Agro-geoinformatics: Theory and Practice (Springer Remote Sensing/Photogrammetry)

✍ Scribed by Liping Di (editor), Berk Üstündağ (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 419
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This volume collects and presents the fundamentals, tools, and processes of utilizing geospatial information technologies to process remotely sensed data for use in agricultural monitoring and management. The issues related to handling digital agro-geoinformation, such as collecting (including field visits and remote sensing), processing, storing, archiving, preservation, retrieving, transmitting, accessing, visualization, analyzing, synthesizing, presenting, and disseminating agro-geoinformation have never before been systematically documented in one volume. The book is edited by International Conference on Agro-Geoinformatics organizers Dr. Liping Di (George Mason University), who coined the term “Agro-Geoinformatics” in 2012, and Dr. Berk Üstündağ (Istanbul Technical University) and are uniquely positioned to curate and edit this foundational text.
The book is composed of eighteen chapters that can each stand alone but also build on each other to give the reader a comprehensive understanding of agro-geoinformatics and what the tools and processes that compose the field can accomplish. Topics covered include land parcel identification, image processing in agricultural observation systems, databasing and managing agricultural data, crop status monitoring, moisture and evapotranspiration assessment, flood damage monitoring, agricultural decision support systems and more.

✦ Table of Contents

