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Ground sample distance

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Distance between pixel centers in remote sensing

In remote sensing, ground sample distance (GSD) in a digital photo of the ground from air or space is the distance between pixel centers measured on the ground. For example, in an image with a one-meter GSD, adjacent pixels image locations are 1 meter apart on the ground.[1] GSD is a measure of one limitation to spatial resolution or image resolution, that is, the limitation due to sampling.[2]

GSD is also referred to as ground-projected sample interval (GSI) and is related to the ground-projected instantaneous field of view (GIFOV).[3]

Formulas

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The GSD can be calculated using the geometry of the imaging setup.

General case (oblique or slant view)

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In the general case where the sensor may be imaging the ground at an oblique angle (i.e., not looking directly down), the GSD is given by:

G S D = R S × p f × cos ( θ ) {\displaystyle \mathrm {GSD} ={\frac {R_{S}\times p}{f\times \cos(\theta )}}} {\displaystyle \mathrm {GSD} ={\frac {R_{S}\times p}{f\times \cos(\theta )}}}

Where:

  • G S D {\displaystyle \mathrm {GSD} } {\displaystyle \mathrm {GSD} } is the ground sample distance, e.g., in cm/px;
  • R S = d 2 + h 2 {\displaystyle R_{S}={\sqrt {d^{2}+h^{2}}}} {\displaystyle R_{S}={\sqrt {d^{2}+h^{2}}}} is the slant range from the sensor to the point on the ground, e.g., in meters:
    • d {\displaystyle d} {\displaystyle d} is the horizontal distance (or offset) from nadir, e.g., in meters;
    • h {\displaystyle h} {\displaystyle h} is the height above ground level (AGL) of the sensor, e.g., in meters.
  • p = P ÷ N {\displaystyle p=P\div N} {\displaystyle p=P\div N} is the physical pixel size of the sensor, e.g., in micrometers:
  • f {\displaystyle f} {\displaystyle f} is the focal length of the camera lens, e.g., in millimeters;
  • θ = arctan ( d ÷ h ) {\displaystyle \theta =\arctan \left(d\div h\right)} {\displaystyle \theta =\arctan \left(d\div h\right)} is the slant angle from nadir (which would correspond to 0°), e.g., in degrees.

The cosine of θ {\displaystyle \theta } {\displaystyle \theta } accounts for the oblique viewing angle, which increases the effective ground footprint of each pixel.

Nadir case (look-down view)

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In the special case of a nadir view, i.e., when the sensor is looking directly downward, the formula is simplified since d = 0 {\displaystyle d=0} {\displaystyle d=0}. Thus, R S = h {\displaystyle R_{S}=h} {\displaystyle R_{S}=h} and θ = 0 {\displaystyle \theta =0} {\displaystyle \theta =0}, the cosine of which is 1. Therefore, the formula becomes:

G S D = h × p f {\displaystyle \mathrm {GSD} ={\frac {h\times p}{f}}} {\displaystyle \mathrm {GSD} ={\frac {h\times p}{f}}}

Where all variables are defined as above.

Planar components derivative formula

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Calculator
d {\displaystyle d} {\displaystyle d} 1000 m
h {\displaystyle h} {\displaystyle h} 2000 m
p {\displaystyle p} {\displaystyle p} 2.9 μm
f {\displaystyle f} {\displaystyle f} 600 mm
G S D {\displaystyle \mathrm {GSD} } {\displaystyle \mathrm {GSD} } 1.20833 cm/px

If the slant range R S {\displaystyle R_{S}} {\displaystyle R_{S}} and slant angle θ {\displaystyle \theta } {\displaystyle \theta } are to be derived from the horizontal and vertical components d {\displaystyle d} {\displaystyle d} and h {\displaystyle h} {\displaystyle h} thereof, after simplification, the formula becomes:

G S D = d 2 + h 2 h × p f {\displaystyle \mathrm {GSD} ={\frac {d^{2}+h^{2}}{h}}\times {\frac {p}{f}}} {\displaystyle \mathrm {GSD} ={\frac {d^{2}+h^{2}}{h}}\times {\frac {p}{f}}}

Where all variables are defined as above.

Optimal off-nadir angle for maximal distance

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Calculator
G S D m a x {\displaystyle \mathrm {GSD_{max}} } {\displaystyle \mathrm {GSD_{max}} } 5 cm
p {\displaystyle p} {\displaystyle p} 2.9 μm
f {\displaystyle f} {\displaystyle f} 600 mm
d = h {\displaystyle d=h\approx } {\displaystyle d=h\approx } 5172 m

To maximize the horizontal imaging distance ( d {\displaystyle d} {\displaystyle d}) for a given optical system while adhering to a specified maximum ground sample distance ( G S D m a x {\displaystyle \mathrm {GSD_{max}} } {\displaystyle \mathrm {GSD_{max}} }) constraint, the optimal imaging geometry is achieved at a 45° off-nadir angle. This corresponds to a height above ground level ( h {\displaystyle h} {\displaystyle h}) equal to the horizontal distance between the target point ( d {\displaystyle d} {\displaystyle d}) and the sensor.

This configuration is useful for planning aerial or satellite imaging operations, for which both resolution and maximum coverable area are critical aspects. The maximum attainable d {\displaystyle d} {\displaystyle d} under resolution constraint can be calculated as follows:

d = h = G S D m a x 2 × f p {\displaystyle d=h={\frac {\mathrm {GSD_{max}} }{2}}\times {\frac {f}{p}}} {\displaystyle d=h={\frac {\mathrm {GSD_{max}} }{2}}\times {\frac {f}{p}}}

Where G S D m a x {\displaystyle \mathrm {GSD_{max}} } {\displaystyle \mathrm {GSD_{max}} } is the desired maximum ground sample distance, and all other variables are defined as above.

Within this constraint, reducing the horizontal distance ( d {\displaystyle d} {\displaystyle d}) without lowering the height ( h {\displaystyle h} {\displaystyle h}) decreases the off-nadir angle and shifts the imaging closer to nadir, thereby improving the ground sample distance. Conversely, decreasing h {\displaystyle h} {\displaystyle h} while keeping d {\displaystyle d} {\displaystyle d} constant increases the off-nadir angle beyond 45°, which degrades the GSD. The 45° configuration provides the widest possible coverage while maintaining the specified GSD limit.

See also

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References

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  1. ^ NZ Aerial Mapping Ltd (2009). "Frequently Asked Questions: What Is Ground Sample Distance?". Archived from the original on 2018年11月29日. Retrieved 2009年07月25日.
  2. ^ Jon C. Leachtenauer and Ronald G. Driggers (2001). Surveillance and Reconnaissance Imaging Systems: Modeling and Performance Prediction. Artech House. pp. 30–31. ISBN 978-1-58053-132-0.
  3. ^ Ronald G. Driggers (2003). Encyclopedia of Optical Engineering. CRC Press. p. 1392. ISBN 978-0-8247-4251-5.

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