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Wyszukujesz frazę "satelita Landsat TM" wg kryterium: Wszystkie pola


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:
Zdjecia satelitarne Landsat TM w ocenie gradacji brudnicy mniszki
Autorzy:
Baniya, N.
Zawila-Niedzwiecki, T.
Majunke, C.
Hauswirth, M.
Powiązania:
https://bibliotekanauki.pl/articles/45615.pdf
Data publikacji:
2006
Wydawca:
Instytut Badawczy Leśnictwa
Tematy:
zdjecia satelitarne
satelita Landsat TM
Lymantria monacha
szkodniki roslin
ochrona roslin
wykorzystanie
brudnica mniszka
monitoring
lesnictwo
gradacja
Źródło:
Leśne Prace Badawcze; 2006, 3; 33-44
1732-9442
2082-8926
Pojawia się w:
Leśne Prace Badawcze
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:
Klasyfikacja gleb słonych doliny Czuj w Kirgistanie na podstawie wielospektralnych obrazów satelitarnych Landsat TM, Landsat ETM+, TERRA ASTER oraz danych naziemnych
Classification of salt-affected soils of the Chuy Valley in Kyrgyzstan using multispectral satellite Landsat TM, Landsat ETM+, TERRA ASTER images and ground-collected data
Autorzy:
Kokoeva, G.
Powiązania:
https://bibliotekanauki.pl/articles/132209.pdf
Data publikacji:
2007
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
gleby słone
klasyfikacja
obraz satelitarny
satelita Landsat
satelita TERRA ASTER
dane naziemne
Kirgistan
salt-affected soils
classification
satellite Landsat
satellite TERRA ASTER
satellite image
ground-collected data
Kyrgyzstan
Opis:
The natural conditions of Kyrgyzstan and consequences of human-induced processes, such as inappropriate methods of irrigation, have led to the extension of salt-affected soils. Extensive areas of irrigated land have been increasingly degraded by salinization from over-irrigation and other forms of inadequate agricultural practices. Between 1985 and 1990, the area of salt-affected soils increased from 666 300 ha to 1170 300 ha (Mamytov, 1995). In recent years salinity processes have been described as one of the problems of agriculture in that area. For the last ten years many none-affected soils of the Chuy Valley have become salinized. According to Mamytov et al. (1991) the total area of salt-affected soils in the Chuy Valley exceeds 259.5 thousands ha, which is more than 42% of the research area. In this research, an attempt has been made to estimate soil salinity quantitatively and also spatially by applying remote sensing techniques. Conventional methods of mapping salt-affected soils consume a lot of energy, time and money. Remote sensing enables us to detect and to map salt-affected soils by using relatively cheap multispectral satellite data such as Landsat TM, Landsat ETM+ and TERRA ASTER. The objective of this study is to identify salt-affected soils by integrating satellite images with ground-collected data. In order to achieve this goal the best algorithms of an unsupervised and a supervised classifi cation have been chosen using TNTmips software. The Normalized Difference Vegetation Index (NDVI) and the Transformed Vegetation Index (TVI) have been applied to distinguish densely and partly vegetation- covered soils, which are not salt-affected. To distinguish areas covered with stone and sands from saline soils the Salinity Index (SI) has been applied. For the differentiation of arable land which is not covered with vegetation the brightness parameter of Tasseled Cap transformation has been used. All these indices were calculated from satellite images. Finding an appropriate interpretation scheme for identifying the saltaffected soils of the Chuy Valley becomes a very important factor infl uencing the accuracy of the supervised classifi cation. The temporal change of salinity accumulation is demonstrated by comparing the classifi cation’s results of the multispectral satellite images from 1994 to those of 2001. This study also includes measurements of spectral properties of collected soil samples for better understanding the difference in classifi cation accuracy of various types of salt-affected soils. Spectral refl ectance was registered from the surfaces of saline and saline-sodic soils using fi eld luminancemeter CIMEL CE 313-21 in the following wavelength bands: 450 nm, 550 nm, 650 nm and 850 nm.
Źródło:
Teledetekcja Środowiska; 2007, 37; 3-50
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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