Accurately identifying Natura 2000 habitat areas with the support of remote sensing techniques is becoming increasingly feasible. Various data types and methods are used for this purpose, and the fusion of data from various sensors and temporal periods (terms) within the phenological cycle allows natural habitats to be precisely identified. This research was aimed at selecting optimal datasets to classify three grassland Natura 2000 habitats (codes 6210, 6410 and 6510) in the Ostoja Nidziańska Natura 2000 site in Poland based on hyperspectral imagery and botanical on-ground reference data acquired in three terms during one vegetative period in 2017 (May, July and September), as well as a digital terrain model (DTM) obtained by airborne laser scanning (ALS). The classifications were carried out using a random forest (RF) algorithm on minimum noise fraction (MNF) transform output bands obtained for single terms, as well as data fusion combining the topographic indices (TOPO) calculated from the DTM, multitemporal hyperspectral data, or a combination of the two. The classification accuracy statistics were analysed in various combinations based on the datasets and their terms of acquisition. Topographic indices improved the classification accuracy of habitats 6210 and 6410, with the greatest impact noted in increased classification accuracy of xerothermic grasslands. The best terms for identifying specific habitats were autumn for 6510 and summer for 6210 and 6410, while the best results overall were obtained by combining data from all terms. The highest obtained values of the F1 coefficient were 84.5% for habitat 6210, 83.2% for habitat 6410, and 69.9% for habitat 6510. Comparing the data fusion results for habitats 6210 and 6410, greater accuracy was obtained by adding topographic indices to multitemporal hyperspectral data, while for habitat 6510, greater accuracy was obtained by fusing only multitemporal hyperspectral data.

Fusion of remote sensing data often improves vegetation mapping, compared to using data from only a single source. The effectiveness of this fusion is subject to many factors, including the type of data, collection method, and purpose of the analysis. In this study, we compare the usefulness of hyperspectral (HS) and Airborne Laser System (ALS) data fusion acquired in separate flights, Multiple Flights Data Fusion (MFDF), and during a single flight through Instrument Fusion (IF) for the classification of non-forest vegetation. An area of 6.75 km2 was selected, where hyperspectral and ALS data was collected during two flights in 2015 and one flight in 2017. This data was used to classify three non-forest Natura 2000 habitats i.e., Xeric sand calcareous grasslands (code 6120), alluvial meadows of river valleys of the Cnidion dubii (code 6440), species-rich Nardus grasslands (code 6230) using a Random Forest classifier. Our findings show that it is not possible to determine which sensor, HS, or ALS used independently leads to a higher classification accuracy for investigated Natura 2000 habitats. Concurrently, increased stability and consistency of classification results was confirmed, regardless of the type of fusion used; IF, MFDF and varied information relevance of single sensor data. The research shows that the manner of data collection, using MFDF or IF, does not determine the level of relevance of ALS or HS data. The analysis of fusion effectiveness, gauged as the accuracy of the classification result and time consumed for data collection, has shown a superiority of IF over MFDF. IF delivered classification results that are more accurate compared to MFDF. IF is always cheaper than MFDF and the difference in effectiveness of both methods becomes more pronounced when the area of aerial data collection becomes larger.

This research focuses on the use of HySpex hyperspectral images for verification of twodimensional hydrodynamic modelling of open-channel flow over loose bed (CCHE2D) and assessment of water quality in the Zegrze Reservoir. The CCHE2D hydrodynamic model results show the distribution of hydraulic parameters of water flow and the sediment concentrations in the reservoir. HySpex images were used to obtain remote sensing indices of water quality. The images were compared to the hydrodynamic model results and field measurements. The analysis of hydrodynamic model results and hyperspectral image indices show the spatial distribution of the water's physico-chemical properties in the reservoir, and poor mixing of the Bug River and the Narew River at their confluence. This study shows that there is synergy potential in using hydrodynamic modelling results and remote sensing indices of water quality for analysis of the reservoir's water quality.