TY  - CONF
AU  - Lakušić, Dmitar
AU  - Kuzmanović, Nevena
AU  - Kovačević, Jovan
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2690
AB  - The generalized habitat map of Serbia is one of the products of the project carried out for the needs of the Institute for Nature Protection of Serbia by the Faculty of Biology University of Belgrade in cooperation with the geomatics company MapSoft d.o.o. The map was created using an adapted methodology for extracting different habitat types from remote sensing data. To interpret the basic habitat types, the following input datasets were used: Sentinel-2 satellite data, EU-DEM terrain elevation data, Basic land cover map, Copernicus pan-European high resolution layers, Open Street Map data, pedological and geological map of Serbia, and numerous training data. The map was created by integrating the rasters of each habitat type into a single raster, separating additional habitat subtypes by crossing them with additional sources (geologic and pedological map, basic land cover map, etc.). The final results are presented in the form of a spatial raster, with 32 cartographic classes defined based on the typology of habitats according to the Rulebook on Habitats of Serbia. In addition, manual vectorization of several other habitat types (springs - 37,284 objects, caves - 499 objects, waterfalls - 109 objects) was performed, and these results are provided in the form of vector point entities.
C3  - 14th Symposium on the Flora of Southeastern Serbia and Neighboring Regions
T1  - Generalized habitat map of Serbia
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2690
ER  - 

@conference{
author = "Lakušić, Dmitar and Kuzmanović, Nevena and Kovačević, Jovan",
year = "2022",
abstract = "The generalized habitat map of Serbia is one of the products of the project carried out for the needs of the Institute for Nature Protection of Serbia by the Faculty of Biology University of Belgrade in cooperation with the geomatics company MapSoft d.o.o. The map was created using an adapted methodology for extracting different habitat types from remote sensing data. To interpret the basic habitat types, the following input datasets were used: Sentinel-2 satellite data, EU-DEM terrain elevation data, Basic land cover map, Copernicus pan-European high resolution layers, Open Street Map data, pedological and geological map of Serbia, and numerous training data. The map was created by integrating the rasters of each habitat type into a single raster, separating additional habitat subtypes by crossing them with additional sources (geologic and pedological map, basic land cover map, etc.). The final results are presented in the form of a spatial raster, with 32 cartographic classes defined based on the typology of habitats according to the Rulebook on Habitats of Serbia. In addition, manual vectorization of several other habitat types (springs - 37,284 objects, caves - 499 objects, waterfalls - 109 objects) was performed, and these results are provided in the form of vector point entities.",
journal = "14th Symposium on the Flora of Southeastern Serbia and Neighboring Regions",
title = "Generalized habitat map of Serbia",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2690"
}

Lakušić, D., Kuzmanović, N.,& Kovačević, J.. (2022). Generalized habitat map of Serbia. in 14th Symposium on the Flora of Southeastern Serbia and Neighboring Regions.
https://hdl.handle.net/21.15107/rcub_grafar_2690

Lakušić D, Kuzmanović N, Kovačević J. Generalized habitat map of Serbia. in 14th Symposium on the Flora of Southeastern Serbia and Neighboring Regions. 2022;.
https://hdl.handle.net/21.15107/rcub_grafar_2690 .

Lakušić, Dmitar, Kuzmanović, Nevena, Kovačević, Jovan, "Generalized habitat map of Serbia" in 14th Symposium on the Flora of Southeastern Serbia and Neighboring Regions (2022),
https://hdl.handle.net/21.15107/rcub_grafar_2690 .

