| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4465071 | International Journal of Applied Earth Observation and Geoinformation | 2011 | 8 Pages |
The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications.
Research highlights▶ Extending the automated cloud cover assessment filters can help to identify and remove transparent cloud edges and cirrus cloud from Landsat ETM+. ▶ Object-based classification can reduce the inevitable commission errors from extending such filters. ▶ Omission errors reduced in the object-based mask than the ACCA mask. ▶ Reliability of subsequent land cover classifications will increase due to less inaccuracies associated with cloud that has been inadvertently excluded from the mask.
