TY  - JOUR
AU  - Antonijević, Ognjen
AU  - Jelić, Slobodan
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3098
AB  - This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.
PB  - Springer
T2  - Journal of Big Data
T1  - Transfer learning approach based on satellite image time series for the crop classification problem
VL  - 10
DO  - 10.1186/s40537-023-00735-2
ER  - 

@article{
author = "Antonijević, Ognjen and Jelić, Slobodan and Bajat, Branislav and Kilibarda, Milan",
year = "2023",
abstract = "This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Transfer learning approach based on satellite image time series for the crop classification problem",
volume = "10",
doi = "10.1186/s40537-023-00735-2"
}

Antonijević, O., Jelić, S., Bajat, B.,& Kilibarda, M.. (2023). Transfer learning approach based on satellite image time series for the crop classification problem. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00735-2

Antonijević O, Jelić S, Bajat B, Kilibarda M. Transfer learning approach based on satellite image time series for the crop classification problem. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00735-2 .

Antonijević, Ognjen, Jelić, Slobodan, Bajat, Branislav, Kilibarda, Milan, "Transfer learning approach based on satellite image time series for the crop classification problem" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00735-2 . .

TY  - JOUR
AU  - Witjes, Martijn
AU  - Parente, Leandro
AU  - J van Diemen, Chris
AU  - Hengl, Tomislav
AU  - Landa, Martin
AU  - Brodský, Lukáš
AU  - Halounova, Lena
AU  - Križan, Josip
AU  - Antonić, Luka
AU  - Ilie, Codrina Maria
AU  - Craciunescu, Vasile
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3343
AB  - A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.
T2  - PeerJ
T1  - A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat
IS  - 10
SP  - e13573
DO  - doi.org/10.7717/peerj.13573
ER  - 

@article{
author = "Witjes, Martijn and Parente, Leandro and J van Diemen, Chris and Hengl, Tomislav and Landa, Martin and Brodský, Lukáš and Halounova, Lena and Križan, Josip and Antonić, Luka and Ilie, Codrina Maria and Craciunescu, Vasile and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka",
year = "2022",
abstract = "A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.",
journal = "PeerJ",
title = "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat",
number = "10",
pages = "e13573",
doi = "doi.org/10.7717/peerj.13573"
}

Witjes, M., Parente, L., J van Diemen, C., Hengl, T., Landa, M., Brodský, L., Halounova, L., Križan, J., Antonić, L., Ilie, C. M., Craciunescu, V., Kilibarda, M., Antonijević, O.,& Glušica, L.. (2022). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ(10), e13573.
https://doi.org/doi.org/10.7717/peerj.13573

Witjes M, Parente L, J van Diemen C, Hengl T, Landa M, Brodský L, Halounova L, Križan J, Antonić L, Ilie CM, Craciunescu V, Kilibarda M, Antonijević O, Glušica L. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ. 2022;(10):e13573.
doi:doi.org/10.7717/peerj.13573 .

Witjes, Martijn, Parente, Leandro, J van Diemen, Chris, Hengl, Tomislav, Landa, Martin, Brodský, Lukáš, Halounova, Lena, Križan, Josip, Antonić, Luka, Ilie, Codrina Maria, Craciunescu, Vasile, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat" in PeerJ, no. 10 (2022):e13573,
https://doi.org/doi.org/10.7717/peerj.13573 . .

TY  - JOUR
AU  - Hengl, Tomislav
AU  - Miller, Matthew A. E.
AU  - Križan, Josip
AU  - Shepherd, Keith D.
AU  - Sila, Andrew
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
AU  - Dobermann, Achim
AU  - Haefele, Stephan M.
AU  - McGrath, Steve P.
AU  - Acquah, Gifty E.
AU  - Collinson, Jamie
AU  - Parente, Leandro
AU  - Sheykhmousa, Mohammadreza
AU  - Saito, Kazuki
AU  - Johnson, Jean‑Martial
AU  - Chamberlin, Jordan
AU  - Silatsa, Francis B. T.
AU  - Yemefack, Martin
AU  - Wendt, John
AU  - MacMillan, Robert A.
AU  - Wheeler, Ichsani
AU  - Crouch, Jonathan
PY  - 2021
UR  - https://www.nature.com/articles/s41598-021-85639-y
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2362
AB  - Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.
T2  - Scientific Reports
T1  - African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning
IS  - 11
SP  - 6130 (2021)
DO  - https://doi.org/10.1038/s41598-021-85639-y
ER  - 

