Spatiotemporal assessment of land use land cover dynamics in Mödling district, Austria, using remote sensing techniques

Alawode, G.L., Oluwajuwon, T.V., Hammed, R.A., Olasuyi, K.E., Krasovskiy, A. ORCID: https://orcid.org/0000-0003-0940-9366, Ogundipe, O.C., & Kraxner, F. (2025). Spatiotemporal assessment of land use land cover dynamics in Mödling district, Austria, using remote sensing techniques. Heliyon 11 (11) e43454. 10.1016/j.heliyon.2025.e43454.

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Abstract

Remotely sensed imagery plays a crucial role in analyzing and monitoring land cover and urban growth. The accuracy and applicability of European CORINE Land Cover (CLC) maps in Land Use and Land Cover (LULC) monitoring across European regions, especially at local scales, have been critiqued and remain limited due to temporal methodological variations. This study aims to understand the dynamics of LULC, assess the effectiveness of vegetation indices in estimating forest cover, and validate the local applicability of CORINE maps in the Lower Austrian district of Mödling in the neighbourhood of Vienna from 1999 to 2022. We employed a supervised maximum likelihood classifier and class-based change detection to analyze multi-decadal multispectral imagery for mapping and quantifying vegetation and land use changes across the district, in comparison with satellite indices and CORINE data. The study identified changing patterns and assessed the accuracy of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) in estimating Mödling's forest cover, determining optimal thresholds for improved assessment. Our findings reveal a slight reduction in Mödling's forest area – decreasing from 39.11 % in 1999 to 36.5 % in 2022 – with an overall reduction of 2.61 %. Agriculture primarily caused forest loss in the early period, expanding by over 37 %. In the most recent decade, settlement expansion, with built-up areas gaining approximately 650 ha, has exacerbated the loss of forest and agricultural lands. Our classification achieved high overall accuracy (92 %–94 %) and Kappa accuracy (0.90–0.93). The supervised classification exhibited a consistent reduction, aligning with CORINE outputs and refuting reports of its limited local applicability and accuracy. Although NDVI and SAVI estimates revealed a non-monotonic trend in forest cover across different years, NDVI performed better than SAVI. The results of this study are vital, providing evidence and recommending effective measures for enhancing monitoring, policy development, and decision-making regarding vegetation conservation, urban development, and overall land management. This research contributes to the limited body of core studies employing spectral imagery and GIS tools to monitor changes in land cover or assess CORINE maps in Austria and across Europe, with a special focus on the peri-urban interface.

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