Contents
Chapter 1: Introduction to Agro-Geoinformatics: Theory and Practices
Chapter 2: Remote Sensing for Agriculture
2.1 Introduction
2.2 Major Agriculture-Related Remote Sensing Data Sources
2.3 Agricultural Applications
2.3.1 Crop Type Identification
2.3.2 Crop Phenology Mapping
2.3.3 Crop Yield Estimation
2.3.4 Crop Evapotranspiration (ET) and Water Use
2.3.5 Soil Moisture Retrieval
2.4 Summary
References
Chapter 3: GIS Fundamentals for Agriculture
3.1 Introduction
3.2 GIS: The Geospatial Approach
3.3 GIS Application in Agriculture
3.3.1 GIS Mapping and Analytical Techniques
3.3.2 Spatial Database for Agricultural Systems
3.3.3 GIS-Based Modeling in Agricultural Application
3.3.3.1 Environment Models Linked to GIS
3.3.3.2 Crop Yield Prediction Based on GIS
3.3.3.3 Agricultural Management Models Using GIS
3.3.4 Decision Support System
3.3.4.1 Traditional Decision Support Systems
3.3.4.2 New Direction and Trends in Decision Support System
3.4 Conclusion
References
Chapter 4: Agro-geoinformatics Data Sources and Sourcing
4.1 Introduction
4.2 Data Sources
4.2.1 Satellite
4.2.2 Airborne Camera
4.2.3 In Situ Sensors
4.2.4 Manual Reports
4.2.5 Summary
4.3 Sourcing
4.3.1 Conventional Sourcing
4.3.2 Cloud Sourcing
4.3.3 Crowdsourcing
4.4 Conclusion
References
Chapter 5: Standards and Interoperability
5.1 Introduction
5.2 Standard Organizations
5.2.1 ISO
5.2.2 OGC
5.2.3 CEN
5.2.4 ANSI
5.3 Typical Standard Development Process
5.4 Types of Standards
5.4.1 Data Content or Encoding Standard
5.4.2 Metadata Content and Encoding Standard
5.4.3 Data Service Standard
5.4.4 Statistical Standards and Methodological Guidelines
5.5 Conclusion
References
Chapter 6: Image Processing Methods in Agricultural Observation Systems
6.1 Introduction
6.2 The Fundamentals of Digital Image Processing
6.2.1 Origins and Definitions
6.2.2 Basic Steps in Image Processing
6.3 Hardware and Software
6.3.1 Image Processing Hardware
6.3.2 Image Processing Software
6.3.3 Mobile Device-Based Image Processing
6.3.4 Cloud-Based Image Processing
6.4 Agricultural Image Data Collection
6.4.1 In Situ Data Collection
6.4.2 Airborne-Based Data Collection
6.4.3 Space-Borne-Based Data Collection
6.4.4 Big Data Challenge in Agricultural Image Data Collection
6.5 Agro-Geoinformation Extraction from Image
6.5.1 Knowledge-Based Expert System
6.5.2 Machine Learning-Based Decision Tree
6.5.3 Artificial Neural Network
6.5.4 A Case Study
6.6 Summary
References
Chapter 7: Data Fusion in Agricultural Information Systems
7.1 Introduction
7.2 Agricultural Information Systems
7.3 Regression Model Example for Real-Time Yield Efficiency Monitoring
7.3.1 Phenological Stage-Based Data Segmentation
7.3.2 Agrometeorological Indices and Regression-Based Data Fusion for Yield Estimation.
7.4 Neural Networks for Data Fusion
7.5 Wavelets in Data Fusion
7.6 Convolutional Neural Networks
7.7 Conclusion
Appendix
References
Chapter 8: Big Data and Its Applications in Agro-Geoinformatics
8.1 Introduction
8.1.1 Challenges in Modern Agriculture
8.1.2 The Role of Big Data in Agriculture
8.2 Agricultural Big Data
8.2.1 Special Features of Agro-Big Data
8.2.2 State-of-the-Art Analysis Methods
8.3 Agro-Geoinformatics
8.3.1 Definition
8.3.2 Agro-Geoinformatics: Connecting Agro-Big Data to Agricultural Applications
8.3.3 Related Research
8.4 Examples of Big Data Application in Agro-Geoinformatics
8.4.1 Agro-Sensor Web
8.4.2 GADMFS
8.4.3 CropScape
8.4.4 VegScape
8.4.5 RF-Class
8.4.6 SMAP Explorer
8.4.7 GeoFairy
8.4.8 CyberConnector COVALI
8.4.9 Geoweaver
8.5 Conclusion
References
Chapter 9: Land Parcel Identification
9.1 Introduction
9.2 Land Parcel and Agricultural Land Parcel
9.2.1 What Is Land Parcel?
9.2.2 Land Parcel in Agriculture
9.2.3 Techniques to Identify Land Parcel
9.3 Managing Land Parcel Information in Agro-Geoinformation Systems for Local Governments, Agencies, and Companies
9.4 Managing Land Parcel Information in Agro-Geoinformation Systems at State and National Levels
9.5 Approaches to Manage Land Parcel Information in Globe Agro-Geoinformation Systems - International Standards
9.6 Conclusion and Discussion
References
Chapter 10: Crop Pattern and Status Monitoring
10.1 Introduction
10.2 Crop Pattern Mapping
10.2.1 Statistical Approach
10.2.2 Remote Sensing Approach
10.2.3 Case Study - Operational National Cropland Mapping Programs
10.2.3.1 USA Cropland Data Layer
10.2.3.2 Canada Crop Inventory
10.2.4 Limitations and Perspectives
10.3 Crop Status Monitoring
10.3.1 Statistical Approach
10.3.2 Remote Sensing Approach
10.3.3 Case Study - Operational Remote Sensing Crop Condition Monitoring
10.3.3.1 National Crop Progress Monitoring System
10.3.3.2 Global Agricultural Monitoring
10.3.3.3 Other Operational Crop Status Monitoring Systems
10.3.4 Limitations and Perspectives
10.4 Conclusions
References
Chapter 11: Crop Growth Modeling and Yield Forecasting
11.1 Introduction
11.2 Statistical Modeling
11.3 Physiological/Physical-Based Modeling
11.4 Remote Sensing Monitoring of Crop Growth
11.5 Data Assimilation
11.5.1 Sequential Data Assimilation Algorithms
11.6 Conclusion
References
Chapter 12: Spatial and Temporal Monitoring System for Agriculture
12.1 Introduction
12.2 Related Work
