Which statistical method is most commonly used to determine accuracy in land classification for a raster-derived map?

The most commonly used statistical method for determining accuracy in land classification for a raster-derived map is computing a Confusion Matrix from ground truthing sample polygons. A Confusion Matrix provides a comprehensive way to assess how well a classification algorithm has performed by comparing the predicted land cover classes from the raster-derived map with the actual classes observed in the ground truth data.

In a Confusion Matrix, various metrics such as overall accuracy, producer’s accuracy, user’s accuracy, and kappa statistics can be derived, offering insights into both the overall effectiveness of the classification and the specific areas where the classification may fail. This method captures the relationship between the predicted classifications and the true classifications, allowing for an in-depth analysis of where errors occur.

Using photo-identifiable points for ground truthing is a valuable data collection method, but it does not inherently provide a statistical evaluation of classification accuracy. Compiling vector polygons for Union Overlay analysis does provide some insights into spatial relationships but is not specifically designed to quantify classification accuracy. Computing the variance in the number of polygons is a statistical measure less relevant to accuracy assessment in this context, as it does not directly relate to the precision of land cover classifications. Therefore, the use of a Confusion Matrix is the most effective and standardized approach for