative Analysis -- 19.3.2.Spectral Matching -- 19.3.3.Vegetation Index Analysis -- 19.3.4.Absorption Features Analysis -- 19.3.5.Hyperspectral Transformation -- 19.3.6.Spectral Mixture Analysis -- 19.3.7.Hyperspectral Image Classification -- |
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Contents note continued: 19.4.Considerations -- 19.5.Challenges and Future Directions -- Acknowledgments -- References -- pt. VIII Land Cover Applications -- ch. 20 Hyperspectral Remote Sensing for Forest Management / Valerie Thomas -- 20.1.Introduction -- 20.2.Complexities of Forest Ecosystems -- 20.3.Forest Management Applications of Hyperspectral Remote Sensing -- 20.3.1.Forest Inventories -- 20.3.1.1.Forest Species Mapping -- 20.3.1.2.Forest Biophysical Variables -- 20.3.2.Carbon Exchange -- 20.3.3.Wildfire Fuel -- 20.4.Potential Future Applications -- 20.4.1.The Need for Repeated Global Measurements -- 20.5.Conclusions -- References -- ch. 21 Hyperspectral Remote Sensing of Wetland Vegetation / Amina Rangoonwala -- 21.1.Introduction -- 21.1.1.Benefits of Hyperspectral Data -- 21.1.2.Chapter Outline -- 21.2.Hyperspectral Remote Sensing of Wetland Forests -- 21.2.1.Mangrove Forests -- 21.2.2.Baldcypress Forests -- 21.2.3.Bottomland Hardwood Forests -- |
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Contents note continued: 2.1.Introduction -- 2.2.HSS Concept -- 2.3.HSS Physics, Principle, and Design -- 2.4.HSS Operational Modes -- 2.4.1.Ground-Based HS Imaging -- 2.4.2.Airborne HSS -- 2.4.3.Spaceborne Imaging -- 2.5.LIDAR and HS Data Integration -- 2.6.Summary and Outlook -- References -- ch. 3 Hyperspectral Remote Sensing in Global Change Studies / Narumon Wiangwang -- 3.1.Introduction -- 3.2.Hyperspectral Sensors and Characteristics -- 3.2.1.Spaceborne Systems -- 3.2.2.Airborne Systems -- 3.2.3.Ground-Based Systems -- 3.3.Hyperspectral Remote Sensing Methods -- 3.3.1.Support Vector Machines -- 3.3.2.Kernel Fisher Discriminant Analysis -- 3.3.3.Matched Filtering -- 3.3.4.Libraries Matching Techniques -- 3.3.5.Derivative Spectroscopy -- 3.3.6.Narrowband Spectral Indices -- 3.3.6.1.Normalized Difference Vegetation Index (NDVI) -- 3.3.6.2.Yellowness Index -- 3.3.6.3.Normalized Difference Water Index -- 3.3.6.4.Red Edge Position Determination -- |
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Contents note continued: 21.3.Hyperspectral Remote Sensing of Invasive Plants -- 21.4.Hyperspectral Remote Sensing of Marsh Wetlands -- 21.4.1.Canopy Reflectance and Structure -- 21.4.2.Detecting Subtle Changes -- 21.5.Summary -- 21.5.1.Hyperspectral Imaging Enhancing Broadband Mapping -- 21.5.2.Hyperspectral Mapping of Invasive Plant Occurrences and Broadband Fusion for Regional Risk Assessment -- 21.5.3.Hyperspectral Imaging and Canopy Structure Influences -- 21.5.4.Hyperspectral Imaging for Detecting Subtle and Abnormal Landscape Change -- 21.5.5.Hyperspectral Methods Summary -- 21.6.Future Directions -- Acknowledgments -- References -- ch. 22 Characterization of Soil Properties Using Reflectance Spectroscopy / E. Ben-Dor -- 22.1.Introduction -- 22.2.Soil -- 22.2.1.General Background -- 22.2.2.Soil Compositions -- 22.2.2.1.Solid Phase -- 22.2.2.2.Liquid and Gas Phases -- 22.3.Soil Spectroscopy -- 22.3.1.Definitions and Limitations -- 22.3.2.Spectral Measurements -- |
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Contents note continued: 22.3.3.Spectral Chromophores -- 22.3.3.1.Chemical Chromophores -- 22.4.Mechanisms of Soil-Radiation Interactions -- 22.4.1.Chemical Process -- 22.4.1.1.Clay Minerals -- 22.4.1.2.Carbonates -- 22.4.1.3.Organic Matter -- 22.4.1.4.Water -- 22.4.1.5.Iron -- 22.4.1.6.Soil Salinity -- 22.4.1.7.Chemical Chromophores: Summary -- 22.5.Physical Processes -- 22.5.1.General -- 22.5.2.Models of Radiation Scattering by Soils -- 22.6.Relationship between Soil Chromophore and Properties -- 22.6.1.Qualitative Aspects -- 22.6.2.Quantitative Aspects of Proximal Soil Spectroscopy -- 22.6.2.1.Historical Notes -- 22.6.2.2.Quantitative Applications -- 22.7.Factors Affecting Soil Reflectance -- 22.7.1.General -- 22.7.2.Biosphere -- 22.7.2.1.Higher Vegetation -- 22.7.2.2.Lower Vegetation -- 22.7.3.Lithosphere -- 22.7.3.1.Soil Cover and Crust -- 22.7.3.2.Surface Affected by Fire -- 22.7.3.3.Soil Moisture -- 22.7.4.Atmosphere -- 22.7.4.1.Gases and Aerosols -- |
