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Bio-optical Modeling and Remote Sensing of Inland Waters [Pehme köide]

Edited by (Indiana University - Purdue University Indianapolis (IUPUI), IN, USA), Edited by (School of Natural Resources, University of Nebraska-Lincoln, NE, USA), Edited by (Department of Geography, University of Georgia, GA, USA)
  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 229x152 mm, kaal: 910 g
  • Ilmumisaeg: 03-May-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128046449
  • ISBN-13: 9780128046449
Teised raamatud teemal:
Bio-optical Modeling and Remote Sensing of Inland WatersSuurem pilt
  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 229x152 mm, kaal: 910 g
  • Ilmumisaeg: 03-May-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128046449
  • ISBN-13: 9780128046449
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128046449.html
Märksõnad:

Bio-Optical Modeling and Remote Sensing of Inland Waters presents the latest developments, state-of-the-art, and future perspectives of bio-optical modeling for each optically active component of inland waters, providing a broad range of applications of water quality monitoring using remote sensing. Rather than discussing optical radiometry theories, the authors explore the applications of these theories to inland aquatic environments. The book not only covers applications, but also discusses new possibilities, making the bio-optical theories operational, a concept that is of great interest to both government and private sector organizations. Bio-Optical Modeling and Remote Sensing of Inland Waters addresses not only the physical theory that makes bio-optical modeling possible, but also theimplementation and applications of bio-optical modeling in inland waters Early chapters introduce the concepts of bio-optical modeling and the classification of bio-optical models and satellite capabilities both in existence and in development. Later chapters target specific optically active components (OACs) for inland waters and present the current status and future direction of bio-optical modeling for the OACs. Concluding sections provide an overview of a governance strategy for global monitoring of inland waters based on earth observation and bio-optical modeling.

  • Presents comprehensive chapters that each target a different optically active component of inland waters
  • Contains contributions from respected and active professionals in the field
  • Presents applications of bio-optical modeling theories that are applicable to researchers, professionals, and government agencies

