diff --git a/Beginners_guide/02_DEA.ipynb b/Beginners_guide/02_DEA.ipynb index 0efe3292e..4be9408da 100644 --- a/Beginners_guide/02_DEA.ipynb +++ b/Beginners_guide/02_DEA.ipynb @@ -83,9 +83,10 @@ "The earliest datasets of optical satellite imagery in DEA date from 1986.\n", "DEA includes data from:\n", "\n", - "* [Landsat 5 TM](https://www.usgs.gov/land-resources/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con) (LS5 TM), operational between March 1984 and January 2013\n", - "* [Landsat 7 ETM+](https://www.usgs.gov/land-resources/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con) (LS7 ETM+), operational since April 1999\n", - "* [Landsat 8 OLI](https://www.usgs.gov/land-resources/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con) (LS8 OLI), operational since February 2013\n", + "* [Landsat 5 TM](https://www.usgs.gov/landsat-missions/landsat-5) (LS5 TM), operational between March 1984 and January 2013\n", + "* [Landsat 7 ETM+](https://www.usgs.gov/landsat-missions/landsat-7) (LS7 ETM+), operational between April 1999 and April 2022\n", + "* [Landsat 8 OLI](https://www.usgs.gov/landsat-missions/landsat-8) (LS8 OLI), operational since February 2013\n", + "* [Landsat 9 OLI](https://www.usgs.gov/landsat-missions/landsat-9) (LS9 OLI), operational since September 2021\n", "* [Sentinel 2A MSI](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) (S2A MSI), operational since June 2015\n", "* [Sentinel 2B MSI](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) (S2B MSI, operational since March 2017\n", "\n", @@ -248,7 +249,7 @@ "\n", "By default, the spatial extent of the DEA data holdings is approximately the Australian coastal shelf. \n", "The actual extent varies based on the sensor and product. \n", - "The current extents of each DEA product can be viewed using the interactive [DEA Datacube Explorer](https://explorer.dea.ga.gov.au)." + "The current extents of each DEA product can be viewed using the interactive [DEA Explorer](https://explorer.dea.ga.gov.au)." ] }, { @@ -257,24 +258,12 @@ "source": [ "## Derived products\n", "\n", - "![DEA products](../Supplementary_data/02_DEA/dea_products.jpg)\n", + "![](../Supplementary_data/02_DEA/dea_products.jpg)\n", "\n", "In addition to ARD satellite data, DEA generates a range of products that are derived from Landsat or Sentinel-2 surface reflectance data.\n", "These products have been developed to characterise and monitor different aspects of Australia's natural and built environment, such as mapping the distribution of water and vegetation across the landscape through time.\n", - "Derived DEA products include:\n", "\n", - "* **Water Observations from Space (WOfS):** WOfS is the world's first continent-scale map of surface water and provides images and data showing where water has been seen in Australia from 1987 to the present. This map can be used to better understand where water usually occurs across the continent and to plan water management strategies. \n", - "\n", - "* **Fractional Cover (FC):** Fractional Cover (FC) is a measurement that splits the landscape into three parts, or fractions; green (leaves, grass, and growing crops), brown (branches, dry grass or hay, and dead leaf litter), and bare ground (soil or rock). DEA uses Fractional Cover to characterise every 25 m square of Australia for any point in time from 1987 to today. This measurement can inform a broad range of natural resource management issues. \n", - "\n", - "* **High and Low Tide Composites (HLTC):** The High and Low Tide Composites (HLTC) are imagery mosaics developed to visualise Australia's coasts, estuaries and reefs at low and high tide, whilst removing the influence of noise features such as clouds, breaking water and sun-glint. These products are highly interpretable, and provide a valuable snapshot of the coastline at different biophysical states.\n", - "\n", - "* **Intertidal Extents Model (ITEM):** The Intertidal Extents Model (ITEM) product utilises 30 years of Earth observation data from the Landsat archive to map the extents and topography of Australia's intertidal mudflats, beaches and reefs; the area exposed between high and low tide.