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Contents note continued: 22.8.Soil Reflectance and Remote Sensing -- 22.8.1.General -- 22.8.2.Imaging Spectroscopy Applications in Soils: Historical Notes -- 22.8.3.Limitations of Imaging Spectroscopy for Soil Mapping -- 22.9.Spectral Proxymation of Soil Using Point and Image Domains: Future Notes -- 22.10.General Summary and Concluding Remarks -- References -- pt. IX Detecting Crop Management, Plant Stress, and Disease -- ch. 23 Analysis of the Effects of Heavy Metals on Vegetation Hyperspectral Reflectance Properties / E. Terrence Slonecker -- 23.1.Introduction -- 23.2.Physiology of Metal Stress in Plants -- 23.3.Basic Spectroscopy of Vegetation -- 23.4.Spectroscopy and Imaging Spectroscopy of Metal Interactions with Plants -- 23.5.Vegetation Indices -- 23.6.Emerging Statistical Methods -- 23.7.Summary and Conclusions -- 23.8.Future Applications -- References -- |
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Contents note continued: 25.2.Applications of Hyperspectral Data in Precision Agriculture -- 25.2.1.Precision Farming Management Considerations -- 25.2.2.Spatial, Spectral, and Temporal Considerations -- 25.2.3.Hyperspectral Narrowband Vegetation Indices -- 25.2.4.Application 1: Soil Management Zoning -- 25.2.5.Application 2: Weed Sensing and Control -- 25.2.6.Application 3: Hyperspectral Imagery for Crop Nitrogen Stress Detection -- 25.2.7.Application 4: Crop Yield Estimation -- 25.2.8.Application 5: Pest and Disease Detection -- 25.3.Conclusions -- References -- pt. X Hyperspectral Data in Global Change Studies -- ch. 26 Hyperspectral Data in Long-Term, Cross-Sensor Continuity Studies / Hiroki Yoshioka -- 26.1.Introduction -- 26.2.Materials -- 26.3.Spectral Compatibility Analyses -- 26.4.Spatial Compatibility Analyses -- 26.5.Algorithm Differences -- 26.6.Angular Effects -- 26.7.Discussions -- Acknowledgments -- References -- |
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Contents note continued: 27.2.3.Moon -- 27.2.3.1.M3 -- 27.2.3.2.Calibration and Analysis Techniques -- 27.2.3.3.Case Studies -- 27.2.4.Mars -- 27.2.4.1.Hyperspectral Instruments -- 27.2.4.2.Calibration and Analysis Techniques -- 27.2.4.3.Case Studies -- 27.2.5.Jupiter -- 27.2.5.1.NIMS -- 27.2.5.2.Calibration and Analysis Techniques -- 27.2.5.3.Case Study -- 27.2.6.Saturn -- 27.2.6.1.VIMS -- 27.2.6.2.Calibration and Analysis Techniques -- 27.2.6.3.Case Study: Ethane in a Titan Polar Lake -- 27.3.Conclusions and Future Challenges -- References -- pt. XII Conclusions and Way Forward -- ch. 28 Hyperspectral Remote Sensing of Vegetation and Agricultural Crops: Knowledge Gain and Knowledge Gap after 40 Years of Research / Alfredo Huete -- 28.1.Critical Needs in Advancing Understanding, Modeling, and Mapping of Vegetation Using Hyperspectral Data -- 28.2.Hughes Phenomenon, Hyperspectral Data Processing Methods and Algorithms, and Overcoming Data Redundancy -- |
