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2016 | T. 55 | 15--26
Tytuł artykułu

Zmienność wskaźników NDVI oraz NDMI na przykładzie analizy uprawy kukurydzy w Etiopii

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Warianty tytułu
Variability of NDVI and NDMI indicators on the example of maize cultivation in Etiopia
Języki publikacji
Celem pracy jest analiza zmian kondycji uprawy kukurydzy na przestrzeni trzech lat, z zastosowaniem danych satelitarnych Landsat-8. Wykorzystano tu obrazy prezentujące rozkład przestrzenny dwóch wskaźników teledetekcyjnych: NDVI oraz NDMI. Pierwszy pozwala badać wielkość biomasy a tym samym potencjalny plon upraw. Drugi natomiast wrażliwy jest na zawartość wody w strukturach komórkowych roślin, co pozwala na detekcję stresu wodnego. Przebadano zróżnicowanie przestrzenne i zmienność tych dwóch wskaźników od 2014 do 2016 roku. Obliczono średnią i odchylenie standardowe dla uprawy oraz wydzielonych w niej 4 stref. Przeanalizowano również zmienność wskaźników na podstawie opracowanych map uprawy. Analiza przedstawiona w pracy jest konkretnym przykładem zastosowania średniorozdzielczych scen Landsat do monitoringu upraw, a tym samym tzw. precyzyjnego rolnictwa, gotowym do zaaplikowania na platformie COMOZ tworzonej w Instytucie Lotnictwa. Statystyki wskazują na konkretne daty kiedy kondycja upraw była najlepsza a kiedy najgorsza. Mapowanie zjawiska pozwala na śledzenie trendów w czasie i przestrzeni. Dodatkowo interpretacja wizualna i pośrednie cechy interpretacyjne obrazu mogą wskazywać na prowadzone na miejscu zabiegi hydrotechniczne.
The aim of the study was to analyze changes in the condition of maize over three years, using satellite data Landsat-8. Spatial distribution and statistics of two remote sensing indices: NDVI and NDMI were shown. First of mentioned allows biomass estimation and thus potential crop yield. The second one is sensitive to water content of the plant cell structure, which allows for the detection of water stress. In the study spatial heterogeneity and variability of these two indicators from 2014 until 2016 were presented. The mean and standard deviation for 4 separated region of interests were calculated. Spatial variability of indices based on developed crops maps were also analyzed The analysis presented in this work is a concrete example of the application of medium-resolution Landsat scenes for monitoring crops and thus the so-called precision farming, ready to implement in COMOZ platform developed in Institute of Aviation. Statistics indicate a specific date when the condition of the crop was the best and when the worst. Mapping the phenomenon allows to track trends over time and space. In addition, the visual interpretation of indirect features of the image can indicate on the agricultural treatments characterization.
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