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RSS FeedsRemote Sensing, Vol. 11, Pages 1434: Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI) (Remote Sensing)

 
 

17 june 2019 16:00:33

 
Remote Sensing, Vol. 11, Pages 1434: Performance Evaluation of Newly Proposed Seaweed Enhancing Index (SEI) (Remote Sensing)
 




Seaweed is a valuable coastal resource for its use in food, cosmetics, and other items. This study proposed new remote sensing based seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) of Landsat 8 satellite data. Nine Landsat 8 satellite images of years 2014, 2016, and 2018 for the January, February, and March months were utilized to test the performance of SEI. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using the SEI, normalized difference vegetation index (NDVI), and floating algae index (FAI). Seaweed locations recorded during a field survey on February 26, 2014, were used to determine threshold values for all three indices. The accuracy of SEI was compared with NDVI while placing FAI as the reference index. The accuracy of NDVI and SEI were assessed by matching their spatial extent of seaweed cover with FAI enhanced seaweed area. SEI images of January 2016, February 2018, and March 2018 enhanced less than 50 percent of the corresponding FAI total seaweed areas. However, on these dates the NDVI performed very well, matching more than 95 percent of FAI seaweed coverage. Except for these three times, the performance of SEI in the remaining six images was either similar to NDVI or even better than NDVI. SEI enhanced 99 percent of FAI seaweed cover on January 2018 image. Overall, seaweed area not covered by FAI was greater in SEI than NDVI in almost all images, which needs to be further explored in future studies by collecting extensive field information to validate SEI mapped additional area beyond the extent of FAI seaweed cover. Based on these results, in the majority of the satellite temporal images selected for this study, the performance of the newly proposed index—SEI, was found either better than or similar to NDVI.


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354 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1432: Improved Spring Vegetation Phenology Calculation Method Using a Coupled Model and Anomalous Point Detection (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1437: Comparative Study on Variable Selection Approaches in Establishment of Remote Sensing Model for Forest Biomass Estimation (Remote Sensing)
 
 
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