Cover Image

Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin

Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sen...

Full description

Saved in:
Bibliographic Details
Main Authors: Muhammad Amiruddin, Zulkifli, Anak Gisen, Jacqueline Isabella, Syarifuddin, Misbari, Shairul Rohaziawati, Samat, Yu, Qian
Format: Article
Language:English
Published: Universiti Putra Malaysia 2024
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf
http://umpir.ump.edu.my/id/eprint/42986/
https://doi.org/10.47836/pjst.32.6.15
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.42986
record_format eprints
spelling my.ump.umpir.429862024-11-27T07:59:30Z http://umpir.ump.edu.my/id/eprint/42986/ Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin Muhammad Amiruddin, Zulkifli Anak Gisen, Jacqueline Isabella Syarifuddin, Misbari Shairul Rohaziawati, Samat Yu, Qian TA Engineering (General). Civil engineering (General) Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals. Universiti Putra Malaysia 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf Muhammad Amiruddin, Zulkifli and Anak Gisen, Jacqueline Isabella and Syarifuddin, Misbari and Shairul Rohaziawati, Samat and Yu, Qian (2024) Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin. Pertanika Journal of Science & Technology (JST), 32 (6). pp. 2699-2722. ISSN 0128-7680. (Published) https://doi.org/10.47836/pjst.32.6.15 10.47836/pjst.32.6.15
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Muhammad Amiruddin, Zulkifli
Anak Gisen, Jacqueline Isabella
Syarifuddin, Misbari
Shairul Rohaziawati, Samat
Yu, Qian
Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
description Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals.
format Article
author Muhammad Amiruddin, Zulkifli
Anak Gisen, Jacqueline Isabella
Syarifuddin, Misbari
Shairul Rohaziawati, Samat
Yu, Qian
author_facet Muhammad Amiruddin, Zulkifli
Anak Gisen, Jacqueline Isabella
Syarifuddin, Misbari
Shairul Rohaziawati, Samat
Yu, Qian
author_sort Muhammad Amiruddin, Zulkifli
title Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
title_short Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
title_full Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
title_fullStr Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
title_full_unstemmed Machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
title_sort machine learning and remote sensing applications for assessing land use and land cover changes for under-monitored basin
publisher Universiti Putra Malaysia
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42986/1/Machine%20learning%20and%20remote%20sensing%20applications%20for%20assessing%20land%20use%20and%20land%20cover%20changes%20for%20under-monitored%20basin.pdf
http://umpir.ump.edu.my/id/eprint/42986/
https://doi.org/10.47836/pjst.32.6.15
_version_ 1822924769830371328
score 13.232681

Similar Items