Ground sample distance explained and why it matters

Illustration of a stockpile with the WingtraOne drone flying over and scanning it, with the scanned area representing GSD

When it comes to aerial data, a surveyor’s job is to capture useful insights that their company can act upon. How useful those insights are depends on the quality of the data collected. 

The most fundamental factor in the quality of the data is ground sample distance (GSD), which will determine the accuracy and precision of the insights and how much they help a project stay on track with minimum overhead.

In this article, we’ll explore the fundamentals of GSD and how you can leverage this knowledge to get the best possible data.

This guide also gives details on why sensors are engineered a certain way to achieve the utmost accuracy and efficiency in projects. We’ll also mention some case studies, the GSDs achieved and why this was important for the overall success of these projects.

Whether you’re a construction site manager, a seasoned surveyor or new to drone technology, this article promises to enhance your understanding and application of GSD in your projects.

Table of Contents

GSD: the ground according to one pixel

The size of the real world captured by a pixel in a digital image, GSD, is based on the flight altitude and camera parameters, which we’ll discuss below. 

When you understand GSD as a relationship between a distance on a mapped surface and a pixel, it’s easy to calculate the size of the ground and features captured in a drone image.

Three surfaces layered on top of each other, showing how the pixel on the ground is captured by the WingtraOne drone
GSD is the amount of actual ground captured by the distance between the center point of two adjacent pixels.

Accuracy and drone ROI

Let’s expand on this logic and say you take aerial images of a railroad track. You set your flight and payload parameters to achieve a GSD of 0.4 in (1 cm). On the surveyed map, you want to check for any gauge deviations on the railroad track. 

To do this, you would measure the distance between the left and right inner part of the railroad tracks. The software will then convert the pixel distance into a real-world distance.

zoomed in orthomosaic map with pixel detail

On the other hand, if you want to know the length down to the sub-centimeter (0.4 in) level, you cannot measure it exactly. Why? 

Each pixel is a single square of visual information without definition. So since in this case any space smaller than 0.4 in (1 cm) measures one pixel, there is no way to zoom in further beyond that square.

The same is true if you need to accurately locate the center of a ground control point or the edge of a building. With a better (lower) GSD, and higher resolution, it will be much easier to see the center point or edge, because each pixel represents a smaller space. In the context of your survey, this kind of detail difference adds up over large areas, and it matters.

How surface GSD impacts volume accuracy

A 1 cm (0.4 in) surface error spread over many meters represents a much bigger volumetric error that can be really expensive in cases of earthwork contract reconciliation and forecasting earthwork that needs to be done.  

For example, if your GSD is 5 cm (2 in), the accuracy error will be at least 5 cm (2 in). And you’ll cube this amount for volumes all along the surface of the stockpile/volume measured.

Stockpile measurement depends on a tight reading around a pile’s surface. The better (lower) your GSD is, the better your accuracy when measuring this surface, which reduces the difference between the real volume and the measured volume to a minimum.

In a road construction project based in Estonia, you can see in real cost, how accurate data makes a difference to budgets around road work (table below). The gravel stockpile volumetric data captured at a good quality in accuracy saved a company hundreds of thousands of dollars, in fact.

Calculation for short one gravel
272,032 m2 x 0.12 m x 38.5 €/m3
million €
Total agreed estimate

Terrestrial capture
A 10 % difference in estimation
000 €
Difference between contractor and subcontractor estimates
Wingtra data
A 2.5 % difference in estimation
500 €
Difference between contractor and subcontractor estimates

Gravel cost adds up. With high-accuracy WingtraOne drone data, surveyors can push the accuracy standard up so the contractor must measure with drones too.

Using terrestrial methods, the difference between real volume and measured volume is averaged around 10 percent. Without drones, at least this much money is under discussion, costing clients or contractors a lot and making reconciliation a huge challenge.

Illustration of a stockpile with markers to show variations in accuracy and GSD
Instead of just looking at the surface area difference, we'll consider the impact on the actual volume. For example, with the current GSD, you might estimate 1,000 cubic centimeters of gravel. However, with a better GSD, you could see that the volume is closer to the actual amount, say 950 cubic centimeters, highlighting the difference in accuracy.

What difference does drone design make to reliable GSD?

As you can see by now, GSD is something enabled or limited by the sensor that captures the pictures. Very basically, the resolution you capture based on the height you fly and the limits of the sensor will avail a certain sample distance value.

And yet the sensor must be carried somehow, so the drone itself makes a difference in terms of how fast it flies as well as what kind of camera it can carry. 

