Sustainable development perspectives in Earth observation /

Sustainable Development Perspectives in Earth Observation offers expert insight into the latest progress made in terrestrial, oceanic, and atmospheric processes, and their interlinkage in the face of changing climate using Earth observation. By addressing the use of advanced datasets, measurement te...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Behera, Mukunda (Editor), Behera, Swadhin K. (Editor), Barik, Saroj Kanta (Editor), Mohapatra, Mrutyunjaya (Editor), Mohapatra, Trilochan (Editor)
Format:	eBook
Language:	English
Published:	Amsterdam : Elsevier, 2025.
Series:	Earth observation
Subjects:	Earth sciences > Remote sensing. Sciences de la terre > Télédétection. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Sustainable Development Perspectives in Earth Observation
Copyright Page
Contents
List of contributors
About the editors
Preface
1 Inter-linkage among climate, terrestrial and marine systems: sustainable development perspectives
1.1 Agriculture, water, and climate change
1.2 Forest and land cover systems and climate change
1.3 Ocean-atmosphere dynamics and climate change
1.4 Conclusion
References
A. Agriculture, water and climate change
2 Geospatial applications for crop assessment
Abbreviations
2.1 Introduction
2.1.1 Role of geospatial technologies in crop assessment
2.2 Spectral response of crop/vegetation
2.2.1 Crop discrimination using spectral observations
2.2.2 Vegetation indices for crop monitoring
2.3 Satellite based crop inventory
2.3.1 Crop classification techniques
2.3.2 Crop classification accuracy: precision in identifying crop types
2.4 Crop parameter retrieval from satellite data
2.4.1 Modeling approaches to retrieve crop parameter
2.4.1.1 Empirical and semi-empirical modeling of crop parameter retrieval
2.4.1.2 Canopy reflectance model
2.4.2 Inversion of canopy reflectance model
2.5 Crop phenology and cropping intensity
2.5.1 Time series normalized difference vegetation index and crop phenology metrics
2.5.2 Time-series normalized difference vegetation index data and cropping season
2.5.3 Cropping intensity
2.6 Crop yield modeling
2.6.1 Statistical approach
2.6.1.1 Linear regression model
2.6.1.2 Time series model
2.6.1.3 Partial least square regression
2.6.2 Crop growth simulation model
2.6.3 Radiation/light use efficiency approach
2.6.4 Machine learning approach
2.7 Geospatial support to crop insurance
2.7.1 Rate making
2.7.2 Crop mapping and monitoring.
2.7.3 Smart sampling for yield measurements
2.7.4 Challenges in applying remote sensing technology for crop insurance
2.8 Conclusions and future perspective
References
3 Agroforestry for sustainable livelihoods: role of geo-informatics, ICT, and citizen science
3.1 Introduction
3.1.1 What is agroforestry?
3.1.2 Ecological benefits of agroforestry
3.1.3 Socioeconomic benefits of agroforestry
3.2 Agroforestry in India
3.3 Type of agroforestry practices
3.4 Use of geo-informatics and data analytics
3.4.1 Use of geo-informatics in agroforestry
3.4.2 Artificial intelligence-based, web-based platform, mobile, and chatbot application in agroforestry systems
3.4.3 Challenges in agroforestry monitoring
3.5 Case studies
3.5.1 Aboveground biomass in agroforestry systems using remote sensing and machine learning
3.5.2 Agroforestry implementation planning using geo-informatics and machine learning
3.6 Challenges and way forward
References
4 Sustaining floriculture and floral fragrance in a changing climate
4.1 Introduction
4.1.1 Biochemical pathways of fragrance production in flowers
4.1.2 Correlation between environmental factors and floral fragrance variation
4.1.3 Diversity in floral scent and its climate linkages
4.1.4 Insights from prior research on floral volatiles
4.1.5 Impact of climate change on flowers
4.1.6 Impact of changing climate on fragrance quality and quantity
4.1.7 Linking fragrance levels to ecological niches
4.1.8 Chemical composition vis-à-vis sensory detection level
4.1.9 ENM based on fragrance level
4.1.10 Species distribution and ecological niche modeling
4.2 Materials and methods
4.2.1 Selection of flower species
4.2.2 Occurrence data
4.2.3 Climatic variables
4.2.4 Ecological niche model parameterization.
4.2.5 Climatic niche characterization
4.2.6 Distribution of potential areas
4.2.7 Distribution under different climate scenarios
4.2.8 Identifying drought prone areas of India
4.3 Results
4.3.1 Model performance
4.3.2 Characterization of climate niche
4.3.3 Distribution of potential areas of cultivation in India
4.3.4 Drought prone areas in India vis-à-vis cultivation of fragrant flowers
4.4 Discussion
4.4.1 Climatic niche and geographic distribution
