1Preface
1116.3.2 Global Information and Early Warning System
2Chapter 1. Fundamentals of Remote Sensing
1126.3.3 USDA/Foreign Agricultural Service
31.1 Definition
1136.3.4 Monitoring Agricultural Resources
41.2 Electromagnetic Interaction
1146.3.5 National Systems
51.2.1 Reflection, Absorption, and Backscatter : Spectral signatures
1156.3.6 Comments on Crop Monitoring Systems
61.2.2 Thermal Emission and Brightness Temperature
1166.4 Next Steps in Crop Monitoring
7A. Thermal infrared
1176.5 Exercise
8B. Microwaves
1186.6 References
91.3 Electromagnetic Spectrum
119Chapter 7. Remote Sensing Applications to Precision Farming
101.4 Remote Sensing Information
1207.1 Introduction
111.5 Conclusion
1217.1.1 Precision Farming
121.6 Exercise
1227.1.2 Remote Sensing Data
131.7 References
1237.2 Applications of Remote Sensing Data in Precision Agriculture
14Chapter 2. Classical Remote Sensing Application
1247.2.1 Precision Farming Management
152.1 Remote Sensing-Based Determination of Conifer Needle Flushing Phenology over Boreal-Dominant Regions
1257.2.2 Spectral, Spatial, and Temporal Considerations
162.1.1 Introduction
1267.2.3 Vegetation Indices
172.1.2 Description of the Study Area and Data Requirements
1277.2.4 Application 1: Soil Mapping
182.1.3 Generation of AGDD Maps
1287.2.5 Application 2: Pest and Disease Detection
192.1.4 Determination of AGDD and NDWI Thresholds for CNG Occurrence
1297.2.6 Application 3: Plant Water Stress Detection
202.1.5 Integration of both AGDD and NDWI Threshold for CNG Occurrence
1307.2.7 Application 4: Crop (Nitrogen) Stress Detection
212.1.6 Determination of AGDD Threshold for CNF Occurrence
1317.2.8 Application 5: Weed Detection and Mapping
222.1.7 Determination of NDWI Thresholds for CNF Occurrence
1327.2.9 Application 6: Herbicide Drift Detection
232.1.8 Integration of both AGDD and NDWI Thresholds
1337.3 Conclusions
242.1.9 Spatial Dynamics of CNF Across the Landscape
1347.4 Exercise
252.2 Information System for Integrated Watershed Management Using Remote Sensing and GIS
1357.5 References
262.2.1 Introduction
136Chapter 8. LiDAR Remote Sensing of Vegetation Biomass
272.2.2 Role of Geographic Information System (GIS) and Remote Sensing (RS) in Watershed Management
1378.1 Introduction
282.2.3 Decision Support System in Watershed Management
1388.2 Remote Sensing of Vegetation Biomass Using Different LiDAR Systems
292.2.4 Need for Advanced and Augmented Techniques for Watershed Management
1398.2.1 Airborne Small-Footprint Discrete-Return Scanning LiDAR
302.2.5 Study Area
140Area-Based Methods
312.2.6 Conceptual Design
141Different Ways of Deriving Area-Based LiDAR Metrics
322.2.7 Tools and Technologies Used
142Useful Height Metrics for Biomass Estimation
33PostgreSQL:
143Other Useful Metrics for Biomass Estimation
34Apache server:
144Common Area-Based Statistical Models
35HyperText Markup Language (HTML):
145Individual Tree Analysis Methods : Challenges of ITA Methods for Biomass Estimation
36Hypertext Preprocessor (PHP):
1468.2.2 Airborne Small-Footprint Discrete-Return Profiling LiDAR
37Map-Server:
147Useful Profiling LiDAR Metrics for Biomass Estimation
38Java Script:
148Challenges of Profiling LiDAR for Biomass Mapping
39pMapper:
1498.2.3 Airborne Medium- and Small-Footprint Waveform LiDARs
402.2.8 System Architecture
150Useful Waveform Metrics for Biomass Estimation
412.3 Remote Estimation of Land Surface Temperature for Different LULC Features of a Moist Deciduous Tropical Forest Region
151Importance of Correcting Attenuated Waveforms
422.3.1 Introduction
1528.2.4 Satellite Large-Footprint Waveform LiDAR : Challenges of Large-Footprint Satellite LiDAR for Biomass Mapping
432.3.2 Image Interpretation for LULC
1538.2.5 Ground-Based Discrete-Return Scanning LiDAR : Challenges of Ground-Based LiDAR for Biomass Estimation
442.3.3 Surface Temperature Estimation
1548.3 General Challenges and Future Directions
452.3.4 Results and Discussions
1558.3.1 Fusion of LiDAR with Other Remotely Sensed Data
462.4 Exercise
156Fusion with Optical Imagery
472.5 References
157Fusion with Radar
48Chapter 3. Advanced Remote Sensing Application
1588.3.2 Generalizability of Biomass Models
493.1 Application of Geo-Spatial Technique for Flood Inundation Mapping of Low Lying Areas
1598.3.3 Biological and Ecological Interpretation of Biomass Models
503.1.1 Introduction
1608.4 Conclusions
513.1.2 Surat City
1618.5 Exercise
523.1.3 Geology and Soil Conditions
1628.6 References
533.1.4 Ground Water Table
163Chapter 9. Remote Sensing Applications to Monitoring Wetland Dynamics
543.1.5 Climate
1649.1 Introduction
553.1.6 Temperature and Rainfall
1659.2 Materials and Methods
563.1.7 Demography/Population in the Study Area
1669.2.1 Study Area and Datasets
57The LTB contains hydraulic structures, namely the Ukai dam, Kakrapar weir, and Singapur weir.