Muu info

An overview and analysis of the current status and future of remote sensing in monitoring inland water quality from space
List of Contributors
ix
Abbreviations and Notations xi
1 Remote Sensing of Inland Waters: Background and Current State-of-the-Art
Igor Ogashawara
Deepak R. Mishra
Anatoly A. Gitelson
1.1 Inland Waters
1(2)
1.2 Remote Sensing of Inland Waters
3(2)
1.3 Fundamental Bio-optical Properties
5(6)
1.4 Bio-optical Models
11(7)
1.4.1 Classification of Bio-optical Models
12(2)
1.4.2 Performance of Bio-optical Models
14(4)
1.5 Book Content
18(7)
References
21(4)
2 Radiative Transfer Theory for Inland Waters
Peter Gege
2.1 Introduction
25(2)
2.2 Basic Principles
27(12)
2.2.1 Interaction of Light with Matter
27(3)
2.2.2 Radiometric Quantities
30(1)
2.2.3 Radiative Transfer Equation
31(2)
2.2.4 Inherent Optical Properties
33(2)
2.2.5 From Microscopic to Macroscopic Material Parameters
35(4)
2.3 Bio-optical Models
39(11)
2.3.1 Water Composition
39(6)
2.3.2 Apparent Optical Properties
45(2)
2.3.3 AOP Models
47(3)
2.4 Light Field Models
50(9)
2.4.1 Incident Radiation
51(2)
2.4.2 Water Surface Effects
53(1)
2.4.3 Underwater Light Field
54(3)
2.4.4 Fluorescence
57(2)
2.4.5 Polarization
59(1)
2.5 Conclusions
59(10)
Acknowledgments
61(1)
References
61(8)
3 Atmospheric Correction for Inland Waters
Wesley J. Moses
Sindy Sterckx
Marcos J. Montes
Liesbeth De Keukelaere
Els Knaeps
3.1 Introduction
69(1)
3.2 Challenges
70(8)
3.2.1 Challenges Due to Physical and Bio-optical Properties
72(3)
3.2.2 Challenges Due to Difficulties in Atmospheric Modeling
75(3)
3.3 Existing Algorithms
78(13)
3.3.1 Atmospheric Correction Algorithms
78(10)
3.3.2 Adjacency Correction Algorithms
88(1)
3.3.3 Case Study: Combined Atmospheric and Adjacency Correction
89(2)
3.4 Conclusion
91(10)
Acknowledgments
94(1)
References
94(7)
4 Bio-optical Modeling of Colored Dissolved Organic Matter
Tiit Kutser
Sampsa Koponen
Kari Y. Kallio
Tonio Fincke
Birgot Paavel
4.1 Carbon in Inland Waters
101(2)
4.2 Optical Properties of CDOM
103(3)
4.3 Remote Sensing of CDOM
106(3)
4.4 CDOM Retrieval With Bio-optical Models
109(12)
4.5 Final Considerations
121(8)
References
122(7)
5 Bio-optical Modeling of Total Suspended Solids
Claudia Giardino
Mariano Bresciani
Federica Braga
Liana Cazzaniga
Liesbeth De Keukelaere
Els Knaeps
Vittorio E. Brando
5.1 Introduction
129(1)
5.2 Optical Properties of Particles
130(8)
5.2.1 Relationship between IOPs and TSS
132(3)
5.2.2 Remote Sensing Algorithms for TSS
135(3)
5.3 Case Studies
138(10)
5.3.1 MERIS time-series---Lake Garda
139(2)
5.3.2 Airborne Imaging Spectrometry---Mantua lakes
141(4)
5.3.3 Multitemporal OLI Data---Po River
145(3)
5.4 Conclusions
148(2)
Acknowledgments
150(1)
References
150(6)
Further Reading
156(1)
6 Bio-optical Modeling of Phytoplankton Chlorophyll-a
Mark W. Matthews
6.1 Introduction
157(3)
6.2 Chlorophylls: The Fundamental Measure of Phytoplankton Biomass and Production
160(2)
6.3 Optical Pathways to Estimate Phytoplankton Chlorophyll-a
162(19)
6.3.1 Phytoplankton Absorption
163(6)
6.3.2 Phytoplankton Fluorescence
169(5)
6.3.3 Phytoplankton Scattering
174(7)
6.4 Conclusion
181(8)
Acknowledgments
182(1)
References
182(7)
7 Bio-optical Modeling of Sun-Induced Chlorophyll-a Fluorescence
Alexander A. Gilerson
Yannick Huot
7.1 Introduction, Basic Concepts, and Current Knowledge
189(3)
7.2 Modeling of Reflectance Spectra with Fluorescence
192(7)
7.2.1 Remote Sensing Reflectance
192(1)
7.2.2 Elastic Reflectance
193(1)
7.2.3 Fluorescence Reflectance
193(2)
7.2.4 Inherent Optical Properties and Attenuation Coefficients
195(4)
7.3 Relationships Between the Fluorescence Magnitude and the Concentrations of Chlorophyll and Other Water Constituents
199(8)
7.3.1 Simplified Fluorescence Model---Theoretical Considerations
199(4)
7.3.2 Simplified Fluorescence Model---Comparison with Field Measurements
203(4)
7.4 Retrieval of the Fluorescence Component from Reflectance Spectra
207(18)
7.4.1 Combined Retrieval of the Fluorescence and Water Constituents
207(4)
7.4.2 Fluorescence Line Height Algorithms and Their Limitations
211(5)
7.4.3 Performance of Fluorescence Algorithms with Satellite Data
216(6)
7.4.4 Retrieval of the Fluorescence Component from Polarimetric Hyperspectral Observations
