Autor: Jarocińska, A. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Korelacje naziemnych i lotniczych teledetekcyjnych wskaźników roślinności dla zlewni Bystrzanki
Correlations of ground- and airborne-level acquired vegetation indices of the Bystrzanka catchment
Autorzy:: Jarocińska, A.
Zagajewski, B.
Powiązania:: https://bibliotekanauki.pl/articles/132257.pdf
Data publikacji:: 2008
Wydawca:: Polskie Towarzystwo Geograficzne
Tematy:: zlewnia rzeki
roślinność
teledetekcja
wskaźnik
river basin
vegetation
indices
remote sensing
Źródło:: Teledetekcja Środowiska; 2008, 40; 100-124
1644-6380
Pojawia się w:: Teledetekcja Środowiska
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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