Fixing links by converting them to relative links in Jupyter notebooks

Beginners_guide/04_Loading_data.ipynb

@@ -6595,7 +6595,7 @@
     "\n",
     "Once you have worked through the beginner's guide, you can join advanced users by exploring:\n",
     "\n",
-    "* A demonstration of how to load cloud-free observations in the [using load_ard](/notebooks/How_to_guides/Using_load_ard/) notebook.\n",
+    "* A demonstration of how to load cloud-free observations in the [using load_ard](../How_to_guides/Using_load_ard/) notebook.\n",
     "* The \"DEA products\" directory in the repository, where you can explore DEA products in depth.\n",
     "* The \"How_to_guides\" directory, which contains a recipe book of common techniques and methods for analysing DEA data.\n",
     "* The \"Real_world_examples\" directory, which provides more complex workflows and analysis case studies."

Beginners_guide/Guided_tutorial.ipynb

@@ -47,7 +47,7 @@
     "Once signed in, the JupyterLab homepage should appear. \n",
     "The JupyterLab interface consists of the main work area (right-hand panel), the left sidebar (containing a file browser and other useful features), and a menu bar along the top:\n",
     "\n",
-    "![Sandbox work area](/_images/sandbox-jupyterlab-startup.png)\n",
+    "![Sandbox work area](../Supplementary_data/Beginners_guide_Guided_tutorial/sandbox-jupyterlab-startup.png)\n",
     "\n",
     "---\n",
     "\n",

DEA_notebooks_template.ipynb

@@ -22,7 +22,7 @@
     "    * Headers must appear in heirachical order (`#`, `##`, `###`, `####`) and there can only be one title (`#`).\n",
     "* Use the [PEP8 standard](https://www.python.org/dev/peps/pep-0008/) for code. To make sure all code in the notebook is consistent, you can use the `jupyterlab_code_formatter` tool: select each code cell, then click `Edit` and then one of the `Apply X Formatter` options (`YAPF` or `Black` are recommended). This will reformat the code in the cell to a consistent style.\n",
     "* For additional guidance, refer to the style conventions and layouts in approved `develop` branch notebooks. Examples include:\n",
-    "    * [How_to_guides/Using_load_ard.ipynb](/notebooks/How_to_guides/Using_load_ard/)\n",
+    "    * [How_to_guides/Using_load_ard.ipynb](../How_to_guides/Using_load_ard/)\n",
     "    * [Real_world_examples/Coastal_erosion.ipynb](./Real_world_examples/Coastal_erosion.ipynb)\n",
     "    * [Tools/dea_tools/datahandling.py](./Tools/dea_tools/datahandling.py)\n",
     "* In the final notebook cell, include a set of relevant tags which are used to build the DEA User Guide's [Tag Index](/genindex.html). \n",

