BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//JQEY3C BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-JQEY3C@talks.osgeo.org DTSTART;TZID=JST:20260902T160000 DTEND;TZID=JST:20260902T163000 DESCRIPTION:Urban Heat Island (UHI) effects are among the most visible clim atic consequences of rapid urbanization\, driven by the expansion of imper vious surfaces\, increasing building density\, and changes in vegetation c over. Satellite remote sensing provides consistent spatial coverage and re peated observations over long time periods\, making it a valuable tool for analyzing these processes. In particular\, land surface temperature (LST) derived from multispectral satellite missions enables systematic monitori ng of thermal conditions in urban environments. Satellite observations hav e therefore become an important source of information for examining how ur ban thermal patterns evolve over time.\nHowever\, interpreting multi-year satellite-derived land surface temperature time series is not always strai ghtforward. LST observations often show strong interannual variability inf luenced by meteorological conditions\, seasonal differences in data availa bility\, and changes in land cover characteristics. Distinguishing a stabl e long-term urban warming signal from short-term fluctuations therefore re mains a methodological challenge in many urban heat island studies. This d ifficulty becomes particularly relevant when relatively short satellite re cords are analyzed\, as individual anomalous years can influence the inter pretation of long-term trends.\nThis study investigates how stable UHI sig nals can be detected in multi-year satellite-derived land surface temperat ure time series using a reproducible open-source geospatial workflow. The analysis is demonstrated for the city of Zagreb\, Croatia\, using a ten-ye ar satellite record (2015–2024) derived from Landsat observations (Lands at 8/9). Instead of analyzing the entire urban area as a single unit\, the study focuses on several representative urban neighborhoods used as analy tical units for evaluating temporal patterns in the temperature time serie s. Using several analytical units also allows temporal patterns to be comp ared across different parts of the city rather than relying on a single ag gregated urban value.\nZagreb represents a typical Central European urban environment characterized by a mixture of dense built-up areas\, residenti al neighborhoods\, urban green spaces\, and peri-urban zones. To capture t his diversity\, several representative neighborhoods were selected as anal ytical units. The selected areas reflect different urban development patte rns within the city\, including contrasts in vegetation cover and building density. This allows the analysis to compare thermal behavior across dist inct types of urban environments within the same metropolitan area. Such v ariation in land cover and urban structure provides a useful setting for e xamining how temperature dynamics differ between neighborhoods with differ ent surface characteristics.\nAnnual warm-season composites of land surfac e temperature are generated to reduce noise associated with individual sat ellite scenes and to provide a consistent representation of thermal condit ions for each observation year. To place temperature dynamics in the conte xt of surface characteristics\, two commonly used spectral indices are ana lyzed alongside LST: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). Analyzing these indices together with temperature values helps interpret whether observed thermal changes are associated with variations in vegetation or built-up surfaces. \nSatellite data processing follows a reproducible workflow that integrate s image preprocessing\, time series aggregation\, and statistical analysis . Individual Landsat scenes are first filtered according to cloud cover an d seasonal criteria. Annual warm-season composites are then generated to r educe scene-level variability and provide consistent yearly observations. LST\, NDVI\, and NDBI layers are subsequently extracted for each selected neighborhood\, forming the basis for the temporal analysis. Using a consis tent processing workflow also helps ensure that results from different yea rs remain comparable.\nParticular attention is given to the temporal behav ior of the data through the analysis of interannual differences between co nsecutive observations. This allows year-to-year variations in land surfac e temperature to be examined together with corresponding changes in vegeta tion and built-up surfaces. Interannual differences help reveal short-term dynamics and provide additional insight into whether temperature changes occur in parallel with changes in surface characteristics. These compariso ns provide an additional perspective on the variability present in the sat ellite record.\nTo assess the robustness of the detected trends\, several analytical approaches are used. Linear trends and non-parametric slope est imates are calculated for the LST time series\, and the results are compar ed across different parts of the observation period. This makes it possibl e to see whether similar trends appear regardless of the method used or th e selected time interval. In this way\, it can be evaluated whether the ob served temperature signal reflects a persistent long-term pattern or mainl y short-term variability in the satellite record. Examining the consistenc y of trend estimates therefore provides an additional check on the stabili ty of the detected patterns.\nThe results help interpret satellite-derived temperature time series in urban heat island research more carefully. Exa mining temporal changes\, interannual differences\, and the consistency of trend estimates helps identify whether similar warming patterns appear ac ross different parts of the time series. This comparison makes it possible to separate persistent temperature trends from short-term variability in the satellite observations.\nSuch an analysis also shows that time plays a key role when interpreting urban heat island dynamics. The use of reprodu cible open-source geospatial tools allows the same workflow to be applied to satellite temperature time series in other urban areas. This makes the approach potentially applicable to other cities where comparable satellite datasets are available. DTSTAMP:20260717T225754Z LOCATION:Cosmos1 SUMMARY:Detecting Stable Urban Heat Island Signals in Satellite Land Surfac e Temperature Time Series: A Case Study of Zagreb\, Croatia - Sanja Šaman ović URL:https://talks.osgeo.org/foss4g-2026/talk/JQEY3C/ END:VEVENT END:VCALENDAR