Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri

Organic is the term 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 soi...

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Main Author:	Mohamad Asri, Mohamad Ariff
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Remote Sensing
Online Access:	https://ir.uitm.edu.my/id/eprint/31205/1/TD_MOHAMAD%20ARIFF%20MOHAMAD%20ASRI%20AP%20R%2019_5.pdf
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id	my-uitm-ir.31205
record_format	uketd_dc
spelling	my-uitm-ir.312052021-12-17T09:03:28Z Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri 2019-07-15 Mohamad Asri, Mohamad Ariff Remote Sensing Soils. Soil science. Including soil surveys, soil chemistry, soil structure, soil-plant relationships Organic is the term 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 (SOC) distribution based on multivariate regression model. This project was used Landsat 8 (OLI) satellite imagery to estimate SOC distribution using remote sensing technique and soil sampling in the District of Arau, Perlis. There were 20soil sampling collected randomly using a handheld Global Positioning System (GPS) unit to the location the of the soil sampling points. The satellite data extract spectral indices, NDV land BSI. All the values will used to assess spatial distribution of SOC at the study area by testing in the multivariate regression model. The result of regression analysis between the observed and predicted SOC using R² = 0.54 value was showed 54% variance of observed SOC can be explained by predicted SOC and it is shows the value is in level of moderate strength of relationship. 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. 2019-07 Thesis https://ir.uitm.edu.my/id/eprint/31205/ https://ir.uitm.edu.my/id/eprint/31205/1/TD_MOHAMAD%20ARIFF%20MOHAMAD%20ASRI%20AP%20R%2019_5.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surverying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Remote Sensing Remote Sensing
spellingShingle	Remote Sensing Remote Sensing Mohamad Asri, Mohamad Ariff Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
description	Organic is the term 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 (SOC) distribution based on multivariate regression model. This project was used Landsat 8 (OLI) satellite imagery to estimate SOC distribution using remote sensing technique and soil sampling in the District of Arau, Perlis. There were 20soil sampling collected randomly using a handheld Global Positioning System (GPS) unit to the location the of the soil sampling points. The satellite data extract spectral indices, NDV land BSI. All the values will used to assess spatial distribution of SOC at the study area by testing in the multivariate regression model. The result of regression analysis between the observed and predicted SOC using R² = 0.54 value was showed 54% variance of observed SOC can be explained by predicted SOC and it is shows the value is in level of moderate strength of relationship. 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	Mohamad Asri, Mohamad Ariff
author_facet	Mohamad Asri, Mohamad Ariff
author_sort	Mohamad Asri, Mohamad Ariff
title	Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
title_short	Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
title_full	Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
title_fullStr	Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
title_full_unstemmed	Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri
title_sort	soil organic carbon mapping using remote sensing technique and multivariate regression method / mohamad ariff mohamad asri
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surverying
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/31205/1/TD_MOHAMAD%20ARIFF%20MOHAMAD%20ASRI%20AP%20R%2019_5.pdf
_version_	1783734106397343744

Soil organic carbon mapping using remote sensing technique and multivariate regression method / Mohamad Ariff Mohamad Asri

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