Stojković, Stefanija

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0000-0002-1700-6674
  • Stojković, Stefanija (5)
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Author's Bibliography

Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom

Stojković, Stefanija; Obrenović, Bojana

(Tehnika, 2022)

TY  - JOUR
AU  - Stojković, Stefanija
AU  - Obrenović, Bojana
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3169
AB  - Cilj ovog istraživanja je da prikaže najoptimalnije rešenje u pogledu položaja i broja orijentacionih tačaka (GCP) za potrebe snimanja terena primenom UAV metode na području prigradskog naselja Duboko kod Umke. Obrada prikupljenih slika izvršena je u softverskom rešenju AgiSoft primenom metode SfM - Structure from Motion (SfM), koja je integrisana u okviru ovog softverskog rešenja. Referentni podaci, dobijeni obradom sa kompletnim skupom od 36 pouzdanih orijentacionih tačaka su proglašeni "tačnim" na samom početku studije. Analizirajući različite kombinacije, kreirane na osnovu četiri pravila definisana u postupku odabira tačaka i upoređujući ih sa "tačnim", kao najbolji rezultat usvojena je kombinacija sa 20 orijentacionih tačaka ravnomerno raspoređenih na području snimanja površine 50 ha.
PB  - Tehnika
T2  - časopis Tehnika
T1  - Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom
VL  - 76,6,731-737
DO  - 10.5937/tehnika2106731S
ER  - 
@article{
author = "Stojković, Stefanija and Obrenović, Bojana",
year = "2022",
abstract = "Cilj ovog istraživanja je da prikaže najoptimalnije rešenje u pogledu položaja i broja orijentacionih tačaka (GCP) za potrebe snimanja terena primenom UAV metode na području prigradskog naselja Duboko kod Umke. Obrada prikupljenih slika izvršena je u softverskom rešenju AgiSoft primenom metode SfM - Structure from Motion (SfM), koja je integrisana u okviru ovog softverskog rešenja. Referentni podaci, dobijeni obradom sa kompletnim skupom od 36 pouzdanih orijentacionih tačaka su proglašeni "tačnim" na samom početku studije. Analizirajući različite kombinacije, kreirane na osnovu četiri pravila definisana u postupku odabira tačaka i upoređujući ih sa "tačnim", kao najbolji rezultat usvojena je kombinacija sa 20 orijentacionih tačaka ravnomerno raspoređenih na području snimanja površine 50 ha.",
publisher = "Tehnika",
journal = "časopis Tehnika",
title = "Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom",
volume = "76,6,731-737",
doi = "10.5937/tehnika2106731S"
}
Stojković, S.,& Obrenović, B.. (2022). Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom. in časopis Tehnika
Tehnika., 76,6,731-737.
https://doi.org/10.5937/tehnika2106731S
Stojković S, Obrenović B. Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom. in časopis Tehnika. 2022;76,6,731-737.
doi:10.5937/tehnika2106731S .
Stojković, Stefanija, Obrenović, Bojana, "Optmizacija broja i rasporeda orijentacionih tačaka konfigurisanih za snimanje UAV metodom" in časopis Tehnika, 76,6,731-737 (2022),
https://doi.org/10.5937/tehnika2106731S . .

Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia)

Stojković, Stefanija; Marković, Dragana; Durlević, Uroš

(Springer, Cham, 2022)