@article{
author = "Hengl, Tomislav and Miller, Matthew A. E. and Križan, Josip and Shepherd, Keith D. and Sila, Andrew and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka and Dobermann, Achim and Haefele, Stephan M. and McGrath, Steve P. and Acquah, Gifty E. and Collinson, Jamie and Parente, Leandro and Sheykhmousa, Mohammadreza and Saito, Kazuki and Johnson, Jean‑Martial and Chamberlin, Jordan and Silatsa, Francis B. T. and Yemefack, Martin and Wendt, John and MacMillan, Robert A. and Wheeler, Ichsani and Crouch, Jonathan",
year = "2021",
abstract = "Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.",
journal = "Scientific Reports",
title = "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning",
number = "11",
pages = "6130 (2021)",
doi = "https://doi.org/10.1038/s41598-021-85639-y"
}

Hengl, T., Miller, M. A. E., Križan, J., Shepherd, K. D., Sila, A., Kilibarda, M., Antonijević, O., Glušica, L., Dobermann, A., Haefele, S. M., McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L., Sheykhmousa, M., Saito, K., Johnson, J., Chamberlin, J., Silatsa, F. B. T., Yemefack, M., Wendt, J., MacMillan, R. A., Wheeler, I.,& Crouch, J.. (2021). African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports(11), 6130 (2021).
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y

Hengl T, Miller MAE, Križan J, Shepherd KD, Sila A, Kilibarda M, Antonijević O, Glušica L, Dobermann A, Haefele SM, McGrath SP, Acquah GE, Collinson J, Parente L, Sheykhmousa M, Saito K, Johnson J, Chamberlin J, Silatsa FBT, Yemefack M, Wendt J, MacMillan RA, Wheeler I, Crouch J. African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports. 2021;(11):6130 (2021).
doi:https://doi.org/10.1038/s41598-021-85639-y .

Hengl, Tomislav, Miller, Matthew A. E., Križan, Josip, Shepherd, Keith D., Sila, Andrew, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, Dobermann, Achim, Haefele, Stephan M., McGrath, Steve P., Acquah, Gifty E., Collinson, Jamie, Parente, Leandro, Sheykhmousa, Mohammadreza, Saito, Kazuki, Johnson, Jean‑Martial, Chamberlin, Jordan, Silatsa, Francis B. T., Yemefack, Martin, Wendt, John, MacMillan, Robert A., Wheeler, Ichsani, Crouch, Jonathan, "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning" in Scientific Reports, no. 11 (2021):6130 (2021),
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y . .

TY  - JOUR
AU  - Bajat, Branislav
AU  - Antonijević, Ognjen
AU  - Kilibarda, Milan
AU  - Sekulić, Aleksandar
AU  - Luković, Jelena
AU  - Doljak, Dejan
AU  - Burić, Dragan
PY  - 2020
UR  - https://spatium.rs/index.php/home/article/view/257
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2414
AB  - The  assessment  of  the  potential  use  of  renewable  energy  resources  requires  reliable  and  precise  data  inputs  for  sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids  of  potential  insolation  and  solar  duration  in  order  to  determine  the  location  of  potential  solar  power  plants  in  Montenegro.  Grids  with  a  25-m  spatial  resolution  of  potential  solar  radiation  and  duration  were  produced  based  on  observational  records  and  publicly  available  high-resolution  digital  elevation  model  provided  by  the  European  Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m² and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy.
PB  - Institut za argitekturu i urbanizam Srbije
T2  - Spatium
T1  - Space-time high-resolution data of the potential insolation and solar duration for Montenegro
IS  - 44
DO  - 10.2298/SPAT2044045B
ER  - 

@article{
author = "Bajat, Branislav and Antonijević, Ognjen and Kilibarda, Milan and Sekulić, Aleksandar and Luković, Jelena and Doljak, Dejan and Burić, Dragan",
year = "2020",
abstract = "The  assessment  of  the  potential  use  of  renewable  energy  resources  requires  reliable  and  precise  data  inputs  for  sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids  of  potential  insolation  and  solar  duration  in  order  to  determine  the  location  of  potential  solar  power  plants  in  Montenegro.  Grids  with  a  25-m  spatial  resolution  of  potential  solar  radiation  and  duration  were  produced  based  on  observational  records  and  publicly  available  high-resolution  digital  elevation  model  provided  by  the  European  Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m² and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy.",
publisher = "Institut za argitekturu i urbanizam Srbije",
journal = "Spatium",
title = "Space-time high-resolution data of the potential insolation and solar duration for Montenegro",
number = "44",
doi = "10.2298/SPAT2044045B"
}

Bajat, B., Antonijević, O., Kilibarda, M., Sekulić, A., Luković, J., Doljak, D.,& Burić, D.. (2020). Space-time high-resolution data of the potential insolation and solar duration for Montenegro. in Spatium
Institut za argitekturu i urbanizam Srbije.(44).
https://doi.org/10.2298/SPAT2044045B

Bajat B, Antonijević O, Kilibarda M, Sekulić A, Luković J, Doljak D, Burić D. Space-time high-resolution data of the potential insolation and solar duration for Montenegro. in Spatium. 2020;(44).
doi:10.2298/SPAT2044045B .

