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    Wyświetlanie 1-2 z 2
    Tytuł:
    The analysis of spatial and temporal changes of land cover and land use in the reclaimed areas with the application of airborne orthophotomaps and LANDSAT images
    Autorzy:
    Szostak, M.
    Wężyk, P.
    Hawryło, P.
    Pietrzykowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/145585.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    reclamation
    GEOBIA
    pixel based classification
    LULC changes
    mapa pokrycia terenu
    klasyfikacja pikselowa
    technologia geoinformatyczna
    Opis:
    The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation. The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classification; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.
    Źródło:
    Geodesy and Cartography; 2015, 64, 1; 75-86
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using a GEOBIA framework for integrating different data sources and classification methods in context of land use/land cover mapping
    Autorzy:
    Osmólska, A.
    Hawryło, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/145304.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    mapa użytkowanych gruntów
    mapa pokrycia terenu
    mapa leśna
    data fusion
    random forest
    supervised classification
    Sentinel-2
    Opis:
    Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
    Źródło:
    Geodesy and Cartography; 2018, 67, 1; 99-116
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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