Kooperativer Bibliotheksverbund

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Format: 1 online resource (317 pages)

Edition: 1st ed.

ISBN: 9780323956178

Note: 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. , 13.8.3 Potential for commercialization and adoption in the agricultural industry.

Additional Edition: Print version: Dutta, Dipanwita Vegetation Dynamics and Crop Stress San Diego : Elsevier Science & Technology,c2024 ISBN 9780323956161

Language: English