DEA_products/DEA_Landsat_Surface_Reflectance.ipynb

@@ -1385,10 +1385,10 @@
     "For example, bright targets such as beaches, buildings and salt lakes often get mistaken for clouds. \n",
     "Edges and fringes of clouds also tend to be more transparent and can be missed by the cloud detection algorithm.\n",
     "\n",
-    "Because of these limitations, it can be advisable to treat individual FMask classifications with caution, and use analysis techniques that are robust to any potential classification errors (e.g. [temporal median](/notebooks/How_to_guides/Generating_composites/) or [geomedian composites](/notebooks/How_to_guides/Generating_geomedian_composites/) that will supress the effect of false positive or negative cloud classifications).\n",
+    "Because of these limitations, it can be advisable to treat individual FMask classifications with caution, and use analysis techniques that are robust to any potential classification errors (e.g. [temporal median](../How_to_guides/Generating_composites/) or [geomedian composites](../How_to_guides/Generating_geomedian_composites/) that will supress the effect of false positive or negative cloud classifications).\n",
     "\n",
     "> **Note:** For more information about Fmask and other Surface Reflectance Observation Attributes, refer to the [Observation Attributes product description listings](/data-products/dea/404/dea-surface-reflectance-oa-landsat-8-oli-tirs) for Landsat 5, 7 and 8.\n",
-    "> For a more detailed guide to masking satellite data (and dealing with common issues such as false positive cloud detection, refer to the [Masking data](/notebooks/How_to_guides/Masking_data/) notebook. "
+    "> For a more detailed guide to masking satellite data (and dealing with common issues such as false positive cloud detection, refer to the [Masking data](../How_to_guides/Masking_data/) notebook. "
    ]
   },
   {
@@ -1408,7 +1408,7 @@
     "\n",
     "In the example below, we show how to load the same Landsat 8 image using `load_ard`, automatically applying a cloud mask to the data.\n",
     "\n",
-    "> **Note:** Find more information about `load_ard` in the detailed [Using load ard](/notebooks/How_to_guides/Using_load_ard/) notebook. "
+    "> **Note:** Find more information about `load_ard` in the detailed [Using load ard](../How_to_guides/Using_load_ard/) notebook. "
    ]
   },
   {
@@ -1531,7 +1531,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> **Note:** For a more detailed guide to cloud masking satellite data (and dealing with common issues such as false positive cloud detection, refer to the [Masking data](/notebooks/How_to_guides/Masking_data/) notebook. "
+    "> **Note:** For a more detailed guide to cloud masking satellite data (and dealing with common issues such as false positive cloud detection, refer to the [Masking data](../How_to_guides/Masking_data/) notebook. "
    ]
   },
   {

DEA_products/DEA_Sentinel2_Surface_Reflectance.ipynb

@@ -2076,7 +2076,7 @@
    "metadata": {},
    "source": [
     "The returned dataset contains all of the bands available for Sentinel-2.\n",
-    "These include `s2cloudless` and `Fmask` layers (used for [cloud masking](/notebooks/How_to_guides/Masking_data/)) and other measurements (e.g. `azimuthal_exiting`, `azimuthal_incident`) that are used for generating the surface reflectance product.\n",
+    "These include `s2cloudless` and `Fmask` layers (used for [cloud masking](../How_to_guides/Masking_data/)) and other measurements (e.g. `azimuthal_exiting`, `azimuthal_incident`) that are used for generating the surface reflectance product.\n",
     "\n",
     "Usually we are not interested in returning all the possible bands, but instead are only interested in a subset of these. If we wish to return only a few `NBAR-T` optical bands and the `s2cloudless` cloud mask, we can pass a `measurements` parameter to `dc.load()` (or, alternatively, amend the initial `query` object to have a `measurements` parameter). \n",
     "\n",
@@ -2703,7 +2703,7 @@
     "This function will load images from both Sentinel-2A and Sentinel-2B, concatenate and sort the observations by time, and apply a cloud mask (by default `Fmask`; [see instructions below](#s2cloudless-cloud-masking-with-load_ard) to use `s2cloudless` instead). \n",
     "The result is an analysis-ready dataset.\n",
     "\n",
-    "You can find more information on this function from the [Using load ard](/notebooks/How_to_guides/Using_load_ard/) notebook. "
+    "You can find more information on this function from the [Using load ard](../How_to_guides/Using_load_ard/) notebook. "
    ]
   },
   {
@@ -2757,7 +2757,7 @@
     "\n",
     "Key differences between `Fmask` and `s2cloudless` are that additional to clouds, `Fmask` also classifies cloud shadows, snow and water whereas `s2cloudless` only classifies clouds. On the other hand, `s2cloudless` offers a probability layer for clouds whereas the cloud classification from `Fmask` is discrete (a pixel is either cloud or it is not). \n",
     "\n",
-    "> **Note:** For more information about applying cloud masks using `Fmask`, see the [Masking data](/notebooks/How_to_guides/Masking_data/) notebook, or the [Cloud masking](DEA_Landsat_Surface_Reflectance.ipynb#Cloud-masking) section of the DEA Landsat Surface Reflectance notebook."
+    "> **Note:** For more information about applying cloud masks using `Fmask`, see the [Masking data](../How_to_guides/Masking_data/) notebook, or the [Cloud masking](DEA_Landsat_Surface_Reflectance.ipynb#Cloud-masking) section of the DEA Landsat Surface Reflectance notebook."
    ]
   },
   {

