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Remote sensing of climate /

Remote Sensing of Climate provides cutting-edge techniques in remote sensing of climate variability and the environment.The book focuses on methods and data analytics, advancements in scientific research in the field, and further proffers recommendations on the possible knowledge gaps and future sci...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Dube, Timothy (Editor)
Format: eBook
Language:English
Published: Amsterdam ; Cambridge, MA : Elsevier, [2024]
Subjects:
Climatology > Remote sensing.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Remote Sensing of Climate
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Chapter 1: Remote sensing of climate variability: An introduction
  • 1. Introduction
  • 2. Drought
  • 3. Drivers of climate variability and climate change
  • 3.1. El Niño and La Nina
  • 3.2. Solar activity
  • 3.3. Volcanic eruptions
  • 3.4. Anthropogenic activities
  • 4. Evolution of climate science
  • 5. Evidence of climate change and variability
  • 6. Remote sensing of climate change and variability
  • 7. Conclusion
  • References
  • Chapter 2: Remote sensing technological advancements and applications in climate variability analysis studies
  • 1. Introduction
  • 2. Overview of climate variability analysis and their associated impacts
  • 3. Remote sensing data applications and associated challenges for climate variability analysis
  • 4. Technological advancements in remote sensing tools and application in climate variability studies
  • 5. Conclusions
  • References
  • Chapter 3: Big data and analytical algorithms for climate variability
  • 1. Introduction
  • 2. Trends in big data analytics for climate change
  • 3. Types of big data analytics
  • 3.1. Descriptive analytics
  • 3.2. Diagnostic analytics
  • 3.3. Prescriptive analytics
  • 3.4. Cognitive analytics
  • 3.5. Behavioral analytics
  • 3.6. Quantitative analytics
  • 4. Types of analytical algorithms
  • 4.1. Regression algorithms
  • 4.2. Classification algorithms
  • 4.3. Clustering algorithms
  • 5. Applications of big data analytics in climate change
  • 5.1. Predictive modeling for extreme weather events
  • 5.2. Energy efficiency and intelligence
  • 5.3. Smart farming, agriculture, and forestry
  • 5.4. Sustainable urban planning and infrastructure
  • 5.5. Natural disaster and disease assessment
  • 6. Conclusion
  • References
  • Chapter 4: Climate variability and agriculture
  • 1. Introduction
  • 2. Monitoring climate variability and its impacts on agriculture
  • 2.1. Monitoring climate variables
  • 2.2. Approaches for monitoring droughts
  • 2.3. Remote sensing applications in drought monitoring
  • 3. Climate change adaptation strategies for agricultural systems
  • 3.1. Types of adaptation strategies in agriculture
  • 3.2. Opportunities and barriers for adapting to climate change in agriculture
  • 4. Climate change mitigation in the agriculture sector
  • 4.1. Cropland emission mitigation
  • 4.2. Enhancing soil carbon sequestration
  • 4.3. Livestock management
  • 5. Case studies showcasing mitigation strategies in agriculture
  • 6. Policy frameworks for climate change and agriculture
  • 7. Gaps and opportunities for policy and institutional support for climate change adaptation and mitigation in agriculture
  • 8. Conclusion
  • References
  • Chapter 5: Climate variability and rangeland ecosystems
  • 1. Introduction
  • 2. Global rangeland spatial extent