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1. Project Overview
Rapid urbanization is one of the most critical anthropogenic drivers of land transformation in emerging metropolitan hubs. Ahmedabad, the largest city and economic center of Gujarat, India, has experienced profound demographic, infrastructural, and spatial expansion over the last two decades.
This research project conducts an independent, multi-decadal spatiotemporal investigation into the Urban Expansion and Land Use Land Cover (LULC) dynamics of Ahmedabad between 2000 and 2024. By leveraging multi-sensor satellite imagery across five temporal epochs (2000, 2008, 2015, 2020, and 2024), the study accurately maps historical land cover transitions, measures the magnitude of built-up sprawl, and evaluates the environmental footprint on peripheral agricultural zones and natural water networks.
2. Study Area
The study region centers on the Ahmedabad Urban Development Authority (AUDA) jurisdiction and the Ahmedabad Municipal Corporation (AMC) administrative territory, covering over 1,000 square kilometers in north-central Gujarat.
Geographically situated along the banks of the Sabarmati River, the region is characterized by flat-to-gently undulating alluvial plains with an average elevation of 53 meters above mean sea level. Over the 24-year study window, major growth axes developed along peripheral arterial corridors—notably the Sardar Patel Ring Road (SP Ring Road), the Sarkhej–Gandhinagar (SG) Highway, and industrial zones stretching toward Sanand and Changodar.
3. Objectives
Classify historical satellite scenes for 2000, 2008, 2015, 2020, and 2024 into five standardized thematic classes (Built-up, Vegetation, Water, Agriculture, Open Land).
Calculate post-classification transition cross-tabulation matrices to quantify the exact rate and volume of agricultural land conversion into urban built-up areas.
Model Digital Elevation Models (DEM), slope, aspect, hillshade, and contour lines to identify how topography and natural drainage corridors influence urban spatial growth.
Produce standardized thematic cartographic map layouts and statistical summaries to inform urban growth and land-use analysis.
4. Data Sources
To maintain scientific reproducibility across a 24-year timeframe, data was acquired from authoritative open Earth observation repositories:
| Data Layer | Provider / Source | Spatial Resolution | Purpose |
|---|---|---|---|
| Landsat 5 TM | USGS / EarthExplorer | 30 meters | 2000 & 2008 Baseline Classification |
| Landsat 8 OLI | USGS / EarthExplorer | 30 meters | 2015 & 2020 Multi-Spectral LULC |
| Sentinel-2 MSI | Copernicus / ESA | 10 / 20 meters | 2024 High-Resolution Verification |
| SRTM DEM | NASA / USGS | 30 meters (1 Arc-Second) | Terrain, Slope & Aspect Extraction |
| Administrative Boundaries | AUDA / AMC Portals | Vector Shapefiles | Study Boundary Masking & Clippings |
5. Satellite Data & Acquisition Strategy
A key challenge in multi-decadal remote sensing is minimizing seasonal atmospheric interference and phenological variation. For this study, all satellite scenes were acquired strictly from the post-monsoon window (October to November) with less than 5% cloud cover.
Level-2 Surface Reflectance (SR) products were utilized, ensuring that atmospheric absorption, Rayleigh scattering, and aerosol effects were systematically corrected before running spectral transformations.
6. GIS Software & Technologies
ArcGIS Pro & Spatial Analyst
Used for geodatabase structure management, raster geoprocessing, 3D Analyst surface interpolation, DEM hillshade generation, contour vectorization, and publishing cartographic layout series.
Explore ArcGIS Pro and ArcPy automationQGIS 3.x & Semi-Automatic Classification (SCP)
Used for radiometric calibration, band stacking, ROI training sample generation, supervised classification algorithms, and post-classification raster cross-tabulation.
Explore QGIS spatial analysis and plugins7. Step-by-Step Methodology
- 1
Satellite Acquisition & Radiometric Preprocessing
Download Landsat 5, 8, and Sentinel-2 tiles. Reproject to UTM Zone 43N, crop to the AUDA administrative boundary mask, and perform band stacking.
- 2
Spectral Index Modeling
Calculate Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) to maximize spectral feature separation between vegetative crops and concrete structures.
