Vegetation dynamics and crop stress : an earth observation perspective /

This book, 'Vegetation Dynamics and Crop Stress,' edited by Dipanwita Dutta, Arnab Kundu, and N.R. Patel, explores the use of remote sensing and artificial intelligence technologies to monitor and analyze vegetation dynamics and crop stress. It covers various methodologies and applications...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	[London] : Academic Press, 2024.
Subjects:	Crops > Effect of stress on. Cultures > Effets du stress sur. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Vegetation Dynamics and Crop Stress
Copyright Page
Contents
List of contributors
1 Advances in earth observation and artificial intelligence in monitoring vegetation dynamics of dryland agroecosystems
1.1 Introduction
1.2 Artificial intelligence methodologies for agriculture
1.2.1 Institutional factors
1.2.2 Market factors
1.2.3 Technological considerations
1.2.4 Stakeholder perception
1.2.5 Machine learning and cloud processing
1.3 Challenges and opportunities
1.4 Conclusion
References
2 Monitoring land use dynamics and diversity of flora in Uttara Kannada district, Karnataka, Central Western Ghats, India
2.1 Introduction
2.2 Materials and methods
2.2.1 Study area
2.2.2 Methods
2.2.2.1 Quantification of land use changes
2.2.2.2 Assessment of flora diversity
2.2.3 Results and discussion
2.2.3.1 Land use changes assessment in Uttara Kannada
2.2.3.2 Flora diversity in Uttara Kannada district
Richness of flora
2.2.3.3 Biomass and carbon sequestration in Uttara Kannada forests
2.2.3.4 Tree species richness, diversity, and dominance
2.2.4 Strategies advocated for the conservation and enrichment of biodiversity
2.3 Conclusion
Acknowledgments
References
3 Assessment of southern Aravalli's vegetation dynamics under climate change using the Google Earth Engine platform
3.1 Introduction
3.2 Materials and methods
3.2.1 Study area
3.2.2 Datasets
3.2.2.1 Platform: Google Earth Engine
3.2.3 Methodology
3.2.3.1 Preprocessing
3.2.3.2 Land use land change and climatic parameters
3.3 Results and discussion
3.3.1 Temperature
3.3.1.1 Minimum temperature
3.3.1.2 Maximum temperature
3.3.2 Actual evapotranspiration
3.3.3 Rainfall
3.3.4 Enhanced vegetation index.
3.3.5 Climatic variables correlation matrix (2000-20)
3.3.6 Land use land cover
3.4 Conclusion
References
4 Spatial patterns of forest fragmentation and human-elephant conflicts in south-western West Bengal, India: a multitempora...
4.1 Introduction
4.2 Materials and methods
4.2.1 Study area
4.2.2 Data sources
4.2.3 Data preprocessing
4.2.4 Classification of satellite datasets
4.2.5 Application of landscape metrics
4.2.6 Detection of landscape changes
4.2.7 Perception appraisal of local villagers
4.3 Results
4.3.1 Changing scenarios of land use/land cover
4.3.2 Dynamics of landscape metrics
4.3.2.1 Patch level metrics of pre- and postmonsoonal phases
4.3.2.2 Class-level metrics of pre- and postmonsoonal phases
4.3.3 Landscape dynamics as perceived by the villagers
4.4 Discussion
4.4.1 Forest fragmentation as a driver of human-elephant conflicts
4.4.2 Socio-ecological effects of human-elephant conflicts
4.5 Conclusions
References
5 Advancement in multisensor remote sensing studies for assessing crop health
5.1 Introduction
5.2 Remote sensing for crop health
5.2.1 Opportunities
5.3 Conclusion
References
6 Land use and land cover classification and change detection of Kaptai National Park of Bangladesh using multitemporal rem...
