Land Use and Land Cover Analysis Using Geomatics Techniques in Amara City

• Autorzy: Al-Saedi, Ali Salah J. , Kadhum, Zahraa M. , Jasim, Basheer S.
• Języki publikacji: EN

Abstrakty

EN

The remote sensing technique is crucial for creating maps showing land use and land cover from a procedure known as image classification. For the process of image classification to be successful, many aspects must be taken into consideration; one of these factors is the availability of high-quality Landsat images. This study aims to classify and map the studied area’s land use and cover using remote sensing and geographic information system techniques. This study is divided into two parts: part one focuses on classifying land use and land cover, while part two evaluates how accurate the classification is. Several classification methods are compared for their efficacy in this study. Some image classification methods have shown promising results when used to remote sensing data. An efficient classifier is necessary for extracting data from remote-sensing images. The maximum likelihood classification was the most effective classifier in our study. In this study, the Maximum Likelihood classification accuracy has achieved an overall accuracy of 91% and an overall kappa accuracy of 86.83%. This study provides essential data for planners and decision-makers to design sustainable environments.

Słowa kluczowe

EN

remote sensing; GIS; supervised classification; land use; land cover

• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2023
• Tom: Vol. 24, iss. 9
• Strony: 161--169
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Al-Saedi, Ali Salah J.

• Surveying Department, Technical Institute of Amara, Southern Technical University, Misan, Iraq

autor

Kadhum, Zahraa M.

• Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq

autor

Jasim, Basheer S.

• Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq,

Bibliografia

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• 11. Perumal, K., and Bhaskaran, R. 2010. Supervised classification performance of multispectral images. ArXiv Preprint ArXiv:1002.4046.
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• 14. Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., Rahman, A. 2020. Land-use land-cover classification by machine learning classifiers for satellite observations – A review. Remote Sensing, 12(7), 1135.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.12912/27197050/173211