Surface Reflectance And Discriminant Analysis For Mapping Of Mangrove Species In Kuala Sepetang Mangrove Forest Reserve, Perak

Simple item page
dc.contributor.authorBeh, Boon Chun
dc.date.accessioned2017-02-13T08:54:52Z
dc.date.available2017-02-13T08:54:52Z
dc.date.issued2016-02
dc.description.abstractThe identification of mangrove species by using traditional approach has been commonly developed and studied. Due to the high cost of field work and the difficulty in assessing the mudflat areas of mangrove ecosystem with conventional methods, remote sensing techniques have been widely used to examine mangrove species at either the scale level or leaf level. In this study, two remote sensing methods have been utilized to identify and discriminate the mangrove species in the Kuala Sepetang Mangrove Forest Reserve, Perak, Malaysia. The first method is to develop an algorithm that was based on species’ surface reflectance to map the mangrove species by using high-resolution CropCam Unmanned Aerial Vehicle (UAV) data. The airborne images were acquired through the three band channels (blue, green and near-infrared) of the Normalized Difference Vegetation Index (NDVI) camera that was mounted on the CropCam UAV. The Top of Atmosphere (TOA) reflectance was retrieved from the mosaicked airborne image, which covered the entire study site with an area of 50.12 ha. The distributions of five mangrove species were successfully mapped by using the retrieved reflectance values. The scatter plot between the predictions and ground pixels revealed a high correlation (R2=0.873) with Root Mean Square Error (RMSE) of 0.476 (less than one species per pixel). The results also indicated that the developed technique was reliable and produced good results with high accuracy of 85%. The second method used statistical analysis to analyze the hyperspectral reflectance data of the mangrove species. Analysis of Variance (ANOVA) and Linear Discriminant Analysis (LDA) tests were applied on the reflectance spectra data. The LDA determined the influential wavelength, which could be used to distinguish the leaf samples among the six mangrove species. Twenty-six significant wavelengths (p<0.05) were obtained in the Very Near Infrared (VNIR), Short Wavelength Infrared I (SWIR I) and Short Wavelength Infrared II (SWIR II) spectral regions. Sixteen discriminant functions were generated by using the 26 influential wavelengths. The score range of each mangrove species in discriminant functions was determined by using the reflectance spectra. Meanwhile, different combinations of discriminant functions were compared to determine the most suitable function to classify the mangrove species. The comparison produced the best accuracy when 11 functions were chosen for the mangrove species classification. Therefore, the score range that was established earlier was used to predict the unknown mangrove leaves by using the 11 discriminant functions. The final results showed that even the attained classification accuracy was lower when identifying certain mangrove species, but the 11 discriminant functions could still determine the correct mangrove species of an unknown mangrove leaf sample from the study area at the leaf scale. Overall, these results clearly indicate that the two remote sensing methods that were applied could successfully discriminate five mangrove species in the Kuala Sepetang Mangrove Forest Reserve, Perak, Malaysia and accomplished the objective of this research.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/3717
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectMangroveen_US
dc.titleSurface Reflectance And Discriminant Analysis For Mapping Of Mangrove Species In Kuala Sepetang Mangrove Forest Reserve, Peraken_US
dc.typeThesisen_US
Files
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Pusat Pengajian Sains Fizik - Tesis