Találati lista | Tudóstér

001-es BibID:	BIBFORM078729
Első szerző:	Balázs Boglárka (geográfus)
Cím:	Extracting water-related features using reflectance data and principal component analysis of Landsat images / Boglárka Balázs, Tibor Bíró, Gareth Dyke, Sudhir Kumar Singh, Szilárd Szabó
Dátum:	2018
ISSN:	0262-6667
Megjegyzések:	This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4?5?7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban multivariate analysis principal component analysis remote sensing Landsat classification uncertainty analysis spectral index
Megjelenés:	Hydrological Sciences Journal. - 63 : 2 (2018), p. 269-284. -
További szerzők:	Bíró Tibor Dyke, Gareth J. Singh, Sudhir Kumar (1970-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	TÁMOP 4.2.4.A/2-11-1-2012-0001 TÁMOP SROP-4.2.2.B-15/1/KONV-2015-0001 egyéb NKFIH 108755 egyéb RH/751/2015 egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM085419
035-os BibID:	(cikkazonosító)1468 (WOS)000543394000115 (Scopus)85085472067
Első szerző:	Szabó Loránd (geográfus)
Cím:	NDVI as a Proxy for Estimating Sedimentation and Vegetation Spread in Artificial Lakes-Monitoring of Spatial and Temporal Changes by Using Satellite Images Overarching Three Decades / Szabó Loránd, Deák Balázs, Bíró Tibor, Dyke Gareth J., Szabó Szilárd
Dátum:	2020
ISSN:	2072-4292
Megjegyzések:	Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk remote sensing sedimentation spectral indices time-series analysis vegetation change wetland monitoring
Megjelenés:	Remote Sensing. - 12 : 9 (2020), p. 1-24. -
További szerzők:	Deák Balázs (1978-) (biológus) Bíró Tibor Dyke, Gareth J. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	NKFIH-1150-6/2019 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM082564
Első szerző:	Szabó Loránd (geográfus)
Cím:	Assessing the efficiency of multispectral satellite and airborne hyperspectral images for land cover mapping in an aquatic environment with emphasis on the water caltrop (Trapa natans) / Szabó Loránd, Burai Péter, Deák Balázs, Dyke, Gareth J., Szabó, Szilárd
Dátum:	2019
ISSN:	0143-1161
Megjegyzések:	A number of clear issues are pertinent when considering whether, or not, to use a remotely sensed dataset. We evaluate these issues here by comparing an aerial hyperspectral image at 1.5 m geometric resolution that comprises 128 narrow bands within a spectral range between 400 nm and 1,000 nm as well as a nine-band Landsat 8 image at 30.0 m geometric resolution. We therefore applied Random Forest (RF) and Support Vector Machine (SVM) classifiers utilizing different input data sets to determine the best thematic accuracy for both types of images by involving all possible bands and then minimized them using variable selection and dimension reduction via Minimum Noise Fraction (MNF). We then compared Landsat images to an aerial hyperspectral one. The results of this analysis revealed that band selections based on variable importance and MNF-transformation improved thematic accuracy assessed as Overall Accuracy (OA). Results reveal a 1.00% improvement in OA via variable selection as 59 bands instead of 128 bands and a 1.50% via MNF-transformation of the hyperspectral image. This improvement was 4.52% in the Landsat image when using a MNFtransformation compared to the best performances without transformation or variable selection. Data also showed that application of Landsat spectral range on hyperspectral bands resulted in different outcomes; specifically, SVM resulted in a 91.50% OA while RF resulted in 95.50% OA. Landscape ecology results show that use of the Landsat image provided fewer land cover patches and that differences encompassed 6.30% of the whole area. We therefore conclude that Landsat data can be used with a number of limitations for accurate ecological mapping.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	International Journal Of Remote Sensing. - 40 : 13 (2019), p. 5192-5215. -
További szerzők:	Burai Péter (1977-) (agrármérnök) Deák Balázs (1978-) (biológus) Dyke, Gareth J. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	EFOP-3.6.1-16-2016-00022 EFOP 4th Thematic Program of the University of Debrecen FIKP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: