TY  - JOUR
AU  - Potić, Ivan
AU  - Srdić, Zoran
AU  - Vakanjac, Boris
AU  - Bakrač, Saša
AU  - Đorđević, Dejan
AU  - Banković, Radoje
AU  - Jovanović, Jasmina M.
PY  - 2023
UR  - http://gery.gef.bg.ac.rs/handle/123456789/1719
AB  - Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.
PB  - Basel : MDPI
T2  - Applied Sciences
T1  - Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia
VL  - 13
IS  - 14
SP  - 8289
DO  - 10.3390/app13148289
ER  - 

@article{
author = "Potić, Ivan and Srdić, Zoran and Vakanjac, Boris and Bakrač, Saša and Đorđević, Dejan and Banković, Radoje and Jovanović, Jasmina M.",
year = "2023",
abstract = "Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.",
publisher = "Basel : MDPI",
journal = "Applied Sciences",
title = "Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia",
volume = "13",
number = "14",
pages = "8289",
doi = "10.3390/app13148289"
}

Potić, I., Srdić, Z., Vakanjac, B., Bakrač, S., Đorđević, D., Banković, R.,& Jovanović, J. M.. (2023). Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia. in Applied Sciences
Basel : MDPI., 13(14), 8289.
https://doi.org/10.3390/app13148289

Potić I, Srdić Z, Vakanjac B, Bakrač S, Đorđević D, Banković R, Jovanović JM. Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia. in Applied Sciences. 2023;13(14):8289.
doi:10.3390/app13148289 .

Potić, Ivan, Srdić, Zoran, Vakanjac, Boris, Bakrač, Saša, Đorđević, Dejan, Banković, Radoje, Jovanović, Jasmina M., "Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia" in Applied Sciences, 13, no. 14 (2023):8289,
https://doi.org/10.3390/app13148289 . .

TY  - JOUR
AU  - Milanović, Miško
AU  - Micić, Tanja
AU  - Lukić, Tin
AU  - Nenadović, Snežana S.
AU  - Basarin, Biljana
AU  - Filipović, Dejan
AU  - Tomić, Milisav
AU  - Samardžić, Ivan
AU  - Srdić, Zoran
AU  - Nikolić, Gojko
AU  - Ninković, Miloš M.
AU  - Sakulski, Dušan
AU  - Ristanović, Branko
PY  - 2019
UR  - https://gery.gef.bg.ac.rs/handle/123456789/1002
AB  - This paper evaluates the application of the Normalised Difference Vegetation Index (NDVI) in the monitoring and assessment of temporal vegetation cover changes (from 2006 to 2014) in three municipalities of Central Serbia: Topola, Jagodina and Kursumlija. Additionally, special focus is placed on the analysis of the forest areas and the possible use of NDVI in the forest management sector. Results of the NDVI applied through Idrisi software identify all vegetation cover types and their typical values for presented case studies and observed periods. Obtained results for Serbian case studies indicate two major observations outlined for the investigated period. It was noticed that vegetation cover is experiencing a certain decrease, and that certain discrepancies exists between the NDVI and official forest area statistics for certain municipalities. The study outlines the positive outcomes of the applied remote sensing techniques, especially for southern Serbian municipalities where illegal logging activities are pronounced. Hence, this method proved very promising for countries performing national forest inventories, such as Serbia, providing local forest managers with several essential up-to-date information about vegetation cover changes on an annual basis.
PB  - Baia Mare : North University of Baia Mare
T2  - Carpathian Journal of Earth and Environmental Sciences
T1  - Application of landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia
VL  - 14
IS  - 1
SP  - 119
EP  - 129
DO  - 10.26471/cjees/2019/014/064
UR  - https://hdl.handle.net/21.15107/rcub_gery_1002
ER  - 

@article{
author = "Milanović, Miško and Micić, Tanja and Lukić, Tin and Nenadović, Snežana S. and Basarin, Biljana and Filipović, Dejan and Tomić, Milisav and Samardžić, Ivan and Srdić, Zoran and Nikolić, Gojko and Ninković, Miloš M. and Sakulski, Dušan and Ristanović, Branko",
year = "2019",
abstract = "This paper evaluates the application of the Normalised Difference Vegetation Index (NDVI) in the monitoring and assessment of temporal vegetation cover changes (from 2006 to 2014) in three municipalities of Central Serbia: Topola, Jagodina and Kursumlija. Additionally, special focus is placed on the analysis of the forest areas and the possible use of NDVI in the forest management sector. Results of the NDVI applied through Idrisi software identify all vegetation cover types and their typical values for presented case studies and observed periods. Obtained results for Serbian case studies indicate two major observations outlined for the investigated period. It was noticed that vegetation cover is experiencing a certain decrease, and that certain discrepancies exists between the NDVI and official forest area statistics for certain municipalities. The study outlines the positive outcomes of the applied remote sensing techniques, especially for southern Serbian municipalities where illegal logging activities are pronounced. Hence, this method proved very promising for countries performing national forest inventories, such as Serbia, providing local forest managers with several essential up-to-date information about vegetation cover changes on an annual basis.",
publisher = "Baia Mare : North University of Baia Mare",
journal = "Carpathian Journal of Earth and Environmental Sciences",
title = "Application of landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia",
volume = "14",
number = "1",
pages = "119-129",
doi = "10.26471/cjees/2019/014/064",
url = "https://hdl.handle.net/21.15107/rcub_gery_1002"
}

Milanović, M., Micić, T., Lukić, T., Nenadović, S. S., Basarin, B., Filipović, D., Tomić, M., Samardžić, I., Srdić, Z., Nikolić, G., Ninković, M. M., Sakulski, D.,& Ristanović, B.. (2019). Application of landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia. in Carpathian Journal of Earth and Environmental Sciences
Baia Mare : North University of Baia Mare., 14(1), 119-129.
https://doi.org/10.26471/cjees/2019/014/064
https://hdl.handle.net/21.15107/rcub_gery_1002

Milanović M, Micić T, Lukić T, Nenadović SS, Basarin B, Filipović D, Tomić M, Samardžić I, Srdić Z, Nikolić G, Ninković MM, Sakulski D, Ristanović B. Application of landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia. in Carpathian Journal of Earth and Environmental Sciences. 2019;14(1):119-129.
doi:10.26471/cjees/2019/014/064
https://hdl.handle.net/21.15107/rcub_gery_1002 .

Milanović, Miško, Micić, Tanja, Lukić, Tin, Nenadović, Snežana S., Basarin, Biljana, Filipović, Dejan, Tomić, Milisav, Samardžić, Ivan, Srdić, Zoran, Nikolić, Gojko, Ninković, Miloš M., Sakulski, Dušan, Ristanović, Branko, "Application of landsat-derived NDVI in monitoring and assessment of vegetation cover changes in central Serbia" in Carpathian Journal of Earth and Environmental Sciences, 14, no. 1 (2019):119-129,
https://doi.org/10.26471/cjees/2019/014/064 .,
https://hdl.handle.net/21.15107/rcub_gery_1002 .