TY  - JOUR
AU  - Kovačević, Jovan
AU  - Cvijetinović, Željko
AU  - Lakušić, Dmitar
AU  - Kuzmanović, Nevena
AU  - Šinžar-Sekulić, Jasmina
AU  - Mitrović, Momir
AU  - Stančić, Nikola
AU  - Brodić, Nenad
AU  - Mihajlović, Dragan
PY  - 2020
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2057
AB  - The inventory of woody vegetation is of great importance for good forest management. Advancements of remote sensing techniques have provided excellent tools for such purposes, reducing the required amount of time and labor, yet with high accuracy and the information richness. Sentinel-2 is one of the relatively new satellite missions, whose 13 spectral bands and short revisit time proved to be very useful when it comes to forest monitoring. In this study, the novel spatio-temporal classification framework for mapping woody vegetation from Sentinel-2 multitemporal data has been proposed. The used framework is based on the probability random forest classification, where temporal information is explicitly defined in the model. Because of this, several predictions are made for each pixel of the study area, which allow for specific spatio-temporal aggregation to be performed. The proposed methodology has been successfully applied for mapping eight potential forest and shrubby vegetation types over the study area of Serbia. Several spatio-temporal aggregation approaches have been tested, divided into two main groups: pixel-based and neighborhood-based. The validation metrics show that determining the most common vegetation type classes in the neighborhood of 5 × 5 pixels provides the best results. The overall accuracy and kappa coefficient obtained from five-fold cross validation of the results are 82.97% and 0.75, respectively. The corresponding producer’s accuracies range from 36.74% to 97.99% and user’s accuracies range from 46.31% to 98.43%. The proposed methodology proved to be applicable for mapping woody vegetation in Serbia and shows a potential to be implemented in other areas as well. Further testing is necessary to confirm such assumptions.
PB  - MDPI
T2  - Remote Sensing
T1  - Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery
IS  - 17
SP  - 2845
VL  - 12
DO  - https://doi.org/10.3390/rs12172845
ER  - 

@article{
author = "Kovačević, Jovan and Cvijetinović, Željko and Lakušić, Dmitar and Kuzmanović, Nevena and Šinžar-Sekulić, Jasmina and Mitrović, Momir and Stančić, Nikola and Brodić, Nenad and Mihajlović, Dragan",
year = "2020",
abstract = "The inventory of woody vegetation is of great importance for good forest management. Advancements of remote sensing techniques have provided excellent tools for such purposes, reducing the required amount of time and labor, yet with high accuracy and the information richness. Sentinel-2 is one of the relatively new satellite missions, whose 13 spectral bands and short revisit time proved to be very useful when it comes to forest monitoring. In this study, the novel spatio-temporal classification framework for mapping woody vegetation from Sentinel-2 multitemporal data has been proposed. The used framework is based on the probability random forest classification, where temporal information is explicitly defined in the model. Because of this, several predictions are made for each pixel of the study area, which allow for specific spatio-temporal aggregation to be performed. The proposed methodology has been successfully applied for mapping eight potential forest and shrubby vegetation types over the study area of Serbia. Several spatio-temporal aggregation approaches have been tested, divided into two main groups: pixel-based and neighborhood-based. The validation metrics show that determining the most common vegetation type classes in the neighborhood of 5 × 5 pixels provides the best results. The overall accuracy and kappa coefficient obtained from five-fold cross validation of the results are 82.97% and 0.75, respectively. The corresponding producer’s accuracies range from 36.74% to 97.99% and user’s accuracies range from 46.31% to 98.43%. The proposed methodology proved to be applicable for mapping woody vegetation in Serbia and shows a potential to be implemented in other areas as well. Further testing is necessary to confirm such assumptions.",
publisher = "MDPI",
journal = "Remote Sensing",
title = "Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery",
number = "17",
pages = "2845",
volume = "12",
doi = "https://doi.org/10.3390/rs12172845"
}

Kovačević, J., Cvijetinović, Ž., Lakušić, D., Kuzmanović, N., Šinžar-Sekulić, J., Mitrović, M., Stančić, N., Brodić, N.,& Mihajlović, D.. (2020). Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery. in Remote Sensing
MDPI., 12(17), 2845.
https://doi.org/https://doi.org/10.3390/rs12172845

Kovačević J, Cvijetinović Ž, Lakušić D, Kuzmanović N, Šinžar-Sekulić J, Mitrović M, Stančić N, Brodić N, Mihajlović D. Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery. in Remote Sensing. 2020;12(17):2845.
doi:https://doi.org/10.3390/rs12172845 .

Kovačević, Jovan, Cvijetinović, Željko, Lakušić, Dmitar, Kuzmanović, Nevena, Šinžar-Sekulić, Jasmina, Mitrović, Momir, Stančić, Nikola, Brodić, Nenad, Mihajlović, Dragan, "Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery" in Remote Sensing, 12, no. 17 (2020):2845,
https://doi.org/https://doi.org/10.3390/rs12172845 . .