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Contents note continued: 28.3.Biophysical and Biochemical Modeling Using Hyperspectral Vegetation Indices and Narrowbands -- 28.4.Cropland Type and Species Discrimination and Classification -- 28.5.Forest Type and Species Discrimination and Classification Using Hyperspectral Data -- 28.6.NPP, Carbon Flux, and Light Use Efficiency Models Using Hyperspectral Data and Indices -- 28.7.Precision Farming Applications -- 28.8.Heavy Metal Effects on Vegetation -- 28.9.Wetland Mapping Using Hyperspectral Data -- 28.10.Pasture Characteristics -- 28.11.Invasive Species Separation from Native Plants -- 28.12.Plant Stress and Red Edge Bands -- 28.13.Plant Litter Mapping -- 28.14.Vegetation Water Content -- 28.15.Soil Characteristics Modeling and Mapping -- 28.16.Cross-Sensor Calibration -- 28.17.Study of Outer Planets and Cross Linkages with Planet Earth: Hyperspectral Approaches -- 28.18.Concluding Thoughts -- Acknowledgments -- References |
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Contents note continued: 3.3.6.5.Crop Chlorophyll Content Prediction -- 3.3.7.Neural Network -- 3.4.Global Change Requirements and Applications -- 3.4.1.Global Change Requirements -- 3.4.2.Global Change Applications -- 3.4.2.1.Water Quantity and Quality -- 3.4.2.2.Carbon Sequestration and Fluxes -- 3.4.2.3.Greenhouse Gas Emissions -- 3.4.2.4.Atmospheric Chemistry -- 3.4.2.5.Vegetation Ecology -- 3.4.2.6.Vegetation Biochemical Properties -- 3.4.2.7.Invasive Plant Species Detection -- 3.4.2.8.Vegetation Health -- 3.5.Hyperspectral Remote Sensing Challenges -- 3.5.1.System Design Challenges -- 3.5.2.Processing and Visualization Challenges -- 3.5.3.Data Volumes and Redundancy -- 3.5.4.Radiometric Calibration -- 3.5.5.Methodological Challenges -- 3.6.Discussion and Future Directions -- References -- pt. III Data Mining, Algorithms, Indices -- ch. 4 Hyperspectral Data Mining / Subodh S. Kulkarni -- 4.1.Introduction -- 4.2.Data Mining Methods -- |
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Contents note continued: 4.3.Feature Selection/Extraction Methods -- 4.3.1.Feature Selection Based on Information Content -- 4.3.1.1.Selection Based on Theoretical Knowledge -- 4.3.1.2.Band Variance -- 4.3.1.3.Information Entropy -- 4.3.2.Projection-Based Methods -- 4.3.2.1.Projection Pursuit -- 4.3.2.2.Principal Component Analysis -- 4.3.2.3.Independent Component Analysis -- 4.3.3.Divergence Measures -- 4.3.3.1.Distance-Based Measures -- 4.3.4.Similarity Measures -- 4.3.4.1.Correlation Coefficient -- 4.3.4.2.Mutual Information Analysis -- 4.3.4.3.Spectral Derivative Analysis -- 4.3.5.Sequential Search Methods -- 4.3.6.Other Methods -- 4.3.6.1.Wavelet Decomposition Method -- 4.4.Information Extraction Methods -- 4.4.1.Statistical Methods -- 4.4.1.1.Multivariate and Partial Least Square Regression -- 4.4.1.2.Discriminant Analysis -- 4.4.2.Unsupervised Classification Methods -- 4.4.2.1.Clustering -- 4.4.2.2.ICA Mixed Model Classification -- 4.4.3.Supervised Classification -- |
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Contents note continued: 4.4.3.1.Spectral Angle Mapping -- 4.4.3.2.Orthogonal Subspace Projection -- 4.4.3.3.Maximum Likelihood Classification -- 4.4.3.4.Artificial Neural Network -- 4.4.3.5.Support Vector Machines -- 1.5.Accuracy Assessment -- 1.6.Applications -- 1.7.Discussions and Future Directions -- Acknowledgment -- References -- ch. 5 Hyperspectral Data Processing Algorithms / Sergio Sanchez -- 5.1.Introduction -- 5.2.Support Vector Machines -- 5.3.Spectral Unmixing of Hyperspectral Data -- 5.3.1.Linear Spectral Unmixing -- 5.3.2.Nonlinear Spectral Unmixing -- 5.4.Experimental Results -- 5.4.1.Analysis of Supervised Hyperspectral Data Classification Using SVMs -- 5.4.2.Analysis of Unsupervised Linear Unmixing of Hyperspectral Data -- 5.4.3.Analysis of Supervised Nonlinear Unmixing of Hyperspectral Data Using MLPs -- 5.5.Conclusions and Future Perspectives -- Acknowledgment -- References -- pt. IV Leaf and Plant Biophysical and Biochemical Properties -- |