- 3
Training Samples & Supervised Classification
Select Region of Interest (ROI) polygons across 5 classes. Run Maximum Likelihood and Random Forest classifiers across all epochs.
- 4
Accuracy Assessment & Validation
Generate stratified random validation points. Compute confusion error matrices, User's Accuracy, Producer's Accuracy, and Overall Kappa coefficients.
- 5
Change Detection Matrix & Terrain Modeling
Execute post-classification cross-tabulation matrices and overlay SRTM DEM derivatives to analyze elevation and slope constraints.
8. Land Use Land Cover (LULC) Classification Scheme
Built-up Land
Residential, commercial, industrial complexes, road infrastructure, asphalt, and high-density urban structures.
Vegetation / Green Cover
Urban parks, riverfront plantations, tree canopies, dense forest patches, and municipal recreational gardens.
Water Bodies
The Sabarmati River channel, Kankaria Lake, Chandola Lake, retention reservoirs, and irrigation canals.
Agricultural Land
Active cropland, irrigated fields, fallow agricultural parcels, and peri-urban rural farming patches.
Barren / Open Land
Vacant development plots, exposed soil, scrublands, riverbed sandbanks, and uncultivated open tracts.
9. Multi-Temporal Change Detection Analysis
Post-classification cross-tabulation change detection matrices were generated for consecutive intervals: 2000–2008, 2008–2015, 2015–2020, and 2020–2024, as well as the complete 24-year cumulative timeframe (2000–2024).
The transition statistics reveal that the predominant vector of land change was the unidirectional conversion of Agricultural Land and Barren Open Land into Built-up Land, with minimal reverse transitions.
10. Topographical Terrain Analysis (DEM, Slope, Aspect, Hillshade)
Using 30-meter SRTM elevation data, digital terrain derivatives were extracted in ArcGIS Pro to evaluate whether geomorphological conditions influenced urban growth corridors:
Digital Elevation Model (DEM)
Elevations range from 35m in southern alluvial depressions to 68m in northern tracts, creating gentle slopes toward the central Sabarmati channel.
Slope Percentage
Over 92% of the study area falls into 0–2% slope (flat), imposing zero physical terrain barriers to outward horizontal urban sprawl.
Aspect & Hillshade
Multi-directional hillshade models illuminated subtle micro-drainage channels critical for urban stormwater retention.
Topographic Contours
Generated 5-meter contour intervals to identify low-lying flood inundation risks along peri-urban settlements.
11. Numerical Classification Results (2000 vs 2024)
| LULC Class | Year 2000 (%) | Year 2024 (%) | Net 24-Year Trend |
|---|---|---|---|
| Built-up Land | ~18.4% | ~29.6% | +60.9% Expansion |
| Agricultural Land | ~54.2% | ~35.7% | -34.2% Reduction |
| Barren / Open Land | ~19.1% | ~25.8% | +35.1% (Transition Phase) |
| Vegetation / Green Cover | ~6.1% | ~6.8% | +11.5% (Riverfront & Parks) |
| Water Bodies | ~2.2% | ~2.1% | Relatively Stable |
12. Key Findings & Spatial Insights
Arterial Transport Corridors Drive Sprawl
Growth was non-concentric: radial expansion heavily clustered along the SG Highway (west toward Sanand) and SP Ring Road ring circumference.
Severe Agricultural Depletion
Over 18.5% of total AUDA land shifted from active agricultural tillage to cleared construction plots and built infrastructure between 2000 and 2024.
Riverfront Regeneration Stability
The Sabarmati Riverfront development created localized green corridors in the urban core, offsetting some internal canopy loss.
13. Thematic Maps & Cartographic Series
The project produced a complete suite of publication-ready thematic cartographic map layouts with standardized north arrows, graphic scale bars, coordinate graticules, and legends:
14. Technologies Used & Technical Discipline Links
15. Research Report & Citations
Citation & Document Availability
Citation: Trivedi, Husain. (2024). Urban Expansion and Land Use / Land Cover (LULC) Change Analysis of Ahmedabad City (2000–2024). Independent Technical Research Report.