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1.

001-es BibID:BIBFORM095531
035-os BibID:(Scopus)85116000868
Első szerző:Eltner, Anette
Cím:Using Thermal an RGB UAV imagery to measure surface flow velocities of rivers / Anette Eltner, David Mader, Noémi Mária Szopos, Bálint Nagy, Jens Grundmann, László Bertalan
Dátum:2021
ISSN:2194-9034
Tárgyszavak:Természettudományok Földtudományok konferenciacikk
folyóiratcikk
image velocimetry
PTV
PIV
RPAS
TIR
Megjelenés:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - 43 : B2 (2021), p. 717-722. -
További szerzők:Mader, David Szopos Noémi Mária (1994-) (geográfus) Nagy Bálint (1994-) (geoinformatika) Grundmann, Jens Bertalan László (1989-) (geográfus)
Pályázati támogatás:DAAD-TKA 307670
Egyéb
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2.

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
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3.

001-es BibID:BIBFORM095974
035-os BibID:(cikkazonosító)2980 (WOS)000682299100001 (Scopus)85112129721
Első szerző:Phinzi, Kwanele
Cím:Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems / Kwanele Phinzi, Dávid Abriha, Szilárd Szabó
Dátum:2021
ISSN:2072-4292
Megjegyzések:The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF's performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM's. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ? 14.4 ha in the dry season and 57.2 - 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
satellite imagery
gully mapping
machine learning
random forest
support vector machine
south africa
semi-arid environment
Megjelenés:Remote Sensing. - 13 : 15 (2021), p. 1-18. -
További szerzők:Abriha Dávid (1995-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:TKP2020-NKA-04
Egyéb
Department of Higher Education and Training (DHET) of South Africa
Egyéb
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4.

001-es BibID:BIBFORM102208
035-os BibID:(cikkazonosító)2645 (WOS)000809095600001 (Scopus)85132310306
Első szerző:Rusnák, Miloš
Cím:Remote Sensing of Riparian Ecosystems / Miloš Rusnák, Tomáš Goga, Lukáš Michaleje, Monika Šulc Michalková, Zdeněk Máčka, László Bertalan, Anna Kidová
Dátum:2022
ISSN:2072-4292
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
riparian zone
vegetation
aerial image
lidar
UAV
Megjelenés:Remote Sensing. - 14 : 11 (2022), p. 1-34. -
További szerzők:Goga, Tomáš Michaleje, Lukáš Michalková, Monika Šulc Máčka, Zdeněk Bertalan László (1989-) (geográfus) Kidová, Anna
Pályázati támogatás:TKP2020-NKA-04
Egyéb
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5.

001-es BibID:BIBFORM087257
035-os BibID:(cikkazonosító)2397 (WOS)000567130200001 (Scopus)85089524467
Első szerző:Schlosser Aletta Dóra (geográfus)
Cím:Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation / Schlosser Aletta Dóra, Szabó Gergely, Bertalan László, Varga Zsolt, Enyedi Péter, Szabó Szilárd
Dátum:2020
ISSN:2072-4292
Megjegyzések:Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer's and 85% user's Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
photogrammetry
RGB indices
image texture
morphometric indices
recursive feature elimination
random forest
support vector machine
multiple adatptive regression spline
partial least square
Megjelenés:Remote Sensing. - 12 : 15 (2020), p. 1-28. -
További szerzők:Szabó Gergely (1975-) (geográfus) Bertalan László (1989-) (geográfus) Varga Zsolt (1968-) (építőmérnök) Enyedi Péter (1982-) (környezettudós) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:NKFI KH 130427
Egyéb
ED_18-1-2019-0028 Thematic Excellence Programme of the ministry for Innovation and Technology in Hungary, framework of the Space Sciences thematic programme of the University of Debrecen
Egyéb
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6.

001-es BibID:BIBFORM082565
035-os BibID:(cikkazonosító)100237
Első szerző:Shimrah, Tuisem
Cím:Quantitative assessment of landscape transformation using earth observation datasets in Shirui Hill of Manipur, India / Tuisem Shimrah, Kiranmay Sarma, Orsolya Gyöngyi Varga, Szilard Szabo, Sudhir Kumar Singh
Dátum:2019
ISSN:2352-9385
Megjegyzések:Shirui hill is situated at the north-eastern part of Manipur, India. This hill is not only the habitat of world famous endemic flower; Lilium mackliniae, but also home to many vulnerable and endangered floral and faunal species.. The aim of work was to assess the ecosystem diversity and landscape transformation Landsat satellite images of year 1988, 2001 and 2013 have been used to study the land use/land cover change and landscape fragmentation using FRAGSTATS. Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. The PCA was performed on the landscape metrics explained 93.0% of the total variance and justified three PCs; of these, both RMSR and AGFI fit very well (0.02 and 0.99, respectively), while PC1 accounted for 42% of the variance and was correlated with CA, TCA, NDCA and AREA_CV, PC2 explained 34% and was correlated with TE, SHAPE_MN, and PC3 explained 19% and was correlated with NP. The phyto-sociological study was carried out to assess the vegetation status. Participatory Rural Appraisal (PRA) method was adopted for socio-economic data collection. The finding of work suggests a rising pressure of human activities on the land and sizable portion of it have been converted into either shifting agriculture or wet paddy land.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
ecosystem diversity
fragmentation
Northeast India
Megjelenés:Remote Sensing Applications. - 15 (2019), p. 1-9. -
További szerzők:Sarma, Kiranmay Varga Orsolya Gyöngyi (1988-) (geográfus) Szabó Szilárd (1974-) (geográfus) Singh, Sudhir Kumar (1970-) (geográfus)
Pályázati támogatás:20428-3/2018/FEKUTSTRAT
FIKP
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7.