DEA_products/DEA_Wetlands_Insight_Tool.ipynb

@@ -69,9 +69,9 @@
     "Licencing information is provided at the bottom of this notebook, and requires attribution.** \n",
     "\n",
     "## Related products\n",
-    "* [DEA Fractional Cover (Landsat)](/notebooks/DEA_products/DEA_Fractional_Cover.html)\n",
+    "* [DEA Fractional Cover (Landsat)](../DEA_products/DEA_Fractional_Cover.html)\n",
     "* [DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat)](/data-products/dea/337/dea-wetness-percentiles-landsat)\n",
-    "* [DEA Water Observations (Landsat)](/notebooks/DEA_products/DEA_Water_Observations.html)"
+    "* [DEA Water Observations (Landsat)](../DEA_products/DEA_Water_Observations.html)"
    ]
   },
   {

How_to_guides/Downloading_data_with_STAC.ipynb

@@ -17,7 +17,7 @@
    "metadata": {},
    "source": [
     "## Background\n",
-    "Digital Earth Australia (DEA) stores a range of data products on [Amazon Web Service's Simple Cloud Storage (S3)](/setup/AWS/data_and_metadata.html) with free public access. These products can be browsed on the interactive [DEA Sandbox Explorer](https://explorer.sandbox.dea.ga.gov.au/products). To make it easier to find data in the DEA archive, the DEA Sandbox Explorer also provides a [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) endpoint for listing or searching metadata (https://explorer.sandbox.dea.ga.gov.au/stac/).\n",
+    "Digital Earth Australia (DEA) stores a range of data products on [Amazon Web Service's Simple Cloud Storage (S3)](https://docs.dea.ga.gov.au/guides/setup/aws/data_and_metadata/) with free public access. These products can be browsed on the interactive [DEA Sandbox Explorer](https://explorer.sandbox.dea.ga.gov.au/products). To make it easier to find data in the DEA archive, the DEA Sandbox Explorer also provides a [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) endpoint for listing or searching metadata (https://explorer.sandbox.dea.ga.gov.au/stac/).\n",
     "\n",
     "STAC is a recently developed specification that provides a common language to describe geospatial information so it can more easily be indexed and discovered. \n",
     "DEA's STAC metadata can be used to quickly identify all available data for a given product, location or time period. \n",

How_to_guides/Machine_learning_with_ODC.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "## Description\n",
     "\n",
-    "This notebook demonstrates a potential workflow using functions from the [dea_tools.classification](/notebooks/Tools/gen/dea_tools.classification/) script to implement a supervised learning landcover classifier within the [ODC (Open Data Cube)](https://www.opendatacube.org/) framework. \n",
+    "This notebook demonstrates a potential workflow using functions from the [dea_tools.classification](../Tools/gen/dea_tools.classification/) script to implement a supervised learning landcover classifier within the [ODC (Open Data Cube)](https://www.opendatacube.org/) framework. \n",
     "\n",
     "For larger model training and prediction implementations this notebook can be adapted into a Python file and run in a distributed fashion.\n",
     "\n",
@@ -726,7 +726,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once the data has been loaded, we can classify it using DEA [predict_xr](/notebooks/Tools/gen/dea_tools.classification/) function:"
+    "Once the data has been loaded, we can classify it using DEA [predict_xr](../Tools/gen/dea_tools.classification/) function:"
    ]
   },
   {

