Learn how to clip images with rasterio using geojson

Table of Contents

1. Introduction
2. Clipping Satellite Imagery
3. Accessing the Required Files
4. Defining the Area of Interest
5. Clipping the Image
6. Visualizing the Clipped Image
7. Manipulating Metadata
8. Exporting the Clipped Image
9. Analyzing the Clipped Image
10. Conclusion

Introduction

In today's class, we will be focusing on the process of clipping satellite imagery. This technique allows us to take a much larger satellite image and define a specific area of interest within it. By doing so, we can extract and work with a smaller portion of the image, making it more efficient and manageable. In this tutorial, we will cover the step-by-step process of clipping an image, from accessing the necessary files to exporting and analyzing the clipped image. Let's get started!

1. Clipping Satellite Imagery

Satellite imagery often consists of vast areas that may be too large for easy analysis and processing. Clipping allows us to extract a specific area of interest from these larger images, enabling us to focus on specific features or phenomena within the image.

2. Accessing the Required Files

Before we begin clipping the image, we need to ensure that we have the necessary files. In this tutorial, we will be using Anaconda, Jupyter Notebook, and various Python libraries such as Rasterio and Matplotlib. Make sure You have these installed and set up in your environment. Additionally, you will need to download the image we will be working with from the appropriate source (e.g., Planet API or Sentinel). This image should be saved in a location accessible to your Jupyter Notebook.

3. Defining the Area of Interest

Once we have our files in place, we can start defining the area of interest. The area of interest specifies the region within the larger image that we want to extract. In this tutorial, we will be using a GeoJSON polygon Shape to represent the area of interest. You can either Create this shape manually or load pre-defined shape files. Ensure that you specify the coordinates correctly and follow the GeoJSON structure.

4. Clipping the Image

With the area of interest defined, we can move forward with the actual clipping process. Using the Rasterio library, we will open the image file and Apply the mask function with the specified area of interest. This function will effectively hide the parts of the image that fall outside the defined area, resulting in a clipped version of the image.

5. Visualizing the Clipped Image

After clipping the image, we can Visualize the results to ensure that the process was successful. Using Matplotlib, we can display the clipped image within our Jupyter Notebook. This will allow us to inspect the image, check for any issues, and assess the quality of the clipping process.

6. Manipulating Metadata

The metadata of the image contains important information about the image's characteristics, such as its width, Height, bands, and coordinate reference system (CRS). Before exporting the clipped image, we may need to adjust some metadata values to accurately represent the new image size. We will demonstrate how to modify the metadata and ensure it aligns with the clipped image.

7. Exporting the Clipped Image

Once We Are satisfied with the clipped image, we can export it for further analysis or use in other GIS software. Using Rasterio, we will create a new file and write the clipped image data along with the updated metadata. This will result in a new GeoTIFF file that contains only the clipped region.

8. Analyzing the Clipped Image

With the clipped image exported, we can proceed to analyze it for specific purposes. In this exercise, we will prompt you to perform a simple analysis, such as counting the number of ships or boats present in the clipped image. This will allow you to practice working with the clipped image and apply any further processing or analysis as required.

9. Conclusion

In conclusion, the process of clipping satellite imagery allows us to extract and work with specific areas of interest within larger images. By utilizing Python libraries such as Rasterio and Matplotlib, we can easily perform this clipping process, visualize the results, and export the clipped image for further analysis. Through this tutorial, you will gain hands-on experience with clipping imagery and gain valuable skills for working with satellite data in your own projects.

Article

Introduction

In today's class, we will be focusing on the process of clipping satellite imagery. This technique allows us to take a much larger satellite image and define a specific area of interest within it. By doing so, we can extract and work with a smaller portion of the image, making it more efficient and manageable. In this tutorial, we will cover the step-by-step process of clipping an image, from accessing the necessary files to exporting and analyzing the clipped image. Let's get started!

Clipping Satellite Imagery

Satellite imagery often consists of vast areas that may be too large for easy analysis and processing. Clipping allows us to extract a specific area of interest from these larger images, enabling us to focus on specific features or phenomena within the image. By defining a smaller area within the image, we can reduce the computational load and make it easier to work with the data.

Accessing the Required Files

Before we begin clipping the image, we need to ensure that we have the necessary files. This includes having Anaconda and Jupyter Notebook installed, as well as the required Python libraries such as Rasterio and Matplotlib. Additionally, we will need the satellite image file that we want to clip. This file should be downloaded and saved in a location accessible to your Jupyter Notebook.

