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: 2025-05-07 (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 # create color raster mkdir -p /tiles/raster/mdc_2013_2022_avg_lst_30m mkdir -p /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived cd /data/mdc_2013_2022_lst_30m cat > /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_color_ramp.txt <<_EOF 33 127 39 4 255 31.20 166 54 3 255 29.04 217 72 1 255 26.70 241 105 19 255 24.36 253 141 60 255 22.02 253 174 107 255 19.68 253 208 162 255 17.34 254 230 206 255 15 255 245 235 255 0 0 0 0 0 _EOF gdaldem color-relief download/LST_Avg_Scenes/AvgMDC_LST_2013_2022.tif /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_color_ramp.txt /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_color.tif -alpha # create color raster and render raster tiles mkdir -p /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived cd /data/mdc_2013_2022_lst_30m echo "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" | base64 -d > /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_legend.png cp /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_legend.png /tiles/raster/mdc_2013_2022_avg_lst_30m/ # render raster tiles cd /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/ gdal2tiles.py -ex --xyz -z 8-16 /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/derived/mdc_2013_2022_avg_lst_30m_color.tif /tiles/raster/mdc_2013_2022_avg_lst_30m # Create derivative directory in data package on osgeo cd /data/mdc_2013_2022_lst_30m/mdc_2013_2022_avg_lst_30m/ mkdir -p derived # Create pg_dump of table SQL in derivate directory on postgis. pg_dump -d $POSTGRES_DB -U $POSTGRES_USER -p $POSTGRES_PORT -h postgis-na4vxyii8b68i518 -t mdc_2013_2022_avg_lst_30m > derived/mdc_2013_2022_avg_lst_30m.sql Create downloadable tarfile of SQL in derivative directory on postgis tar -czf derived/mdc_2013_2022_avg_lst_30m.sql.tar.gz meta_dcat_mdc_2013_2022_avg_lst_30m.json -C derived mdc_2013_2022_avg_lst_30m.sql