Unlocking Land Potential with Suitability Analysis Using MCDA in QGIS: A Case Study of Bengaluru

Introduction

Suitability analysis is a vital component of spatial planning, allowing decision-makers to identify the most appropriate locations for specific land uses based on multiple criteria.

In a world facing rapid urbanization, food insecurity, and climate change, the demand for smart land use decisions has never been greater. Whether it's identifying the best site for agriculture, infrastructure, or conservation, making the right choice means balancing environmental, social, and economic factors.

Choosing the best land for development, agriculture, or conservation in the rapidly shifting landscape of today necessitates striking a balance between a number of elements. To determine the best land areas, we use Multi-Criteria Decision Analysis (MCDA) in QGIS to examine important spatial layers such as slope, land surface temperature (LST), land use/land cover (LULC), soil suitability, and precipitation.

With the help of geospatial data and decision-making reasoning adapted to actual planning problems, this blog helps you unleash the potential of your land.

Study Area of Bangalore Region

The Bangalore region, known for its rapid urban growth and diverse ecological zones, presents both opportunities and challenges for sustainable land development. Its complex terrain, varying land use patterns, and dynamic infrastructure expansion make it an ideal case for a GIS-based suitability assessment.

Slope Analysis

Slope was derived from the DEM to assess terrain steepness across the Bengaluru region. The raw slope values ranged from 0° to 36°, indicating flat to steep areas. To make this layer usable in the MCDA process, it was reclassified into four categories: Flat, Gently Sloping,

Moderately Sloping, and Steep. These categories help identify areas more suitable for development and agriculture, with flat terrain being the most favourable. This reclassified slope data was then integrated into the weighted overlay for land suitability assessment.

LST Analysis

Land Surface Temperature (LST) for the Bengaluru region was extracted from MODIS data to understand thermal stress distribution. The temperature ranged from approximately 27°C to 33°C, highlighting urban heat variations. The data was then reclassified into four suitability zones: Hot, Less Suitable, Moderate, and Very Suitable (Cool). Cooler zones were considered more suitable for development and environmental resilience. This reclassified LST layer played a key role in the MCDA-based land suitability evaluation.

LULC (Land Use Land Cover) Analysis

The LULC data was used to understand the spatial distribution of different land types, such as built-up areas, cropland, forest, and water bodies. The original satellite-derived LULC raster was reclassified based on land suitability for development. For instance, built-up and cropland areas were assigned higher suitability values, while forests and water bodies were assigned lower or restricted suitability scores. This reclassified raster was then used as one of the key criteria in the MCDA process to guide site suitability for sustainable land use planning.

Flood Susceptibility Analysis

Flood-prone areas were identified using JRC Global Surface Water data in Google Earth Engine. Seasonal and ephemeral water zones were extracted to highlight regions vulnerable to flooding. The output was then exported as a raster, reclassified based on flood risk, and integrated into the MCDA model. Areas frequently exposed to flood events were assigned lower suitability scores to ensure resilient land-use planning.

Soil Suitability Analysis

Soil sample points from the Bangalore region were interpolated using Inverse Distance Weighting (IDW) to generate a continuous soil suitability surface. The input attribute “Suitability” was used to reflect soil health and fertility. The resulting raster was reclassified into classes ranging from very low to high suitability. This layer was incorporated into the MCDA to emphasize areas with better agronomic potential for sustainable land development.

Site Suitability Analysis – Bengaluru Region

Then future development in the Bengaluru region, we conducted a Multi-Criteria Decision Analysis (MCDA) using QGIS. This spatial technique enables the integration of diverse datasets to evaluate the land's potential by considering both environmental and physical constraints. Each criterion was processed, standardized, and weighted to build a composite suitability surface.

At least we need to consider five parameters for site suitability analysis

Slope: Flatter terrain was given higher preference due to its suitability for infrastructure and ease of access.

Land Surface Temperature (LST): Cooler zones were marked more suitable, supporting environmental comfort and reduced urban heat.

Land Use Land Cover (LULC): Green covers and open areas were favored for sustainable development.

Precipitation: Areas with moderate rainfall were preferred to balance water availability and flood risks.

Soil Suitability: Based on interpolated values from sample data, reclassified into fertility and texture categories relevant for growth and support.

Reclassification

Each raster dataset was normalized into suitability classes (1 = Low Suitability, up to 4 = Very High Suitability). This ensured uniformity in analysis across different scales and units.

Weight Assignment

Based on the relative importance of each factor, the following weights were assigned:

Criteria	Weight (%)
LULC	25%
SLOPE	20%
LST	15%
PRECIPITATION	15%
SOIL	25%

Using QGIS Raster Calculator (or automated via arcpy in Python), the weighted layers were combined with the formula:

The final suitability raster, derived from the MCDA-weighted overlay analysis, was symbiological rendered using classified colour gradients and composited over a satellite basemap.

The final suitability map was categorized into three intuitive classes:

• High Suitable (light yellow)

• Moderate Suitable

• Low Suitable (deep red to blue)

This helped visualize the spatial distribution of development potential.

Conclusion:

The Multi-Criteria Decision Analysis (MCDA) approach effectively integrated diverse geospatial datasets to assess land suitability across the Bengaluru region. By combining layers such as slope, land surface temperature (LST), land use/land cover (LULC), precipitation, and soil characteristics, the analysis provided a comprehensive understanding of environmental and physical constraints. Each criterion was standardized and weighted based on relevance to sustainable land planning. The final output—a classified suitability map—highlights zones ranging from low to highly suitable areas for development. This spatial decision-support tool enables urban planners, researchers, and policymakers to make informed, data-driven decisions. The project demonstrates the power of GIS and remote sensing in land resource management and regional planning. Future work can incorporate socio-economic and infrastructure layers for enhanced precision.