4.4.2 Adaptation and survival strategies in the context of climatic suitability
4.4.3 Potential applications in floral variety conservation and horticulture
4.4.4 Limitations of the study and future prospects
References
5 Crop insurance using geo-information: a strategy for climate change mitigation
5.1 Introduction
5.2 Need of insurance
5.3 Types of crop insurance
5.4 Remote sensing and crop insurance
5.5 Study area
5.6 Materials and methods
5.7 Preprocessing of synthetic aperture radar data
5.8 Extraction of water pixels
5.9 Crop damage assessment
5.10 GIS model generation for crop risk analysis
5.11 Results and discussion
5.11.1 Cropland identification
5.11.2 Crop vulnerability assessment
5.11.3 Crop damage assessment
5.11.4 Crop insurance model
5.12 Conclusions
Acknowledgments
References
6 Terrestrial water cycle in future climate over India
6.1 Introduction
6.2 Materials and methods
6.2.1 Study region
6.2.2 Data used
6.2.2.1 Satellite record of actual evapotranspiration
6.2.2.2 Ground observations
6.2.2.3 Shared socioeconomic pathways
6.2.2.4 Climate model simulations
6.2.2.4.1 Potential evapotranspiration
6.2.3 Methodology
6.2.3.1 Climate data operators
6.2.3.2 Extraction of data
6.2.3.3 Model ensemble mean time series
6.2.3.4 Taylor diagram.
6.2.3.5 Biogeographic zones
6.3 Results and discussion
6.3.1 Performance of CMIP6 model simulations
6.3.1.1 Potential evapotranspiration
6.3.1.2 Precipitation
6.3.2 Future projections
6.3.2.1 Trends in evapotranspiration and precipitation
6.3.2.1.1 Trend in evapotranspiration
6.3.2.1.2 Trend in precipitation
6.3.2.2 Intraannual variabilities
6.3.2.3 Water budget
6.4 Conclusions
Acknowledgments
Author contribution
Funding
Data availability
References
7 Impacts of ocean atmospheric phenomena on hydroclimate extremes
7.1 Introduction
7.1.1 El Niño Southern Oscillation and its indices
7.1.1.1 Southern Oscillation Index
7.1.1.2 Oceanic Niño Index
7.1.1.3 Equatorial Southern Oscillation Index
7.1.1.4 Niño 1+2, Niño 3, Niño 3.4, and Niño 4 indices
7.1.1.5 Trans-Niño Index
7.1.1.6 Multivariate ENSO Index
7.1.2 Pacific decadal oscillation
7.1.3 North Atlantic oscillation
7.1.4 Atlantic multidecadal oscillations
7.1.5 Madden Julian Oscillation
7.1.6 Indian Ocean Dipole
7.1.7 Equatorial Indian Ocean Oscillation
7.1.7.1 EQWIN index
7.2 Impacts of OA phenomena on hydroclimatic extremes
7.2.1 Precipitation
7.2.2 Temperature
7.2.3 Heat waves
7.2.4 Cold extremes
7.2.5 Drought
7.2.6 Compound extremes
7.3 Conclusions
References
8 Geospatial technology for coastal water resources management
8.1 Introduction
8.1.1 Importance of coastal water resources management
8.1.2 Challenges in coastal water resources management
8.1.3 Overview of traditional ecological knowledge and modern geospatial technology
8.2 Current coastal restoration management
8.2.1 Existing strategies and practices
8.2.2 Limitations of current approaches
8.3 The role of local knowledge in coastal restoration.
8.3.1 Definition and essence of traditional ecological knowledge
8.3.2 Values of integrating traditional ecological knowledge in coastal management
8.4 Modern geospatial technology in coastal management
8.4.1 Overview of geospatial technologies
8.4.2 Applications in coastal water resources management
8.5 Rationale for data integration to support decision making
8.5.1 Benefits of integrating traditional ecological knowledge and scientific knowledge
8.5.2 Challenges and considerations in data integration
8.6 Methodological framework
8.6.1 Data collection and analysis techniques
8.6.2 Integrating traditional ecological knowledge and geospatial technology
8.6.3 Analyzing cases of integration specific to costal problems
8.7 Case studies and examples
8.7.1 Specific instances of integration in various regions
8.7.2 Lessons learned and best practices
8.8 Challenges and limitations
8.9 Future directions and recommendations
8.10 Summary and conclusions
References
9 Comprehensive insights into dam break analysis: a mini review
9.1 Introduction
9.1.1 General
9.1.2 Impacts of dam breach failures
9.2 Historical studies on dam break analysis
9.3 Literature review
9.3.1 General
9.3.2 HEC-RAS
9.3.3 DAMBRK
9.3.4 FLDWAV
9.3.5 MIKE 11
9.4 Some important models of dam break analysis
9.4.1 Dam break forecasting model (DAMBRK)
9.4.2 Hydrologic engineering center-river analysis system
9.4.3 MIKE 11
9.4.4 FLDWAV
9.4.5 Other dam break models
9.5 Integration of remote sensing techniques in dam break analysis
9.5.1 Monitoring surface water change due to dam break using satellite remote sensing
9.6 Dam breach parameters
9.6.1 Dam breach parameters calculation
9.6.1.1 Froehlich (1995)
9.6.1.2 Froehlich (2008).