How_to_guides/Opening_GeoTIFFs_NetCDFs.ipynb

@@ -18,7 +18,7 @@
     "It can be useful open an external raster dataset that you have previously saved to GeoTIFF or NetCDF into a Jupyter notebook in order to conduct further analysis or combine it with other Digital Earth Australia (DEA) data. \n",
     "In this example, we will demonstrate how to load in one or multiple GeoTIFF or NetCDF files originally exported to files from a Landsat-8 time-series into an `xarray.Dataset` for further analysis.\n",
     "\n",
-    "For advice on exporting raster data, refer to the [Exporting GeoTIFFs](/notebooks/How_to_guides/Exporting_GeoTIFFs.ipynb) notebook.\n",
+    "For advice on exporting raster data, refer to the [Exporting GeoTIFFs](../How_to_guides/Exporting_GeoTIFFs.ipynb) notebook.\n",
     "\n",
     "For more information on the `xarray` and `rioxarray` functions used:\n",
     "\n",

Interactive_apps/Change_filmstrips.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "source": [
     "## Description\n",
-    "In this example, Digital Earth Australia Landsat data is extracted for a given time range and location, and combined using the geometric median (\"geomedian\") statistic to reveal the median or 'typical' appearance of the landscape for a series of time periods (for more information about geomedians, see the [Geomedian composites notebook](/notebooks/How_to_guides/Geomedian_composites/)).\n",
+    "In this example, Digital Earth Australia Landsat data is extracted for a given time range and location, and combined using the geometric median (\"geomedian\") statistic to reveal the median or 'typical' appearance of the landscape for a series of time periods (for more information about geomedians, see the [Geomedian composites notebook](../How_to_guides/Geomedian_composites/)).\n",
     "\n",
     "For coastal applications, the analysis can be customised to select only satellite images obtained during a specific tidal range (e.g. low, average or high tide). \n",
     "\n",

Interactive_apps/Exporting_satellite_images.ipynb

@@ -41,7 +41,7 @@
    "source": [
     "## Description\n",
     "This notebook provides an interactive tool for selecting, loading, processing and exporting satellite imagery as a high quality image file. \n",
-    "This can be used in combination with the interactive [Digital Earth Australia Maps](maps.dea.ga.gov.au) platform to identify an image of interest, then download it using this notebook for use in other applications.\n",
+    "This can be used in combination with the interactive [Digital Earth Australia Maps](https://maps.dea.ga.gov.au/) platform to identify an image of interest, then download it using this notebook for use in other applications.\n",
     "\n",
     "The tool requires no coding experience. \n",
     "It supports Sentinel-2 and Landsat data, creating True and False colour images, and advanced features like applying pansharpening to increase the resolution of Landsat 7, 8 and 9 imagery, power transformations to darken bright features, or unsharp masking to sharpen imagery.\n",

Interactive_apps/Generating_satellite_animations.ipynb

@@ -42,7 +42,7 @@
    "source": [
     "## Description\n",
     "This notebook provides an interactive tool for selecting, loading, processing and exporting satellite imagery as a high quality animated MP4 and GIF files. \n",
-    "This can be used in combination with the interactive [Digital Earth Australia Maps](maps.dea.ga.gov.au) platform to identify an image of interest, then download it using this notebook for use in other applications.\n",
+    "This can be used in combination with the interactive [Digital Earth Australia Maps](https://maps.dea.ga.gov.au/) platform to identify an image of interest, then download it using this notebook for use in other applications.\n",
     "\n",
     "The tool requires no coding experience. \n",
     "\n",