\n", - "\n", - "* **National Intertidal Digital Elevation Model (NIDEM):** The National Intertidal Digital Elevation Model (NIDEM) is a national dataset that maps the three-dimensional structure of Australia’s intertidal zone. NIDEM provides a first-of-its kind source of intertidal elevation data for Australia’s entire coastline. \n", - "\n", - "Each of the products above have dataset-specific naming conventions, measurements, resolutions, data types and coordinate reference systems.\n", - "For more information about DEA's derived products, refer to the [DEA website](http://www.ga.gov.au/dea/products), the [Content Management Interface](https://knowledge.dea.ga.gov.au/) (CMI) containing detailed product metadata, or the \"DEA_products\" notebooks in this repository." + "For more information about DEA's derived products, refer to the [DEA website](http://www.ga.gov.au/dea/products), the [Data Products page of the DEA Knowledge Hub](https://knowledge.dea.ga.gov.au/), or the \"DEA_products\" notebooks on the DEA Sandbox (e.g. [Introduction to DEA Surface Reflectance (Landsat, Collection 3)](../DEA_products/DEA_Landsat_Surface_Reflectance.ipynb))." ] }, { @@ -282,7 +271,7 @@ "metadata": {}, "source": [ "## Recommended next steps\n", - "For more detailed information on the concepts introduced in this notebook, please see the [DEA User Guide](https://knowledge.dea.ga.gov.au/) and [Open Data Cube Manual](https://datacube-core.readthedocs.io/en/latest/).\n", + "For more detailed information on the concepts introduced in this notebook, please see the [DEA Knowledge Hub](https://knowledge.dea.ga.gov.au/) and [Open Data Cube Manual](https://datacube-core.readthedocs.io/en/latest/).\n", "For more information on the development of the DEA platform, please see [Dhu et al. 2017](https://doi.org/10.1080/20964471.2017.1402490).\n", "\n", "To continue with the beginner's guide, the following notebooks are designed to be worked through in the following order:\n", @@ -319,7 +308,7 @@ "**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", "If you would like to report an issue with this notebook, you can file one on [GitHub](https://github.com/GeoscienceAustralia/dea-notebooks).\n", "\n", - "**Last modified:** December 2023" + "**Last modified:** April 2024" ] }, { diff --git a/DEA_products/DEA_Coastlines.ipynb b/DEA_products/DEA_Coastlines.ipynb index 270314a67..4639055a5 100644 --- a/DEA_products/DEA_Coastlines.ipynb +++ b/DEA_products/DEA_Coastlines.ipynb @@ -37,9 +37,6 @@ "* Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/j.ecss.2021.107712\n", "* Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., & Sixsmith, J. (2019). Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra. *Remote Sensing*, 11(24), 2984. Available: https://www.mdpi.com/2072-4292/11/24/2984\n", "\n", - "> **Note:** For more technical information about the DEA Coastlines product, visit the official Geoscience Australia [DEA Coastlines product description](https://knowledge.dea.ga.gov.au/data/product/dea-coastlines)..\n", - "> To explore DEA Coastlines on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEACoastlines).\n", - "\n", "## Description\n", "\n", "This notebook will demonstrate how to load data from the [Digital Earth Australia Coastlines](https://knowledge.dea.ga.gov.au/data/product/dea-coastlines) product using the Digital Earth Australia datacube. \n", @@ -48,6 +45,13 @@ "1. Loading DEA Coastlines annual shoreline data using the `get_coastlines` function.\n", "2. Loading DEA Coastlines rates of change statistics data using the `get_coastlines` function.\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Coastlines product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-coastlines) for detailed technical information including methods, quality, and data access.\n", + "To explore DEA Coastlines on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEACoastlines).\n", + " \n", + "
\n", + "\n", "***\n" ] }, @@ -1240,7 +1244,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Fractional_Cover.ipynb b/DEA_products/DEA_Fractional_Cover.ipynb index 7b5e500b4..4d159164b 100644 --- a/DEA_products/DEA_Fractional_Cover.ipynb +++ b/DEA_products/DEA_Fractional_Cover.ipynb @@ -40,9 +40,7 @@ "* vegetation studies\n", "* fuel load estimation\n", "* ecosystem modelling\n", - "* land cover mapping\n", - "\n", - "> **Note:** For more technical information about DEA Fractional Cover, visit the official [Geoscience Australia DEA Fractional Cover product description](https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat)." + "* land cover mapping\n" ] }, { @@ -59,6 +57,12 @@ "4. Inspecting unmixing error outputs\n", "4. Masking out missing or invalid data and unclear or wet pixels, and using this to track percentages of green and brown vegetation and bare soil over time\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Fractional Cover product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat) for detailed technical information including methods, quality, and data access.\n", + " \n", + "
