# Habitat Loss and Other Threats to the Survival of _Parnasius apollo_ ([PERSON], 1758) in Serbia
## 2 Materials and Methods
### Climate and Thermal Data Analysis  
Land Surface Temperature (LST) is a critical environmental parameter that reflects surface-level thermal conditions and serves as a sensitive indicator of both ecological state and climate variability [74; 75]. In this study, LST data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor onboard the Sentinel-3 satellite were used to assess spatial and temporal patterns of surface temperature across selected sites. These data provided key insights into potential thermal stress and were used to evaluate changes in habitat quality and local climate dynamics. The SLSTR sensor offers high-precision thermal infrared measurements with a spatial resolution of \(\sim\)1 km and a revisit interval of 1-2 days. It operates across multiple thermal infrared (TIR) and mid-infrared (MIR) bands, with a focus on split-window channels at 11 \(\upmu\)m and 12 \(\upmu\)m. These bands support robust LST estimation via established split-window algorithms, which account for atmospheric transmittance and surface emissivity variations. Under typical conditions, accuracy ranges from 1 to 2 K, with improved precision during nighttime observations due to more stable thermal gradients and reduced surface heterogeneity [76; 77; 78]. In this study, LST values were used as independent variables and interpreted directly, without modifications to the retrieval algorithm. The emphasis was placed on spatial and temporal variation in surface temperatures as indicators of ecosystem shifts. Sentinel-3-derived LST datasets proved especially valuable for identifying microclimatic differentiation among habitats and for detecting early signals of ecological stress. Geospatial data were processed using GIS 3.40. software with a focus on spatial visualization, statistical comparison, and integration with land cover reference layers. Although only one variable (LST) was utilized in this segment of the analysis, it yielded meaningful insights into thermal regime variability and its implications for vegetation and habitat function. In addition, this study employed long-term satellite time series from NASA's MODIS sensors onboard the Terra and Aqua platforms. The dataset spans the period from January 2010 to December 2024 and comprises monthly aggregated values from multiple ecological monitoring locations in Serbia. Data were accessed via Google Earth Engine [79], which enabled consistent preprocessing, spatial harmonization, and quality control across the full temporal extent. Spatial resolution ranged from 500 m to 1 km, depending on the specific MODIS product; temporal resolution corresponds to monthly means derived from 8-day or 16-day composites. The dataset included both biophysical and climatological variables relevant to land surface processes. Each entry was georeferenced by site name, year, and month. Daytime and nighttime LST values (LST_Day_1 km_C, LST_Night_1 km_C) were extracted from the MOD1A2 Version 6 product [80], along with standard deviations capturing intra-month thermal variability (LST_Day_1 km_stdDev, LST_Night_1 km_stdDev). The Temperature Condition Index (TCI) was calculated as a normalized measure of thermal stress, based on long-term LST extremes. Lower TCI values indicate increased heat stress and reduced vegetative performance. Snow_Cover, derived from the MOD10A2 product [81], provided additional seasonal context, representing the fractional area covered by snow at 500 m resolution. Key vegetation indices included FPAR (fraction of absorbed photosynthetically active radiation) and LAI (leaf area index), both obtained from MOD15A2H Version 6 [82]. These metrics characterize canopy density, radiation capture, and photosynthetic potential. Primary productivity was assessed using GPP (Gross Primary Production) and NPP (Net Primary Production) from MOD17A2H Version 6 [83], expressed in gC/m\({}^{2}\)/month. These indices quantify carbon assimilation rates and biomass accumulation across temporal scales.