Multispectral remote sensing for nitrogen fertilizer management in oil palm
Environmental concerns are growing about excessive applying nitrogen (N) fertilizers specially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, a...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/67916/ http://psasir.upm.edu.my/id/eprint/67916/1/FK%202018%2050.pdf |
| Summary: | Environmental concerns are growing about excessive applying nitrogen (N) fertilizers
specially in oil palm. Some conventional methods which are used to assess the amount
of nutrient in oil palm are time consuming, expensive, and involve frond destruction.
Remote sensing as a non-destructive, affordable and efficient method are widely used to
detect the concentration of chlorophyll (Chl) from canopy plants using several
Vegetation Indices (VIs) because there is a strong relative between the concentration of
N in the leaves and canopy Chl content. The objectives of this research were (i) to
evaluate and compare the performance of various Vegetation Indices (VIs) for measuring
N status in oil palm canopy using SPOT7 imagery (ii) to develop a regression formula
that can predict the N content using satellite data (iii) to assess the regression formula
performance on testing datasets by testing the correlation between the predicted and
measured N contents. Spot 7 was acquired in a 6 ha oil palm planted area in Pahang,
Malaysia. To predict N content 28 VIs based on spectral range of SPOT 7 satellite image
were evaluated. Several regression models were applied to determine the highest
correlation between VIs and actual N content from leaf sampling. MSAVI generated the
highest correlation (R2 = 0.93). MTVI1 and Triangular VI had the highest second and
third correlations with N content (R2= 0.926 and 0.923 respectively).
The accuracy assessment of developed model was evaluated using several statistical
parameters such as Independent T-test, and p-value. The accuracy assessment of
developed model was more than 77%. |
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