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    Tytuł:
    Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend
    Autorzy:
    Golenia, M.
    Zagajewski, B.
    Ochtyra, A.
    Hościło, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/92466.pdf
    Data publikacji:
    2015
    Wydawca:
    Oddział Kartograficzny Polskiego Towarzystwa Geograficznego
    Tematy:
    classification
    Corine Land Cover
    Landsat
    artificial neural networks
    Warsaw
    Opis:
    Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.
    Źródło:
    Polish Cartographical Review; 2015, 47, 4; 203-212
    2450-6974
    Pojawia się w:
    Polish Cartographical Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains
    Autorzy:
    Jędrych, M.
    Zagajewski, B.
    Marcinkowska-Ochtyra, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/92448.pdf
    Data publikacji:
    2017
    Wydawca:
    Oddział Kartograficzny Polskiego Towarzystwa Geograficznego
    Tematy:
    Sentinel-2
    EnMAP
    classification
    alpine vegetation
    satellite systems
    Opis:
    Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier. Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.
    Źródło:
    Polish Cartographical Review; 2017, 49, 3; 107-119
    2450-6974
    Pojawia się w:
    Polish Cartographical Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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