Defining the Area of Interest

Once we have all the required files in place, we can start defining the area of interest within the satellite image. The area of interest represents the specific region or feature that we want to extract from the larger image. In this tutorial, we will be using a GeoJSON polygon shape to define the area of interest. This shape can be created manually or imported from existing shape files. It's important to ensure that the coordinates are specified correctly and follow the GeoJSON structure. By defining the area of interest, we can narrow down our focus and extract Relevant information from the larger image.

Clipping the Image

With the area of interest defined, we can move forward with the actual clipping process. Using the Rasterio library, we will open the satellite image file and apply the mask function with the defined area of interest. The mask function allows us to hide the parts of the image that fall outside the area of interest, effectively creating a clipped version of the image. This clipped image will only contain the specified area, making it easier to work with and analyze. The clipping process helps in reducing the complexity of the image and extracting relevant information for further analysis.

Visualizing the Clipped Image

After clipping the image, it's important to visually inspect the results to ensure that the process was successful. Using Matplotlib, we can display the clipped image within our Jupyter Notebook. This visualization allows us to assess the quality of the clipping process, check for any issues, and gain a better understanding of the extracted area. By visualizing the clipped image, we can ensure that the relevant portion of the satellite image has been successfully extracted and is ready for further analysis.

Manipulating Metadata

The metadata of the satellite image contains important information about its characteristics, such as its width, height, bands, and coordinate reference system (CRS). Before exporting the clipped image, we may need to manipulate some of the metadata values to accurately represent the new image size. Using the existing metadata as a base, we can modify the width and height to match the Dimensions of the clipped image. This ensures that any subsequent analysis or processing recognizes the correct image dimensions, allowing for accurate interpretation and comparison.

Exporting the Clipped Image

Once we are satisfied with the clipped image, we can proceed to export it for further analysis or use in other GIS software. Using the Rasterio library, we will create a new file and write the clipped image data along with the updated metadata. This results in a new GeoTIFF file that contains only the clipped region. This exported file can then be easily shared, analyzed, or further processed as needed. By exporting the clipped image, we can utilize it in various applications, such as map-making, data visualization, or remote sensing analysis.

Analyzing the Clipped Image

With the clipped image exported, we can now focus on analyzing the specific area of interest. Depending on the objective, we can perform various analyses on the extracted region. For example, we may count the number of ships or boats present in the clipped image, assess the vegetation cover, monitor changes over time, or identify specific features within the area. The clipped image provides a more manageable dataset, allowing for targeted analysis and interpretation. By conducting in-depth analysis, we can gain valuable insights and information from the clipped image.

Conclusion

Clipping satellite imagery is a powerful technique that enables us to extract specific areas of interest from larger satellite images. By defining a smaller region within the image, we can focus on particular features, phenomena, or regions of importance. This process makes it easier to work with the data, reduces computational load, and allows for targeted analysis. Through this tutorial, you have learned the step-by-step process of clipping satellite imagery, from accessing the required files to exporting and analyzing the clipped image. By applying these techniques, you can effectively extract relevant information from satellite imagery for a wide range of applications and studies.

Highlights

• Clipping satellite imagery allows us to extract specific areas of interest from larger images.
• By defining a smaller region, we can focus on specific features or phenomena within the image.
• Clipping helps in reducing computational load and makes it easier to work with the data.
• The process involves accessing the required files, defining the area of interest, clipping the image, visualizing the results, manipulating metadata, exporting the clipped image, and analyzing the specific region.
• Python libraries such as Rasterio and Matplotlib are used for the clipping process.
• The clipped image can be further analyzed and utilized for various applications.

FAQ

Q: What is the purpose of clipping satellite imagery? A: The purpose of clipping satellite imagery is to extract specific areas of interest from larger images, enabling focused analysis and reducing computational load.

Q: How can I define the area of interest within a satellite image? A: The area of interest can be defined using a GeoJSON polygon shape, specifying the coordinates that encompass the desired region within the larger image.

Q: Can I manipulate the metadata of a clipped image? A: Yes, the metadata of a clipped image can be manipulated to accurately represent the new image size or characteristics.

Q: What are some possible analyses that can be performed on a clipped image? A: Some possible analyses include counting objects within the clipped region, assessing vegetation cover, monitoring changes over time, or identifying specific features within the area of interest.

Q: What Python libraries are used for clipping satellite imagery? A: Rasterio and Matplotlib are commonly used Python libraries for clipping satellite imagery and visualizing the results.

Q: How can the clipped image be exported for further analysis? A: The clipped image can be exported as a new GeoTIFF file using the Rasterio library, preserving the clipped region and relevant metadata.