Investigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan

dc.contributor.authorRaza, Ali
dc.contributor.authorSyed, Neyha Rubab
dc.contributor.authorFahmeed, Romana
dc.contributor.authorAcharki, Siham
dc.contributor.authorAljohani, Taghreed Hamdi
dc.contributor.authorHussain, Sajjad
dc.contributor.authorZubair, Muhammad
dc.contributor.authorZahra, Syeda Mishal
dc.contributor.authorMd. Towfiqul Islam, Abu Reza
dc.contributor.authorAlmohamad, Hussein
dc.contributor.authorGhassan Abdo, Hazem
dc.date.accessioned2025-11-17T07:28:37Z
dc.date.available2025-11-17T07:28:37Z
dc.date.issued2024-04-22
dc.descriptionArticle
dc.description.abstractMonitoring and understanding Land Use/Land Cover (LU/LC) is critical for sustainable development, as it can impact various environmental, social, and economic systems. For example, deforestation and land degradation can lead to soil erosion, loss of biodiversity, and greenhouse gas emissions, affecting the quality of soil, air, and water resources. The present research examined changes in (LU/LC) within the underdeveloped regions of Balochistan and Sindh provinces, which are situated in Pakistan. In order to monitor temporal variations of LU/LC, we employed Geographic Information System (GIS) technique, to conduct an analysis of satellite imagery obtained from the Landsat 8 Operational Land Imager (OLI) during the time period spanning from 2013 to 2023. In order to obtain an accurate LU/LC classification, we used principal component analysis (PCA) and a supervised classification approach using the maximum likelihood algorithm (MLC). According to the results of our study, there was a decrease in the extent of water bodies (− 593.24 km2) and vegetation (− 68.50 km2) by − 3.43% and − 0.40% respectively. In contrast, the area occupied by settlements in the investigated region had a 2.23% rise, reaching a total of 385.66 square kilometers. Similarly, the extent of barren land also expanded by 1.60%, encompassing a total area of 276.04 square kilometers, during the course of the last decade. The overall accuracy (94.25% and 95.75%) and K value (91.75% and 93.50%) were achieved during the year 2013 and 2023 respectively. The enhancement of agricultural output in Pakistan is of utmost importance in order to improve the income of farmers, mitigate food scarcity, stimulate economic growth, and facilitate the expansion of exports. To enhance agricultural productivity, it is recommended that the government undertake targeted initiatives that aimed at enhancing water infrastructure and optimizing land use to foster a sustainable ecological framework. Integrating the sustainable ecological framework provides a foundation for informed decision-making and effective resource management. By identifying areas of urban expansion, agricultural intensification, or alterations in natural LU/LC, stakeholders can design targeted conservation strategies, mitigating potential environmental degradation and promoting biodiversity conservation. In conclusion, the integration of GIS and Remote Sensing (RS) may effectively facilitate the monitoring of land use patterns over a period of time. This combined approach offers valuable insights and recommendations for the judicious and optimal management of land resources, as well as informing policy decisions.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/15756
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/15756
dc.language.isoen_US
dc.sourceDIU Institutional Repository
dc.subjectLand use/land cover
dc.subjectUnder develop region
dc.subjectPrincipal component analysis
dc.subjectSupervised classification
dc.subjectOperational land imager
dc.subjectEnvironment
dc.titleInvestigation of changes in land use/land cover using principal component analysis and supervised classification from operational land imager satellite data: a case study of under developed regions, Pakistan
dc.typeArticle

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