## Materials and Methods
### Thermal Survey of the Coastal Zone  
Thermal UAV surveys of two key study sites (Norashen Peninsula and the mouth of Masrik River, see Figure 2) were performed to enable more accurate detection of the shoreline position, recording processes of submerged vegetation decay, and detection of spatial patterns and interfusion processes of waters characterized by different temperature gradients (influx of rivers into the lake, water bodies of lagoons and backwaters).  
DJI Phantom 4 UAV was used with an integrated thermal camera (FLIR BOSON) providing thermal video imagery within the spectral range of 7.5-13.5 \(\upmu\)m. The survey was performed during predawn hours at two flight levels of 300 and 400 m above the surface. The survey route was planned so that the overlap of adjacent traverses reaches 80% and includes obligatory flights over key sites having maximal temperature gradients due to diversity and contrast of land cover types.  
Modern thermal cameras used on UAVs are bolometers and have their own peculiarities in terms of brightness temperatures registration [47; 48]. They typically show dependence between registered brightness values and camera-to-subject distance. Furthermore, data quality and processing algorithms are significantly affected by camera distortion responsible for the effect of a temperature difference between the center and (<<colder>>) edges of a frame. This effect diminishes frame-overlap, and automated algorithm of matching and aligning frames cannot be used because of very low quality of reciprocal orientation of frames and high noise levels in the resulting data.  
When using a digital photogrammetric system (DPS) with UAV-based thermal aerial photography data, the processing technique is essentially similar to that used for optical aerial imagery [49; 50] with the addition of stages required for processing the video stream recorded by thermal cameras. More accurate camera calibration is also necessary. Nevertheless, the peculiarities of UAV-based thermal aerial photography data often preclude the use of automated procedures provided by standard packages. Instead, some operations (e.g., positioning of matching points) must be performed manually because certain objects have different temperature gradients in terms of recognition, which is variable from the survey beginning to its end.  
The processing algorithm of thermal survey data, performed in Agisoft MetaShape software, included the stages presented in Figure 4. Video record frames were converted into a set of images in increments of 20 frames. Under the camera, calibration utilized values of the camera parameter input were set as a matrix pixel size of 0.012 \(\times\) 0.012 mm and a focal distance of 4.3 mm.  
The final ortho-mosaic is a rasterized image of brightness values with a radiometric resolution of 8 bit (256 scale units per pixel). This information cannot be directly interpreted as a temperature without terrestrial sensor calibration. In order to calibrate and classify thermal survey data as brightness temperature, several specific objects with maximal temperature value gradients should be chosen. In the course of UAV flights, these objects are surveyed at fixed intervals to determine the main regularities of the temperature change during different time periods.  
Figure 4: The processing flowchart of UAV-derived optical (RGB) imagery and thermal video stream.