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001-es BibID:	BIBFORM113915
035-os BibID:	(WoS)001044509700001 (Scopus)85167334773
Első szerző:	Likó Szilárd Balázs
Cím:	Deep learning-based training data augmentation combined with post-classification improves the classification accuracy for dominant and scattered invasive forest tree species / Szilárd Balázs Likó, Imre J. Holb, Viktor Oláh, Péter Burai, Szilárd Szabó
Dátum:	2023
ISSN:	2056-3485
Megjegyzések:	Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning-based training data augmentation (TDA) and post-classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post-classification with segmentation improved the total accuracy to 86.2%. The class-level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk ailanthus black locust Convolutional Neural Network multiresolution segmentation Random Forest Support Vector Machine
Megjelenés:	Remote Sensing in Ecology and Conservation. - [Epub ahead of print] (2023), p.1-17. -
További szerzők:	Holb Imre (1973-) (agrármérnök) Oláh Viktor (1980-) (biológus) Burai Péter (1977-) (agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	K138079 Egyéb K138503 Egyéb K131478 Egyéb KKP144068 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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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
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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
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