Spatio-temporal dynamics of land use and land cover in Selangor (2005–2020): insights from MODIS and Google Earth Engine for sustainable urban planning

This study investigates the spatio-temporal changes in land use and land cover (LULC) in Selangor, Malaysia, from 2005 to 2020 using Moderate Resolution Imaging Spectroradiometer (MODIS) data processed via the Google Earth Engine (GEE). As the most urbanised state in Malaysia, Selangor has undergone rapid transformation, with significant shifts from vegetation and agricultural land to built-up areas due to urbanisation, industrial growth, and infrastructure development. Employing MODIS land cover datasets for four reference years (2005, 2010, 2015, and 2020) and validated classification techniques, five primary land cover classes, built-up areas, vegetation, agricultural land, open spaces, and water bodies, were analysed. Results show that built-up areas increased consistently, rising from 18.9% in 2005 to 20.67% in 2020, while vegetative cover declined from 68.91% to 65.06% over the same period. Agricultural areas exhibited fluctuating trends, reflecting shifts in land-use policy and food production strategies. Open areas, defined as non-urban and non-agricultural cleared or vacant land (including construction sites, parks, and recreation grounds), expanded until 2015 but contracted by 2020, reflecting their transitional nature as lands earmarked for future development. Water bodies remained relatively unchanged. Accuracy assessments yielded over 80% overall classification accuracy, confirming the reliability of the analysis. This study highlights the effectiveness of cloud-based remote sensing for monitoring urban expansion and environmental change. The findings serve as vital geospatial evidence for policymakers and urban planners to devise data-driven, sustainable land management strategies. By integrating spatial analysis with high-resolution temporal monitoring, this research contributes to informed decision-making aligned with environmental preservation and urban resilience goals.

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Publication Details

Journal
Economics and Environment
Published
2025-10-23
DOI
https://doi.org/10.34659/eis.2025.94.3.1203
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
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article

Spatio-temporal dynamics of land use and land cover in Selangor (2005–2020): insights from MODIS and Google Earth Engine for sustainable urban planning

Ang Kean Hua, Luqman Shamsudin
Economics and Environment
Land Use and Ecosystem Services
article

Spatio-temporal dynamics of land use and land cover in Selangor (2005–2020): insights from MODIS and Google Earth Engine for sustainable urban planning

Ang Kean Hua, Luqman Shamsudin
article en

Abstract

This study investigates the spatio-temporal changes in land use and land cover (LULC) in Selangor, Malaysia, from 2005 to 2020 using Moderate Resolution Imaging Spectroradiometer (MODIS) data processed via the Google Earth Engine (GEE). As the most urbanised state in Malaysia, Selangor has undergone rapid transformation, with significant shifts from vegetation and agricultural land to built-up areas due to urbanisation, industrial growth, and infrastructure development. Employing MODIS land cover datasets for four reference years (2005, 2010, 2015, and 2020) and validated classification techniques, five primary land cover classes, built-up areas, vegetation, agricultural land, open spaces, and water bodies, were analysed. Results show that built-up areas increased consistently, rising from 18.9% in 2005 to 20.67% in 2020, while vegetative cover declined from 68.91% to 65.06% over the same period. Agricultural areas exhibited fluctuating trends, reflecting shifts in land-use policy and food production strategies. Open areas, defined as non-urban and non-agricultural cleared or vacant land (including construction sites, parks, and recreation grounds), expanded until 2015 but contracted by 2020, reflecting their transitional nature as lands earmarked for future development. Water bodies remained relatively unchanged. Accuracy assessments yielded over 80% overall classification accuracy, confirming the reliability of the analysis. This study highlights the effectiveness of cloud-based remote sensing for monitoring urban expansion and environmental change. The findings serve as vital geospatial evidence for policymakers and urban planners to devise data-driven, sustainable land management strategies. By integrating spatial analysis with high-resolution temporal monitoring, this research contributes to informed decision-making aligned with environmental preservation and urban resilience goals.

Economics and EnvironmentVol. 94(3)
University of Malaya (MY)
Openalex Percentile: Top 72%
Land Use and Ecosystem Services
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