Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island...

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Bibliographic Details
Published in:Journal of imaging 2015-12, Vol.1 (1), p.85-114
Main Authors: Grybas, Heather, Congalton, Russell
Format: Article
Language:English
Subjects:
Accuracy
Change detection
Classification
Damage assessment
Ecosystems
Flood damage
Image analysis
Image classification
Impact damage
Land cover
Landsat satellites
Methods
Multivariate analysis
Pixels
Remote sensing
Sea level
Sensors
Storm damage
Storms
Tidal waves
Vegetation
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Summary:The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island as a result of Hurricane Sandy. Several single-date classifications were performed on the pre and post hurricane imagery utilized using both a pixel-based and object-based approach with the Random Forest classifier. Univariate image differencing and a post classification comparison were used to conduct the change detection. This study found that the addition of the coastal blue band to the Landsat 8 sensor did not improve classification accuracy and there was also no statistically significant improvement in classification accuracy using Landsat 8 compared to Landsat 5. Furthermore, there was no significant difference found between object-based and pixel-based classification. Change totals were estimated on Assateague Island following Hurricane Sandy and were found to be minimal, occurring predominately in the most active sections of the island in terms of land cover change, however, the post classification detected significantly more change, mainly due to classification errors in the single-date maps used.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging1010085