Flood Risk and Vulnerability Analysis Project - Atlantic Climate ...

coordinates of the SPOT imagery using control points. A reasonable root mean squared error
(RMSE) was achieved during the registration process, but it is important to note that the
classified EOSD dataset was processed using coarser resolution than the reference SPOT data;
therefore, spatial shifts may still exist but are minor. After georeferencing was complete, a final
EOSD mosaic dataset was created.
3.3.3 Land Cover Classification
Land cover classification was performed through a multi-step process detailed below. General
steps include; (1) image preprocessing and mosaicing, (2) ISODATA unsupervised classification
and aggregation, (3) change detection processes, (4) classification corrections and manual
edits, (5) land cover tabulations by watershed, and (6) accuracy assessment. Accuracy
assessment results are presented in the results section.
3.3.3.1 Image Preprocessing
Due to the large geographic area and the high frequency of clouds and ice in the study area,
more than 100 SPOT images and image fragments were used in this study. For each
watershed, mosaics of images with similar reflectance characteristics were combined to create
larger images where possible. Histogram matching was used to reduce pixel variation within
aggregated images. For the remaining, adjacent datasets with high levels of variation in pixel
values at overlapping areas (likely due to temporal resolution), performing an atmospheric
correction process to apparent reflectance values would not have provided any benefit (see
additional information in Recommendations). Clouds and ice were then masked from areas
where they represented a large proportion of existing imagery in order to remove these skewed
pixel values during the classification.
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