Annual NLCD Characteristics, Constraints, and Caveats
This page provides information about specific artifacts that were discovered in review of Annual National Land Cover Database (NLCD) Collection 1.0 or provided by user feedback. These show up in the products listed below.
The annual NLCD Collection 1 Science Products contain known caveats and limitations. Overall considerations regarding the full data set or the NLCD approach are described below.
Land Cover
- Water Bodies: Pixelated developed, barren, and other out-of-place land cover calls over water, particularly Lake Superior, are attributed to input issues related to artifacts in the leaf-on/off imagery used.
- Shrubs/Grassland Classification Artifacts: Linear artifacts of “blockiness” can be seen in some geographic areas, particularly the desert Southwest where the new spatial AI/ML approach had difficulty differentiating between shrub/scrub and grassland/herbaceous.
Fractional Impervious Surface
- Value/Range Truncation: Linear regression predictions produced values outside of the accepted 0-100 value range and were not properly truncated. This can cause values to be greater than 100 and, in some cases, cause a buffer underflow for the UINT8 data type.
Land Cover Confidence
- Value/Range Truncation: Linear regression predictions and the nature of the ensemble approach can give values greater than 100, which were not properly truncated.
- Confidence Value Misrepresentations: The wrong class can be referenced for the associated confidence value due to modeling on an expanded set of classes which get cross-walked to the final land cover calls. This often results in a much lower confidence value than intended.
Back to the About Annual NLCD page.
About Annual NLCD
This page provides information about specific artifacts that were discovered in review of Annual National Land Cover Database (NLCD) Collection 1.0 or provided by user feedback. These show up in the products listed below.
The annual NLCD Collection 1 Science Products contain known caveats and limitations. Overall considerations regarding the full data set or the NLCD approach are described below.
Land Cover
- Water Bodies: Pixelated developed, barren, and other out-of-place land cover calls over water, particularly Lake Superior, are attributed to input issues related to artifacts in the leaf-on/off imagery used.
- Shrubs/Grassland Classification Artifacts: Linear artifacts of “blockiness” can be seen in some geographic areas, particularly the desert Southwest where the new spatial AI/ML approach had difficulty differentiating between shrub/scrub and grassland/herbaceous.
Fractional Impervious Surface
- Value/Range Truncation: Linear regression predictions produced values outside of the accepted 0-100 value range and were not properly truncated. This can cause values to be greater than 100 and, in some cases, cause a buffer underflow for the UINT8 data type.
Land Cover Confidence
- Value/Range Truncation: Linear regression predictions and the nature of the ensemble approach can give values greater than 100, which were not properly truncated.
- Confidence Value Misrepresentations: The wrong class can be referenced for the associated confidence value due to modeling on an expanded set of classes which get cross-walked to the final land cover calls. This often results in a much lower confidence value than intended.
Back to the About Annual NLCD page.