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Wyszukujesz frazę "multi-temporal classification" wg kryterium: Temat


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Tytuł:
Porównanie klasyfikacji wieloczasowych zdjęć satelitarnych MODIS
Comparison of multi-temporal classification of MODIS satellite data
Autorzy:
Lewińska, K. E.
Powiązania:
https://bibliotekanauki.pl/articles/132371.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
klasyfikacja wieloczasowa
pokrycie terenu
MODIS
multi-temporal classification
land cover
Opis:
Since the 1970’s remote sensing enable constant monitoring of land cover and land use, which are considered as the most crucial environmental data. Obtaining this information at global, regional and local scales, becomes the goal of many research and application programs and has allowed for the deeper understanding of the entire Earth system. In December 1999 NASA launched the EOS Terra Satellite, followed in 2002 by Aqua, both equipped with MODIS (Moderate Resolution Imaging Spectroradiometer). Thanks to its technical specification and free distribution of the majority of its products, MODIS is considered to be the most important sensor for the global vegetation mapping. Although has been originally designed for large-scale analysis, MODIS is also used in many regional research programs. This paper presents results of two approaches of multitemporal land cover classification of MODIS data. For the study polygon of 22100 square kilometres, situated in the western Poland, four single day surface reflectance data sets, of spatial resolution of 250m and 500m, were collected for the year 2007 – one for spring and autumn, and two for summer. In the first approach supervised land cover classification was conducted for each set of single day data separately. On the basis of obtained results, final classification was elaborated as an effect of analyse of the sequence of changes for each pixel. In second approach, independent classification was conducted for the aggregation of all possessed data. The accuracy of all classifications’ results was checked against Corine Land Cover 2000 database using 4200 randomly distributed points. Obtained statistics show that comparing with single-day classifications as well as with classification of aggregated data, multi-temporal approach based on the analysis of sequence, enabled crucial improvement of accuracy of the classification.
Źródło:
Teledetekcja Środowiska; 2011, 46; 3-13
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forest species mapping using airborne hyperspectral APEX data
Autorzy:
Tagliabue, Giulia
Panigada, Cinzia
Colombo, Roberto
Fava, Francesco
Cilia, Chiara
Baret, Frédéric
Vreys, Kristin
Meuleman, Koen
Rossini, Micol
Powiązania:
https://bibliotekanauki.pl/articles/1035947.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Warszawski. Wydział Geografii i Studiów Regionalnych
Tematy:
Vegetation map
Hyperspectral
Aerial
Supervised classification
Multi-temporal dataset
Forest ecosystem
Opis:
The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.
Źródło:
Miscellanea Geographica. Regional Studies on Development; 2016, 20, 1; 28-33
0867-6046
2084-6118
Pojawia się w:
Miscellanea Geographica. Regional Studies on Development
Dostawca treści:
Biblioteka Nauki
Artykuł
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