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Contents note continued: 7.2.2.Physically Based Model Inversion Method -- 7.2.2.1.Modeling Method for Broadleaf Chlorophyll Content Estimation -- 7.2.2.2.Modeling Method for Needleleaf Chlorophyll Content Estimation -- 7.3.Methods for Estimating Forest Canopy Chlorophyll Content -- 7.3.1.Empirical and Semiempirical Methods -- 7.3.2.Physically Based Modeling Method -- 7.3.2.1.Modeling Method for Closed Forest Canopies -- 7.3.2.2.Modeling Methods for Open Forest Canopies -- 7.4.Conclusions and Applications -- Acknowledgments -- References -- ch. 8 Estimating Leaf Nitrogen Concentration (LNC) of Cereal Crops with Hyperspectral Data / Xia Yao -- 8.1.Introduction -- 8.2.Materials and Methods -- 8.2.1.Experimental Design -- 8.2.2.Measurement and Data Analysis -- 8.2.2.1.Measurement of Canopy Hyperspectral Reflectance -- 8.2.2.2.Determination of Leaf Nitrogen Concentration -- 8.2.2.3.Data Analysis -- 8.3.Results -- 8.3.1.Canopy LNC in Rice and Wheat during Different Growing Periods -- |
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Contents note continued: 8.3.2.Relationships of Canopy LNC with Hyperspectral Reflectance -- 8.3.3.Relationships of Canopy LNC with Spectral Indices: SAVI during Early Growing Periods -- 8.3.4.Relationships of Canopy LNC with Spectral Indices: DVI, RVI, and NDVI during Mid-Late Growing Period -- 8.3.5.Relationships of Canopy LNC with Spectral Indices: SAVI and RVI during Whole Growing Period -- 8.3.6.Common Sensitive Wavelength Ranges of Canopy LNC -- 8.3.7.Quantitative Models for Estimating LNC -- 8.3.7.1.Common Sensitive Wavelength -- 8.3.7.2.Center Bands with Optimum Bandwidths -- 8.3.8.Performance of Spectral Indices in Previous Studies -- 8.4.Discussion -- 8.4.1.Selection of Spectral Indices during Different Growing Periods -- 8.4.2.Common Sensitive Wavelength Ranges of Canopy LNC -- 8.4.3.Accuracy and Universality of Monitoring Models for Canopy LNC -- 8.5.Conclusions -- References -- ch. 9 Characterization on Pastures Using Field and Imaging Spectrometers / Izaya Numata -- |
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Contents note continued: 9.1.Introduction -- 9.2.Field and Imaging Spectrometers for Pasture Characterization -- 9.3.Controlling Factors for Biophysical and Biochemical Characterstics of Pasture -- 9.3.1.Structure -- 9.3.2.Foliar Chemical Composition -- 9.3.3.Nonphotosynthetic Vegetation and Background Effects -- 9.4.Hyperspectral Approaches for Pasture Characterization -- 9.4.1.Vegetation Indices -- 9.4.2.Red Edge -- 9.4.3.Normalized Absorption Features by Continuum Removal -- 9.4.4.Spectral Mixture Analysis -- 9.4.5.Statistical Methods -- 9.5.Application of Hyperspectral Remote Sensing for Pasture Estimation -- 9.5.1.Biomass -- 9.5.2.Leaf Area Index -- 9.5.3.Nutrients -- 9.5.4.Degradation Analysis -- 9.5.5.Species Discrimination -- 9.6.Conclusions -- References -- ch. 10 Optical Remote Sensing of Vegetation Water Content / Panigada Cinzia -- 10.1.Introduction -- 10.2.Laboratory and Field Measurements of Vegetation Water Content -- |
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Contents note continued: ch. 24 Hyperspectral Narrowbands and Their Indices on Assessing Nitrogen Contents of Cotton Crop Applications / Chengcheng Gang -- 24.1.Introduction -- 24.2.Materials and Methods -- 24.2.1.Experiment Designs and Treatments -- 24.2.2.Observed Dates -- 24.2.3.Canopy Hyperspectral Reflectance Measurements -- 24.2.4.Biomass Measurements -- 24.2.5.Agronomic Variable Measurements -- 24.2.6.Data Process and Analysis -- 24.3.Results and Analysis -- 24.3.1.Biomass Analysis under Different Nitrogen Treatments -- 24.3.2.Difference of Canopy Spectral Reflectance under Different Nitrogen Treatments -- 24.3.3.Changes of Normalized Difference Spectra Characteristic -- 24.3.4.Multiple Variable Comparison Analysis under Different Treatments -- 24.4.Discussions -- 24.5.Conclusions -- References -- ch. 25 Using Hyperspectral Data in Precision Farming Applications / Thomas E. Cleveland -- 25.1.Introduction -- 25.1.1.Precision Farming -- 25.1.2.Hyperspectral Data -- |