TY  - JOUR
AU  - Stojković, Stefanija
AU  - Marković, Dragana
AU  - Durlević, Uroš
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3170
AB  - Remote sensing, presented in the form of high-resolution satellite images, is essential in monitoring snow cover and the state of the environment. Due to the increase in the spatial resolution of the images, the Sentinel-2 mission improved the research of natural conditions and processes on the topographic surface. This paper presents changes in the snow cover of National Park Šar planina during 3 winter seasons: from December 2018 to April 2021. The study is based on the analysis of 33 satellite images where snow cover has been estimated using a combination of Normalized Difference Snow Index (NDSI) and S3 indices with suitable thresholds. According to the final results, the trend of decreasing the area of snow cover was observed. During the first season 91.75% of the study area was covered with snow, while in the next two seasons these percentages were lower - 79.94% and 72.84% retrospectively. The final result coincides with the trend of climate change, which is reflected in the increasingly intense warming of glaciers in the largest mountains and the decreasing amount of snowfall. It is believed that the obtained results will provide an adequate overview of the local population in terms of tourism and agricultural activities, as well as proper management of water resources in settlements that provide fresh water from reservoirs of mountain rivers that spring on the Šar Planina.
PB  - Springer, Cham
T2  - Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022 - "Advanced Technologies, Systems, and Applications VII", 2023, 507-519
T1  - Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia)
SP  - 507-519
DO  - 10.1007/978-3-031-17697-5_39
ER  - 
@article{
author = "Stojković, Stefanija and Marković, Dragana and Durlević, Uroš",
year = "2022",
abstract = "Remote sensing, presented in the form of high-resolution satellite images, is essential in monitoring snow cover and the state of the environment. Due to the increase in the spatial resolution of the images, the Sentinel-2 mission improved the research of natural conditions and processes on the topographic surface. This paper presents changes in the snow cover of National Park Šar planina during 3 winter seasons: from December 2018 to April 2021. The study is based on the analysis of 33 satellite images where snow cover has been estimated using a combination of Normalized Difference Snow Index (NDSI) and S3 indices with suitable thresholds. According to the final results, the trend of decreasing the area of snow cover was observed. During the first season 91.75% of the study area was covered with snow, while in the next two seasons these percentages were lower - 79.94% and 72.84% retrospectively. The final result coincides with the trend of climate change, which is reflected in the increasingly intense warming of glaciers in the largest mountains and the decreasing amount of snowfall. It is believed that the obtained results will provide an adequate overview of the local population in terms of tourism and agricultural activities, as well as proper management of water resources in settlements that provide fresh water from reservoirs of mountain rivers that spring on the Šar Planina.",
publisher = "Springer, Cham",
journal = "Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022 - "Advanced Technologies, Systems, and Applications VII", 2023, 507-519",
title = "Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia)",
pages = "507-519",
doi = "10.1007/978-3-031-17697-5_39"
}
Stojković, S., Marković, D.,& Durlević, U.. (2022). Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia). in Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022 - "Advanced Technologies, Systems, and Applications VII", 2023, 507-519
Springer, Cham., 507-519.
https://doi.org/10.1007/978-3-031-17697-5_39
Stojković S, Marković D, Durlević U. Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia). in Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022 - "Advanced Technologies, Systems, and Applications VII", 2023, 507-519. 2022;:507-519.
doi:10.1007/978-3-031-17697-5_39 .
Stojković, Stefanija, Marković, Dragana, Durlević, Uroš, "Snow Cover Estimation Using Sentinel-2 High Spatial Resolution Data. A Case Study: National Park Šar Planina (Serbia)" in Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2022 - "Advanced Technologies, Systems, and Applications VII", 2023, 507-519 (2022):507-519,
https://doi.org/10.1007/978-3-031-17697-5_39 . .
2

Within-field correlation between satellite-derived vegetation indices and grain yield of wheat

Blagojević, Dragana; Stojković, Stefanija; Brdar, Sanja; Crnojević, Vladimir

(BioSense Institute, Novi Sad, 2021)