\n", + "\n", "***" ] }, @@ -1761,7 +1765,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_High_and_Low_Tide_Imagery.ipynb b/DEA_products/DEA_High_and_Low_Tide_Imagery.ipynb index 9bd55de8f..90065d326 100644 --- a/DEA_products/DEA_High_and_Low_Tide_Imagery.ipynb +++ b/DEA_products/DEA_High_and_Low_Tide_Imagery.ipynb @@ -36,9 +36,7 @@ "* Visualising the full observed extent of the tidal range around the Australian continental coastline\n", "\n", "### Publications\n", - "* Sagar, S., Phillips, C., Bala, B., Roberts, D., & Lymburner, L. (2018). [Generating continental scale pixel-based surface reflectance composites in coastal regions with the use of a multi-resolution tidal model](https://www.mdpi.com/2072-4292/10/3/480). Remote Sensing, 10(3), 480.\n", - "\n", - "> **Note:** For more technical information about DEA High and Low Tide Imagery, visit the official [Geoscience Australia DEA High and Low Tide Imagery product description](https://knowledge.dea.ga.gov.au/data/product/dea-high-and-low-tide-imagery-landsat)." + "* Sagar, S., Phillips, C., Bala, B., Roberts, D., & Lymburner, L. (2018). [Generating continental scale pixel-based surface reflectance composites in coastal regions with the use of a multi-resolution tidal model](https://www.mdpi.com/2072-4292/10/3/480). Remote Sensing, 10(3), 480.\n" ] }, { @@ -54,6 +52,12 @@ "3. Plotting low and high tide data in true and false colour\n", "4. Converting low and high tide data to a remote sensing water index (NDWI), and use this to map wet pixels at low and high tide\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA High and Low Tide Imagery product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-high-and-low-tide-imagery-landsat) for detailed technical information including methods, quality, and data access.\n", + "\n", + "
\n", + "\n", "***" ] }, @@ -1106,7 +1110,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1120,7 +1124,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Intertidal.ipynb b/DEA_products/DEA_Intertidal.ipynb index 8655dc425..d59daac7a 100644 --- a/DEA_products/DEA_Intertidal.ipynb +++ b/DEA_products/DEA_Intertidal.ipynb @@ -55,8 +55,7 @@ "source": [ "## Description\n", "\n", - "This notebook introduces the Digital Earth Australia (DEA) Intertidal product suite. \n", - "In addition to access here in the DEA Sandbox, they can also be explored via [DEA Maps](https://maps.dea.ga.gov.au/) and are available directly via [DEA Explorer](https://explorer.dea.ga.gov.au/). \n", + "This notebook introduces the Digital Earth Australia (DEA) Intertidal product suite.\n", "In this notebook, users will:\n", "\n", "- Learn about the datasets and how they are interrelated\n", @@ -66,9 +65,10 @@ "By the end of this notebook, users should have an understanding of what these datasets are, how they can be used and any limitations they should be aware of.\n", "\n", "
\n", - "\n", - "**Note:** Visit the [DEA Intertidal product description](https://knowledge.dea.ga.gov.au/data/product/dea-intertidal/?tab=overview) for technical information about DEA Intertidal including a comprehensive review of the methods employed in the generation of datasets in this product suite.\n", - "\n", + " \n", + "**Note:** Visit the [DEA Intertidal product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-intertidal) for detailed technical information including methods, quality, and data access.\n", + "To explore DEA Intertidal on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEAIntertidal).\n", + " \n", "
