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    Tytuł:
    The analysis of spatial and temporal changes of land cover and land use in the reclaimed areas with the application of airborne orthophotomaps and LANDSAT images
    Autorzy:
    Szostak, M.
    Wężyk, P.
    Hawryło, P.
    Pietrzykowski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/145585.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    reclamation
    GEOBIA
    pixel based classification
    LULC changes
    mapa pokrycia terenu
    klasyfikacja pikselowa
    technologia geoinformatyczna
    Opis:
    The aim of this study was to investigate the possible use of geoinformatics tools and generally available geodata for mapping land cover/use on the reclaimed areas. The choice of subject was dictated by the growing number of such areas and the related problem of their restoration. Modern technology, including GIS, photogrammetry and remote sensing are relevant in assessing the reclamation effects and monitoring of changes taking place on such sites. The LULC classes mapping, supported with thorough knowledge of the operator, is useful tool for the proper reclamation process evaluation. The study was performed for two post-mine sites: reclaimed external spoil heap of the sulfur mine Machów and areas after exploitation of sulfur mine Jeziórko, which are located in the Tarnobrzeski district. The research materials consisted of aerial orthophotos, which were the basis of on-screen vectorization; LANDSAT satellite images, which were used in the pixel and object based classification; and the CORINE Land Cover database as a general reference to the global maps of land cover and land use.
    Źródło:
    Geodesy and Cartography; 2015, 64, 1; 75-86
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring the secondary forest succession and land cover/use changes of the Błędów Desert (Poland) using geospatial analyses
    Autorzy:
    Szostak, Marta
    Wężyk, Piotr
    Hawryło, Paweł
    Puchała, Marta
    Powiązania:
    https://bibliotekanauki.pl/articles/1052864.pdf
    Data publikacji:
    2016-09-15
    Wydawca:
    Uniwersytet im. Adama Mickiewicza w Poznaniu
    Tematy:
    LULC changes
    Geographic Object Based Image Analysis (GEOBIA)
    pixel-based classification
    GIS analyses
    secondary forest succession
    Opis:
    The role of image classification based on multi-source, multi-temporal and multi-resolution remote sensed data is on the rise in the environmental studies due to the availability of new satellite sensors, easier access to aerial orthoimages and the automation of image analysis algorithms. The remote sensing technology provides accurate information on the spatial and temporal distribution of land use and land cover (LULC) classes. The presented study focuses on LULC change dynamics (especially secondary forest succession) that occurred between 1974 and 2010 in the Błędów Desert (an area of approx. 1210 ha; a unique refuge habitat – NATURA 2000; South Poland). The methods included: photointerpretation and on screen digitalization of KH-9 CORONA (1974), aerial orthoimages (2009) and satellite images (LANDSAT 7 ETM+, 1999 and BlackBridge – RapidEye, 2010) and GIS spatial analyses. The results of the study have confirmed the high dynamic of the overgrowth process of the Błędów Desert by secondary forest and shrub vegetation. The bare soils covered 19.3% of the desert area in 1974, the initial vegetation and bush correspondingly 23.1% and 30.5%. In the years 2009/2010 the mentioned classes contained: the bare soils approx. 1.1%, the initial vegetation– 8.7% and bush – 15.8%. The performed classifications and GIS analyses confirmed a continuous increase in the area covered by forests, from 11.6% (KH-9) up to 24.2%, about 25 years later (LANDSAT 7) and in the following 11 years, has shown an increase up to 35.7% (RapidEye 2010).
    Źródło:
    Quaestiones Geographicae; 2016, 35, 3; 5-13
    0137-477X
    2081-6383
    Pojawia się w:
    Quaestiones Geographicae
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mapy podstawowych form pokrycia i użytkowania terenu zlewni Raby powyżej Zbiornika Dobczyckiego - porównanie dokładności klasyfikacji pikselowej i obiektowej obrazów LANDSAT TM
    Mapping of basic land-use/land cover types in upper Raba watershed - accuracy comparison of pixel-based and object-based approaches to LANDSAT TM images classification
    Autorzy:
    Badurska, M.
    Drzewiecki, W.
    Tokarczyk, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/132383.pdf
    Data publikacji:
    2009
    Wydawca:
    Polskie Towarzystwo Geograficzne
    Tematy:
    Landsat TM
    klasyfikacja pikselowa
    klasyfikacja obiektowa
    ocena
    dokładność klasyfikacji
    zlewnia Raby
    pokrycie i użytkowanie terenu
    pixel based classification
    object-based classification
    classification accuracy assessment
    Raba watershed
    land-use
    land cover
    Opis:
    The research presented in the paper has been aimed at mapping the basic types of land-use in the upper Raba watershed (south Poland). The maps have been prepared for a study of the influence of land-use changes within the watershed on the sediment yields introduced into the reservoir. Because the erosion models used for sediment yields prediction need only to identify the main land-use / land cover classes (arable land, meadows and pastures, forests, waters, developed areas), the maps have been based on classification of middle-resolution satellite images (Landsat TM). In the research the results of traditional pixel-based classification were compared to the ones obtained in the object based approach. Six different Landsat TM images were classified. The methodology of both classification approaches have been described in the paper. The accuracy assessment of the classification results was based on their comparison with the land use types defined by the photo interpretation of colour composite images. The assessment was done by two operators. Each of them used different set of two hundred and fifty randomly generated sample points. In most cases the pixel-based approach resulted in higher overall accuracy. However, if overall accuracy confidence intervals are taken into consideration, none of the methods can be definitely recognised as a better one.
    Źródło:
    Teledetekcja Środowiska; 2009, 42; 15-21
    1644-6380
    Pojawia się w:
    Teledetekcja Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessing the accuracy of the pixel-based algorithms in classifying the urban land use, using the multi spectral image of the IKONOS satellite (Case study, Uromia city)
    Autorzy:
    Safaralizade, E.
    Husseinzade, R.
    Pashazade, G.
    Khosravi, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/11078.pdf
    Data publikacji:
    2014
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    pixel-based algorithm
    urban land
    land use
    multispectral image
    IKONOS satellite
    classification
    urbanization
    urban planning
    Uromia city
    Opis:
    With the development of urbanization and expansion of urban land use, the need to up to date maps, has drawn the attention of the urban planners. With the advancement of the remote sensing technology and accessibility to images with high resolution powers, the classification of these land uses could be executed in different ways. In the current research, different algorithms for classifying the pixel-based were tested on the land use of the city of Urmia, using the multi spectral images of the IKONOS satellite. Here, in this method, the algorithms of the supervised classification of the maximum likelihood, minimum distance to mean and parallel piped were executed on seven land use classes. Results obtained using the error matrix indicated that the algorithm for classifying the maximum likelihood has an overall accuracy of 88/93 % and the Kappa coefficient of 0/86 while for the algorithms of minimum distance to mean and parallel piped , the overall accuracy are 05/79 % and 40/70 % respectively. Also, the accuracy of the producer and that of the user in most land use classes in the method of maximum likelihood are higher compared to the other algorithms.
    Źródło:
    International Letters of Natural Sciences; 2014, 06
    2300-9675
    Pojawia się w:
    International Letters of Natural Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques
    Autorzy:
    Powar, Sudhir K.
    Panhalkar, Sachin S.
    Patil, Abhijit S.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055771.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    pixel-based classification (PBC)
    object-based classification(OBC)
    maximum likelihood classifier
    multi-resolution segmentation
    Opis:
    Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 2; 61--75
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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