001-es BibID:BIBFORM044957
Első szerző:Szabó Gergely (geográfus)
Cím:Accuracy Assessment of the ASTER GDEM and the SRTM Databases : A Case Study, Hungary / Szabó Gergely, Szabó Szilárd, Mecser Nikoletta, Karika Anita
Dátum:2013
Megjegyzések:The ASTER GDEMand SRTMdatabasesarethemost widespreadglobaldigital surface modelsof the Earth.However,it is important to know thedisadvantages and weaknesses of thesedatabases,i.e. the direction-dependentaccuracy and the difference between the digital and therealsurface. Our goal was toexamine these kindsofdeficienciesofsurface modelsand to point the rate of thevariance. We have found that there are significant differences between the surfacesand the aspect affects the rate of the error.
Tárgyszavak:Természettudományok Földtudományok konferenciacikk
DEM
SRTM
GDEM
Doktori iskola
Megjelenés:35th Symposium on Remote Sensing CD. - 1 (2013), p. 1-6. -
További szerzők:Szabó Szilárd (1974-) (geográfus) Mecser Nikoletta (1988-) (geográfus) Karika Anita (1986-) (geográfus)
Pályázati támogatás:TÁMOP-4.2.2/B-10/1-2010-0024
TÁMOP
Földtudományok Doktori Iskola
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8.

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
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9.

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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10.

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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11.

001-es BibID:BIBFORM089188
035-os BibID:(cikkazonosító)3652 (WOS)000589355700001 (Scopus)85096038754
Első szerző:Szabó Zsuzsanna (környezetgazdálkodási és vidékfejlesztési agrármérnök)
Cím:Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches / Zsuzsanna Csatáriné Szabó, Tomáš Mikita, Gábor Négyesi, Orsolya Gyöngyi Varga, Péter Burai, László Takács-Szilágyi, Szilárd Szabó
Dátum:2020
ISBN:2072-4292
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény hazai lapban
folyóiratcikk
Megjelenés:Remote Sensing. - 12 : 21 (2020), p. 1-29. -
További szerzők:Mikita, Tomáš Négyesi Gábor (1980-) (geográfus) Varga Orsolya Gyöngyi (1988-) (geográfus) Burai Péter (1977-) (agrármérnök) Takács-Szilágyi László Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:NKFIH KH 130427
Egyéb
TNN123457
Egyéb
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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12.

001-es BibID:BIBFORM070788
Első szerző:Szabó Zsuzsanna (környezetgazdálkodási és vidékfejlesztési agrármérnök)
Cím:Airborne LiDAR point cloud in mapping of fluvial forms : a case study of a Hungarian floodplain / Szabó Zsuzsanna, Tóth Csaba Albert, Tomor Tamás, Szabó Szilárd
Dátum:2017
ISSN:1548-1603 1943-7226
Megjegyzések:The aim of this paper was to analyse the ground and low vegetation points of a LiDAR point cloud from the aspect of the generated digital terrain model (DTM). We determined the height difference between the surveyed surface and the DTM and the level of interspersion of ground and low vegetation points in a floodplain. Finally, we performed a supervised classification with topographic (elevation, slope, aspect) variables and an NDVI layer to identify swales and point bars as floodplain forms. Cross sections of field surveys provided reference data to express the magnitude of the bias on the DTM caused by the vegetation, and we proved that the bias can reach the 60% of the relative height and depth of the floodplain forms (mean error was 0.15?0.12 m). A contagion type landscape metric, the Aggregation Index, provided an appropriate tool to analyse and quantify the interspersion of the ground and vegetation points: indicating a high level of interspersion of the classified points, i.e. proved that vegetation points where the last echoes reflected from the vegetation became ground points. Floodplain classification performed best with the common use of DTM, slope, aspect and NDVI coverages, with 71% overall accuracy.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
geomorphology
fluvial landform
point cloud; landscape indices
landscape indices
Aggregation Index
Megjelenés:GIScience & Remote Sensing. - 54 : 6 (2017), p. 862-880. -
További szerzők:Tóth Csaba Albert (1971-) (geográfus) Tomor Tamás (1976-) (geográfus) Szabó Szilárd (1974-) (geográfus)
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