old_dirs.md

• Install miniconda for python 2.7 (default here)

  • cd ~
  • wget "http://repo.continuum.io/miniconda/Miniconda-3.5.5-Linux-x86_64.sh"
  • bash Miniconda-3.5.5-Linux-x86_64.sh
  • Use all defaults... this will create ~/miniconda

• Create a Python 3 environment to atmospherically correct landsat-8 data

  • restart a new terminal to get access to conda
  • conda create -n arcsi python=3
  • source activate arcsi
  • conda install -c https://conda.binstar.org/osgeo arcsi tuiview
  • export GDAL_DRIVER_PATH=~/miniconda/envs/arcsi/gdalplugins
  • export GDAL_DATA=~/miniconda/envs/arcsi/share/gdal

• Download a Landsat-8 scene

  • Option 1:
    • Go to: http://earthexplorer.usgs.gov/
    • Login
    • Select and download a Scene
    • Upload it to an S3 bucket, make the file it public and copy it to ~/data/landsat8 using wget
  • Option 2:
    • Get an existing scene from our own S3 and copy it over
    • cd $MENA_DIR_/data/landsat8
    • mkdir ./OutputImages
    • mkdir ./LC80090462013357LGN00
    • cd LC80090462013357LGN00
    • wget "https://s3.amazonaws.com/mena_data/LC80090462013357LGN00.tar.gz"
    • tar -xf LC80090462013357LGN00.tar.gz
    • rm LC80090462013357LGN00.tar.gz
    • cd ..

• Atmospheric Correction of Landsat Image

  • Conversion to Radiance [Note: This might not be necessary]

    • arcsi.py -s ls8 -f KEA --stats -p RAD -o ./OutputImages -i LC80090462013357LGN00/LC80090462013357LGN00_MTL.txt

  • Conversion to Top of Atmosphere Reflectance [Note: This might not be necessary]

    • arcsi.py -s ls8 -f KEA --stats -p RAD TOA -o ./OutputImages -i LC80090462013357LGN00/LC80090462013357LGN00_MTL.txt

  • Convert to Surface Reflectance

    • arcsi.py -s ls8 -f KEA --stats -p RAD SREFSTDMDL --aeropro Continental --atmospro MidlatitudeSummer --aot 0.25 -o ./OutputImages -i LC80090462013357LGN00/LC80090462013357LGN00_MTL.txt

  • Convert to tif to avoid requiring KEA Driver if you want to download file to another machine - also reproject to ESPG:4326 while at it

    • gdalwarp -of GTIFF -t_srs EPSG:4326 ./OutputImages/LS8_20131223_lat20lon7253_r46p9_rad_srefstdmdl.kea ./OutputImages/LS8_20131223_lat20lon7253_r46p9_rad_srefstdmdl.tif

  • Copy back to scene folder and rename it

    • mv ./OutputImages/LS8_20131223_lat20lon7253_r46p9_rad_srefstdmdl.tif LC80090462013357LGN00/LC80090462013357LGN00_SREF.tif

  • Same for other scene [optional]

    • arcsi.py -s ls8 -f KEA --stats -p RAD SREFSTDMDL --aeropro Continental --atmospro MidlatitudeSummer --aot 0.25 -o ./OutputImages -i LC80090472013357LGN00/LC80090472013357LGN00_MTL.txt
    • gdalwarp -of GTIFF -t_srs EPSG:4326 ./OutputImages/LS8_20131223_lat19lon7286_r47p9_rad_srefstdmdl.kea ./LC80090472013357LGN00/LC80090472013357LGN00_SREF.tif

  • Reproject BQA band [Not necessary anymore]

    • gdalwarp -t_srs EPSG:4326 ./LC80090472013357LGN00/LC80090472013357LGN00_BQA.tif ./LC80090472013357LGN00/LC80090472013357LGN00_BQA_4326.tif

  • Generate Composite for V&V [ 4-3-2 and rest optional]

    • landsat8_composite_toa.py --scene LC80090472013357LGN00 --red 4 --green 3 --blue 2
    • landsat8_composite_toa.py --scene LC80090472013357LGN00 --red 5 --green 6 --blue 4
    • landsat8_composite_toa.py --scene LC80090472013357LGN00 --red 7 --green 5 --blue 4

  • Generate water map, vectors and browse image

    • landsat8_toa_watermap.py --scene LC80090472013357LGN00 -v
    • landsat8_to_topojson.py --scene LC80090472013357LGN00 --vrt haiti_hand.vrt -v
    • landsat8_browseimage.py --scene LC80090472013357LGN00 -v

• Process Landsat Image (Assuming a atmospherically corrected EPSG:4326 tif file in given Landsat8 directory)

  • cd $MENA_DIR/python
  • landsat8_to_topojson.py --scene LC80090462013357 --vrt haiti_hand.vrt
  • NOTE:
    • visualize surface_water.json with mapshaper.org or geojson.io
    • visualize surface_water.osm with JOSM to generate a reference water trace

• Process MODIS Imagery

  • cd $MENA_DIR/python
  • modis.py -y 2012 -d 234 -t 080W020N -p 2 -v