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 . .