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.
ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R 2 and MAE of 0.0518 m 3 /m 3 , 0.7312 and 0.0374 m 3 /m 3 , respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.
Извод: Процес набавке је од великог значаја за пословни успех предузећа, а ефективност организације пословања, у великој мери, зависи од способности да се искористи околина у набавци ресурса потребних за функционисање. Циљ истраживања је идентификација главних проблема у газдовању шумама на Јужноморавском шумском подручју (ЈМШП), производњи и продаји букове техничке обловине, као и утврђивању карактеристика предузећа и организације процеса набавке дрвне сировине. Подаци су прикупљени у периоду 2014-2017. год., анкетирањем 13 представника малих и средњих и једног великог предузећа, која послују на територији ЈМШП, као и интервјуисањем пет дипломираних инжењера шумарства запослених у Шумском газдинству "Врање" (Jавно предузеће "Србијашуме"). Ситуација на ЈМШП се карактерише неповољном сортиментном структуром букових изданачких шума, недовољном отвореношћу шумских комплекса и недостатком мобилизације дрвних ресурса из приватних шума. Анализирана предузећа су, већином, микро и мала (79%), основана су пре мање од 10 година (64,3%), а половина се бави пиланском и прерадом нижег степена финализације. Сва предузећа набављају и користе букову техничку обловину. Дрвна сировина се набавља из јавног и приватног сектора, а транспорт сировине се обавља преко посредника (92,9%) и по "лошој" и "веома лошој" саобраћајној инфраструктури (71%). Ипак, већина купаца техничке обловине је "делимично задовољна" и "задовољна" (92,9%) испорученим квантитетом и квалитетом сировине. Постоји значајна заступљеност предузећа (86%), која поред техничке обловине набављају и користе и друге дрвне производе (плоче влакнатице и друге репродукционе материјале) од добављача из других региона. На основу анализе свих прикупљених података, дефинисана су места за унапређење процеса снабдевања дрвном сировином и слабости постојећих ланаца снабдевања дрвном сировином, као и дати предлози унапређења организације набавке дрвне сировине на ЈМШП.
ABSTRACT:High resolution (10 m and 20 m) optical imagery satellite Sentinel-2 brings a new perspective to Earth observation. Its frequent revisit time enables monitoring the Earth surface with high reliability. Since Sentinel-2 data is provided free of charge by the European Space Agency, its mass use for variety of purposes is expected. Quality evaluation of Sentinel-2 data is thus necessary. Quality analysis in this experiment is based on comparison of Sentinel-2 imagery with reference data (orthophoto). From the possible set of features to compare (point features, texture lines, objects, etc.) line segments were chosen because visual analysis suggested that scale differences matter least for these features. The experiment was thus designed to compare long line segments (e.g. airstrips, roads, etc.) in both datasets as the most representative entities. Edge detection was applied to both images and corresponding edges were manually selected. The statistical parameter which describes the geometrical relation between different images (and between datasets in general) covering the same area is calculated as the distance between corresponding curves in two datasets. The experiment was conducted for two different test sites, Austria and Serbia. From 21 lines with a total length of ca. 120 km the average offset of 6.031 m (0.60 pixel of Sentinel-2) was obtained for Austria, whereas for Serbia the average offset of 12.720 m (1.27 pixel of Sentinel-2) was obtained out of 10 lines with a total length of ca. 38 km.
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