\n", "\n", "---" @@ -4487,564 +4487,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "state": { - "047f884129804eca9b72340d416b27eb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_48ec168d58c34f92a1ab2104f5b2fecd", - "style": "IPY_MODEL_34a0779e14a843839f0c091e2dbda74c", - "value": " 8/? (0.0 seconds remaining at 13.96 frames/s)" - } - }, - "0ba49123e7a1411f9a968966f27a3bf9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "106dafff03ea4db2bb9fe347c78c520d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "GridBoxModel", - "state": { - "children": [ - "IPY_MODEL_8fb482e1fbfc45b7a390c8b72cba489e", - "IPY_MODEL_c41fb279eeb24568a536171824287df0" - ], - "layout": "IPY_MODEL_e3cffebcefa9498cbfff6a127269232c" - } - }, - "117ee836bd1b45738e55d38080d2a861": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_ecb3bc1cce084295873f4943799b735d", - "style": "IPY_MODEL_e8cdb32ee1314559ae2c5592fec7d22a", - "value": " 86%" - } - }, - "119eb5330e7f4f69a55b1557e175375d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "layout": "IPY_MODEL_b13f7d72b2a24f3b87182742bfebfb3b", - "max": 7, - "style": "IPY_MODEL_bac11e1ffad1489ab74a821e38e55dbd", - "value": 6 - } - }, - "1a30579d9ce146a7ac93a0a97a8dacd6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "1b1c833ac802436bb70bbd84ea6f2131": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "1bf938beb08c43ae8118dbea88571f6d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "257cf1c1f45942688b0ddbfe83d95a6e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_45f8b3d213ef41dd882041d4ba5c66f3", - "style": "IPY_MODEL_f2cacd7e8ee34152ae32457c0d72d0c2" - } - }, - "2ff2ea694f014443a4ffae5c86ddd7d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_30f64735cf7b4956ac2ed44efdb014db", - "style": "IPY_MODEL_fe0b0a79b170481fb46e561501d29a12" - } - }, - "30f64735cf7b4956ac2ed44efdb014db": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "328c58cdd1d34ce98d40663f507b1d4a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "34a0779e14a843839f0c091e2dbda74c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "4408c8d8acf1494f9c672b8c5555edd9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "45f8b3d213ef41dd882041d4ba5c66f3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "48ec168d58c34f92a1ab2104f5b2fecd": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "4b0344556246433eb5be665228a54a2a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_86806429ae5d470a91d7b80f06c8ffd3", - "style": "IPY_MODEL_961ac38416794ba5a129b84f5e54a0b4" - } - }, - "4d7cecb196c74f55ad525313769ad9a3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_area": "widget002" - } - }, - "508fc2ca07ff44d99b7b70229a74459f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_area": "widget003" - } - }, - "52c36fb70b4f4c45a23b583333c4f2c9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "54031a143520493cbd01a7f37025da40": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "58cd4fe43e6b43898d3eaa6a4d65db56": { - "model_module": "@jupyter-widgets/output", - "model_module_version": "1.0.0", - "model_name": "OutputModel", - "state": { - "layout": "IPY_MODEL_508fc2ca07ff44d99b7b70229a74459f", - "outputs": [ - { - "data": { - "text/html": "", - "text/plain": "" - }, - "metadata": {}, - "output_type": "display_data" - } - ] - } - }, - "5f80681f248f4501ab043e18b084403e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_1bf938beb08c43ae8118dbea88571f6d", - "style": "IPY_MODEL_bfdc625daa50406ca67a7b101da221f4", - "value": " 8/? (0.0 seconds remaining at 14.00 frames/s)" - } - }, - "649626bc014246079264745347481455": { - "model_module": "@jupyter-widgets/output", - "model_module_version": "1.0.0", - "model_name": "OutputModel", - "state": { - "layout": "IPY_MODEL_be43be1e96bc444995db42a8ab46d491", - "outputs": [ - { - "data": { - "text/html": "", - "text/plain": "" - }, - "metadata": {}, - "output_type": "display_data" - } - ] - } - }, - "66351b50b3db429083c1394e77eb900e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_961371d07e854a44ad65fcca13294bed", - "style": "IPY_MODEL_1a30579d9ce146a7ac93a0a97a8dacd6", - "value": " 8/? (0.0 seconds remaining at 14.30 frames/s)" - } - }, - "6c8d1aa339cb49a8960653afe8e0ef0d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "layout": "IPY_MODEL_52c36fb70b4f4c45a23b583333c4f2c9", - "max": 7, - "style": "IPY_MODEL_f3ba90e3e8d14fa8bd97518613cf11f2", - "value": 7 - } - }, - "7b15f23974c6438ca651f912d643bcbc": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "7cfd83d94a024f61916c9c67d2aafeac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_2ff2ea694f014443a4ffae5c86ddd7d9", - "IPY_MODEL_aa142fbac7ca4a34abad019d4b8e7f83", - "IPY_MODEL_5f80681f248f4501ab043e18b084403e" - ], - "layout": "IPY_MODEL_7b15f23974c6438ca651f912d643bcbc" - } - }, - "844a2b57c6674c43a0ec23bebb221638": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_template_areas": "\"widget001 widget002 widget003\"", - "grid_template_columns": "repeat(3, 1fr)", - "grid_template_rows": "repeat(1, 1fr)" - } - }, - "86806429ae5d470a91d7b80f06c8ffd3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "87a147878f36431d941002db42da368a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_4b0344556246433eb5be665228a54a2a", - "IPY_MODEL_6c8d1aa339cb49a8960653afe8e0ef0d", - "IPY_MODEL_047f884129804eca9b72340d416b27eb" - ], - "layout": "IPY_MODEL_328c58cdd1d34ce98d40663f507b1d4a" - } - }, - "8a2c338fb2284bd8acd4ba0c8cf04bc8": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "layout": "IPY_MODEL_54031a143520493cbd01a7f37025da40", - "max": 7, - "style": "IPY_MODEL_8c520245371744abaf2f64e6efd16464", - "value": 7 - } - }, - "8c520245371744abaf2f64e6efd16464": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "8fb482e1fbfc45b7a390c8b72cba489e": { - "model_module": "@jupyter-widgets/output", - "model_module_version": "1.0.0", - "model_name": "OutputModel", - "state": { - "layout": "IPY_MODEL_a176c4872bea4542bddb02155a641bcc", - "outputs": [ - { - "data": { - "text/html": "", - "text/plain": "" - }, - "metadata": {}, - "output_type": "display_data" - } - ] - } - }, - "8fc278efb0594dfabe3101eb7129dd8a": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_area": "widget001" - } - }, - "961371d07e854a44ad65fcca13294bed": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "961ac38416794ba5a129b84f5e54a0b4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "a176c4872bea4542bddb02155a641bcc": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_area": "widget001" - } - }, - "a1ad07ce751846f6bb339a45d336ca11": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "aa142fbac7ca4a34abad019d4b8e7f83": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "FloatProgressModel", - "state": { - "layout": "IPY_MODEL_f7bb9e40f3714012a576cf51e9d040c9", - "max": 7, - "style": "IPY_MODEL_d5569953d56b4ac2a60c04f734d5f341", - "value": 7 - } - }, - "b13f7d72b2a24f3b87182742bfebfb3b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "bac11e1ffad1489ab74a821e38e55dbd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "be43be1e96bc444995db42a8ab46d491": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_area": "widget002" - } - }, - "bfdc625daa50406ca67a7b101da221f4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "bfdc9b7646994d47980d9d6b6b034296": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_1b1c833ac802436bb70bbd84ea6f2131", - "style": "IPY_MODEL_a1ad07ce751846f6bb339a45d336ca11", - "value": " 6/7 (0.0 seconds remaining at 22.10 frames/s)" - } - }, - "c008aadb05ec44e7860d037bef194d18": { - "model_module": "@jupyter-widgets/output", - "model_module_version": "1.0.0", - "model_name": "OutputModel", - "state": { - "layout": "IPY_MODEL_8fc278efb0594dfabe3101eb7129dd8a", - "outputs": [ - { - "data": { - "text/html": "", - "text/plain": "" - }, - "metadata": {}, - "output_type": "display_data" - } - ] - } - }, - "c41fb279eeb24568a536171824287df0": { - "model_module": "@jupyter-widgets/output", - "model_module_version": "1.0.0", - "model_name": "OutputModel", - "state": { - "layout": "IPY_MODEL_4d7cecb196c74f55ad525313769ad9a3", - "outputs": [ - { - "data": { - "text/html": "", - "text/plain": "" - }, - "metadata": {}, - "output_type": "display_data" - } - ] - } - }, - "d5569953d56b4ac2a60c04f734d5f341": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "e3cffebcefa9498cbfff6a127269232c": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "grid_template_areas": "\"widget001 widget002\"", - "grid_template_columns": "repeat(2, 1fr)", - "grid_template_rows": "repeat(1, 