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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Main Author:	Khouzani, Mohammad Yadegari
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Nitrogen fertilizers Remote sensing Remote-sensing images
Online Access:	http://psasir.upm.edu.my/id/eprint/67916/1/FK%202018%2050%20IR.pdf
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id	my-upm-ir.67916
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spelling	my-upm-ir.679162019-04-05T00:09:33Z Multispectral remote sensing for nitrogen fertilizer management in oil palm 2017-12 Khouzani, Mohammad Yadegari 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%. Nitrogen fertilizers Remote sensing Remote-sensing images 2017-12 Thesis http://psasir.upm.edu.my/id/eprint/67916/ http://psasir.upm.edu.my/id/eprint/67916/1/FK%202018%2050%20IR.pdf text en public masters Universiti Putra Malaysia Nitrogen fertilizers Remote sensing Remote-sensing images
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Nitrogen fertilizers Remote sensing Remote-sensing images
spellingShingle	Nitrogen fertilizers Remote sensing Remote-sensing images Khouzani, Mohammad Yadegari Multispectral remote sensing for nitrogen fertilizer management in oil palm
description	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%.
format	Thesis
qualification_level	Master's degree
author	Khouzani, Mohammad Yadegari
author_facet	Khouzani, Mohammad Yadegari
author_sort	Khouzani, Mohammad Yadegari
title	Multispectral remote sensing for nitrogen fertilizer management in oil palm
title_short	Multispectral remote sensing for nitrogen fertilizer management in oil palm
title_full	Multispectral remote sensing for nitrogen fertilizer management in oil palm
title_fullStr	Multispectral remote sensing for nitrogen fertilizer management in oil palm
title_full_unstemmed	Multispectral remote sensing for nitrogen fertilizer management in oil palm
title_sort	multispectral remote sensing for nitrogen fertilizer management in oil palm
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/67916/1/FK%202018%2050%20IR.pdf
_version_	1747812538943275008

Multispectral remote sensing for nitrogen fertilizer management in oil palm

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