The Effects of Land Use and Land Cover Geoinformation Raster Generalization in the Analysis of LUCC in Portugal

Abstract

Multiple land use and land cover (LUC) datasets are available for the analysis of LUC changes (LUCC) in distinct territories. Sometimes, different LUCC results are produced to characterize these changes for the same territory and the same period. These differences reflect: (1) The different properties of LUC geoinformation (GI) used in the LUCC assessment, and (2) different criteria used for vector-to-raster conversion, namely, those deriving from outputs with different spatial resolutions. In this research, we analyze LUCC in mainland Portugal using two LUC datasets with different properties: Corine Land Cover (CLC 2006 and 2012) and LUC official maps of Portugal (Carta de Ocupação do Solo, COS 2007 and 2010) provided by the European Environment Agency (EEA) and the General Directorate for Territorial Development (DGT). Each LUC dataset has undergone vector-to-raster conversion, with different resolutions (10, 25, 50, 100, and 200 m). LUCC were analyzed based on the vector GI of each LUC dataset, and with LUC raster outputs using different resolutions. Initially, it was observed that the areas with different LUC types in two LUC datasets in vector format were not similar—a fact explained by the different properties of this type of GI. When using raster GI to perform the analysis of LUCC, it was observed that at high resolutions, the results are identical to the results obtained when using vector GI, but this ratio decreases with increased cell size. In the analysis of LUCC results obtained with raster LUC GI, the outputs with pixel size greater than 100 m do not follow the same trend of LUCC obtained with high raster resolutions or using LUCC obtained with vector GI. These results point out the importance of the factor form and the area of the polygons, and different effects of amalgamation and dilation in the vector-to-raster conversion process, more evident at low resolutions. These findings are important for future evaluations of LUCC that integrate raster GI and vector/raster conversions, because the different LUC GI resolution in line with accuracy can explain the different results obtained in the evaluation of LUCC. The present work demonstrates this fact, i.e., the effects of vector-to-raster conversions using various resolutions culminated in different results of LUCC.

Effects of different land use and land cover data on the landslide susceptibility zonation of road networks

“…LUC is an important conditioning factor in landslide susceptibility (Pisano et al, 2017), and the high accountability index results prove this fact (Table 4). There are several studies about the influence of land use cover changes on landslide susceptibility (e.g., Karsli et al, 2009;Mugagga et al, 2012;Promper et al, 2014;Reichenbach et al, 2014), although to the best of our knowledge there are no approaches that analyze the influence of different LUC datasets with different properties (date and base maps used on the production, spatial resolution, scale, minimum mapping unit, or others) on the landslide susceptibility results. When the landslide predisposing factors are collected, the LUC dataset must be selected according to its abovementioned properties and not only on the basis of its availability and free-of-charge conditions.…”

“…Beyond the influence of different environmental factors (e.g., lithology, slope angle, slope morphology, topography, soils, and hydrology) on the spatial distribution of landslides, land use and land cover (LUC) dynamics are also an important factor on landslide susceptibility assessment (Guillard and . Certain land use and land cover changes (LUCCs) (e.g., deforestation, slope ruptures to road construction, steep slopes) increase the number of unstable slopes (Reichenbach et al, 2014), i.e., promoting the propensity for landslide occurrence, and can have an important impact on landslide activity (Beguería, 2006;Glade, 2003;Mugagga et al, 2012;Persichillo et al, 2017;van Westen et al, 2008).…”

Detection and classification of changes in agriculture, forest, and shrublands for land cover map updating in Portugal

“…The area associated with change between the maps corresponds to 5.4% of the total area. The rate of change for continental Portugal is usually smaller 12,13 , but typically statistics rely on maps such as COS and CORINE Land Cover, which have a stronger emphasis on land use. On the contrary, the new maps focus more on land cover and therefore represent changes often not mapped, such as the impact of wildfires and clear-cuts, which do not represent directly land use change.…”

The Land-Use Change Dynamics Based on the CORINE Data in the Period 1990–2018 in the European Archipelagos of the Macaronesia Region: Azores, Canary Islands, and Madeira

“…The main number of new researches has conducted territorial studies based on GIS Tools and CORINE data methodical approaches to landscape fragmentation. Especially in environmental studies as well as on research about changes in land degradation-i.e., in areas with different types of land cover countries, regions, islands, or cities [30][31][32][33], including urban growth monitoring and urban sprawl comparations' land-use forecasts, modeling of road travel speeds or fragmentation of property rights [34].…”