With quadcopter or traditional fixed-wing drones, the system can only support so much weight in a payload. Full-frame and higher resolution sensors are heavy, so only VTOL drones feature passive-lift flight that lets them carry such a camera plus the vertical landing to protect it when it reaches the ground.

Illustration showing how sensor sizes vary from drone company to company. Including DJI Phantom, sensefly S.O.D.A, Aeria X, and Wingtra's RGB61
The size of a payload’s sensor impacts the quality of images, especially those taken from a lower altitude and at high speeds. Measuring the real resolution of images with the same GSD gives an idea of the difference a high-quality payload makes.

To get a better idea of this, visit our drone comparison reports to see the real resolution differences across images taken by cropped and full-frame payloads. 

Sensors aside, the key physical determinant of GSD and resolution is altitude. Most of the time drones used for serious mapping purposes are flying at about 120 m. So if you have a high-enough resolution payload, you can still achieve less than 2 cm GSD and 310 ha at that altitude. Lighter, lower-quality payloads with maximum resolution in the 20 MP range will require lower flying and reduce the coverage when aiming for GSDs in the same range.

So altitude (and GSD) impacts your project’s efficiency, since the lower you go, the more time you will need in the field to cover your project, and in the office to process the data.

Coverage with the RGB61 sensor
Wingtra’s flagship payload, the RGB61, can achieve a 0.75 in/px (1.9 cm/px) GSD at 120m altitude and cover 310 ha (766 ac).

So, why choose a high-quality drone?

The above information makes it clear that if you’re not using a high-quality sensor and a stable drone that flies according to fixed-wing passive lift, you’re already losing out on accuracy if you want to map efficiently. 

Remember that GSD is directly related to the number of pixels in a single shot. So with a high-resolution, 61MP sensor, you will fly up to 2x higher and cover up to 11x more ground in the same time as a multicopter drone with a 20MP sensor flying with the same GSD settings. High-end, heavier sensors require a high-quality VTOL drone to carry them over a broad area, safely.

The right sensor for the right ground sample distance

Many drones compete to carry the latest and greatest sensors, especially VTOLs, which can carry heavier ones. Yet even if they carry the same sensors, it is how the sensor is configured and engineered that will enable reliable GSD according to your needs. 

Wingtra in particular invests a lot in the research and development of its payloads. For example, the RGB61 is an engineered sensor based off of the Sony A7R Mark IV, the best 61 MP in its class for aerial data capture. From this foundation, engineers worked with different settings, including shutter speed and exposure.

 Their aim? Find the perfect balance for fast, sharp and consistent image capture, given the speed the drone flies and the conditions it is expected to perform in.

The larger the sensor, the shorter the shutter speed you can get away with—maximizing light penetration and minimizing blur.

The conditions you are flying in will also change, impacting how that shutter speed works toward the final result. So engineers have adapted the RGB61 with adjustable light settings inside the flight planning app. The difference this makes in saturation and contrast, no matter the ambient conditions, is clear.
Side-by-side comparison of in-app light settings between RX1R II and the RGB61
With our latest RGB addition, the RGB61, you can now control lighting levels right in the app. Fly on sunny or cloudy days, plus, its high-quality lens enables better color saturation and contrast for sharper details.

Range of GSD in UAV photogrammetry

In drone photogrammetry, GSD ranges vary depending on the drone, sensor and the altitude you fly at. You can gather sub-inch (low-cm) level accuracy to multiple feet (meters) accuracy, depending on your needs. How efficiently this happens is another story. 

Achieving sub-inch level (low-cm) GSD is essential for well-rounded analytics and precise data. This level of visibility and tightness of measurement proves effective when measuring stockpiles, distances between power lines, details in topographical maps and more. You’ll get the insights you need and that you can count on.

While we’re on the subject of choosing the right GSD, it’s also important to consider data load. That’s because a better GSD will also mean higher resolution, richer images containing more data. 

This means heavier data load and longer processing times. So knowing what GSD you need based on accuracy and resolution requirements is critical for capturing data that is not too heavy. Another consideration here is data load. impacting the usefulness of the data collected.

What GSD do you need for your project?

Case studies and real-world applications

Let’s look at some case studies that highlight how the right drone and low GSD levels were crucial in data collection and money-saving.

Large-scale construction project

Rancho Mission Viejo, known for developing large-scale communities, required precise earthwork quantity tracking. They then hired a surveying company called TurnPoint Geomatics to track the work being done, who would then give Rancho Mission Viejo the right price for the contractors, and avoid them spending more money than needed.

