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001-es BibID:	BIBFORM076537
035-os BibID:	(WoS)000458299900007 (Scopus)85059086542
Első szerző:	Szabó Szilárd (geográfus)
Cím:	NDVI dynamics as reflected in climatic variables: spatial and temporal trends : a case study of Hungary / Szilárd Szabó, László Elemér, Zoltán Kovács, Zoltán Püspöki, Ádám Kertész, Sudhir Kumar Singh, Boglárka Balázs
Dátum:	2019
ISSN:	1548-1603 1943-7226
Megjegyzések:	Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961-2010) and the MODIS NDVI images (2000-2016) and evaluated the time period covered by both (2000-2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961-2008 and 2000-2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk climate change trend CARPATCLIM principal component analysis topographic variables MODIS
Megjelenés:	GIScience & Remote Sensing. - 56 : 4 (2019), p. 624-644. -
További szerzők:	László Elemér (1987-) (meteorológus előrejelző szakiránnyal) Kovács Zoltán (1988-) (geográfus) Püspöki Zoltán (1972-) (geológus) Kertész Ádám (1948-) Singh, Sudhir Kumar (1970-) (geográfus) Balázs Boglárka (1985-) (geográfus)
Pályázati támogatás:	TÁMOP-4.2.4.A/2-11-1-2012-0001 TÁMOP NKFIH 108755 Egyéb
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001-es BibID:	BIBFORM091945
035-os BibID:	(cikkazonosító)857 (WOS)000628506100001 (Scopus)85102203063
Első szerző:	Varga Orsolya Gyöngyi (geográfus)
Cím:	Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning / Orsolya Gyöngyi Varga, Zoltán Kovács, László Bekő, Péter Burai, Zsuzsanna Csatáriné Szabó, Imre Holb, Sarawut Ninsawat, Szilárd Szabó
Dátum:	2021
ISSN:	2072-4292
Megjegyzések:	We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km ? 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA's accuracy, and PlanetScope's data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1?78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk landsat sentinel planet CLC2018 Recursive Feature Elimination validation representativeness Random Forest Linear Discriminant Analysis
Megjelenés:	Remote Sensing. - 13 : 5 (2021), p. 1-24. -
További szerzők:	Kovács Zoltán (1988-) (geográfus) Bekő László (1986-) (okleveles vidékfejlesztési agrármérnök) Burai Péter (1977-) (agrármérnök) Szabó Zsuzsanna (1985-) (környezetgazdálkodási és vidékfejlesztési agrármérnök) Holb Imre (1973-) (agrármérnök) Ninsawat, Sarawut Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	TNN 123457 Egyéb ÚNKP-19-3-III-DE-94 Egyéb TKP2020-NKA-04 Egyéb
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