Application Of Remote Sensing & GIS for Effective Agricultural Management

1. Application Of Remote Sensing & GIS for Effective Agricultural Management By Dr Jibanananda Roy Consultant, SkyMap Global

2. Introduction Agricultural Practices And Effect On Man And Environment • Unplanned Agricultural Practices • Overuse of Natural Resources • Unscientific Use of Chemicals & Fertilizers • Man made and Natural Changes in Environment & Ecology • Biodiversity

3. Introduction Agricultural Practices And Effect On Man And Environment • Degradation of Land and Environment • Reduction in Crop Yield • Environmental Pollution • Human Health Hazard • Natural Disasters

4. REQUIREMENT Sustainable Agricultural Management Plan • Region Specific Management Plan • Effective Use of Technology • Long Term Management Plan • Awareness Among Stakeholders • Analysis Of Social, Political, Infrastructural And Financial Issues • Preservation Of Natural System

5. TECHNOLOGY Availability And Use • Satellite Remote Sensing Data • Multispectral & Hyperspectral • SAR • Digital Terrain Model • Derived Data • Weather – Historical And Predictive • Software And Systems • Periodic Monitoring And Corrective Action • Human Experts

6. OBJECTIVE • Propose methodologies for continuous crop health monitoring and yield prediction using satellite images, field survey data and ancillary information • Analysis of historical data and field survey using Remote Sensing to develop a model for crop yield prediction. • Productivity analysis of different crops with respect to other similar areas or historical data. • Identification of parameters for productivity. • Agricultural zoning. • A sustainable development plan.

7. Data Analysis • During Last 30 Years Several Effective Models Have Been Developed And Implemented • Models Are Case Specfic – Regional, Agriculture Type • Direct Models • Integration Of Field Survey • Issues Of All Stakeholders • Multi-criteria Decision Support System

8. SCALE • Large Contiguous Area With Similar Crop • Mixed Cultivation – Orchard, Vegetables, Horticulture • Single Crop Vs Multiple Crops • Periodicity Of Crop Yield

9. Standard Models • Normalized Difference Vegetation Index (NDVI) • Other Band Ratios • Leaf Area Index (LAI) • Spectral Signature – Crop Health & Crop Yield • Band Combinations • Fusion Of Multiple Satellite Data

10. Advanced Models • Models Based On Narrow Band Hyperspectral Satellite Data • Use Of Red Edge Band With NIR • Crop Type Delineation • Crop Health Monitoring • Crop Yield Estimation

11. Use Of SAR Data • Crop Type Analysis Based On Texture • Estimation Of Soil Moisture • Crop Type Delineation • Crop Health Monitoring • Crop Yield Estimation

12. Mixed Cultivation • Smaller Area With Different Type Of Crops • High Crop Yield At Specific Areas • Low Yield In Several Areas • Individual Plot Level Management Plan • No Macroscopic Plan • Bad Water Management • High Pollution • Land Degradation, Soil Erosion • Bad Disaster Management During Flood and Other Natural Calamities • Ineffective Use Of Satellite Data • Awareness

13. Issues With Satellite Data • Not Viable For Small Land Holdings And Mixed Cultivation • May Not Be Effective For Pest Attack Information Due To Inherent Delay In Providing Data • Limitation Of Predictive Model Development • Data Degradation Due To Atmospheric Disturbances

14. Effective Use Of Satellite Data • Analysis Of Large Area • Model Development With Field Collection Of Spectral Parameters • Effective Use Of SAR Data • Fusion Or Mixed Mode Analysis Of Coarse And High Resolution Data • Continuous Data Collection, Analysis and Notification • Refinement Of Model Based On Past Data • Infrastructure Development Plan for Storage And Delivery

15. CONCLUSION • Agricultural Zoning Based On Natural Factors • Satellite Data Can Be Effectively Used By Analyzing Large Agricultural Zone. • Field Survey For Model Development By Analyzing Spectral Characteristics. • Development Of Better Water Management Plan • Continuous Monitoring And Model Refinement • Development Of Rule Based System Analyzing Multiple Factors Using GIS and Rule Based Software.

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