The Rising Tide: Visualizing the Impact of a 5-Meter Sea Level Increase on Mumbai with GIS Technology

Introduction:

Start with a brief overview of climate change and its potential impacts, specifically focusing on sea level rise. Introduce Mumbai as a case study for this analysis due to its coastal location and significance. Mention the use of GIS technology (ArcGIS Pro and QGIS) and various datasets, including Google's Open Buildings, SRTM DEM images, coastal and maritime boundaries, and vector layers for roads, railways, water bodies, and green spaces.

Background:

• Climate Change and Sea Level Rise: Explain the science behind sea level rise as a consequence of global warming.
• Mumbai's Vulnerability: Discuss why Mumbai is particularly at risk, including its geographic, economic, and demographic aspects.
• GIS in Climate Change Analysis: Briefly introduce GIS technology and its importance in analyzing and visualizing climate change impacts.

Data and Methodology:

• Data Sources: Describe each data source used in the study, including their origins, contents, and relevance.
• GIS Tools and Techniques: Elaborate on how ArcGIS Pro and QGIS were utilized to overlay and analyze the data. Discuss the process of simulating a 5-meter sea level rise.
• Challenges and Limitations: Address any challenges encountered during the analysis, including data accuracy, projection issues, or software limitations.

Analysis and Findings:

• Impacted Areas: Detail which parts of Mumbai are most at risk, supported by maps and visuals created through GIS. Discuss the implications for residential areas, infrastructure, and key landmarks.
• Infrastructure at Risk: Focus on critical infrastructure such as roads, railways, and utilities. Analyze the potential disruptions and the cascading effects on the city's functionality.
• Environmental and Social Impacts: Assess the impact on green spaces, water bodies, and the broader ecological system. Discuss how communities, especially marginalized ones, are affected.
• Comparative Analysis: If possible, compare Mumbai's situation with other coastal cities globally to provide a broader context.

Implications and Solutions:

• Mitigation Strategies: Discuss potential mitigation strategies to combat sea level rise, including both global (e.g., reducing carbon emissions) and local (e.g., building sea walls) approaches.
• Adaptation Measures: Suggest how Mumbai can adapt to the inevitable changes, such as through urban planning and community resilience programs.
• The Role of Technology: Highlight how GIS and other technologies can aid in both understanding the problem and implementing solutions.

Conclusion:

Summarize the key findings and the importance of taking immediate action to mitigate and adapt to sea level rise. Emphasize the role of GIS technology in planning for climate change impacts. End with a call to action for policymakers, researchers, and the general public.

References:

Include all the data sources, research papers, and additional reading materials used for your analysis.

Introduction Draft:

The specter of climate change looms large over the globe, manifesting in extreme weather events, temperature anomalies, and a phenomenon deeply concerning for coastal cities worldwide: sea level rise. Mumbai, India's bustling financial capital, stands on the frontline of this environmental challenge. Given its geographical location, economic significance, and dense population, understanding Mumbai's vulnerability to rising sea levels is not just a local concern but a global imperative.

This article employs Geographic Information Systems (GIS) technology, specifically ArcGIS Pro and QGIS, to visualize and analyze the impact of a hypothetical 5-meter rise in sea levels on Mumbai. Through an intricate layering of data from Google's Open Buildings, SRTM DEM images, and various vector layers depicting the city's infrastructure and natural geography, we embark on a comprehensive examination of what the future might hold for this vibrant city under siege by the changing climate.

Generating 1 km x 1 km Square Polygons Automatically in Google Earth Engine

 Introduction:

Google Earth Engine (GEE) is a powerful platform that allows users to analyze and visualize geospatial data at an unprecedented scale. In this tutorial, we'll explore how to create 1 km x 1 km square polygons automatically using Google Earth Engine. This can be incredibly useful for tasks such as defining study areas, generating sample regions, or creating consistent spatial units for analysis. Let's dive in!

Step 1: Setting Up Google Earth Engine Environment

If you haven't already, sign up for a Google Earth Engine account and access the GEE Code Editor. This cloud-based platform provides an interactive environment to write and run code for geospatial analysis.

Step 2: Defining the Area of Interest (AOI)

Start by defining the geographical coordinates or a region of interest where you want to create the 1 km x 1 km square polygons. For instance, you might be interested in a specific location for a study.

Figure 1: Autogenerate 1sq km polygon

Step 3: Writing the Code

In the GEE Code Editor, use JavaScript to write the code for generating the square polygons.

CODE:--------------------------------------------------------

var targetArea = ee.Number(1e6)

function toSquare(buffer) {

  return point.buffer(buffer, 1).bounds()

}

function getBuffer(area) {

  return ee.Number(area).sqrt().divide(2)

}

var buffer = getBuffer(targetArea)

var minBuffer = buffer.multiply(0.99)

var maxBuffer = buffer.multiply(1.01)

var square, area

for (var i = 0; i < 100; i++) {

  var buffer = minBuffer.add(maxBuffer).divide(2)

  square = toSquare(buffer)

  area = square.area(1)

  minBuffer = ee.Number(ee.Algorithms.If(area.lt(targetArea), buffer, minBuffer))

  maxBuffer = ee.Number(ee.Algorithms.If(area.gt(targetArea), buffer, maxBuffer))

}

print(square)

print("Area of Square", area)

Map.addLayer(square)

Map.centerObject(square)

//Export to Drive

var generatedGeometry = square;

var geojsonFeature = ee.Feature(generatedGeometry);

var geojson = geojsonFeature.toGeoJSONString();

Export.string.toDrive({

  data: geojson,

  description: '1sq_km',

  folder: 'GEE',

  fileFormat: 'GeoJSON'

});

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Step 4: Customizing the Output

You can customize the appearance of the generated square polygon by adjusting the visualization parameters. For instance, you can change the color, opacity, and border properties of the polygon to suit your needs.

Step 5: Exporting and Sharing

Once you're satisfied with the generated square polygon, you can explore further analyses within Google Earth Engine or export the polygon for use in other GIS software. Here I export the polygon in GeoJSON format in my google drive.

Conclusion:

Automatically generating 1 km x 1 km square polygons in Google Earth Engine is a straightforward process that can assist in various geospatial tasks. This tutorial demonstrated the basic steps to create a square polygon around a given center point. As you become more familiar with Google Earth Engine's capabilities, you can build on this foundation to create more complex polygons, integrate other datasets, and perform advanced spatial analyses.

Remember that Google Earth Engine's extensive documentation and user community are excellent resources for expanding your knowledge and exploring the platform's full potential. 

Happy mapping!