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Prague: Short-term history of land cover changes explored by satellite images

Publikace na Přírodovědecká fakulta |
2018

Tento text není v aktuálním jazyce dostupný. Zobrazuje se verze "en".Abstrakt

Information about urban areas and their land cover changes has significant implications for urban growth studies modeling, environmental impacts and public health. Although only accounting for a small proportion of the country land surface, Prague acts as a base for the country's economy and culture and, to a certain degree, also for its population.

The city is home to about 1.4 million people, while its surrounding urban zone is estimated to have a population of 2.2 million people. With the rapid growth of the urban population and intensive human activities, urban areas will undoubtedly continue to grow in extent.

Despite activities concerned with mapping urban land changes at multi-year intervals, detailed information on urban development in time over rapid urbanization is not available in most studies. The study describes short-term mapping of urban areas in Prague and its closest surroundings over the period from 1984 to 2017.

Mapping for selected years was carried out using a number of Landsat images that employ spectral information from base bands and other information on vegetation indices, such as the Normalized Difference Vegetation Index (NDVI,) Normalized Difference Moisture Index (NDMI) and Modified Soil- Adjusted Vegetation Index (MSAVI), and also land surface temperatures. The selected Landsat images and the vegetation indices are arranged into time series in order to explore land cover changes on a short-term historical scale.

The original areas of the city corresponding to small land cover changes can be used for accuracy assessment, while the areas developing rapidly over the past decades located mainly in the suburbs are explored by the available Landsat images. In addition to the Landsat data, the study is extended to include thematic maps and the results from band collection statistics, such as correlation matrices.

The contribution also reflects GIS capabilities for assessment and modeling of land cover changes and their utilization in decision-making procedures for similar regions. (C) 2018 Nova Science Publishers, Inc. All rights reserved.