Interpreting Daytime Resolution
Aside from sensor type, which determines the type of data that the satellite detects, the resolution selection has the biggest impact on your satellite image and the data you can extract from it. Thorough understanding of resolution is critical to effectively utilize your image and its application. In this blog we will be looking at daytime resolutions only.
What is Ground Sampling Distance (GSD) Resolution?
GSD stands for Ground Sampling Distance in the context of satellite imagery and remote sensing. It refers to the physical size of one pixel in an image, measured in real-world units such as meters. GSD represents the level of detail and spatial resolution in satellite imagery.
In simpler terms, GSD tells you how much area on the Earth's surface each pixel in the image represents. A smaller GSD, like 30 cm, means that each pixel represents a smaller area and can capture finer details. Conversely, a larger GSD, like 75 cm, means that each pixel covers a larger area and provides less detail.
So what’s the difference?
Let’s take a look.
30 cm Resolution:
Description: At 30 cm GSD, each pixel in the image represents a tiny 30 cm x 30 cm area on Earth's surface, providing incredibly fine-grained detail.
Use Case: This level of detail is vital for applications like monitoring things like construction and activity (parking lots, boats, etc.), and urban planning.
50 cm Resolution:
Description: With a GSD of 50 cm, each pixel captures a slightly larger 50 cm x 50 cm section on the ground, still offering high-resolution imagery.
Use Case: Useful for mapping small scale features, such as individual trees and cars, and detailed infrastructure planning in urban areas.
75 cm Resolution:
Description: A GSD of 75 cm provides a bit coarser view, with each pixel representing a 75 cm x 75 cm area.
Use Case: This resolution can be used for mapping larger vegetation areas, assessing medium-sized construction sites, and urban expansion analysis.
It's also worth noting that the scale of your project and the type of data you're interested in can impact the resolution you need. For example, if you're studying a large area, you may need a lower resolution to capture the entire area in one image. In such cases, utilizing open data sources like Sentinel 2 can be particularly valuable. Open data is completely free on SkyFi with images updated every five days. Open data is freely accessible data with a resolution of 10 meters, which, while offering less detail at the pixel level, allows you to analyze vast regions without incurring additional costs.
Open Data – 10 m Resolution (Sentinel 2):
Description: With a GSD of 10 meters, each pixel represents a 10 m x 10 m area on Earth's surface. While less detailed at the pixel level compared to higher resolutions, it offers a broader view suitable for regional assessments.
Use Case: Open data at 10-meter resolution is ideal for large-scale land cover mapping, monitoring changes in vegetation cover over entire regions, assessing the extent of natural disasters, and tracking agricultural trends across vast agricultural areas. And the best part? It's available for free, making it an accessible choice for many remote sensing applications on a grand scale.
Considering Cost and AOI Size
Lastly, it's important to consider the cost and minimum area of interest (AOI) size when selecting a resolution. While high resolution imagery may have a higher price per square kilometer, the minimum AOI requirement is often smaller, which can make it less expensive than medium resolution imagery for smaller projects.
In conclusion, interpreting resolution can be complicated, but understanding how it impacts the level of detail you can see and what factors to consider when selecting a resolution can help you make the best decision for your intended use case.