Real_world_examples/Burnt_area_mapping_near_realtime.ipynb

@@ -45,7 +45,7 @@
     "\n",
     "This notebook is designed to map the extent of fires within Australia over the previous fortnight dependent on the availability of suitable imagery. The output of this notebook does not measure the severity of fires.\n",
     "\n",
-    "For users interested in mapping historical fires, please use the [Burnt Area mapping using Sentinel-2 data notebook](/notebooks/Real_world_examples/Burnt_area_mapping.html) instead.\n",
+    "For users interested in mapping historical fires, please use the [Burnt Area mapping using Sentinel-2 data notebook](../Real_world_examples/Burnt_area_mapping.html) instead.\n",
     "\n",
     "### Normalised Burn Ratio (NBR) and delta Normalised Burn Ratio (dNBR)\n",
     "\n",
@@ -61,7 +61,7 @@
     "d\\mathrm{NBR = NBR_{baseline} - NBR_{post-fire}}\n",
     "$$\n",
     "\n",
-    "More information on NBR and dNBR can be found in the [Burnt Area mapping using Sentinel-2 data notebook.](/notebooks/Real_world_examples/Burnt_area_mapping.html)\n",
+    "More information on NBR and dNBR can be found in the [Burnt Area mapping using Sentinel-2 data notebook.](../Real_world_examples/Burnt_area_mapping.html)\n",
     "\n",
     "### Relativized Burn Ratio (RBR)\n",
     "The Relativized Burn Ratio (RBR) is a variation of the Relativized delta Normalised Burn Ratio (RdNBR) developed by [SA parks](https://www.mdpi.com/2072-4292/6/3/1827) that solves some of the numerical problems with the original RdNBR algorithm. Like the RdNBR, the RBR aims to improve burnt area mapping over burnt areas that had low levels of pre-fire vegetation by considering the baseline NBR measurement to prevent these areas being thresholded out.\n",

Real_world_examples/Coastal_erosion.ipynb

@@ -12,7 +12,7 @@
     "[ga_ls5t_ard_3](https://explorer.sandbox.dea.ga.gov.au/ga_ls5t_ard_3),\n",
     "[ga_ls7e_ard_3](https://explorer.sandbox.dea.ga.gov.au/ga_ls7e_ard_3),\n",
     "[ga_ls8c_ard_3](https://explorer.sandbox.dea.ga.gov.au/ga_ls8c_ard_3)\n",
-    "* **Prerequisites:** For more information about the methods used in this analysis, refer to the [DEA Coastlines](../DEA_products/DEA_Coastlines.ipynb) and [Tidal modelling](/notebooks/How_to_guides/Tidal_modelling/) notebooks"
+    "* **Prerequisites:** For more information about the methods used in this analysis, refer to the [DEA Coastlines](../DEA_products/DEA_Coastlines.ipynb) and [Tidal modelling](../How_to_guides/Tidal_modelling/) notebooks"
    ]
   },
   {
@@ -1536,7 +1536,7 @@
     "## Model tide heights\n",
     "The location of the shoreline can vary greatly from low to high tide. In the code below, we aim to reduce the effect of tides by modelling tide height data, and keeping only the satellite images that were taken at specific tidal conditions. For example, if `tide_range = (0.25, 0.75)`, we are telling the analysis to focus only on satellite images taken at mid-tide (e.g. when the tide was between the lowest 25th percentileand highest 75th percentile of tide heights).\n",
     "\n",
-    "The `tidal_tag` function below uses the [FES2014 tidal model](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html) to calculate the height of the tide at the exact moment each satellite image in our dataset was taken, and adds this as a new `tide_m` attribute in our dataset (for more information about this function, refer to the [Tidal modelling notebook](/notebooks/How_to_guides/Tidal_modelling/)). \n",
+    "The `tidal_tag` function below uses the [FES2014 tidal model](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/global-tide-fes/description-fes2014.html) to calculate the height of the tide at the exact moment each satellite image in our dataset was taken, and adds this as a new `tide_m` attribute in our dataset (for more information about this function, refer to the [Tidal modelling notebook](../How_to_guides/Tidal_modelling/)). \n",
     "\n",
     "> **Note:** this function can only model tides correctly if the centre of your study area is located over water. If this isn't the case, you can specify a custom tide modelling location by passing a coordinate to `tidepost_lat` and `tidepost_lon` (e.g. `tidepost_lat=-27.73, tidepost_lon=153.46`)."
    ]