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collections/HAUC1_land_surface_temperature_analysis/HAUC1.md

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-## Tool description
+#### TIPS ON USING THIS TOOL
+Use this tool to explore Land Surface Temperature (LST) and its relationships to land cover, and land use, providing valuable insights for sustainable urban and climate adaptation strategies. You can constrain the LST information depending on Imperviousness, Land Cover, and Population Density. Once all constraints are set, you can select an administrative area at Gemeinde level to display a graph showing the relationships between LST and land cover or imperviousness, and export the information into a csv file for your further analyses.
 
-Placeholder for tool description
+#### WHY THIS DATA
+
+*LST Composite*
+
+The Land Surface Temperature (LST) dataset provides crucial insights into surface temperatures during the summer months. This dataset includes per-pixel estimates of surface temperatures at a spatial resolution of 70 meters. The data is derived from ECOSTRESS observations collected between and predictions were made every third day between June 1st to August 31st. The dataset offers two distinct compositions: Mean LST, which represents the average temperature across multiple observations per pixel, and Max LST, which captures the highest recorded temperatures for each pixel over the same period. This dataset enables detailed analysis of heat patterns and temperature variability, supporting research into climate change impacts and urban heat island effects.
+
+*Imperviousness*
+
+Impervious areas refer to regions where natural surfaces, like soil or water, have been replaced with artificial, non-permeable materials. This transformation can lead to significant environmental issues, such as soil degradation, reduced biodiversity, and increased flooding risks. This dataset indicates the percentage of land covered by artificial materials and was developed using semi-automatic methods on Sentinel 1 and 2 satellite data. This dataset will provide per-pixel estimates of impervious cover, ranging from 0 to 100%, at a 70-meter resolution.
+
+*Land Cover Layer*
+
+The land cover layer is part The LULUCF (Land Use, Land Use Change, and Forestry) Time Series dataset provides comprehensive insights into mid- to long-term land use transitions from 2015 to 2023. This dataset was created using a combination of satellite imagery, including Sentinel data, CLMS products, and national datasets, such as orthophotos and national forest maps, to ensure the highest accuracy. The time series offers per-pixel classifications at a detailed level, highlighting areas that have experienced stable transitions from one land use category to another over the nine-year period, at a resolution of 70m.
+
+![](https://raw.githubusercontent.com/eurodatacube/eodash-assets/main/collections/HAUC1_land_surface_temperature_analysis/2024_LST_AT_municipality_statistics_processed.png)
+
+*Global Human Settlement Layer*
+
+The Global Human Settlement Layer (GHSL) dataset provides valuable insights into the distribution of the residential population across different epochs, offering multitemporal data from 1975 to 2030. This dataset, developed through disaggregation methods, uses census or administrative unit data alongside the classification and volume of built-up areas mapped in GHSL. The GHSL data supports population-related studies and planning by offering detailed, spatially disaggregated estimates, with values expressed as decimals representing the absolute number of people per grid cell, at a 70-meter resolution.