6.1 Introduction
6.2 Materials and methods
6.2.1 Study area
6.2.2 Satellite data collection and analysis
6.2.2.1 Image classification and accuracy assessment
6.2.2.2 Accuracy assessment
6.3 Results and discussion
6.4 Conclusion
References
7 Interannual and seasonal dynamics of NDVI in correlation with precipitation and temperature in Delhi NCR
7.1 Introduction
7.2 Study area
7.3 Materials and methods
7.3.1 Data sources and preprocessing
7.3.1.1 NDVI from MODIS terra for vegetation dynamics.
7.3.1.2 Precipitation and temperature data source
7.3.2 Methodology
7.4 Results and discussion
7.4.1 Winter season
7.4.2 Spring season
7.4.3 Summer season
7.4.4 Monsoon season
7.4.5 Autumn season
7.4.6 Annual
7.5 Conclusions
Acknowledgments
References
8 Vegetation dynamics and its response to climate change at Bhitarkanika mangrove forest, Odisha, east coast of India
8.1 Introduction
8.2 Study area
8.3 Data and methods
8.3.1 Normalized Difference Vegetation Index-vegetation analysis
8.3.2 Climatology of the study area
8.4 Results and discussion
8.4.1 Impacts of cyclonic storms
8.4.2 Trends of temperature, rainfall, NDVI, and their relationship with SPEI
8.4.3 Land use land cover change (1977-2020)
8.5 Conclusion
Acknowledgment
References
9 Monitoring yearly forest cover dynamics in the Indian Sundarban region during 2000-20: a geospatial approach
9.1 Introduction
9.2 Study area
9.3 Methodology
9.3.1 Land use land cover classifications
9.3.2 Conversion to TOA radiance
9.3.3 Normalized difference vegetation index
9.3.4 Advanced vegetation index
9.3.5 Bare soil index
9.3.6 Shadow index
9.3.7 Thermal index
9.3.8 Scale shadow index
9.3.9 Vegetation density
9.3.10 Forest canopy density
9.4 Results and discussion
9.4.1 Land use land cover pattern
9.4.2 Spatio-temporal pattern of NDVI
9.4.2.1 Year-wise evaluation of NDVI patterns
9.4.2.2 CD block-wise NDVI analysis
9.4.3 Forest canopy density analysis
9.4.3.1 Year-wise evaluation of FCD
9.4.3.2 CD block-wise FCD analysis
9.5 Conclusions
References
10 Real-time monitoring irrigation impact on crop dynamics using Earth Observation sensors in Mashi Dam Command Area, Tonk,...
10.1 Introduction
10.2 Materials and methods
10.2.1 Study area
10.2.2 Methodology.
10.2.2.1 Satellite data access from climate engine platform
10.2.2.2 Extracting vegetation indices
10.2.2.3 Crop growth metrics
10.2.2.4 Yield prediction modeling from multiple linear regression techniques
10.3 Results and discussion
10.4 Conclusions
References
11 Agricultural crop phenology and crop water stress monitoring in south-eastern regions of Bangladesh using Landsat satell...
11.1 Introduction
11.2 Materials and methods
11.2.1 Background of the study area
11.2.2 Data used
11.2.3 Crop pattern identification and validation through field-based in situ data and satellite spectral profile
11.2.4 Agricultural crop phenology and crop water stress mapping method
11.3 Results and discussion
11.3.1 Crop phenology monitoring using NDVI and LSWI
11.3.2 Relationship study between LSWI and NDVI
11.3.3 Some photographs taken by authors to identify winter season crops during field investigation in the months of Januar...
11.4 Major findings and recommendations
11.5 Conclusion
Acknowledgments
References
12 Remote sensing vis a vis ground truthing in agricultural crops for growth and stress identification
12.1 Introduction
12.2 Materials and methods
12.2.1 Component of remote sensing
12.2.1.1 Source of energy or illumination (sun or self-emission)
12.2.1.2 Transmission of energy from source to target
12.2.1.3 Contact with the target
12.2.1.4 Energy transmission to sensor
12.2.1.5 Detection and measurement of reflected energy by sensor
12.2.1.6 Interpretation and analysis of image
12.2.1.7 Application
12.2.2 Ground truthing equipped with GPS-GIS system
12.2.3 Agricultural application
12.2.3.1 Crop area estimation
12.2.3.2 Crop stress monitoring
12.2.4 Remote sensing indices and their significance.
12.2.5 Satellite data classification and categorization
12.3 Conclusions
References
13 Applications of hyperspectral imaging and spectroscopy in agriculture
13.1 Introduction
13.2 Overview of hyperspectral imaging techniques
13.2.1 Different imaging techniques: push-broom, whisk-broom, and snapshot
13.2.2 Spectral range and resolution considerations
13.2.3 Introduction to SPECTROSCOPY TECHNIQUES
13.2.4 Applications in agriculture
13.3 Types of spectroscopy
13.3.1 Importance of spectral libraries and calibration methods
13.4 Hyperspectral imaging and spectroscopy applications in agriculture
13.4.1 Crop health monitoring and stress detection
13.4.2 Disease and pest detection
13.4.3 Crop yield estimation
13.4.4 Nutrient analysis and fertilizer optimization
13.4.5 Soil quality assessment
13.4.6 Weed detection and management
13.5 Challenges and limitations of the application of hyperspectral imaging and spectroscopy in agriculture
13.5.1 Data preprocessing challenges and techniques
13.5.2 Equipment costs and availability
13.5.3 Integration with precision agriculture systems
13.5.4 Scaling up from laboratory to field applications
13.6 Data analysis and interpretation
13.7 Selection of representative case studies highlighting the practical application of hyperspectral imaging and spectrosc...
13.7.1 Case study: hyperspectral imaging for crop disease detection
13.7.2 Case study: spectroscopy for nutrient analysis in plants
13.7.3 Case study: hyperspectral imaging for weed detection and management
13.7.4 Case study: crop yield estimation using hyperspectral imaging
13.8 Future perspectives
13.8.1 Emerging trends and advancements in hyperspectral imaging and spectroscopy
13.8.2 Integration with other technologies.