1fr)" - } - }, - "e8cdb32ee1314559ae2c5592fec7d22a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ecb3bc1cce084295873f4943799b735d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "efaa306b105e49de8ab0db4e9296231e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_257cf1c1f45942688b0ddbfe83d95a6e", - "IPY_MODEL_8a2c338fb2284bd8acd4ba0c8cf04bc8", - "IPY_MODEL_66351b50b3db429083c1394e77eb900e" - ], - "layout": "IPY_MODEL_0ba49123e7a1411f9a968966f27a3bf9" - } - }, - "f2cacd7e8ee34152ae32457c0d72d0c2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "f3ba90e3e8d14fa8bd97518613cf11f2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "f7bb9e40f3714012a576cf51e9d040c9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "f7d32d67edc44c57957e426ba480c4d1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "GridBoxModel", - "state": { - "children": [ - "IPY_MODEL_c008aadb05ec44e7860d037bef194d18", - "IPY_MODEL_649626bc014246079264745347481455", - "IPY_MODEL_58cd4fe43e6b43898d3eaa6a4d65db56" - ], - "layout": "IPY_MODEL_844a2b57c6674c43a0ec23bebb221638" - } - }, - "fcf999a38f37450e85f70433601b234a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_117ee836bd1b45738e55d38080d2a861", - "IPY_MODEL_119eb5330e7f4f69a55b1557e175375d", - "IPY_MODEL_bfdc9b7646994d47980d9d6b6b034296" - ], - "layout": "IPY_MODEL_4408c8d8acf1494f9c672b8c5555edd9" - } - }, - "fe0b0a79b170481fb46e561501d29a12": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - } - }, + "state": {}, "version_major": 2, "version_minor": 0 } diff --git a/DEA_products/DEA_Land_Cover.ipynb b/DEA_products/DEA_Land_Cover.ipynb index b93e5bd34..27516e553 100644 --- a/DEA_products/DEA_Land_Cover.ipynb +++ b/DEA_products/DEA_Land_Cover.ipynb @@ -51,9 +51,7 @@ "### Publications\n", "\n", "* Lucas R, Mueller N, Siggins A, Owers C, Clewley D, Bunting P, Kooymans C, Tissott B, Lewis B, Lymburner L, Metternicht G (2019) 'Land Cover Mapping using Digital Earth Australia', Data, 4(4):143, doi: [10.3390/data4040143](https://www.mdpi.com/2306-5729/4/4/143/htm).\n", - "* Owers C, Lucas R, Clewley D, Planque C, Punalekar S, Tissott B, Chua S, Bunting P, Mueller N, Metternicht G (2021) 'Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development', Big Earth Data, 5(3):368-390, doi:[10.1080/20964471.2021.1948179](https://www.tandfonline.com/doi/full/10.1080/20964471.2021.1948179?scroll=top&needAccess=true).\n", - "\n", - "> **Note:** For more technical information about DEA Land Cover, visit the official Geoscience Australia [DEA Land Cover product description](https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat) page." + "* Owers C, Lucas R, Clewley D, Planque C, Punalekar S, Tissott B, Chua S, Bunting P, Mueller N, Metternicht G (2021) 'Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development', Big Earth Data, 5(3):368-390, doi:[10.1080/20964471.2021.1948179](https://www.tandfonline.com/doi/full/10.1080/20964471.2021.1948179?scroll=top&needAccess=true).\n" ] }, { @@ -69,6 +67,13 @@ "2. Choosing and loading DEA Land Cover data for an area of interest.\n", "3. Plotting DEA Land Cover data.\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Land Cover product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat) for detailed technical information including methods, quality, and data access.\n", + "To explore DEA Land Cover on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEALandCover).\n", + "\n", + "
\n", + "\n", "***" ] }, @@ -1873,7 +1878,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Landsat_Surface_Reflectance.ipynb b/DEA_products/DEA_Landsat_Surface_Reflectance.ipynb index 5a19780a5..e85c82ddd 100644 --- a/DEA_products/DEA_Landsat_Surface_Reflectance.ipynb +++ b/DEA_products/DEA_Landsat_Surface_Reflectance.ipynb @@ -52,8 +52,6 @@ "* Li, F., Jupp, D. L. B., Reddy, S., Lymburner, L., Mueller, N., Tan, P., & Islam, A. (2010). An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data. *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*, 3(3), 257–270. https://doi.org/10.1109/JSTARS.2010.2042281\n", "* Li, F., Jupp, D. L. B., Thankappan, M., Lymburner, L., Mueller, N., Lewis, A., & Held, A. (2012). A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. *Remote Sensing of Environment*, 124, 756–770. https://doi.org/10.1016/j.rse.2012.06.018\n", "\n", - "> **Note:** For more technical information about the DEA Landsat Surface Reflectance products, visit the official Geoscience Australia [DEA Landsat Surface Reflectance product listings](https://knowledge.dea.ga.gov.au/data-products?combine=&program%5Bdea%5D=dea&catalog%5B12884%5D=12884).\n", - "\n", "## Description\n", "\n", "This notebook will demonstrate how to load data from the DEA Landsat Surface Reflectance products using the Digital Earth Australia datacube. \n", @@ -66,6 +64,12 @@ "5. [Advanced: Loading Landsat data in its native projection and resolution](#Native-load)\n", "6. [Advanced: Filtering Landsat data by product metadata](#Filtering-by-metadata)\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Surface Reflectance product documentation](https://knowledge.dea.ga.gov.au/data/category/dea-surface-reflectance/) for detailed technical information including methods, quality, and data access.\n", + "\n", + "
