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Wyszukujesz frazę "Change detection" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Monitoring and forecasting spatio-temporal LULC for Akure rainforest habitat in Nigeria
Autorzy:
Aliyu, Yahaya A
Youngu, Terwase T.
Abubakar, Aliyu Z.
Bala, Adamu
Jesulowo, Christianah I.
Powiązania:
https://bibliotekanauki.pl/articles/1444929.pdf
Data publikacji:
2020
Wydawca:
Politechnika Warszawska. Wydział Geodezji i Kartografii
Tematy:
LULC
change detection
Landsat
Cellular Automata Markov model
Nigeria
wykrywanie zmian
program Landsat
model Markowa
automaty komórkowe
Opis:
For several decades, Nigerian cities have been experiencing a decline in their biodiversity resulting from rapid land use land cover (LULC) changes. Anticipating short/long-term consequences, this study hypothesised the effects of LULC variables in Akure, a developing tropical rainforest city in south-west Nigeria. A differentiated trend of urban LULC was determined over a period covering 1999–2019. The study showed the net change for bare land, built-up area, cultivated land, forest cover and grassland over the two decades to be -292.68 km2, +325.79 km2, +88.65 km2, +8.62 km2 and -131.38 km2, respectively. With a projected population increase of about 46.85%, the study identified that the built-up land cover increased from 1.98% to 48.61%. The change detection analysis revealed an upsurge in built area class. The expansion indicated a significant inverse correlation with the bare land class (50.97% to 8.66%) and grassland class (36.33% to 17.94%) over the study period. The study observed that the land consumption rate (in hectares) steadily increased by 0.00505, 0.00362 and 0.0687, in the year 1999, 2009 and 2019, respectively. This rate of increase is higher than studies conducted in more populated cities. The Cellular Automata (CA) Markovian analysis predicted a 37.92% growth of the study area will be the built-up area in the next two decades (2039). The 20-year prediction for Akure built-up area is within range when compared to CA Markov prediction for other cities across the globe. The findings of this study will guide future planning for rational LULC
Źródło:
Reports on Geodesy and Geoinformatics; 2020, 110; 29-38
2391-8365
2391-8152
Pojawia się w:
Reports on Geodesy and Geoinformatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detekcja zmian pokrycia terenu na zdjęciach satelitarnych Landsat - porównanie trzech metod
Land cover change detection using Landsat imagery - comparison of three methods
Autorzy:
Niedzielko, J.
Lewiński, S.
Powiązania:
https://bibliotekanauki.pl/articles/132345.pdf
Data publikacji:
2012
Wydawca:
Polskie Towarzystwo Geograficzne
Tematy:
pokrycie terenu
wykrywanie zmian
Landsat
różnica obrazów
klasyfikacja nadzorowana
analiza głównych składowych
land cover
change detection
image difference
supervised classification
principal components analysis
Opis:
Environmental changes are amongst the most important research subjects in geography. The changes may be natural, but also may be caused by human activity. Land cover is a significant component of the changing environment. Monitoring of its changes involves usage of satellite techniques. Landsat mission provides comparable data since forty years, very useful in land cover studies. Utilization of satellite techniques in such researches is developing quickly. This paper is an example of methods that enable quick and quite accurate assessment of range and spatial distribution of land cover changes. Practical application of image difference, principal component analysis and supervised classification to detect land cover changes is presented. Methods are applied to study area containing different land cover classes. Accuracy of methods was tested and compared. Combining methods presented in earlier researches, five new methods were developed: image difference, image difference with classification, classification, principal component analysis, principal component analysis with classification. Methods were applied to three different input datasets: pairs of images with different level of preprocessing. First dataset was a pair of georeferenced Landsat Thematic Mapper images. The second dataset was the same pair of images, atmospherically corrected using dark object subtraction method. Normalization of one image to the other provided the third dataset. Accuracy assessment was executed. Results were obtained from confusion matrices. Overall accuracy of methods was high, from 77% to 91%. Supervised classification was the most accurate method. Combining fully automatic methods with supervised classification has increased overall accuracy of automatic change detection, however not significantly. Studies on combining change detection methods should be continued. Future studies should concentrate on the automation of change detection process.
Źródło:
Teledetekcja Środowiska; 2012, 47; 87-98
1644-6380
Pojawia się w:
Teledetekcja Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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