TY  - CONF
AU  - Blagojević, Dragana
AU  - Stojković, Stefanija
AU  - Brdar, Sanja
AU  - Crnojević, Vladimir
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3182
AB  - This research aimed to inspect the correlation coefficients, during the crop growth stages, between vegetation indices (VIs) derived from Sentinel-2 imagery and grain winter wheat yield derived from yield monitoring and select the most promising indices for monitoring crop growth and yield estimation. METHOD / DESIGN: The satellite images in 10m resolution were selected based on crop growth stages, from the end of tillering phase (beginning of March 2019) until the full ripening (end of June 2019). For the analysis, the BBCH-scale for cereals was used. Yield observations were performed at harvest on five fields in one season and twelve VIs were calculated across 10 growth stages. To designate their correlation and dependence, a statistical comparison of the VIs and yield was made. The Pearson’s and Spearman’s correlation coefficients were calculated, and their statistical significance was tested using p-value (at p=0.01, p=0.05). RESULTS: According to the crop growth stages, the highest correlation coefficients were detected from the early boot stage (BBCH 41) until the middle of development of the fruiting stage (BBCH 73 – early milk). In that period the correlation coefficients varied from 0.39 to 0.84 depending on the field. Based on the location, the highest correlation coefficient values for all 12 indices were recorded for the parcel named C-6 (April 15), and the lowest values for the parcel named C-10 (June 29). Most of the indices showed statistically significant dependence (at the p<0.01 and p<0.05 significant levels) on the yield in the first five growth stages except the chlorophyll vegetation index (CVI) for the parcel named C-11 (p=0.21, p=0.39). CONCLUSIONS: To conclude, the last growth stage named ripening showed the lowest values both for correlation coefficient and statistical significance which means that VIs also had low values because the reflectance is weak in this growth stage and wheat is about to be harvested. In the first five stages, VIs showed significantly high spectral reflectance values since in this period the leaf is full of chlorophyll pigments. Analyzing the correlation coefficient in different stages of wheat growth, we look at the current state of crops and have the opportunity to take appropriate measures in time to increase yields or save inputs at specific locations.
PB  - BioSense Institute, Novi Sad
C3  - International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference
T1  - Within-field correlation between satellite-derived vegetation indices and grain yield of wheat
SP  - 218
DO  - 10.13140/RG.2.2.22001.20324
ER  - 
@conference{
author = "Blagojević, Dragana and Stojković, Stefanija and Brdar, Sanja and Crnojević, Vladimir",
year = "2021",
abstract = "This research aimed to inspect the correlation coefficients, during the crop growth stages, between vegetation indices (VIs) derived from Sentinel-2 imagery and grain winter wheat yield derived from yield monitoring and select the most promising indices for monitoring crop growth and yield estimation. METHOD / DESIGN: The satellite images in 10m resolution were selected based on crop growth stages, from the end of tillering phase (beginning of March 2019) until the full ripening (end of June 2019). For the analysis, the BBCH-scale for cereals was used. Yield observations were performed at harvest on five fields in one season and twelve VIs were calculated across 10 growth stages. To designate their correlation and dependence, a statistical comparison of the VIs and yield was made. The Pearson’s and Spearman’s correlation coefficients were calculated, and their statistical significance was tested using p-value (at p=0.01, p=0.05). RESULTS: According to the crop growth stages, the highest correlation coefficients were detected from the early boot stage (BBCH 41) until the middle of development of the fruiting stage (BBCH 73 – early milk). In that period the correlation coefficients varied from 0.39 to 0.84 depending on the field. Based on the location, the highest correlation coefficient values for all 12 indices were recorded for the parcel named C-6 (April 15), and the lowest values for the parcel named C-10 (June 29). Most of the indices showed statistically significant dependence (at the p<0.01 and p<0.05 significant levels) on the yield in the first five growth stages except the chlorophyll vegetation index (CVI) for the parcel named C-11 (p=0.21, p=0.39). CONCLUSIONS: To conclude, the last growth stage named ripening showed the lowest values both for correlation coefficient and statistical significance which means that VIs also had low values because the reflectance is weak in this growth stage and wheat is about to be harvested. In the first five stages, VIs showed significantly high spectral reflectance values since in this period the leaf is full of chlorophyll pigments. Analyzing the correlation coefficient in different stages of wheat growth, we look at the current state of crops and have the opportunity to take appropriate measures in time to increase yields or save inputs at specific locations.",
publisher = "BioSense Institute, Novi Sad",
journal = "International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference",
title = "Within-field correlation between satellite-derived vegetation indices and grain yield of wheat",
pages = "218",
doi = "10.13140/RG.2.2.22001.20324"
}
Blagojević, D., Stojković, S., Brdar, S.,& Crnojević, V.. (2021). Within-field correlation between satellite-derived vegetation indices and grain yield of wheat. in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference
BioSense Institute, Novi Sad., 218.
https://doi.org/10.13140/RG.2.2.22001.20324
Blagojević D, Stojković S, Brdar S, Crnojević V. Within-field correlation between satellite-derived vegetation indices and grain yield of wheat. in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference. 2021;:218.
doi:10.13140/RG.2.2.22001.20324 .
Blagojević, Dragana, Stojković, Stefanija, Brdar, Sanja, Crnojević, Vladimir, "Within-field correlation between satellite-derived vegetation indices and grain yield of wheat" in International Bioscience Conference and the 8th International PSU – UNS Bioscience Conference (2021):218,
https://doi.org/10.13140/RG.2.2.22001.20324 . .

