Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman

Organic were the terms use to represent the materials that combined with or derived from living organisms. The quantity of organic matter in soil is frequently used as an indicator of the possible sustainability in a soil system. Soil organic matter was significant part in nutrient cycle and fixi...

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Main Author:	Abdul Rahman, Muhammad Radhi
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Global Positioning System Remote Sensing Bacteria
Online Access:	https://ir.uitm.edu.my/id/eprint/22444/1/TD_MUHAMMAD%20RADHI%20ABDUL%20RAHMAN%20AP%20R%2018.5.PDF
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id	my-uitm-ir.22444
record_format	uketd_dc
spelling	my-uitm-ir.224442018-12-13T09:00:00Z Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman 2018-12 Abdul Rahman, Muhammad Radhi Global Positioning System Remote Sensing Bacteria Organic were the terms use to represent the materials that combined with or derived from living organisms. The quantity of organic matter in soil is frequently used as an indicator of the possible sustainability in a soil system. Soil organic matter was significant part in nutrient cycle and fixing soil structure. Organic carbon in soil was important to build up good health in soil environment and vital in supplying the needs of the ecosystem. This project aims to identify the Soil Organic Carbon distribution based on multivariate regression model. This project was used satellite imagery, SPOT 5 to estimate SOC distribution using remote sensing technique and soil sampling in the Ladang Harumanis, UiTM Aran, Perlis. There were nine soil samplings were picked randomly collected using a handheld Global Positioning System (GPS) unit to location the position of the sampling points. The satellite data derived spectral indices, NDVI and BSl were used to assess spatial distribution of SOC in the study area by testing in the multivariate regression model. The result of regression analysis between the observed and predicted SOC using = 0.10 value was showed only 10% accurate because of the lack of number of soil samples and same land use type which no really soil variations that reflected this result. This information of this study can gave advanced understanding by using the remote sensing approach which had many advantages regarding conventional approach before would be important technique thus increase the effectivity of the soil management method. 2018-12 Thesis https://ir.uitm.edu.my/id/eprint/22444/ https://ir.uitm.edu.my/id/eprint/22444/1/TD_MUHAMMAD%20RADHI%20ABDUL%20RAHMAN%20AP%20R%2018.5.PDF other en public degree Universiti Teknologi Mara Perlis Faculty of architecture, planning and surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Global Positioning System Remote Sensing Bacteria
spellingShingle	Global Positioning System Remote Sensing Bacteria Abdul Rahman, Muhammad Radhi Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
description	Organic were the terms use to represent the materials that combined with or derived from living organisms. The quantity of organic matter in soil is frequently used as an indicator of the possible sustainability in a soil system. Soil organic matter was significant part in nutrient cycle and fixing soil structure. Organic carbon in soil was important to build up good health in soil environment and vital in supplying the needs of the ecosystem. This project aims to identify the Soil Organic Carbon distribution based on multivariate regression model. This project was used satellite imagery, SPOT 5 to estimate SOC distribution using remote sensing technique and soil sampling in the Ladang Harumanis, UiTM Aran, Perlis. There were nine soil samplings were picked randomly collected using a handheld Global Positioning System (GPS) unit to location the position of the sampling points. The satellite data derived spectral indices, NDVI and BSl were used to assess spatial distribution of SOC in the study area by testing in the multivariate regression model. The result of regression analysis between the observed and predicted SOC using = 0.10 value was showed only 10% accurate because of the lack of number of soil samples and same land use type which no really soil variations that reflected this result. This information of this study can gave advanced understanding by using the remote sensing approach which had many advantages regarding conventional approach before would be important technique thus increase the effectivity of the soil management method.
format	Thesis
qualification_level	Bachelor degree
author	Abdul Rahman, Muhammad Radhi
author_facet	Abdul Rahman, Muhammad Radhi
author_sort	Abdul Rahman, Muhammad Radhi
title	Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
title_short	Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
title_full	Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
title_fullStr	Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
title_full_unstemmed	Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman
title_sort	soil organic carbon mapping using remote sensing technique and multivariate regression model / muhammad radhi abdul rahman
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of architecture, planning and surveying
publishDate	2018
url	https://ir.uitm.edu.my/id/eprint/22444/1/TD_MUHAMMAD%20RADHI%20ABDUL%20RAHMAN%20AP%20R%2018.5.PDF
_version_	1783733818440548352

Soil organic carbon mapping using remote sensing technique and multivariate regression model / Muhammad Radhi Abdul Rahman

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