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ŠUMARSKI LIST 7-8/2023 str. 62     <-- 62 -->        PDF

regularly updated global mosaic of imagery that is available through the Google Maps and Google Earth platforms (Google Earth Web and Google Earth Pro Desktop).
Examples of test areas are shown in Figure 5. Based on the given images, it is possible to see the amount of overgrown agricultural land in the area of Brezici. The land use in Table 3 is collected from Real Estate Cadastre Database, and compared to the land use of classified Sentinel-2 images.
After the visual analyses, raster data were converted into vector data to obtain the amount of lost and created forest areas. The final result shows that three times more forest has been deciphered today than was the case in 1976 when a survey was carried out to establish the cadastre, the data which are still used today, Table 4.
CONCLUSION AND DISCUSSION
ZAKLJUČAK I DISKUSIJA
According to the Real Estate Cadastre Database, 26% of the total territory of the cadastral municipality of Brezici is covered in forest, whereas according to the Sentinel-2 images, 69% of the territory is separated under some form of tall vegetation. Of course, the accuracy and spatial resolution of the Sentinel-2 images, as well as significant shortcomings during classification due to the lack of a field-collected training sample, must be considered. However, taking into account all the disadvantages of using Sentinel-2 images, the existence of a huge difference between the real situation on the field and the data from the Real Estate Cadastre Database is unquestionable. The Real Estate Cadastre Database does not accurately reflect the extent of tree and shrub invasion on land where agricultural production has ceased.
The study shows the possibility of using Sentinel-2 images to monitor LULC changes regarding the implementation of EU programs and updating the Real Estate Cadastre Database. Remote sensing and the Copernicus program provide countless free geodata that provide information on the spatial and temporal distribution of land cover (land classes) and the spatial range of vegetation.
Future research should be directed towards developing appropriate training data, which requires going out into the field and allocating appropriate funds for research and determining multiple classes of data, aiming to better classify Sentinel-2 images. By better classifying Sentinel-2 images, in the sense of extracting more classes of data, a more accurate and precise model is obtained that will meet the needs of the Real Estate Cadastre Database updating in a much more detailed scope. Additionally, the use of some commercial satellite images and the financial profitability concerning the conventional ways of establishing the real estate cadastre should also be considered.
REFERENCES
LITERATURA
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Aschbacher, J., 2017: ESA’s earth observation strategy and Copernicus, Satellite earth observations and their impact on society and policy, Springer, 81.–86., Singapore.
Barakat, A., R. Khellouk, A. El Jazouli, F. Touhami, S. Nadem, 2018: Monitoring of forest cover dynamics in eastern area of Béni-Mellal Province using ASTER and Sentinel-2A multispectral data, Geology, Ecology, and Landscapes, 2(3): 203.–215.
Belgiu, M., L. Dragut, 2016: Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24.–31.
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Bucha, T., J. Papčo, I. Sačkov, J. Pajtik, J. M. Sedliak, I. Barka, J. Feranec, 2021: Woody Above-Ground Biomass Estimation on