UM Geospatial Digital Special Collections

Citation: Nkosi Muse (2024). Miami-Dade Average Land Surface Temperature 2013-2022 [gis dataset]. University of Miami GDSC. https://doi.org/10.17604/0zp4-6280. last updated: 2024-10-03 (update frequency: As needed). Abstract: This data collection is comprised of statistical summary as an average from LST scenes (processed in ArcGIS) that were captured at 11 AM EDT/12 PM EST by the Landsat 8 satellite (2013-2022). Scenes are Level 2-1, retrieved from the USGS Earth Explorer. Processed LST rasters in TIFF format are composed of 30x30m pixels that contain a LST value in Celsius (°C) for that area. Each raster image is clipped to Miami-Dade County boundaries (downloaded from the Miami-Dade County Open Data Hub). Key words: Miami-Dade County, LST Open Geospatial Consortium Standards: raster tiles | raster tile url: https://gdsc.idsc.miami.edu/rastertiles/mdc_2013_2022_avg_lst_30m/{z}/{x}/{y}.png | WMS (coming soon) | WFS (coming soon) Analytical Functions: gdsc_zonal_statistics(table=mdc_2013_2022_avg_lst_30m,lat=25.718,lng=-80.279,radius=500) ETL documentation

ETL for mdc_2013_2022_avg_lst_30m

######### # GDSC ETL is performed in two steps: shell script to ETL data into postgres # and then additional transformations with SQL in postGIS (if needed). #### ######### # Step 1 - bash script (pseudo code) #### # create directory structure and move into it mkdir -p /data/mdc_2013_2022_lst_30m/{download,mdc_2013_2022_avg_lst_30m/etl} && cd /data/mdc_2013_2022_lst_30m # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # download if update flag is set: if do_update == 1 then wget -O download/mdc_2013_2022_lst_30m.zip 'https://scholarship.miami.edu/view/fileRedirect?instCode=01UOML_INST&filePid=13440658420002976&download=true' # unzip to download directory if update flag is set if do_update == 1 then unzip -d download download/mdc_2013_2022_lst_30m.zip && rm download/mdc_2013_2022_lst_30m.zip # load raster into postGIS with: raster2pgsql -s 32617 -d -C -I LST_Avg_Scenes/AvgMDC_LST_2013_2022.tif -F -t 256x256 mdc_2013_2022_avg_lst_30m > load_raster.sql psql < load_raster.sql rm load_raster.sql # render raster tiles mkdir -p /tiles/raster/mdc_2013_2022_avg_lst_30m cd /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/ cat > mdc_2013_2022_avg_lst_30m_color_ramp.txt <<_EOF 0 0 0 0 0 20.0 171 168 125 255 20.5 194 171 117 255 21.0 193 157 97 255 21.5 195 138 83 255 22.0 190 112 76 255 22.5 175 77 77 255 23.5 158 41 76 255 24.0 135 32 62 255 24.5 110 21 49 255 25.0 86 12 37 255 27.0 61 2 22 255 _EOF gdaldem color-relief download/LST_Avg_Scenes/AvgMDC_LST_2013_2022.tif mdc_2013_2022_avg_lst_30m_color_ramp.txt download/LST_Avg_Scenes/AvgMDC_LST_2013_2022_color.tif -alpha gdal2tiles.py -ex -z 8-16 download/LST_Avg_Scenes/AvgMDC_LST_2013_2022_color.tif /tiles/raster/mdc_2013_2022_avg_lst_30m/