VIRTUAL MICRO-INTERNSHIP

1. Set Up Your Environment

• Install Python: If you don't have Python installed, download it for free from the official website: https://www.python.org/.

• Install Required Libraries: Use the pip command in your terminal or command prompt to install the necessary library:

Bash

pip install earthengine-api

2. Create a Google Earth Engine Account

Visit https://earthengine.google.com/ and sign up for a free account. This account will allow you to access and process data on the Google Earth Engine platform.

3. Initialise Earth Engine

Open a Python script or Jupyter Notebook and import the ee library:

Python

import ee

Authenticate your GEE account using the following line:

Python

ee.Authenticate()

Note: You'll be prompted to open a link in your web browser and enter a code to complete the authentication process.

4. Define Your Region of Interest (ROI)

• Use online tools like Google Maps or dedicated GIS software to define the coastal region you're interested in analysing.

• Convert the ROI boundaries into a GEE geometry object using the ee.Geometry.Rectangle class. Here's an example:

Python

geometry = ee.Geometry.Rectangle([-0.35, 53.75, -0.15, 53.85]) # Adjust coordinates for your specific region

5. Load Data

LiDAR Data:

• We'll be using the National Elevation Dataset (NED) provided by the USGS.

• Access the data directly within GEE using the ee.ImageCollection class and the collection ID 'USGS/NED'.

Python

lidar_image = ee.ImageCollection('USGS/NED')

Sentinel-2 Data:

• Google Earth Engine offers access to a vast collection of satellite imagery, including Sentinel-2 data from the European Space Agency (ESA).

• Use the ee.ImageCollection class and the collection ID 'COPERNICUS/S2' to load Sentinel-2 imagery.

Python

sentinel_image = ee.ImageCollection('COPERNICUS/S2')

Alternative Sources:

• While this guide focuses on freely available data within GEE, you may explore additional LiDAR datasets from organisations like the Environment Agency (https://environment.data.gov.uk/) or the British Geological Survey (BGS) (https://www.bgs.ac.uk/map-viewers/geology-of-britain-viewer/).

6. Filter and Prepare Data

Filtering by Region and Time:

• Use the filterBounds method with your defined geometry (geometry) to filter the data for your specific ROI.

• The filterDate method allows you to select a desired time range. In this example, we're selecting data from 2015-01-01 to 2020-12-31:

Python

lidar_image_filtered = lidar_image.filterBounds(geometry).median().clip(geometry) sentinel_image_filtered = sentinel_image.filterBounds(geometry) \ .filterDate('2015-01-01', '2020-12-31').median().clip(geometry)

• The median method calculates the median pixel value across all images in the collection.

• The clip method ensures data processing only occurs within the defined ROI boundaries.

Ensuring Consistent Projection:

• It's essential to have data in a common projection system for accurate analysis. Utilize the project method to project the Sentinel-2 data to a desired projection, such as EPSG:4326 (WGS84):

Python

sentinel_image_projected = sentinel_image_filtered.project(crs='EPSG:4326')

7. Extract Features

Elevation:

• Extract elevation information from the filtered LiDAR data using the select method to choose the specific band containing elevation values:

Python

elevation = lidar_image_filtered.select('elevation')

Vegetation Indices:

• Calculate the NDVI (Normalized Difference Vegetation Index) from the Sentinel-2 data. The normalizedDifference method takes two bands (B8 and B4) as input for calculating NDVI:

Python

ndvi = sentinel_image_filtered.normalizedDifference(['B8', 'B4'])

NDVI helps assess changes in vegetation cover over time.

Introduction (200-300 words)

• Purpose: Clearly state the objective of the study (e.g., identifying coastal erosion hotspots).

• Methodology: Briefly describe the data used (LiDAR, satellite imagery), analysis techniques (e.g., PCA, image differencing), and tools (e.g., GEE).

Data and Methods (300-500 words)

• Data Acquisition: Explain how you obtained LiDAR and satellite data for your region.

• Data Preprocessing: Describe steps taken to prepare data (e.g., filtering, projection).

• Feature Extraction: List features extracted (e.g., elevation, NDVI).

• Change Detection: Explain the techniques used to identify changes (e.g., PCA, image differencing).

Results (500-700 words)

• Key Findings: Present significant areas of change (e.g., erosion hotspots, vegetation loss).

