Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad

Over time, a growing human population that accelerates land use and land cover (LULC) change has placed a massive burden on natural resources. Changes in LULC have become an essential issue for decision makers and environmentalists. Monitoring and evaluating LULC changes over large areas become crit...

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Main Author:	Ahmad, Noorain
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Aerial geography Geographic information systems
Online Access:	https://ir.uitm.edu.my/id/eprint/69367/1/69367.pdf
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id	my-uitm-ir.69367
record_format	uketd_dc
spelling	my-uitm-ir.693672022-12-01T07:59:06Z Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad 2022 Ahmad, Noorain Aerial geography Geographic information systems Over time, a growing human population that accelerates land use and land cover (LULC) change has placed a massive burden on natural resources. Changes in LULC have become an essential issue for decision makers and environmentalists. Monitoring and evaluating LULC changes over large areas become critical. Understanding the functional diversity of machine learning classifiers is important due to increased geospatial data from satellite remote sensing. The potential of Google Earth Engine (GEE) as cloud-based computing to know the changes of the map area in a long period is interesting to study. Therefore, this study aims to evaluate the LULC classification map using different classifiers in Kedah between 2014 and 2021 conducted on Google Earth Engine. The objective of this study is i) to classify LULC carried out on the Google Earth Engine Platform using three (3) different classifiers (Random Forest, smile CART, and Minimum Distance) for Landsat 8 images in Kedah between 2014 and 2021, ii) To compare their performance for three (3) classifiers using accuracy assessment, and iii) to produce land use land cover maps for the years 2014 and 2021 for each classification. Landsat 8 images are obtained from GEE, and all the processing involved is done on this platform. The study prove that the best classifier was Random Forest and the OA = 80.50%, kappa = 0.73 for 2014 while in year 2021 OA = 80.88%, kappa 0.75. This study shows the GEE cloud platform's efficiency in generating spatial temporal classification maps with high accuracy and takes a short time, and is easy to modify. The final output for this study was the LULC maps for the years 2014 and 2021 will benefit local authorities and policy makers for their planning and sustainable management. 2022 Thesis https://ir.uitm.edu.my/id/eprint/69367/ https://ir.uitm.edu.my/id/eprint/69367/1/69367.pdf text en public degree Universiti Teknologi MARA, Perlis Faculty of Architecture, Planning and Surveying Anshah, Siti Aminah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Anshah, Siti Aminah
topic	Aerial geography Geographic information systems
spellingShingle	Aerial geography Geographic information systems Ahmad, Noorain Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
description	Over time, a growing human population that accelerates land use and land cover (LULC) change has placed a massive burden on natural resources. Changes in LULC have become an essential issue for decision makers and environmentalists. Monitoring and evaluating LULC changes over large areas become critical. Understanding the functional diversity of machine learning classifiers is important due to increased geospatial data from satellite remote sensing. The potential of Google Earth Engine (GEE) as cloud-based computing to know the changes of the map area in a long period is interesting to study. Therefore, this study aims to evaluate the LULC classification map using different classifiers in Kedah between 2014 and 2021 conducted on Google Earth Engine. The objective of this study is i) to classify LULC carried out on the Google Earth Engine Platform using three (3) different classifiers (Random Forest, smile CART, and Minimum Distance) for Landsat 8 images in Kedah between 2014 and 2021, ii) To compare their performance for three (3) classifiers using accuracy assessment, and iii) to produce land use land cover maps for the years 2014 and 2021 for each classification. Landsat 8 images are obtained from GEE, and all the processing involved is done on this platform. The study prove that the best classifier was Random Forest and the OA = 80.50%, kappa = 0.73 for 2014 while in year 2021 OA = 80.88%, kappa 0.75. This study shows the GEE cloud platform's efficiency in generating spatial temporal classification maps with high accuracy and takes a short time, and is easy to modify. The final output for this study was the LULC maps for the years 2014 and 2021 will benefit local authorities and policy makers for their planning and sustainable management.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad, Noorain
author_facet	Ahmad, Noorain
author_sort	Ahmad, Noorain
title	Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
title_short	Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
title_full	Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
title_fullStr	Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
title_full_unstemmed	Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad
title_sort	land use land cover classification from different classifiers in kedah between 2014 and 2021 using google earth engine / noorain ahmad
granting_institution	Universiti Teknologi MARA, Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/69367/1/69367.pdf
_version_	1783735869998366720

Land use land cover classification from different classifiers in Kedah between 2014 and 2021 using Google Earth engine / Noorain Ahmad

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