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Wyszukujesz frazę "satelita Landsat" wg kryterium: Wszystkie pola

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    Wyświetlanie 1-4 z 4
    Tytuł:
    Zdjęcia satelitarne Landsat-Thematic Mapper w ocenie stanu lasu
    Kosmicheskie snimki so sputnika Lehndsat TM dlja ocenki sostojanija lesov
    Landsat TM satellite data for forest quality assessment
    Autorzy:
    Zawila-Niedzwiedzki, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/813103.pdf
    Data publikacji:
    1989
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    lesnictwo
    lasy
    stan lasu
    metody oceny
    zdjecia satelitarne
    satelita Landsat TM
    Źródło:
    Sylwan; 1989, 133, 07
    0039-7660
    Pojawia się w:
    Sylwan
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Określanie lesistości Polesia Ukraińskiego na podstawie wyników klasyfikacji sezonowych obrazów kompozytowych Landsat 8 OLI
    Estimation of forest cover in Ukrainian Polissia using classification of seasonal composite Landsat 8 OLI images
    Autorzy:
    Lakyda, P.
    Myroniuk, V.
    Bilous, A.
    Boiko, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/979663.pdf
    Data publikacji:
    2019
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    lesnictwo
    Ukraina
    Polesie
    lesistosc
    teledetekcja
    zdjecia satelitarne
    satelita Landsat 8 OLI
    forest cover
    remote sensing
    random forest
    ikonos−2
    ndvi
    Opis:
    Training dataset for modelling of forest cover was created after classification of multispectral satel− lite imagery IKONOS−2 with spatial resolution 3.2 m (acquisition date – 12.08.2011). As a result, we created binary forest cover map with 2 categories: ‘forest’ and ‘not−forest’. That allowed us to compute the tree canopy cover for each pixel of Landsat 8 OLI, using vector grid with cell size of 30×30 m. Classification model was developed using training dataset that included 17,000 observations, 10,000 of them represented results of IKONOS−2 classification. Aiming to avoid errors of agricultural lands inclusion into forest mask because of lack of data, additionally we collected about 7000 random observations with canopy cover 0% that had been evenly distributed within unforested area. Random Forest (RF) model we developed allowed us to create continuous map of forests within study area that represents in each pixel value of tree canopy closeness (0−100%). To convert it into a discrete map, we recoded all values less than 30% as ‘no data’ and values from 30 to 100% as 1. Forest mask for two selected administrative districts of Chernihiv region (NE Ukraine) was created after screening map from small pixel groups that covered area less than 0.5 ha. Obtained results were compared with Global Forest Change (GFC) map and proved that GFC data can be used for forest mapping with tree canopy closeness threshold 40%. On considerable areas of abandoned agricultural lands in the analysed regions of Ukraine, forest stands are formed by Scots pine, silver birch, black alder and aspen. Existence of such forests substantially increases (on 6−8%) the forested area of Gorodnya and Snovsk districts of Chernihiv region – comparing to official forest inventory data. However, such stands are not protected and have high risks to be severed by wildfires, illegal cuttings with aim to renew the agricultural production, by diseases, insects and other natural disturbances.
    Źródło:
    Sylwan; 2019, 163, 09; 754-764
    0039-7660
    Pojawia się w:
    Sylwan
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring wylesień lasów deszczowych Amazonii na podstawie radiometrycznych przetworzeń zdjęć satelitarnych
    The monitoring of the Amazon rainforest deforestation on the basis of a radiometric analysis of satellite images
    Autorzy:
    Ugarte, H.F.
    Zawiła-Niedźwiecki, T.
    Dos Santos, J.R.
    Maldonado, F.D.
    Powiązania:
    https://bibliotekanauki.pl/articles/1012565.pdf
    Data publikacji:
    2008
    Wydawca:
    Polskie Towarzystwo Leśne
    Tematy:
    zdjecia satelitarne
    satelita CBERS-2
    satelita Landsat ETM plus
    Amazonia
    teledetekcja satelitarna
    lesnictwo tropikalne
    monitoring
    wylesienia
    lasy tropikalne
    satellite remote sensing
    tropical forest
    deforestation
    bolivia
    the amazon
    Opis:
    The article discusses the results of research on radiometric processing of multitemporal satellite images in order to monitor deforestation in the Amazon region. Images acquired by Landsat ETM+ and CBERS−2 were used. The proposed algorithms enabled an objective analysis of deforestation and regeneration distribution within tropical rainforests. The advantage of the method is its simple algorithm and lack of need for atmospheric correction.
    Źródło:
    Sylwan; 2008, 152, 07; 3-8
    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
      Wyświetlanie 1-4 z 4

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