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GIS-based land cover analysis and prediction based on open-source software and data

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Dawid W., Bielecka E.
Quaestiones Geographicae
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2022
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vol. 41
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issue 3
75-86
EN
The study aims at land cover prediction based on cellular automata and artificial neural network (CA-ANN) method implemented in the Methods Of Land Use Change Evaluation (MOLUSCE) tool. The Tricity region and the neighbouring counties of Gdański and Kartuzy were taken as the research areas, and coordination of information on the environment (CORINE Land Cover, CLC, CLMS 2022) data for 2006, 2012 and 2018 were used to analyse, simulate and predict land cover for 2024, the next reference year of the CORINE inventory. The results revealed an increase in artificial surfaces, with the highest value during the period 2006–2012 (86.56 km2). In total, during the period 2006–2018, the growth in urbanised area amounted to 95.37 km2. The 2024 prediction showed that artificial surfaces increased by 9.19 km2, resulting in a decline in agricultural land.
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The Potential of Fuzzy Logic for Quantitative Land Cover Change Analysis Basing on Historical Topographic Maps

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Zachwatowicz M.
Miscellanea Geographica. Regional Studies on Development
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2011
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vol. 15
231-240
EN
This paper describes an approach that allows to reduce error propagation when comparing historical topographic maps. By linking the fuzzy set theory with simple map algebra and Kappa statistics, the uncertainty resulting from dissimilar quality of the maps can at least be partly eliminated and a distinction between ‘true’ and ‘false’ land cover changes can be made.
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