CCL

Összesen 6 találat.
#/oldal:
Részletezés:
Rendezés:

1.

001-es BibID:BIBFORM121564
035-os BibID:(Scopus)85190263402 (WoS)001201036600001
Első szerző:Alsafadi, Karam
Cím:Response of Ecosystem Carbon-Water Fluxes to ExtremeDrought in West Asia / Karam Alsafadi, Bashar Bashir, Safwan Mohammed, Hazem Ghassan Abdo, Ali Mokhtar, Abdullah Alsalman, Wenzhi Cao
Dátum:2024
ISSN:2072-4292
Megjegyzések:Global warming has resulted in increases in the intensity, frequency, and duration of drought in most land areas at the regional and global scales. Nevertheless, comprehensive understanding of how water use efficiency (WUE), gross primary production (GPP), and actual evapotranspiration (AET)-induced water losses respond to exceptional drought and whether the responses are influenced by drought severity (DS) is still limited. Herein, we assess the fluctuation in the standardized precipitation evapotranspiration index (SPEI) over the Middle East from 1982 to 2017 to detect the drought events and further examine standardized anomalies of GPP, WUE, and AET responses to multiyear exceptional droughts, which are separated into five groups designed to characterize the severity of extreme drought. The intensification of the five drought events (based on its DS) increased the WUE, decreased the GPP and AET from D5 to D1, where both the positive and negative variance among the DS group was statistically significant. The results showed that the positive values of standardized WUE with the corresponding values of the negative GPP and AET were dominant (44.3% of the study area), where the AET values decreased more than the GPP, and the WUE fluctuation in this region is mostly controlled by physical processes, i.e., evaporation. Drought's consequences on ecosystem carbon-water interactions ranged significantly among eco-system types due to the unique hydrothermal conditions of each biome. Our study indicates that forthcoming droughts, along with heightened climate variability, pose increased risks to semi-arid and sub-humid ecosystems, potentially leading to biome restructuring, starting with low-productivity, water-sensitive grasslands. Our assessment of WUE enhances understanding of water-carbon cycle linkages and aids in projecting ecosystem responses to climate change.
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
water use efficiency
drought severity
detrending analysis
hydroclimatic processes
gross primary production
middle east
Megjelenés:Remote Sensing. - 16 : 7 (2024), p. 1-27. -
További szerzők:Bashir, Bashar Mohammed Safwan (1985-) (agrármérnök) Abdo, Hazem Ghassan Mokhtar, Ali Alsalman, Abdullah Cao, Wenzhi
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

2.

001-es BibID:BIBFORM105966
035-os BibID:(WOS)000904199700001 (Scopus)85144826669
Első szerző:Alsafadi, Karam
Cím:Assessment of Carbon Productivity Trends and Their Resilience to Drought Disturbances in the Middle East Based on Multi-Decadal Space-Based Datasets / Karam Alsafadi, Shuoben Bi, Bashar Bashir, Safwan Mohammed, Saad Sh. Sammen, Abdullah Alsalman, Amit Kumar Srivastava, Ahmed El Kenawy
Dátum:2022
ISSN:2072-4292
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
SEDI
carbon cycle
eddy covariance
FLUXNET
satellite-based GPP
light-use-efficiency model
Megjelenés:Remote Sensing. - 14 : 24 (2022), p. 1-25. -
További szerzők:Bi, Shuoben Bashir, Bashar Mohammed Safwan (1985-) (agrármérnök) Sammen, Saad Sh. Alsalman, Abdullah Srivastava, Amit Kumar El Kenawy, Ahmed M.
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

3.

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:2024
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. - 10 : 2 (2024), p. 203-219. -
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
Borító:

4.

001-es BibID:BIBFORM087603
Első szerző:Mohammed Safwan (agrármérnök)
Cím:Estimation of soil erosion risk in southern part of Syria by using RUSLE integrating geo informatics approach / Safwan Mohammed, Karam Alsafadi, Swapan Talukdar, Samer Kiwan, Sami Hennawi, Omran Alshiehabi, Mohammed Sharaf, Endre Harsanyi
Dátum:2020
ISSN:2352-9385
Tárgyszavak:Agrártudományok Növénytermesztési és kertészeti tudományok kutatási jelentés
folyóiratcikk
Megjelenés:Remote Sensing Applications: Society and Environment. - 20 (2020), p. 1-13. -
További szerzők:Alsafadi, Karam Talukdar, Swapan Kiwan, Samer Hennawi, Sami Alshiehabi, Omran Sharaf, Mohammed Harsányi Endre (1976-) (agrármérnök)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

5.

001-es BibID:BIBFORM131577
Első szerző:Tamás János (környezetgazdálkodási agrármérnök)
Cím:Land Cover Mapping Using High-Resolution Satellite Imagery and a Comparative Machine Learning Approach to Enhance Regional Water Resource Management / János Tamás, Angura Louis, Zsolt Zoltán Fehér, Attila Nagy
Dátum:2025
ISSN:2072-4292
Megjegyzések:Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB`s performance in 2022 remained less consistent, GTB`s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making
Tárgyszavak:Természettudományok Környezettudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Remote Sensing. - 17 : 15 (2025), p. 1-20. -
További szerzők:Louis Angura (1995-) (környezetgazdálkodási agrármérnök) Fehér Zsolt Zoltán (1984-) (geoinformatika) Nagy Attila (1982-) (környezetgazdálkodási agrármérnök)
Pályázati támogatás:RRF 2.3.1 21 2022 00008
Egyéb
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:

6.

001-es BibID:BIBFORM091945
035-os BibID:(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
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Rekordok letöltése1 
Corvina könyvtári katalógus v10.11.18-SNAPSHOT © 2024 Monguz kft. Minden jog fenntartva.