Terrestrial remote sensing toolkit

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BRG The University of Queensland
Fundamentals

- Main page
- Pre-processing
- Field data collection
- Mapping approaches
- Change and trends
- Delivery
  - Post classification
  - Error assessment
    - Sampling design
    - Reference data
    - Error matrix
    - Accuracy measures
    - Quantitative products
    - Interpreting accuracy
  - Final map creation

# Number of samples

The number of samples required depends on:

- number of map classes / mapping categories
- level of detail/rigour required in assessment.

Apply sampling theory to determine number of samples per image information class, e.g., at least 50 + observations per class for 80-95% expected accuracy. A commonly applied rule of thumb specifies a minimum of 50 samples per cover class.

The number of samples is a BALANCE between a number that is statistically sound and practically attainable.

### Example formula to determine number of samples N required

N = required sample size, Z = # of standard deviations associated with a certain percentage or confidence level, E = allowable error, p = expected percent accuracy , q = (100- p)

For example,

We may require; p = 85% , q = (100 - p ) = 15%, E = 5% and Z = 2 standard deviations