🗺️ Geographic Information System (GIS)
Definition, components, raster vs vector data, spatial analysis operations, GIS software, and agricultural applications.
This lesson builds core elective concepts in BSc Agriculture with practical applications and exam-oriented clarity.
Geographic Information System (GIS)
Definition
A Geographic Information System (GIS) is a computer-based framework for capturing, storing, querying, analyzing, and displaying data that is tied to a geographic location. GIS links spatial data (where something is) with attribute data (what it is), enabling powerful spatial queries and analyses that conventional databases cannot perform.
A GIS answers questions like:
- Where are the soils with pH < 6.0 in this district?
- Which villages are within 5 km of a river and have no drainage?
- What is the total wheat-growing area in blocks receiving < 500 mm rainfall?
Components of GIS
GIS systems consist of five interacting components:
- Hardware: Computers, servers, GPS receivers, digitizing tablets, plotters, scanners
- Software: GIS application software (ArcGIS, QGIS, GRASS) for data input, storage, analysis, and visualization
- Data: The most critical component — spatial data (maps, images) and attribute data (tabular records)
- Methods: Defined procedures and workflows for data capture, processing, and analysis
- People: Trained operators, analysts, and decision-makers who use GIS outputs
Types of Spatial Data
Vector Data
Vector data represents geographic features using geometric objects:
- Points: Discrete locations with no area — soil sampling sites, tube wells, weather stations, GPS waypoints
- Lines (Polylines): Linear features with length but no area — roads, canals, rivers, field boundaries
- Polygons: Enclosed areas — agricultural fields, administrative districts, soil type zones, forest patches
Advantages of vector data:
- Precise boundary representation
- Compact file size for discrete features
- Easy attribute querying ("select all fields > 2 ha")
- Topological relationships (adjacency, containment) are explicit
Raster Data
Raster data represents the world as a regular grid of cells (pixels), where each cell holds a value:
- Satellite images (each pixel = reflectance in multiple spectral bands)
- Digital Elevation Models — each pixel = elevation in metres
- Interpolated surface maps (soil pH, rainfall)
- Classified land use maps (each pixel = land use category)
Key raster property — Spatial Resolution: The size of each pixel. A 10m resolution means each pixel covers a 10×10 m area on the ground. Finer resolution = more detail = larger file size.
Advantages of raster data:
- Natural representation for continuous phenomena (elevation, temperature, rainfall)
- Simple data structure — mathematical operations are straightforward
- Satellite imagery is inherently raster
Raster vs Vector Comparison
| Feature | Vector | Raster |
|---|---|---|
| Representation | Points, lines, polygons | Regular grid of pixels |
| Best for | Discrete features, boundaries | Continuous surfaces, imagery |
| File size | Small for sparse features | Can be large at fine resolution |
| Spatial accuracy | Precise | Limited by pixel size |
| Map algebra | Not applicable | Directly applicable |
| Examples | Soil map, road network | Satellite image, DEM, rainfall map |
Coordinate Systems and Projections
Geographic Coordinate System
Locations expressed as latitude (north-south, −90° to +90°) and longitude (east-west, −180° to +180°) in degrees. Suitable for global data but distances/areas are distorted.
Projected Coordinate System
The curved Earth is projected onto a flat plane. UTM (Universal Transverse Mercator) divides the world into 60 zones; coordinates in metres. India spans UTM zones 42–47N.
Datum
A datum is a reference model of Earth's shape:
- WGS84: World Geodetic System 1984 — used by GPS, Google Maps, most modern data
- Everest 1830: Traditional Indian datum — older Survey of India maps use this; coordinate offset from WGS84 can be hundreds of metres
Always check and match coordinate systems before overlaying layers in GIS.
Spatial Data Formats
| Format | Type | Description |
|---|---|---|
| Shapefile (.shp) | Vector | ESRI format; actually 3+ files (.shp, .dbf, .shx); industry standard |
| GeoJSON | Vector | Text-based JSON format; web-friendly; open standard |
| GeoPackage (.gpkg) | Vector/Raster | SQLite-based; single file; recommended open standard |
| KML/KMZ | Vector | Google Earth format; used for sharing/visualization |
| GeoTIFF (.tif) | Raster | Georeferenced raster image; contains embedded projection info |
| ESRI Grid / IMG | Raster | Common raster formats in ArcGIS workflows |
| NetCDF / HDF | Raster | Multi-dimensional data; meteorological, climate datasets |
Spatial Analysis Operations
Overlay Analysis
Combining two or more spatial layers to produce a new layer:
- Union: Combines all features from both layers
- Intersection: Keeps only areas where both layers overlap
- Difference (Erase): Removes overlapping area from first layer
Agricultural use: Overlay soil type map with crop suitability table → identify areas suitable for cotton but currently under other crops.