12.3 Spatial and Temporal Monitoring Systems for Agriculture
12.3.1 Web Service-Based Near-Real-Time Global Agricultural Drought Monitoring System
12.3.2 Web Service-Based Near-Real-Time US Vegetation Condition Monitoring System
12.3.3 Web Service-Based Near-Real-Time US Flood and Progress Monitoring System
12.4 Conclusion
References
Chapter 13: Spatial Data Usage in Turkish Agriculture
13.1 Introduction
13.2 Parcel-Based Support Payment System
13.3 Land Parcel Identification System
13.3.1 Orthophoto Production
13.3.1.1 Geodetic Works
13.3.1.2 Post-processing of Aerial Imagery (AI)
13.3.1.3 DEM Production for the AI Areas
13.3.1.4 Orthophoto Production, Mosaicking, and Tile Cutting from the AI and SI
13.3.1.5 Radiometric Enhancement
13.3.1.6 Pan-Sharpening
13.3.2 Constraints for Aerial Imagery
13.3.2.1 Cloud Cover Percentage
13.3.2.2 Sun Angle
13.3.2.3 Crop Phenology
13.3.3 Orthophoto Features
13.3.4 Vector Data in LPIS
13.3.4.1 Generation
13.3.4.2 Controls
13.3.5 Usage of LPIS Data with Collaboration of Cadastre
13.4 Potential Usage of Spatial Database
13.5 Geostatistics through Spatial Database
13.5.1 Interpolation Methodology
13.6 Conclusion
References
Chapter 14: Geospatial Land Use and Land Cover Data for Improving Agricultural Area Sampling Frames
14.1 Introduction
14.2 Background
14.2.1 Related Work
14.2.2 NASS Area Sampling Frames
14.2.3 NASS Cropland Data Layer
14.2.4 NASS Cultivated Layer
14.3 Study areas
14.4 Automated Stratification Methodology
14.4.1 Stratification Method
14.4.2 Automatic Stratification Analysis and Results Evaluation
14.4.3 Comparison of Traditional and Automatic Stratification Results
14.5 Integration of Automatic Stratification into NASS Operations
14.5.1 Ancillary Data for Manual Review and Editing Process
14.5.2 Integration Process
14.6 Integration Results
14.6.1 Stratification Accuracy
14.6.2 Mean Stratum Percent Cultivation Range, Standard Deviations, and PSU Size
14.7 Integration Discussion
14.7.1 Stratification Accuracy
14.7.2 Mean Stratum Percent Cultivation Range, Standard Deviations, and PSU Size
14.7.3 Labor Cost
14.8 Conclusion
References
Chapter 15: Mapping and Monitoring of Soil Moisture, Evapotranspiration, and Agricultural Drought
15.1 Introduction
15.2 Soil Moisture
15.2.1 Methodology
15.2.2 Data
15.2.3 Results
15.3 Evapotranspiration
15.3.1 Methods
15.3.2 Data
15.3.3 Results
15.3.3.1 Validation at US-Skr
15.3.3.2 Validation at ARM-SGP Stations
15.4 Agricultural Drought
15.4.1 Normalized Difference Vegetation Index (NDVI)
15.4.2 Vegetation Condition Index (VCI)
15.4.3 Results
15.5 Conclusions
References
Chapter 16: Flood Monitoring and Crop Damage Assessment
16.1 Introduction
16.2 Remote Sensing on Flood Event Monitoring
16.2.1 Traditional Gauge-Based Flood Monitoring
16.2.2 Remote Sensing-Based Flood Monitoring
16.2.2.1 Remote Sensing in Flood Forecasting
16.2.2.2 Remote Sensing in Flood Mapping
16.2.3 GIS-Based Flood Modeling and Early Warning System
16.2.4 Event and Duration of the Flood
16.3 Flood Crop Damage Assessments
16.3.1 Classification Method
16.3.2 Band Ratioing (Vegetation Indices)
16.4 Case Study: NDVI-Based Corn Loss Assessment through Regression Model
16.4.1 Flood Event
16.4.2 Data
16.4.3 Study Area
16.4.4 Method
16.4.4.1 Pure Pixel Selection
16.4.4.2 Normal NDVI
16.4.4.3 NDVI Smoothing
16.4.4.4 Area under the Curve
16.4.4.5 Regression Model
16.4.5 Result
16.4.5.1 Regression Result
16.4.5.2 Model Estimation
16.5 Conclusion
References
Chapter 17: Remote Sensing-Based Mapping of Plastic-Mulched Land Cover
17.1 Introduction
17.2 A Decision-Tree Classifier for Extracting PML Using Landsat Imagery
17.2.1 Methodology
17.2.1.1 The Detectable Features of PML
17.2.1.2 Construction of the Decision-Tree Classifier
17.2.2 A Specific Example
17.2.2.1 Data Sets and Preprocessing
17.2.2.2 Experiment Results
17.3 A Threshold Model for Mapping PML Using MODIS Time Series Data
17.3.1 Methodology
17.3.2 A Specific Example
17.3.2.1 Data Sets and Preprocessing
17.3.2.2 Determination of Threshold Condition and Value
17.3.2.3 Detecting and Mapping PML
17.4 Subpixel Mapping of PML from MODIS Imagery Using Spatial Attraction Models
17.4.1 Methodology
17.4.1.1 Subpixel Mapping Theory
17.4.1.2 Subpixel/Pixel Spatial Attraction Model (SPSAM)
17.4.1.3 MSPSAM and MSAM
17.4.1.4 Improved Spatial Attraction Model (ISAM)
17.4.2 A Specific Example
17.4.2.1 Data Sets and Preprocessing
17.4.2.2 Experiment Results
17.5 Conclusion
References
Chapter 18: Design and Implementation of Geospatial Data Services for Agriculture
18.1 Introduction
18.2 Geospatial Data for Agriculture
18.2.1 Data Categories
18.2.1.1 CDL Data
18.2.1.2 Vegetation Index Data
18.2.1.3 Hydrological Data
18.2.1.4 Temperature Data
18.2.2 Data Life Cycle
18.3 Geospatial Interoperability and Standardization
18.3.1 Geospatial Web Service Interoperability Standards
18.3.2 Content Interoperability Standards
18.4 Geospatial Web Service Architecture for Agriculture
18.4.1 A Specific Example: CropScape
18.4.1.1 Application Layer
18.4.1.2 Service Layer
18.4.1.3 Data Layer
18.5 Geospatial Data Service Functionalities for Agriculture
18.5.1 Agricultural Data Management
18.5.2 Agricultural Data Analytics
18.6 Conclusion
References
Index