Bengaluru Green Cover Change (2010-2023): A Google Earth Engine Analysis

Introduction

Bengaluru, often referred to as the Silicon Valley of India, is a hub of economic and technological development. However, with rapid urbanization over the years, the city has faced significant environmental challenges, particularly the depletion of its green cover. Between 2010 and 2023, Bengaluru experienced a population boom of over 50%, leading to increased demand for housing, transportation, and commercial infrastructure. This urban expansion has frequently come at the expense of natural ecosystems and vegetation. The use of advanced geospatial technologies like Google Earth Engine (GEE) allows us to quantify and analyze these changes, providing actionable insights for policymakers, conservationists, and urban planners.

This study focuses on detecting and visualizing green cover changes in Bengaluru over the last decade. Using Landsat data and supervised classification techniques, the analysis highlights the areas of vegetation loss, gain, and stability, providing a comprehensive understanding of the city's evolving landscape.

Step 1: Setting Up the Study Area and Background

The first step involves defining the study area for analysis, such as Bengaluru, and setting a blank background layer to ensure visualization clarity. This provides a clean map base and helps focus on the target area.

Step 2: Loading and Preprocessing Landsat Data

We use Landsat 5 imagery for the year 2010 and Landsat 9 imagery for the year 2023. By filtering for specific dates, cloud cover, and bounding the data to the study area, we create median composites for analysis.

For 2010 Landsat 5 Data:

We filter images taken in 2010, with less than 1% cloud cover, and calculate the median pixel value for each band.

For 2023 Landsat 9 Data:

A similar approach is used for 2023, but with the Landsat 9 dataset.

Step 3: Training Data Preparation

To classify land cover types, we need training data. This involves merging green Cover samples and other land cover classes. Each sample is labeled with a class field.

Step 4: Supervised Classification

Using a random forest classifier, we classify the land cover types for both years. The dataset is split into training and testing subsets, and the classifier is trained on the training set.

Classification for 2010:

Classification for 2023:

Step 5: Reclassification and Change Detection

We align the classes between years and calculate:

• Green Cover Loss: Green Cover in 2010 but not in 2023.

• Green Cover Gain: Green Cover absent in 2010 but present in 2023.

• Unchanged Areas: No change between 2010 and 2023.

Step 6: Visualization of Results

Visualize the results with distinct color codes:

• Red: Green cover loss.

• Green: Green cover gain.

• Yellow: Unchanged areas.

Step 7: Calculating Areas

Using pixel areas, compute the area for green cover loss, gain, and unchanged regions.

Step 8: Exporting the Results

Export the classified images and change detection results for further analysis.

Step 9: Adding a Legend

A legend provides clarity on the visualization.

Conclusion

The analysis of Bengaluru's green cover between 2010 and 2023 reveals a worrying trend of significant vegetation loss due to urban expansion. Red-colored regions on the final output map represent areas where green cover has been depleted, often due to deforestation, infrastructure development, and encroachment on natural ecosystems. In contrast, the green-colored regions, which indicate vegetation gain, are minimal and do not compensate for the extensive loss observed. Yellow-colored areas represent unchanged areas, highlighting stable regions that need continued protection.

Key findings from this study include:

1. Extensive green cover loss due to urban development and encroachment.

2. Minimal vegetation gain compared to the scale of vegetation depletion.

3. The critical need for sustainable urban planning and green initiatives.

To address this environmental concern, collaborative efforts are essential. Community-driven initiatives such as neighbourhood tree-planting drives, improved waste management practices, and rainwater harvesting can contribute to green cover restoration. Authorities should implement Corporate Social Responsibility (CSR) programs, incentivize green projects, and focus on lake restoration and urban afforestation to reverse the damage.

This study highlights the utility of Google Earth Engine as a tool for monitoring and managing environmental changes. By adopting similar methodologies, urban planners and conservationists can develop data-driven strategies to promote sustainable development and ensure the preservation of natural ecosystems in rapidly urbanizing regions.