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:Author: Brian Hamlin
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:Reviewer: Alex Leith
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:Version: osgeolive 16
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:License: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
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@LOGO_odc@
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@OSGEO_KIND_odc@
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@NAME_odc@
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================================================================================
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Open Data Cube
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Open Data Cube (ODC) is a community of people and organisations building capability
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for working with earth observation data. At the core, the ODC is a software library and
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set of command line tools. And keeping the core together we have a group of organisations
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collaborating on strategy and who may maintain specific implementations of the ODC.
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The objective of the ODC is to increase the impact of satellite data by providing an open
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and freely accessible exploitation tool, and to foster a community to develop, sustain,
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and grow the breadth and depth of applications.
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At a technical level, an implementation of the ODC is made up of three things:
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data, an index and software:
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* Data is usually file based, either in local directories of GeoTIFFs or NetCDF files,
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but data can be anything that GDAL can read, including Cloud Optimised GeoTIFFs stored on AWS’ S3.
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* For the Index, the ODC uses PostgreSQL as a database to store a list of Products
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(a specific data type, like Landsat 8 Analysis Ready Data) and Datasets (a single instance
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of a Product, for example, a single Landsat 8 scene). The index enables a user to ask for
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data at a time and location, without needing to know specifically where the required files
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are stored and how to access them.
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* The Software at the core of the ODC is a Python library that enables a user to index data
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(add records to the Index), to ingest data (optimise indexed data for performance), to query data
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(returning data in a standard data format) and a wide range of other functions related
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to managing data.
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@SCREENSHOT_odc@
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Core Features
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--------------------------------------------------------------------------------
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* Journal quality cartography.
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* Gridding and contouring functions.
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Details
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--------------------------------------------------------------------------------
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**Website:** @WEB_odc@
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**Licence:** Apache License Version 2
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**Software Version:** |version-odc|
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**Supported Platforms:** GNU/Linux, Mac OSX, MS Windows
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**API Interfaces:** Command line
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**Support:** https:///
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@VMDK_odc@
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@QUICKSTART_odc@
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.. presentation-note
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Generic Mapping Tools, or GMT is a collection of tools that allow users to manipulate (x,y) and (x,y,z) data sets
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:Author: Brian M Hamlin
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:Reviewer: Alex Leith
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:Version: osgeolive16.0
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:License: Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
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:Copyright: 2023 by The OSGeo Foundation
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@LOGO_odc@
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@OSGEO_KIND_odc@
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@VMDK_odc@
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********************************************************************************
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@NAME_odc@ Quickstart
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********************************************************************************
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The Open Data Cube Core provides an integrated gridded data
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analysis environment for decades of analysis ready earth observation
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satellite and related data from multiple satellite and other acquisition
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systems.
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In this quick Open Data Cube tutorial we will use tools.
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.. contents:: Contents
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:local:
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Create a map
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============
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As our input dataset we will use TODO from TODO dataset, which is accessible by GDAL using the GDAL-GRASS driver.
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From a quick look at the DTM metadata, open a terminal and use the GDAL command `gdalinfo`:
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::
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gdalinfo /home/user/grassdata/nc_basic_spm_grass7/PERMANENT/cellhd/elevation
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.. Cameron Review Comment:
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For each command described, we should show the output of the command, either as text
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or screenshot.
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A user should be able to look at this quickstart, and work out what to expect, without
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running the quickstart.
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We can see the DTM is in a projected coordinate system and the data range is between 50 and 160 meters.
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We can convert the DTM in a GMT Compatible netCDF file format with `gdal_translate` as follow:
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::
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gdal_translate -of GMT /home/user/grassdata/nc_basic_spm_grass7/PERMANENT/cellhd/elevation elevation.gmt
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For our GMT example we will first transform the dataset into geographic coordinates Longitude-Latitude (EPSG:4326) using the `gdalwarp` command:
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::
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gdalwarp elevation.gmt geo_elevation.gmt -t_srs EPSG:4326
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Make a color palette
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====================
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To assign a color map to our grid we can make a GMT color palette tables
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with the `makecpt` command. We will set the range and number of colors with the flag `-T`
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and assign a colormap with the flag `-C`.
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::
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gmt makecpt -Chaxby -T50/160/10 -Z > elevation.cpt
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You can experiment with other colortables, see `man gmt makecpt` for a complete list.
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Define the gradient
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===================
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For our shaded relief example we need to specify show how the color intensity is distributed along the grid.
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To do so, we will compute the directional derivative, or gradient, from our grid using the `grdgradient` command.
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In `grdgradient` we will use the `-Ne` flag to normalize the output using a cumulative Laplace distribution and the `-A` flag for Azimuthal direction for a directional derivative.
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::
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gmt grdgradient geo_elevation.gmt -Ne0.8 -A100 -fg -Ggradient.nc
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The output is a netCDF file which can then be used as color intensity by the `grdimage` command as follow:
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::
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gmt grdimage geo_elevation.gmt -Igradient.nc -JM6i -P -Ba -Celevation.cpt -K > GMT_tut_OSGeoLive.ps
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Display the color bar
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=====================
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Finally we can add a colorbar with `psscale`:
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::
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gmt psscale -DjTC+w5i/0.25i+h+o0/-1i -Rgeo_elevation.gmt -J -Celevation.cpt -I0.4 -By+lm -O >> GMT_tut_OSGeoLive.ps
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Here the flag `-D` is used to select the colorbar position `TC: Top Center`, the size `5x0.25` expressed in inches `i` the orientation by `h: horizontal`
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and the flag `-By` to set a label `m` on the `y axis`.
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The output ps document should look like:
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.. image:: /images/projects/gmt/GMT_tut_OSGeoLive.png
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:scale: 70 %
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:alt: North Caroline - sample elevation data
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:align: center
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Things to try
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================================================================================
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.. TODO do these needs to be installed to go thru the tutorial?
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https://readthedocs.org
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* Install the following packages:
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* datacube-core
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* python3-pystac
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* `TODO tutorial <https://docs.generic-mapping-tools.org/latest/tutorial>`
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What next?
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==========
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* Documentation: http://datacube-core.readthedocs.io/en/latest/
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* Website: @WEB_odc@
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* Support: Slack <http://slack.opendatacube.org>
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