Initially using manual GPS mapping and soon switching to the DJI Phantom 4 RTK for surveying, they noticed this method still required extensive time for ground control setup and three hours to fly the complete site

Seeking a more efficient solution and adopting the WingtraOne drone, they could cover large areas with high accuracy in just 30 minutes compared to several hours with the multirotor.

Turnpoint Geomatics main parcel image

Overall they benefited from: 

  • Simplified operational processes and ease of use
  • Daily high-accuracy views of the entire worksite
  • Minimal discrepancy between measured and actual earthworks, enhancing overall project efficiency
  • Significantly reduced man-hours and enhanced safety by minimizing exposure to moving equipment

While the DJI drone was capable of achieving the required accuracy, you’d have to fly it much lower to the ground for it. This reduces the area covered per image, which reduces the already-limited area it can cover in its relatively short flight time. 

Compare this with WingtraOne, which can fly at 400 feet with a 61MP camera at high speeds, for way more area coverage in less time. Not only does this cut the costs in terms of field time but, as mentioned in this case, it also allows surveyors to spend more time on quality control, which reduces mistakes and costs

The combination of Wingtra data and CAD lines offered a photorealistic comparison of planned vs. actual work, enabling real-time quality control and the identification of discrepancies as they occurred. This capability also created a historical record of the site, crucial for future reference if issues arise.

Smoother mining operations

BNI coal, a mining company based in North Dakota, procures millions of tons of lignite coal every year. Requiring extensive and careful mining surveying, they incorporated the WingtraOne drone into their workflow, increasing their ability to easily collect site information and do more accurate stock volume measurements over large areas, fast.

Some key benefits that they enjoy: 

  • More frequent, accurate and cost-effective surveying methods with the WingtraOne Gen II after stepping away from manned aircraft surveys
  • Equipped with a Sony RX1R II payload, they mapped extensive gravel stockpiles over 81.4 hectares (201 acres) in 42 minutes, achieving a GSD of 0.63 inches (1.61 cm)
  • More coverage from each flight for complete surveys in tight weather windows and less field time
  • Improved on-site safety, reducing risks in hazardous areas, especially for stockpile surveys
  • Improved landing precision and better control, crucial for areas surrounded by farmland frequented by low-flying aircraft
Precise GSD was pivotal for BNI Coal, enabling more accurate volume measurements, improved safety protocols, and enhanced overall efficiency in their mining operations. Such a high level of detail is vital in industries like mining, where precise data directly influences operational decisions and safety standards.


What is ground sample distance?

Ground sampling distance (GSD) is a key concept in surveying, particularly when it comes to remote sensing and drone photogrammetry

When you’re surveying an area from the sky with a drone, each pixel in a captured image represents a square of actual area on the ground. The GSD is the size of one of these squares.

Illustration with two adjacent pixels and the distance between showing what GSD is

Technically, GSD is the distance between the centers of two adjacent pixels measured on the ground. It’s an indication of how much area on the ground one pixel covers. 

If you have a GSD of 10 centimeters, that means one pixel in your image corresponds to a 10 cm by 10 cm square on the ground. The smaller the GSD, the higher the resolution of the image, and the more detail you can see because each pixel represents a smaller area on the ground.

How does your sensor affect GSD?

GSD is inversely proportional to resolution, a lower GSD setting results in higher-resolution images, and a higher GSD setting results in lower-quality images. 

The physical size of the sensor and the number of pixels it contains influence the level of detail in the images captured.

Essentially, a larger sensor with more pixels will enable a lower GSD from a higher flight altitude, allowing for finer, more detailed imagery and more efficient capture. Therefore, selecting the right drone that can carry the right sensor is key to achieving the desired GSD for your project.

How does GSD affect your data?

A specific example of how GSD affects data accuracy can be seen in the context of construction site surveys. Imagine a scenario where a drone with a high GSD of 10 cm per pixel is used to survey a site for earthwork volume calculations. 

This lower resolution might not capture small but critical features such as slight elevations or depressions in the terrain. As a result, the calculated volumes based on this data could be significantly inaccurate.

This inaccuracy can lead to underestimation or overestimation of materials required, impacting project costs and timelines. In contrast, a drone with a lower GSD of 2 cm per pixel would provide much more detailed and accurate data, enabling precise volume calculations and effective resource planning.

How is GSD calculated?

GSD = sensor width (mm)×flight height (m) /focal length (mm)×image width (pixels) 


    • Sensor width is the width of the camera sensor in centimeters
    • Flight height is the distance between the sensor and the ground
    • Focal length is the distance between the camera lens and the sensor, which determines the field of view
    • Image width measured in pixels

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