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001-es BibID:BIBFORM083405
035-os BibID:(WOS)000352275900002 (Scopus)84926284886
Első szerző:Burai Péter (agrármérnök)
Cím:Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery / Péter Burai, Balázs Deák, Orsolya Valkó, Tamás Tomor
Dátum:2015
ISSN:2072-4292
Megjegyzések:Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier-MLC), machine learning algorithms (support vector machine-SVM, random forest-RF) and feature extraction (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400-1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2-15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes.
Tárgyszavak:Természettudományok Biológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
grassland
habitat mapping
hyperspectral
maximum likelihood classifier
minimum noise fraction
nature conservation
open landscape
random forest
support vector machine
Megjelenés:Remote Sensing. - 7 : 2 (2015), p. 2046-2066. -
További szerzők:Deák Balázs (1978-) (biológus) Valkó Orsolya (1985-) (biológus) Tomor Tamás (1976-) (geográfus)
Pályázati támogatás:INSPIRE - KEOP-6.3.0/2F/09-2010-0012
Egyéb
TÁMOP-4.2.3.-12/1/KONV-0047
TÁMOP
OTKA PD 111807
OTKA
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2.

001-es BibID:BIBFORM082766
Első szerző:Burai Péter (agrármérnök)
Cím:Airborne hyperspectral remote sensing for identification grassland vegetation / Burai Péter, Tomor Tamás, Bekő László, Deák Balázs
Dátum:2015
ISSN:2194-9034
Megjegyzések:In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400?1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Vegetation Mapping
Hyperspectral
Image Classification
Maximum Likelihood Classifier
Random Forest
Support Vector Machine
Open Landscape
Megjelenés:ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - XL-3/W3 (2015), p. 427-431. -
További szerzők:Tomor Tamás (1976-) (geográfus) Bekő László (1986-) (okleveles vidékfejlesztési agrármérnök) Deák Balázs (1978-) (biológus)
Pályázati támogatás:TÁMOP- 4.2.2.D-15/1/ KONV-2015-0010
TÁMOP
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3.

001-es BibID:BIBFORM084233
Első szerző:Mücke, Werner (távérzékelési mérnök)
Cím:Comparison of discrete and full-waveform ALS for dead wood detection / Werner Mücke; Markus Hollaus.; Norbert Pfeife; Anke Schroiff; Deák Balázs
Dátum:2013
ISSN:2194-9050
Megjegyzések:The amount of dead wood is a significant parameter for the description and assessment of forest habitat quality under the terms of the Habitats directive and Natura 2000 guidelines. EU member states are obliged by the Natura 2000 regulations to report on habitat quality in a regular interval of six years. To fulfil this task, the areas should be surveyed in the field, which requires an enormous amount of workload if done only by conventional field work. In this study the applicability of airborne laser scanning data as the single data source for the detection of downed trees in forest habitats is investigated. A focus is laid on the comparison of point clouds with only discrete (XYZ) and full-waveform (including echo width) information as input data. In our paper we present an automatic workflow which is able to detect downed trees with high completeness for both data sets (77.8% for discrete and 75.6% for full-waveform data). Due to large amount of false positives, the correctness using discrete ALS data is poorer (63.1%) than for full-waveform data (89.9%). It was found that the quality of the result is also influenced by factors such as dimension, state of decay, vegetation density and penetration of the foliage by the laser.
Tárgyszavak:Természettudományok Biológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Forestry
LIDAR
Ecosystem
Inventory
Monitoring
Megjelenés:ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. - II-5/W2 (2013), p. 199-204. -
További szerzők:Hollaus, Markus Pfeifer, Norbert (1971-) (geodéziai mérnök) Schroiff, Anke Deák Balázs (1978-) (biológus)
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4.

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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5.

001-es BibID:BIBFORM083267
035-os BibID:(WOS)000353685200032 (Scopus)84926367025
Első szerző:Zlinszky András
Cím:Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning / András Zlinszky, Balázs Deák, Adam Kania, Anke Schroiff, Norbert Pfeifer
Dátum:2015
ISSN:2072-4292
Megjegyzések:Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility with the traditional assessment methodology has not been achieved yet. We aimed to test Airborne Laser Scanning (ALS) as a source for mapping all variables required by the local official conservation status assessment scheme and to develop an automated method that calculates Natura 2000 conservation status at 0.5 m raster resolution for 24 km 2 of Pannonic Salt Steppe habitat (code 1530). We used multi-temporal (summer and winter) ALS point clouds with full-waveform recording and a density of 10 pt/m 2. Some required variables were derived from ALS product rasters; others involved vegetation classification layers calculated by machine learning and fuzzy categorization. Thresholds separating favorable and unfavorable values of each variable required by the national assessment scheme were manually calibrated from 10 plots where field-based assessment was carried out. Rasters representing positive and negative scores for each input variable OPEN ACCESS Remote Sens. 2015, 7 2992 were integrated in a ruleset that exactly follows the Hungarian Natura 2000 assessment scheme for grasslands. Accuracy of each parameter and the final conservation status score and category was evaluated by 10 independent assessment plots. We conclude that ALS is a suitable data source for Natura 2000 assessments in grasslands, and that the national grassland assessment scheme can successfully be used as a GIS processing model for conservation status, ensuring that the output is directly comparable with traditional field based assessments.
Tárgyszavak:Természettudományok Biológiai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Natura 2000
conservation status
Airborne Laser Scanning
LiDAR
grasslands
Pannonic Salt Steppe
habitat assessment
habitat quality
Megjelenés:Remote Sensing. - 7 : 3 (2015), p. 2991-3019. -
További szerzők:Deák Balázs (1978-) (biológus) Kania, Adam (1974-) (program fejlesztő) Schroiff, Anke Pfeifer, Norbert (1971-) (geodéziai mérnök)
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6.

001-es BibID:BIBFORM081438
Első szerző:Zlinszky András
Cím:Biodiversity mapping via Natura 2000 conservation status and ebv assessment using airborne laser scanning in alkali grasslands / A. Zlinszky, B. Deák, A. Kania, A. Schroiff, N. Pfeifer
Dátum:2016
ISSN:2194-9034
Megjegyzések:Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5x0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.
Tárgyszavak:Természettudományok Biológiai tudományok konferenciacikk
folyóiratcikk
LIDAR
Essential Biodiversity Variables
Natura 2000
Conservation status
Biodiversity assessment
Megjelenés:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - 41 : B8 (2016), p. 1293-1299. -
További szerzők:Deák Balázs (1978-) (biológus) Kania, Adam (1974-) (program fejlesztő) Schroiff, Anke Pfeifer, Norbert (1971-) (geodéziai mérnök)
Pályázati támogatás:OTKA PD 115627
OTKA
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