🌾 GIS and Spatial Data Analysis

GIS turns scattered farm observations into mappable decision layers, helping agronomists convert variability into practical, zone-based actions.

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What is GIS?

Geographic Information System (GIS) is an integrated system designed to capture, store, manipulate, analyze, manage, and display all types of geographical (spatially referenced) data. It links location information (where things are) with descriptive information (what things are like).

GIS answers questions like:

• Where are the areas most prone to drought in Vidarbha?
• Which soils in this district have critical nitrogen deficiency?
• What is the area under wheat cultivation in Punjab this season?

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Five Components of GIS

Component	Description
Hardware	Computers, GPS receivers, scanners, plotters, servers
Software	ArcGIS, QGIS, GRASS GIS, Google Earth Engine
Data	Spatial data (maps, satellite imagery) + attribute data (tables)
People	GIS analysts, remote sensing scientists, field data collectors
Procedures	Workflows for data collection, processing, analysis, reporting

Types of Spatial Data

GIS handles two fundamental data types:

Vector Data

Represents discrete geographic features using geometric objects:

• Points: Single location — weather station, soil sample site, borewell location
• Lines (Polylines): Linear features — roads, canals, field boundaries
• Polygons: Enclosed areas — farm fields, land parcels, district boundaries

Each feature has an attribute table storing descriptive data (e.g., a soil polygon may have attributes: soil type, pH, organic carbon %, texture class).

Raster Data

Represents continuous spatial phenomena as a grid of cells (pixels):

• Each cell has a value (e.g., NDVI value, elevation in metres, soil pH)
• Resolution defines pixel size — 5.8 m (Resourcesat LISS-IV), 30 m (Landsat), 10 m (Sentinel-2)
• Examples: satellite imagery, DEM (Digital Elevation Model), interpolated soil maps

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GIS Software

Software	Type	Key Features
ArcGIS (ESRI)	Commercial	Industry standard, ArcPro, Model Builder, geostatistics
QGIS	Free/Open-source	Cross-platform, plugin ecosystem, equal to ArcGIS for most tasks
GRASS GIS	Free/Open-source	Powerful raster analysis, used in research and academia
Google Earth Engine	Cloud-based	Petabyte-scale satellite data analysis via JavaScript/Python API
SAGA GIS	Free/Open-source	Strong terrain analysis tools

Key GIS Operations

Overlay Analysis

Combines two or more spatial layers to produce a new layer:

• Union: Combines all features from both layers
• Intersection: Retains only areas common to both layers
• Identity: Overlays one layer onto another preserving the identity layer extent

Example: Overlay soil pH map with crop suitability map to identify optimal zones for groundnut cultivation.

Buffer Analysis

Creates a zone of specified distance around a feature:

• Buffer 500 m around pesticide spray zones to identify exposure risk to water bodies
• Buffer along canal networks to map irrigable land

Spatial Interpolation

Estimates values at unsampled locations based on sampled points:

• IDW (Inverse Distance Weighting): Assumes nearer points have more influence
• Kriging: Geostatistical method that accounts for spatial autocorrelation; produces prediction + error surfaces; preferred for soil property mapping
• Spline: Fits a mathematical surface through sample points

Reclassification

Reassigns raster values into new classes:

• Reclassify NDVI map (0–1) into: stressed (<0.3), moderate (0.3–0.5), healthy (>0.5)

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Spatial Queries

GIS allows both attribute queries (SQL-based) and spatial queries:

• Select by Attribute: "Select all fields where soil organic carbon < 0.5%"
• Select by Location: "Select all villages within 10 km of a market yard"
• Spatial Join: Attach attributes from one layer to another based on location

Coordinate Systems

System	Description	Use in India
WGS84	World Geodetic System 1984; used by GPS	Standard for GPS data
UTM (Universal Transverse Mercator)	Zone-based projection; minimal distortion	Large-scale mapping
Geographic (Lat/Long)	Degrees of latitude and longitude	Global datasets
Survey of India toposheet grid	National grid; 1:50,000 / 1:250,000 scales	Land records, legacy maps

For Indian agricultural GIS projects, WGS84 with UTM Zone 43N or 44N (depending on region) is commonly used.

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Agricultural Applications of GIS

1. Soil Mapping

• GPS-georeferenced soil samples analyzed in lab
• Results interpolated (Kriging) to create continuous soil property maps
• Output: management zone maps for variable rate fertilization

2. Land Use / Land Cover (LULC) Classification

• Satellite imagery classified into: cropland, forest, wasteland, water body, settlement
• Change detection: compare LULC maps across years to monitor agricultural expansion or land degradation

3. Watershed Delineation

• DEM processed through GIS (Flow Direction → Flow Accumulation → Watershed) to delineate catchment boundaries
• Critical for soil and water conservation planning

4. Crop Monitoring

• Multi-temporal NDVI maps track crop growth stages
• Identify stress patches within fields for targeted intervention

5. Flood Risk Mapping

• DEM + rainfall data → flood susceptibility model
• Overlay with cropland map to assess crop loss risk

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ISRO Resources for Indian Agriculture GIS

Bhuvan Portal (bhuvan.nrsc.gov.in):

• Free access to Resourcesat-2 imagery: LISS-III (23.5 m resolution) and LISS-IV (5.8 m resolution)
• Thematic layers: soil maps, wasteland atlas, crop maps, administrative boundaries
• Supports web GIS and data download for analysis

Resourcesat-2A Satellite: Carries LISS-III (multispectral, 23.5 m) and AWiFS (56 m, wide-swath for state-level crop monitoring) sensors.

Creating Farm Management Zones Using GIS: Step-by-Step

1. Collect field boundary: GPS field survey → shapefile
2. Collect soil data: Grid-based soil sampling (1 sample/ha) with GPS coordinates
3. Lab analysis: pH, N, P, K, OC for each sample
4. Input data to GIS: Import sample points with attributes
5. Interpolate: Kriging for each soil parameter → raster maps
6. Reclassify: Each raster into 3–5 management classes
7. Overlay: Combine reclassified layers → composite management zone map
8. Export: Create prescription maps for variable rate fertilizer application

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GIS transforms raw spatial data into actionable farm-level decisions — making it the analytical engine at the heart of precision agriculture.

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Summary Cheat Sheet

Topic	Key Point
GIS purpose	Integrates location and attribute data for farm analysis
Core data	Vector layers, raster layers, and tabular attributes
Key operations	Overlay, buffering, interpolation, reclassification
Farm value	Builds management zones and variable-rate prescriptions

References

3 sources

QGIS and ArcGIS documentation for spatial analysis workflows.

ISRO Bhuvan and NRSC resources for agricultural geospatial data.

ICAR precision farming modules using GIS decision support.

In practice, summary cheat sheet should be interpreted with reference to local crop conditions, resource constraints, and expected outcomes over time. When applying this concept, compare alternatives using cost, risk, and feasibility so the selected approach remains technically sound and economically viable.