Bajat, Branislav, Antonijević, Ognjen, Kilibarda, Milan, Sekulić, Aleksandar, Luković, Jelena, Doljak, Dejan, Burić, Dragan, "Space-time high-resolution data of the potential insolation and solar duration for Montenegro" in Spatium, no. 44 (2020),
https://doi.org/10.2298/SPAT2044045B . .

TY  - CONF
AU  - Miljković, Stefan
AU  - Antonijević, Ognjen
AU  - Kilibarda, Milan
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1586
AB  - During the establishment of first order trigonometric network in Serbia, the influence of Earth’s gravitational field on measurements was not considered. As a consequence, all points have position errors. With additional measurements and calculations, these errors were later determined for X and Y coordinates of each point. This paper uses geostatistical methods (regression kriging) to estimate error prediction model for any location in Serbia.
PB  - Građevinski fakultet, Subotica
C3  - Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018
T1  - Prediction of position errors of points in first order trigonometric network
T1  - Predikcija položajne greške tačaka trigonometrijske mreže prvog reda
EP  - 725
SP  - 717
VL  - 34
DO  - 10.14415/konferencijaGFS2018.070
ER  - 

@conference{
author = "Miljković, Stefan and Antonijević, Ognjen and Kilibarda, Milan",
year = "2018",
abstract = "During the establishment of first order trigonometric network in Serbia, the influence of Earth’s gravitational field on measurements was not considered. As a consequence, all points have position errors. With additional measurements and calculations, these errors were later determined for X and Y coordinates of each point. This paper uses geostatistical methods (regression kriging) to estimate error prediction model for any location in Serbia.",
publisher = "Građevinski fakultet, Subotica",
journal = "Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018",
title = "Prediction of position errors of points in first order trigonometric network, Predikcija položajne greške tačaka trigonometrijske mreže prvog reda",
pages = "725-717",
volume = "34",
doi = "10.14415/konferencijaGFS2018.070"
}

Miljković, S., Antonijević, O.,& Kilibarda, M.. (2018). Prediction of position errors of points in first order trigonometric network. in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018
Građevinski fakultet, Subotica., 34, 717-725.
https://doi.org/10.14415/konferencijaGFS2018.070

Miljković S, Antonijević O, Kilibarda M. Prediction of position errors of points in first order trigonometric network. in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018. 2018;34:717-725.
doi:10.14415/konferencijaGFS2018.070 .

Miljković, Stefan, Antonijević, Ognjen, Kilibarda, Milan, "Prediction of position errors of points in first order trigonometric network" in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018, 34 (2018):717-725,
https://doi.org/10.14415/konferencijaGFS2018.070 . .

TY  - CONF
AU  - Protić, Dragutin
AU  - Milutinović, S.
AU  - Antonijević, Ognjen
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1427
PB  - Faculty of Civil Engineering, Belgrade
C3  - Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
T1  - Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1427
ER  - 

@conference{
author = "Protić, Dragutin and Milutinović, S. and Antonijević, Ognjen and Sekulić, Aleksandar and Kilibarda, Milan",
year = "2016",
publisher = "Faculty of Civil Engineering, Belgrade",
journal = "Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016",
title = "Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1427"
}

Protić, D., Milutinović, S., Antonijević, O., Sekulić, A.,& Kilibarda, M.. (2016). Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics. in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
Faculty of Civil Engineering, Belgrade..
https://hdl.handle.net/21.15107/rcub_grafar_1427

Protić D, Milutinović S, Antonijević O, Sekulić A, Kilibarda M. Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics. in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016. 2016;.
https://hdl.handle.net/21.15107/rcub_grafar_1427 .

Protić, Dragutin, Milutinović, S., Antonijević, Ognjen, Sekulić, Aleksandar, Kilibarda, Milan, "Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics" in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016 (2016),
https://hdl.handle.net/21.15107/rcub_grafar_1427 .