222(2)
7.4.5 Application of SICF to the Detection of Algal Blooms
224(1)
7.5 Summary
225(8)
Acknowledgments
226(1)
References
226(7)
8 Bio-optical Modeling of Phycocyanin
Linhai Li
Kaishan Song
8.1 Introduction
233(2)
8.2 Theoretical Basis for Remote Sensing of Phycocyanin
235(2)
8.3 Literature Review of Remote Sensing Algorithms of Phycocyanin
237(8)
8.3.1 Empirical Algorithms
238(1)
8.3.2 Semi-empirical Algorithms
238(3)
8.3.3 Semi-analytical Algorithms
241(4)
8.4 Evaluation of Representative Algorithms Using a Large Field Dataset
245(9)
8.4.1 Description of the Field Dataset
245(2)
8.4.2 Evaluation of the Estimation Accuracy
247(2)
8.4.3 Evaluation of a Band Ratio Algorithm
249(1)
8.4.4 Evaluation of Semi-analytical Models
250(1)
8.4.5 Evaluation of Two Baseline Algorithms Using AOP and IOP
251(1)
8.4.6 Discussion of Factors Influencing the Remote Estimation of Phycocyanin
252(2)
8.5 Mapping PC Using Airborne Images
254(3)
8.6 Summary and Future Work
257(6)
Acknowledgements
258(1)
References
258(5)
9 Bio-optical Modeling and Remote Sensing of Aquatic Macrophytes
Tim J. Malthus
9.1 Introduction
263(2)
9.2 Spectral Characteristics of Aquatic Macrophytes
265(9)
9.3 Application of Remote Sensing Systems
274(9)
9.4 Discrimination and Classification
283(2)
9.5 Determination of Macrophyte Biophysical Properties
285(4)
9.5.1 Use of Indices
286(3)
9.6 Bio-optical Modeling of Aquatic Macrophytes
289(5)
9.7 Discussion and Priorities for Further Research
294(6)
9.7.1 In Situ Measurement of Spectral Signatures
295(1)
9.7.2 Signature Analysis
296(1)
9.7.3 Bio-optical Modeling
297(1)
9.7.4 Relationships with Biophysical Properties
297(1)
9.7.5 Inversion Algorithms
298(1)
9.7.6 Assessment of Remote Sensing Platforms
298(1)
9.7.7 Regional Assessment/Global Monitoring
299(1)
9.7.8 Role in Management
300(1)
References 300(9)
Index 309
Mishras research expertise is in the area of application of geospatial science to monitor environment particularly vegetation and water resources in the southeastern U.S. His research on radiative transfer model and water column correction procedure to map underwater coral reef habitats from space have been citied in numerous journal articles and replicated by many researchers and resource managers. His research on predicting toxic algal growth (cyanobacteria) and phytoplankton in inland waters has attracted attention of agencies such as GA Power, EPA, and Center for Disease Control. Mishra currently serves on the Editorial board of two international journals, GIScience and Remote Sensing and MDPIs Remote Sensing, and is an active reviewer for 34 international journals. He edited a special issue for GIScience and Remote Sensing during 2013 entitled Coastal Remote Sensing” which was published in early 2014. Currently, he is co-editing two special issues entitled Remote Sensing of Water resources” and Remote sensing in coastal environments” for MDPIs Remote Sensing. Geographer by degree and limnologist by heart Ogashawara found through remote sensing the opportunity to connect these two areas. Since high school he learned about tropical limnology while researching at the International Institute of Ecology, Brazil. During college he worked on relating weather types and cyanobacteria blooms and as an MSc student at the Brazilian National Institute for Space Research, he attempted to use bio-optical modeling to monitor water quality in tropical hydroelectric reservoirs. Currently he is a PhD student at Indiana University Purdue University at Indianapolis, working with bio-optical modeling to identify and predict cyanobacteria in inland waters. Gitelsons expertise is in the area of remote sensing of aquatic and terrestrial environments. His research on radiative transfer in terrestrial vegetation and water resulted in development models for estimating water constituent concentrations and pigment contents in vegetation. They have been citied in numerous journal articles and used widely around the world. He has published more than 150 papers in peer-reviewed journals (http://calmit.unl.edu/people/agitelson2). Gitelson serves on the Editorial board of several journals, Remote Sensing of Environment and Remote Sensing are among them.