Reviewing the potential of Sentinel-2 in assessing the drought

Varghese, Dani; Radulović, Mirjana; Stojković, Stefanija; Crnojević, Vladimir

(MDPI, 2021)

TY  - JOUR
AU  - Varghese, Dani
AU  - Radulović, Mirjana
AU  - Stojković, Stefanija
AU  - Crnojević, Vladimir
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3168
AB  - This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.
PB  - MDPI
T1  - Reviewing the potential of Sentinel-2 in assessing the drought
VL  - 13: 3355
DO  - 10.3390/rs13173355
ER  - 
@article{
author = "Varghese, Dani and Radulović, Mirjana and Stojković, Stefanija and Crnojević, Vladimir",
year = "2021",
abstract = "This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.",
publisher = "MDPI",
title = "Reviewing the potential of Sentinel-2 in assessing the drought",
volume = "13: 3355",
doi = "10.3390/rs13173355"
}
Varghese, D., Radulović, M., Stojković, S.,& Crnojević, V.. (2021). Reviewing the potential of Sentinel-2 in assessing the drought. 
MDPI., 13: 3355.
https://doi.org/10.3390/rs13173355
Varghese D, Radulović M, Stojković S, Crnojević V. Reviewing the potential of Sentinel-2 in assessing the drought. 2021;13: 3355.
doi:10.3390/rs13173355 .
Varghese, Dani, Radulović, Mirjana, Stojković, Stefanija, Crnojević, Vladimir, "Reviewing the potential of Sentinel-2 in assessing the drought", 13: 3355 (2021),
https://doi.org/10.3390/rs13173355 . .
5
25

Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data

Radulović, Mirjana; Stojković, Stefanija; Pejak, Branislav; Lugonja, Predrag; Brdar, Sanja

(2021)

TY  - CONF
AU  - Radulović, Mirjana
AU  - Stojković, Stefanija
AU  - Pejak, Branislav
AU  - Lugonja, Predrag
AU  - Brdar, Sanja
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3167
AB  - In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management.
With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields.
Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy.
T1  - Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data
DO  - 10.5281/zenodo.4943800
ER  - 
@conference{
author = "Radulović, Mirjana and Stojković, Stefanija and Pejak, Branislav and Lugonja, Predrag and Brdar, Sanja",
year = "2021",
abstract = "In the 21st century, the establishment of efficient water resource management is crucial for ensuring world water and food security. Irrigation is a significant artificial process in the hydrological cycle and presents the only way to balance between mentioned issues, where collecting knowledge is essential for developing adaptive and sustainable strategies. Considering that, the precise information about the spatio-temporal distribution of irrigated fields on a national scale is thus the initial key step for agricultural water resource management.
With a high spatial, spectral, and temporal resolution, Sentinel-2 provides new possibilities in this field. This research focuses on using multispectral satellite imagery and advanced machine learning models for detecting irrigation and rainfed fields on a plot scale. Dry year images during irrigation season were used for vegetation indices calculation for three crop types: maize, soybean, and sugar beet. These three databases were used separately for training the Random Forest classifier. The results showed high overall accuracy for each three crops where soybean reached the highest 0.91, maize 0.89, while sugar beet reached 0.76. According to the results, the assumption is that the difference in accuracy between crops could be caused by the difference in the geospatial characteristic of the area, amount of data, omission in labeling crop types and rainfed fields.
Irrigated agricultural fields present a challenge for classification and mapping considering the heterogeneity of the area, climate impact, and diverse crop types. This study showed that classification could be done using Sentinel-2 images, but further analysis including climate and soil data could improve the classification. This methodology has the potential to produce an annual irrigation map which is very important information for optimizing water use and making sustainable agricultural policy.",
title = "Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data",
doi = "10.5281/zenodo.4943800"
}
Radulović, M., Stojković, S., Pejak, B., Lugonja, P.,& Brdar, S.. (2021). Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data. .
https://doi.org/10.5281/zenodo.4943800
Radulović M, Stojković S, Pejak B, Lugonja P, Brdar S. Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data. 2021;.
doi:10.5281/zenodo.4943800 .
Radulović, Mirjana, Stojković, Stefanija, Pejak, Branislav, Lugonja, Predrag, Brdar, Sanja, "Classification of irrigated and rainfed croplands in Vojvodina Province (North Serbia) using Sentinel-2 data" (2021),
https://doi.org/10.5281/zenodo.4943800 . .