Determination of NDVI using landsat 8 satellite imagery data and Its relationship with chlorophyll A and chlorophyll B content in teresstrial plant species leaves at Hutan Semarak in UiTM Perlis / Nur Atiqah Othman
Chlorophyll is the most important molecules that are necessary to initiate photosynthesis process and also serves as vegetation’s stress indicator. Nowadays, the chlorophyll content can be estimated by using satellite imagery data which offers less time-consuming and convenient method. Remote sensin...
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/36523/1/36523.pdf |
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| Summary: | Chlorophyll is the most important molecules that are necessary to initiate photosynthesis process and also serves as vegetation’s stress indicator. Nowadays, the chlorophyll content can be estimated by using satellite imagery data which offers less time-consuming and convenient method. Remote sensing technology is widely used in estimating the chlorophyll content. The main objectives of this study are to identify the chlorophyll content using satellite imagery data and laboratory measurement for different species of plants at UiTM Perlis, and also to determine the relationship between vegetation indices from satellite imagery data and measurement of chlorophyll content in the laboratory for different vegetation species within study area. Chlorophyll content will be retrieved by using NDVI from Landsat 8 application. Spectrophotometric method also applied in determination of chlorophyll content for leaves sample which the chlorophyll pigment will be extracted from leaf tissue in the laboratory. Then, the chlorophyll extracts will be analyze using spectrophotometric analysis, by measuring absorbance at particular wavelength and using simultaneous equations to calculate the concentration of the chlorophyll pigment. The result shows that each different species have different concentration of chlorophyll content. The result of OIF indicates that band combination of 2-5-6 as the most suitable band to map vegetation areas. For supervised classification, all three Classifier (Minimum Distance Classifier, Spectral Angle Classifier and Spectral Correlation Classifier) gives same result with 80% of accuracy assessment and 0.775 of Kappa Statistic. The relationship between NDVI and determination of chlorophyll content in the laboratory is measured by using SPSS Application. While, the NDVI increases with increasing chlorophyll content since there is a positive relationship between NDVI and chlorophyll content that measured in the laboratory in different species. Therefore, vegetation indices in remote sensing application can helps to detect. |
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