• Visualisation: Include maps, charts, or animations to illustrate results.

• Quantify Change: Use metrics (e.g., area of change, rate of erosion) to quantify the extent of change.

Discussion (300-500 words)

• Interpret Findings: Relate changes to coastal processes (e.g., erosion, sea-level rise).

• Assess Risk: Discuss potential risks associated with the changes (e.g., flooding, habitat loss).

• Compare with Existing Studies: Compare your findings with previous research in the area.

Conclusions (100-200 words)

• Summarise Key Findings: Briefly recap the main results.

• Implications: Discuss the implications of your findings for coastal management.

• Future Research: Suggest potential areas for further investigation.

Remember:

• Use clear language and avoid technical jargon.

• Tailor the report to your specific findings.

• Proofread carefully for errors and clarity.

Total Duration: 2-3 minutes

1. Introduction (20-30 seconds)

   • Purpose: Start by stating the objective of your study, e.g., "In this video, I will discuss how we identified coastal erosion hotspots using remote sensing data."

   • Methodology: Briefly mention the data sources (LiDAR and satellite imagery) and the tools used (e.g., Google Earth Engine).

2. Data and Methods (40-60 seconds)

   • Data Acquisition: Explain how you obtained your LiDAR and satellite data, including specific sources.

   • Data Preprocessing: Briefly outline steps taken to prepare the data, like filtering and projection.

   • Change Detection: Describe the techniques used to identify changes in the data, such as PCA or image differencing.

3. Results (40-60 seconds)

   • Key Findings: Highlight significant areas of change, such as erosion hotspots or vegetation loss.

   • Visualisation: Present a few key visuals (maps or charts) to illustrate your findings effectively.

   • Quantify Change: Share metrics that show the extent of change, like area of erosion.

4. Discussion and Conclusions (30-40 seconds)

   • Interpret Findings: Discuss what the changes imply for coastal processes and management.

   • Summarise Key Findings: Recap the main results and their significance for future planning.

Tips for Your Video:

• Speak clearly and at a moderate pace to ensure understanding.

• Use visuals effectively; consider showing relevant images or charts while discussing them.

• Keep your content focused and avoid unnecessary jargon.

• Rehearse to ensure you stay within the time limit and maintain a smooth delivery.

Total Duration: 5-10 slides

1. Slide 1: Title Slide

   • Region of Choice

   • Your Name

   • Date

2. Slide 2: Introduction (1 slide)

   • Purpose: Clearly state the objective of your study (e.g., "Identifying coastal erosion hotspots using remote sensing data").

     Methodology: Briefly mention the data sources (LiDAR and satellite imagery) and tools (e.g., Google Earth Engine).

3. Slide 3: Data Acquisition (1 slide)

   • Data Sources: List where you obtained the LiDAR and satellite data (e.g., UK LiDAR, European Sentinel data).

   • Accessing Data: Provide a brief explanation of the process for obtaining the data.

4. Slide 4: Data Preprocessing (1 slide)

   • Steps Taken: Describe key preprocessing steps (e.g., filtering, projection).

   • Tools Used: Mention any specific tools or software employed for preprocessing.

5. Slide 5: Change Detection Methods (1 slide)

   • Techniques Used: List techniques for identifying changes (e.g., PCA, image differencing).

   • Rationale: Briefly explain why these techniques were chosen.

6. Slide 6: Key Findings (1 slide)

   • Significant Changes: Highlight major areas of change (e.g., erosion hotspots, vegetation loss).

   • Visualisations: Include relevant maps or charts to illustrate your findings.

7. Slide 7: Quantifying Change (1 slide)

   • Metrics: Share quantitative data on the extent of changes observed (e.g., area of erosion, rate of change).

   • Implications: Discuss what these metrics suggest about coastal health.

8. Slide 8: Discussion (1 slide)

   • Interpret Findings: Relate changes to coastal processes (e.g., erosion, sea-level rise).

   • Assess Risk: Discuss potential risks associated with these changes (e.g., flooding, habitat loss).

9. Slide 9: Conclusions (1 slide)

   • Summary of Key Findings: Recap the main results and their significance.

   • Future Research: Suggest potential areas for further investigation.

Tips for Your Presentation:

• Keep slides visually engaging: use images and charts

• Proofread your slides for clarity and correctness.