Buffer Analysis
Creates a zone of specified distance around features:
- Point buffer: 2 km buffer around a pesticide depot
- Line buffer: 500 m buffer around a river (flood risk zone)
- Polygon buffer: 1 km buffer around a protected area
Clip
Extracts features of one layer that fall within the boundary of another (e.g., clip national soil map to district boundary).
Interpolation
Estimates values at unsampled locations using values from sampled points:
- IDW (Inverse Distance Weighting): Closer points have greater influence; simple and fast
- Kriging: Geostatistical method using variogram; theoretically optimal; accounts for spatial autocorrelation; preferred for soil sampling data
Agricultural use: 30 GPS-tagged soil pH samples across a block → kriging interpolation → continuous pH surface map → prescription lime application.
Reclassification
Assigns new values to raster cells based on old values (e.g., reclassify NDVI image into 5 crop health classes).
Map Algebra (Raster Calculator)
Mathematical operations on raster layers:
- NDVI = (NIR band − Red band) / (NIR band + Red band)
- Composite suitability index = (0.4 × soil score) + (0.3 × rainfall score) + (0.3 × slope score)
Watershed Delineation
Using a DEM, GIS can automatically delineate watershed boundaries, calculate drainage direction and accumulation, and map stream networks — essential for watershed development programmes.
GIS Software
| Software | License | Key Features | Agricultural Use |
|---|---|---|---|
| ArcGIS Pro (ESRI) | Commercial (₹50k–2L/yr) | Most comprehensive; ModelBuilder; full 3D; best enterprise support | State-level projects, government agencies |
| QGIS | Free, open-source | Full-featured; large plugin library; Python scripting; active community | Research, universities, NGOs, field use |
| Google Earth Pro | Free | Easy visualization; historical imagery; basic measurement tools | Quick field boundary checks, farmer demos |
| Google Earth Engine | Free (research) | Cloud-based; petabytes of satellite data; JavaScript/Python API | Large-scale crop mapping, time series analysis |
| GRASS GIS | Free, open-source | Powerful raster analysis; hydrological tools | Watershed analysis, DEM processing |
| ERDAS IMAGINE | Commercial | Advanced image processing, classification | Satellite image analysis, LULC mapping |
Google Earth Engine (GEE) deserves special mention: it provides access to the entire Landsat and Sentinel-2 archives (petabytes of data) in the cloud, eliminating the need to download images. Researchers can run continental-scale crop mapping analyses in minutes using JavaScript or Python APIs — all free for academic use.
Digital Elevation Model (DEM)
A DEM is a raster dataset where each cell value represents elevation above sea level:
| DEM Source | Resolution | Coverage | Cost |
|---|---|---|---|
| SRTM (NASA) | 30m / 90m | Global | Free |
| ASTER GDEM | 30m | Global | Free |
| Cartosat-1 DEM (ISRO) | 30m | India | Available via NRSC |
| LiDAR DEM | <1m | Survey area | Expensive |
Applications in agriculture:
- Slope and aspect calculation → slope-based erosion risk maps
- Drainage network delineation → watershed planning
- Terrain analysis for terrace design
- Solar radiation modelling for crop suitability
GIS in Indian Agriculture
Land Use / Land Cover Mapping
National Remote Sensing Centre (NRSC) produces annual LULC maps of India at 1:50,000 scale using IRS LISS-III data — 19 Level-I and 55 Level-II classes including cropland, fallow, plantation, waterbodies.
Soil Resource Mapping
NBSS&LUP (National Bureau of Soil Survey & Land Use Planning, Nagpur) has mapped soils at 1:50,000 (district level) and 1:250,000 (state level) scales using IRS imagery combined with field surveys.
MGNREGS Planning
GIS is used to plan and monitor Mahatma Gandhi National Rural Employment Guarantee Scheme works — watershed structures, plantation sites, drainage channels — at village level.
Crop Insurance (PMFBY)
Remote sensing + GIS for area verification reduces fraudulent claims; cluster-level yield estimation using satellite data is replacing some crop-cutting experiments.
Market Accessibility Analysis
GIS network analysis identifies villages beyond 10 km from a regulated market (APMC) — informs policy for new market infrastructure.
Overview
GIS is the analytical engine of geoinformatics. It handles both vector (points, lines, polygons) and raster (pixel grid) data formats, links spatial data with attribute tables, and performs overlay, buffer, interpolation, and map algebra operations. QGIS is the recommended free tool for students and researchers. Google Earth Engine has revolutionized large-scale analysis by making petabytes of satellite data accessible in the cloud. In India, GIS underpins LULC mapping, soil surveys, MGNREGS planning, and crop insurance under PMFBY.
Summary Cheat Sheet
| Topic | Key takeaway |
|---|---|
| Main focus | Definition, components, raster vs vector data, spatial analysis operations, GIS software, and agricultural applications. |
| Section context | Revise this lesson with the rest of Geoinformatics Basics for stronger conceptual continuity. |
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