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Contents note continued: ch. 6 Nondestructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids, and Anthocyanins) Contents: Evaluating a Semianalytical Three-Band Model / Anatoly A. Gitelson -- 6.1.Introduction -- 6.2.Background -- 6.2.1.Chlorophylls -- 6.2.2.Carotenoids -- 6.2.3.Anthocyanins -- 6.3.Spectral Features of Leaf Reflectance -- 6.4.Conceptual Three-Band Model -- 6.5.Estimation of Leaf Pigment Content -- 6.5.1.Chlorophyll -- 6.5.1.1.Spectral Bands Tuning -- 6.5.1.2.Model Performance -- 6.5.2.Carotenoids -- 6.5.2.1.Spectral Bands Tuning -- 6.5.2.2.Model Performance -- 6.5.3.Anthocyanins -- 6.5.3.1.Spectral Bands Tuning -- 6.5.3.2.Model Performance -- 6.6.Conclusions and Future Prospects -- Acknowledgments -- References -- ch. 7 Forest Leaf Chlorophyll Study Using Hyperspectral Remote Sensing / Yongqin Zhang -- 7.1.Introduction -- 7.2.Methods for Estimating Leaf Chlorophyll Content -- 7.2.1.Empirical Method for Leaf Chlorophyll Content Estimation -- |
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Contents note continued: pt. XI Hyperspectral Remote Sensing of Outer Planets -- ch. 27 Hyperspectral Analysis of Rocky Surfaces on the Earth and Other Planetary Bodies / Paul E. Geissler -- 27.1.Introduction -- 27.1.1.Planetary Bodies and Hyperspectral Instruments -- 27.1.1.1.Earth -- 27.1.1.2.Mercury -- 27.1.1.3.Moon -- 27.1.1.4.Mars -- 27.1.1.5.Jupiter -- 27.1.1.6.Saturn -- 27.1.1.7.Future/Upcoming Instruments -- 27.1.1.8.Synergy with Remote Measurements outside the VNIR/SWIR Spectral Range -- 27.1.2.Overview of Hyperspectral Analysis Techniques -- 27.1.2.1.Radiometric Calibration -- 27.1.2.2.Atmospheric Correction/Compensation -- 27.1.2.3.Spectral Indices -- 27.1.2.4.Spectral Mixture Modeling -- 27.2.Hyperspectral Missions and Case Studies -- 27.2.1.Earth -- 27.2.1.1.AVIRIS and Hyperion -- 27.2.1.2.Calibration and Analysis Techniques -- 27.2.1.3.Case Study -- 27.2.2.Mercury -- 27.2.2.1.MASCS -- 27.2.2.2.Calibration and Analysis Techniques -- 27.2.2.3.Case Study -- |
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Machine generated contents note: pt. I Introduction and Overview -- ch. 1 Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands / Alfredo Huete -- 1.1.Introduction and Rationale -- 1.2.Hyperspectral Remote Sensing of Vegetation and Agricultural Crops -- 1.3.Hyperspectral Data Composition for Study of Vegetation and Agricultural Crops -- 1.4.Methods and Approaches of Hyperspectral Data Analysis for Vegetation and Agricultural Crops -- 1.4.1.Lambda (λ1) by Lambda (λ2) Plots -- 1.4.2.Principal Component Analysis -- 1.4.3.Other Hyperspectral Data Mining Algorithms -- 1.5.Optimal Hyperspectral Narrowbands: Hyperspectral Vegetation Indices to Study Vegetation and Crop Biophysical and Biochemical Properties -- 1.5.1.Hyperspectral Two Band Vegetation Index -- 1.5.2.Hyperspectral Multiple-Band Models -- 1.5.3.Hyperspectral Derivative Greenness Vegetation Indices -- 1.5.4.Hyperspectral Hybrid Vegetation Indices -- |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Crops -- Remote sensing.
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Multispectral imaging.
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Plants -- Remote sensing.
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Vegetation monitoring.
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| Author |
Huete, Alfredo.
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Lyon, J. G. (John G.)
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Thenkabail, Prasad Srinivasa, 1958-
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| LC no. |
2011036043 |
| ISBN |
9781439845370 (hardcover : alk. paper) |
