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Wyszukujesz frazę "Marcinkowska-Ochtyra, A." wg kryterium: Autor

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    Tytuł:
    Mapa geomorfologiczna województw pomorskiego i warmińsko-mazurskiego z wykorzystaniem metod geoinformatycznych
    Geomorphological map of pomorskie and warmińsko-mazurskie voivodeships using geoinformatics methods
    Autorzy:
    Marcinkowska, A.
    Ochtyra, A.
    Olędzki, J. R.
    Wołk-Musiał, E.
    Zagajewski, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/132207.pdf
    Data publikacji:
    2013
    Wydawca:
    Polskie Towarzystwo Geograficzne
    Tematy:
    forma
    rzeźba
    geomorfologia
    Landsat
    mapa cyfrowa
    województwo podlaskie
    województwo pomorskie
    województwo warmińsko-mazurskie
    wektoryzacja
    zdjęcie satelitarne
    landform
    geomorphology
    digital map
    podlaskie voivodeship
    pomorskie voivodeship
    warmińsko-mazurskie voivodeship
    vectorization
    satellite image
    Opis:
    The aim of this study was to prepare geomorphological maps of pomorskie and warminsko-mazurskie voivodeships in scale 1:300 000. Analysis primarily were based on the General Geomorphological Map of Poland 1:500 000 and Landsat 5 TM satellite images in RGB 453 composition, and alternatively with Geological Map of Poland 1:200 000, Topographic Map of Poland 1:100 000 and Digital Terrain Model from Shuttle Radar Topography Mission. These materials were processed into digital form and imported them PUWG 1992 coordinate system. Based on them was lead interpretation and vectorization of geomorphological forms. It was detailing the boundaries in accordance with the content of the General Geomorphological Map of Poland 1:500 000. Then polygons were coded according to the numbering of J. Borzuchowski (2010). Very important was process to design a legend and then editing maps. The last stage of this study was to prepare a composition for printing maps. The effect of studies are geomorphological maps of pomorskie and warminsko-mazurskie voivodeships in scale 1:300 000, and an interactive databases in ESRI shapefile format (*.shp).
    Źródło:
    Teledetekcja Środowiska; 2013, 49; 43-79
    1644-6380
    Pojawia się w:
    Teledetekcja Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Określenie składu gatunkowego lasów Góry Chojnik (Karkonoski Park Narodowy) z wykorzystaniem lotniczych danych hiperspektralnych APEX
    Identification of tree species in Mt Chojnik (Karkonoski National Park) forest using airborne hyperspectal APEX data
    Autorzy:
    Raczko, E.
    Zagajewski, B.
    Ochtyra, A.
    Jarocińska, A.
    Marcinkowska-Ochtyra, A.
    Dobrowolski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/989774.pdf
    Data publikacji:
    2015
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    lesnictwo
    Karkonoski Park Narodowy
    gory
    Chojnik
    lasy
    sklad gatunkowy
    metody badan
    teledetekcja
    pomiary hiperspektralne
    skaner APEX
    svm classification
    apex hyperspectral data
    species structure
    Opis:
    We used hyperspectral data from APEX scanner (288 spectral bands in 380−2500 nm spectral range; 3,5 m spatial resolution) to classify five tree species occurring in the area of Mt. Chojnik in the Karkonoski National Park (south−western Poland). Data used to delimit learning and verification polygons were acquired during field research in August 2013, when ground truth polygons were acquired using device equipped with GPS receiver. Raw APEX data went through radiometric and geometric correction at VITO office. To reduce processing time, 40 most informative bands were selected using information content analysis. The Support Vector Machines (SVM) algorithm was used for classification of the following tree species: Fagus sylvatica L., Betula pendula Roth, Pinus sylvestris L., Picea alba L. Karst and Larix decidua Mill. Final classification had 78.66% overall accuracy with Kappa coefficient equal to 0.71. The best classified species included beech (87.09%) and pine (83.96%), while the worst results were obtained for larch (60.29%). Low accuracy for larch could be caused by the fact that most of larch trees in the research area grow in small patches, which made it hard to specify large enough sample of training data. All classified tree species had producer's accuracy of at least 60%, with the highest value reaching 87%. User's accuracies were from 53% for pine to 85% for beech. It is possible to classify tree species using hyperspectral data with moderate to high accuracy even if the data used lacked atmospheric correction. Further work will focus on improving the classification accuracy and use of neural networks based classification methods. Results from this paper will serve as basis for tree species map of the Karkonoski National Park.
    Źródło:
    Sylwan; 2015, 159, 07; 593-599
    0039-7660
    Pojawia się w:
    Sylwan
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ocena kondycji drzewostanów Tatrzańskiego Parku Narodowego za pomocą metody drzewa decyzyjnego oraz wielospektralnych obrazów satelitarnych Landsat 5 TM
    Assessment of the condition of forests in the Tatra National Park using decision tree method and multispectral Landsat TM satellite images
    Autorzy:
    Ochtyra, A.
    Zagajewski, B.
    Kozłowska, A.
    Marcinkowska-Ochtyra, A.
    Jarocińska, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/972978.pdf
    Data publikacji:
    2016
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    drzewostany
    kondycja drzew
    metody oceny
    drzewa decyzyjne
    teledetekcja satelitarna
    obrazy satelitarne
    satelita Landsat TM
    leśnictwo
    lasy górskie
    Tatrzański Park Narodowy
    forest
    assessment of condition
    vegetation indices
    remote sensing
    the Tatras
    Landsat TM
    Opis:
    The paper presents a method of Landsat 5 Thematic Mapper satellite image processing to assess the condition of forests in the Tatra National Park (southern Poland). Selected images were acquired on 1987/09/01, 2005/09/02 and 2011/09/03 from the same sensor with maximum time interval for the first and last scene and from similar phenological period. Firstly, the data were radiometrically corrected using the ATCOR 2/3 software and Digital Terrain Model from the ASTER mission. Quality of the correction was assessed calculating RMSE for reflectance values from images and resampled spectral characteristics collected in terrain. RMSE was in range 3−10%. Next, basing on Landsat images, Normalized Difference Infrared Index (NDII) and a Maximum Likelihood supervised classificatory, following dominant land cover types were identified: forests (including dwarf pine), grasslands, rocks, lakes, shadows (additionally clouds were dis−tinguished on data from 1987/09/01). It allowed to select forest areas with producer accuracy not worse than 97.69% and user accuracy not worse than 98.31%. On corrected Landsat images Normalized Difference Vegetation Index (NDVI, an overall vegetation state) and Moisture Stress Index (MSI, canopy water content) were calculated. Vegetation indices discriminated forest state using the decision tree method. The worst overall condition was observed for the 1987 (about 21% of forest stands were in the worst condition and 87% were in medium condition), while the best one in 2005 (75.51% forest stands were in good condition and 10.66% were in the best condition). In case of 2011, the overall condition was quite good, but there were large areas with poor condition caused by bark beetle outbreaks. Proposed method allows for a fast and objective assessment of forest condition. It is possible to detect damaged areas or stands in poor condition. It can be complement for traditional methods of monitoring and management in forestry and nature protection.
    Źródło:
    Sylwan; 2016, 160, 03; 256-264
    0039-7660
    Pojawia się w:
    Sylwan
    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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