1679.2.3 Wetland Dynamics Analysis
58Ukai Dam
1689.2.4 Landscape Analysis
59Kakrapar Weir
1699.3 Results
60Singapur Weir
170Period 1977–1987
613.1.8 Methodology
171Period 1987–2000
623.1.9 Results and Discussion
172Period 2000–2005
633.1.10 Validation
173Period 2005–2011
643.2 Land Use Fragmentation Analysis Using Remote Sensing and Fragstats
1749.3.2 Landscape Pattern Dynamics
653.2.1 Introduction
1759.3.3 Patch Shape Changes
663.2.2 Study Area
1769.4 Conclusions and Discussion
673.2.3 Classification of Satellite Data
1779.5 Exercise
683.2.4 Fragmentation Analysis
1789.6 References
693.2.6 Landscape & Class Level Metrics
179Chapter 10. Tree Species Classification Using Remote Sensing
703.2.7 Land Fragmented Class Analysis
18010.1 Introduction
713.2.8 Estimating Effects on Water Quality
18110.2 Advanced Remote Sensing Sensors/Systems
723.3 Exercise
18210.3 Techniques and Methods
733.4 References
18310.3.1 Spectral Mixture Analysis
74Chapter 4. Remote Sensing Applications for Natural Resources
18410.3.2 Object-Based Image Analysis Method
754.1 Introduction
18510.3.3 Hierarchical Mapping System
764.2 Design- and Model-Based Sampling Design Strategies
18610.3.4 Hyperspectral Transformation and Feature Extraction
774.3 Role of Remotely Sensed Data in Sampling Design Strategies
18710.3.5 Advanced Classifiers
784.4 Optimal Spatial Resolution for Sampling and Mapping
18810.4 Considerations and Future Directions
794.5 Optimal Temporal Resolution for Sampling and Mapping
18910.4.1 Considerations
804.6 Improvement of Sampling Design Strategies
19010.4.2 Future Directions
814.7 Local Variability–Based Sampling Design Strategy
19110.5 Exercise
824.8 Conclusion
19210.6 References
834.9 Exercise
193Chapter 11. Remote Sensing Applications to Modelling Biomass
844.10 References
19411.1 Introduction
85Chapter 5. Application of Remote Sensing in Ecosystem
19511.2 Overview of Algorithms and Methods
865.1 Introduction
19611.3 Case Study—Fisheries
875.2 Forest Landscape Dynamics
19711.4 Summary and Comments on the Future
885.2.1 Model Initialization
19811.5 Exercise
895.2.2 Model Validation/Benchmarking
19911.6 References
905.2.3 Model-Data Integration
200Glossary
915.3 Lake Landscape
201Index
925.3.1 Extraction of Lakes from Remote Sensing Images
202A
935.3.2 Use of Lake Data for Model Initialization
203B
945.3.3 Potential Use of Lake Data for Model Validation/Benchmarking
204C
955.4 Conclusion
205D
965.5 Exercise
206E
975.6 References
207F
98Chapter 6. Remote Sensing Applications on Crop Monitoring
208G
996.1 Introduction
209H
1006.1.1 Early Warning
210I
1016.1.2 Crop Production Monitoring
211L
1026.1.3 Agricultural Sustainability
212M
1036.2 Advances in Methodology
213N
1046.2.1 Crop Condition Monitoring
214O
1056.2.2 Agricultural Drought Monitoring
215P
1066.2.3 Crop Acreage Estimation
216R
1076.2.4 Crop Yield Estimation
217S
1086.2.5 Crop Phenophase Monitoring
218T
1096.3 Global and National Operational Systems
219V
1106.3.1 Crop Watch
220W
