Working with Satellite Imagery and Google Earth Engine in Python
Looking up at the stars on a clear night is a beautiful experience. But have you ever wondered what all the lights on Earth look like at night?
You can analyze these patterns through satellite imagery, and in this article, we’ll explore how to use Google Earth Engine, Python’s powerful platform to work with planetary data, for this purpose.to Google Earth Engine
Google Earth Engine is an extraordinary tool that enables developers to explore different geospatial data sources and analyze them right in their browsers. Earth Engine provides access to massive-scale, multi-sensor image datasets.
With Google Earth Engine, developers can perform high-performance computing operations with ease. The program is supported by Google’s massive infrastructure, allowing parallelized operations on huge datasets, with thousands of processors working in tandem.
How to Install and Authenticate
To use Google Earth Engine with Python, you need to authenticate first. To authenticate, you require the credentials provided by Google.
To acquire them, follow the Google Earth Engine Python API setup instructions. Before authenticating, you must install the Google Earth Engine Python API library.
The project sources for the ee Python package live in the Earth Engine API GitHub Repository. If you prefer to install the package directly from the GitHub repository, you can run the following command in your terminal: $ pip install google-api-python-client google-auth google-auth-oauthlib google-auth-httplib2 earthengine-api.
How to Load the Image Dataset
Google Earth Engine datasets are in cloud storage. To load an image collection for nighttime lights, use “DMSP OLS Nighttime Lights Time Series”.
The `.collection()` method is the imageCollection constructor.
Working with the Image Collection
The image collection contains information on bands – these are the different types of electromagnetic radiation recorded by the satellite sensors. The dataset we are using considers information about the nighttime lights recorded by satellites.
Therefore, a proper way of using the dataset is to construct some bands that are relevant to the temporal patterns for the nighttime lights, such as `createTimeBand()`, which represents the time by a decimal storing the year since January 1st, 1970.
Visualizing the Night Lights
To visualize the nighttime lights dataset in a more interactive manner using Python, we suggest using the web mapping library Folium, which is easy to set up and comes with the ability to integrate with Leaflet.js. Visualizing images in Folium involves loading the dataset we want and adding it to a Folium map using the `add_ee_layer` method.
The method `ee.ImageCollection.reduce()` can help perform low-level linear regression of pixel values through time. Import the `ee.ImageCollection` and run the reduce method, followed by the `linearFit()` method to retrieve the night lights’ linear regression model for pixel values.
Pass the `ee.Image` object and `Band names` to the `add_ee_layer` function to display the nighttime lights map.
Using Folium for Interactive Mapsto Folium
Folium is a powerful Python library that makes it easy to visualize data interactively on a Leaflet.js map. It enables users to explore and visualize geospatial data in web maps that can be embedded in Jupyter notebooks or published as standalone HTML files.
The basic premise of Folium is to create maps, add data overlays, and customize the interactive components as needed.
Defining How to Display Google Earth Images in Folium
To display Google Earth Engine images in Folium, we can use the `add_ee_layer` method, which takes both `ee.Image` objects and parameter definitions. The parameters are defined using the `ee.Image.visualize()` method, where the parameter name is the argument name, and the value is the argument value.
Creating the Map and Adding Parameters
Now that we have successfully defined how to display Google Earth images in Folium, the next step is to create the map itself and add some parameters to facilitate navigation. The `Map()` function from the `folium` library is used to create the map, followed by adding parameters such as the `location`, `zoom_start` to define the center and level of zoom of the map.
In conclusion, working with satellite imagery and Google Earth Engine in Python is a great way to gain insights from geospatial data at a large scale. The Earth Engine API allows Python users to work with many Earth science tools and datasets, making it straightforward to analyze geospatial data, including nighttime lights time-series data.
By using Python’s technology stack libraries and Folium, we can create insightful interactive maps that can be embedded in web applications or used as standalone HTML files. Conclusion:
As discussed in the previous sections, working with satellite imagery and Google Earth Engine in Python is a valuable tool in gaining insights into geospatial data at a large scale.
From exploring different geospatial data sources to analyzing them in real-time, Google Earth Engine allows developers to perform high-performance computing operations with ease. But what if we want to use different image datasets or analyze different data features?
In this section, we will explore further exploration with different bands and datasets, such as detecting changes in Sentinel-1 imagery and other geo-spatial analyses.
Detecting Changes in Sentinel-1 Imagery
Sentinel-1 is a synthetic aperture radar satellite that provides high-quality imagery for a range of applications, including land cover classification, urban mapping, disaster monitoring, and more. The images captured by Sentinel-1 can provide incredible detail on geo-spatial features such as topography, water bodies, vegetation, and changes in land usage.
By using Google Earth Engine, Python users can take advantage of Sentinel-1’s imagery to detect changes in the landscape. Sentinel-1 is unique in that it can capture images through clouds and in various weather conditions, making it useful for natural disaster monitoring.
By utilizing the advanced algorithmic capabilities of Google Earth Engine with Sentinel-1’s satellite imagery, developers can detect changes in the landscape, such as changes in water bodies’ levels, erosion, land use, agricultural activity, and even natural and human-made disasters.
Geo-Spatial Analysis with Google Earth Engine
Google Earth Engine is not limited to just satellite imagery. The platform can also handle a range of geo-spatial datasets, such as topographic data, vector data, and more.
With its ability to work with multiple data sets at high speed, Google Earth Engine is the ideal platform for geo-spatial analysis. Users can perform a range of GIS operations, such as joining, filtering, and aggregating data to provide insights into various geographic features.
For instance, a developer can bring together a range of data sets such as traffic data, land cover data, and terrain data, to give insights into traffic patterns through time, land use patterns, and how terrain features affect traffic flow. Moreover, Google Earth Engine can also be used to study changes in ecological systems, such as monitoring vegetation cover, deforestation, and desertification.
These capabilities make Google Earth Engine a valuable tool in studying natural ecosystems, biological systems, and geological systems.
Google Earth Engine, in combination with Python, is an unparalleled platform for working with geospatial data. The platform’s high-performance computing capabilities allow for real-time analysis and processing of large-scale datasets, and the platform can handle a range of data formats.
By combining different data sets, developers can gain insights into different geographic features, such as changes in land use, water, vegetation, and more. With new geo-spatial data sources emerging, the possibilities of exploring the world continue to grow.
Thanks to Google Earth Engine, we can continue to make sense of our world at scale and in real-time, generating insights that help us understand this planet better. As researchers continue to explore new ways of utilizing the data present, we can expect continued progress towards making our world a better place.
In conclusion, this article has highlighted the importance of working with satellite imagery and Google Earth Engine in Python to explore geospatial data at scale. We have discussed how the platform allows developers to access massive-scale, multi-sensor image datasets and perform high-performance computing operations with ease.
Using Google Earth Engine with Python, developers can explore different geospatial data sources and analyze them in real-time. Additionally, we’ve explored how developers can integrate Folium to create insightful interactive maps that can be embedded in web applications or used as standalone HTML files.
Lastly, we have discussed how Google Earth Engine can be used to detect changes in Sentinel-1 imagery and for geo-spatial analysis. With new geo-spatial data sources emerging, platforms like Google Earth Engine will continue to play a vital role in exploring and understanding our world.