20.500.12556/RUL-176483 Beyond monthly composites maximizing information retention in satellite image time series for forest stand classification This study investigates the effectiveness of data pre-processing and classifier selection in forest stand classification using Satellite Image Time Series (SITS). We compare the performance of Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) on monthly composites and dense time series. While the monthly RF achieves an average accuracy of 74.1%, the use of LightGBM results in lower performance on monthly composites. Our approach, which utilizes synthetic bands generated based on the available Sentinel−2 SITS, improved RF performance by 13.2 percentage points, exceeding the improvement observed when using 10-day composites. This highlights the loss of information that occurs when using composites. LightGBM improved the results by an additional 1.9 percentage points. However, without additional pre-processing, LightGBM can use the raw SITS and outperform these results with an F1 score of 0.906. The generated map was further improved by using margin values to highlight uncertainties and mask areas of uncertainty. Overall, while monthly composites provide a good starting point, the best results are obtained with raw SITS, which allows efficient processing for larger regions without additional pre-processing. random forest LightGBM forest stand classification synthetic data Sentinel−2 naključni gozdovi LightGBM klasifikacija gozdnih sestojev sintetični podatki Sentinel-2 true false true Angleški jezik Slovenski jezik Članek v reviji 2025-12-02 10:51:26 2025-12-02 10:51:28 2025-12-21 04:15:02 0000-00-00 00:00:00 2025 0 0 19 str. no. 1 Vol. 58 2025 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.932.2 2279-7254 10.1080/22797254.2025.2585241 258780419 RAZ_Racic_Matej_2025.pdf RAZ_Racic_Matej_2025.pdf 1 6FFF75A5DAD8AD3025B21BF7F4DDFA75 5ec9c2b290776ae6073de477d676c493022992f7661ddfb2d503189050468bf9 af0134d8-cf64-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=222833 https://www.tandfonline.com/doi/full/10.1080/22797254.2025.2585241#abstract 1 170c643c-cf64-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=222832 Fakulteta za računalništvo in informatiko 0 0 0