\n", + "\n", "***\n" ] }, @@ -1900,7 +1904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Mangroves.ipynb b/DEA_products/DEA_Mangroves.ipynb index 82731966e..274ce4b0b 100644 --- a/DEA_products/DEA_Mangroves.ipynb +++ b/DEA_products/DEA_Mangroves.ipynb @@ -54,12 +54,13 @@ { "cell_type": "markdown", "metadata": { + "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "### Publications\n", "\n", - "[Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., & Held, A., (2020). Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238, 111185.](https://doi.org/10.1016/j.rse.2019.05.004)" + "* Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., & Held, A., (2020). Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238, 111185. Available at: https://doi.org/10.1016/j.rse.2019.05.004" ] }, { @@ -75,7 +76,15 @@ "3. Plot a single timestep image\n", "4. Create and view an animation of the whole timeseries\n", "5. Plot change over time by graphing the timeseries of each class\n", - "6. Identify hotspot change areas within each class" + "6. Identify hotspot change areas within each class\n", + "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Mangroves product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-mangrove-canopy-cover-landsat) for detailed technical information including methods, quality, and data access.\n", + " \n", + "
\n", + "\n", + "***" ] }, { @@ -1459,7 +1468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Sentinel2_Surface_Reflectance.ipynb b/DEA_products/DEA_Sentinel2_Surface_Reflectance.ipynb index 1d0bfdb5f..5eb6cb2e1 100644 --- a/DEA_products/DEA_Sentinel2_Surface_Reflectance.ipynb +++ b/DEA_products/DEA_Sentinel2_Surface_Reflectance.ipynb @@ -81,18 +81,14 @@ " - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping)\n", " - water extent\n", "\n", - "\n", "* The development of refined information products, such as:\n", "\n", " - areal units of detected surface water\n", " - areal units of deforestation\n", " - yield predictions of agricultural parcels\n", "\n", - "\n", "* Compliance surveys\n", - "* Emergency management\n", - "\n", - "> **Note:** For more technical information about DEA Surface Reflectance, visit the official Geoscience Australia DEA Surface Reflectance product descriptions for [Sentinel-2A](https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2a-msi) and [Sentinel-2B](https://knowledge.dea.ga.gov.au/data/product/dea-surface-reflectance-sentinel-2b-msi)." + "* Emergency management\n" ] }, { @@ -111,6 +107,12 @@ "6. [Dropping cloudy observations](#Dropping-cloudy-scenes)\n", "7. [Advanced: Filtering by metadata to remove poorly georeferenced scenes](#Filtering-by-metadata-to-remove-poorly-georeferenced-scenes)\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Surface Reflectance product documentation](https://knowledge.dea.ga.gov.au/data/category/dea-surface-reflectance/) for detailed technical information including methods, quality, and data access.\n", + "\n", + "
\n", + "\n", "***" ] }, @@ -3405,276 +3407,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "state": { - "114f867be2154c6880b3a30ab3cdff54": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_9c22aba1aa3f46ba8c8abe0f18882f3e", - "IPY_MODEL_c49747ee9d0f467e911840023a8f0f9f" - ], - "layout": "IPY_MODEL_86d188ed5cca404e91ee63c8e2e3e70b" - } - }, - "1a9c3a14aa664a5cb66b40b49fcd3730": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "description_width": "" - } - }, - "2c9b9254ad8346b68ac91d40a5689771": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "IntProgressModel", - "state": { - "layout": "IPY_MODEL_49e06eccad144204ab52e6da140778ce", - "max": 81, - "style": "IPY_MODEL_7dda22eab93b4ae381bcfec237c66ef4", - "value": 81 - } - }, - "316fec15a7744259b5c51bd2bd56d023": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "width": "100%" - } - }, - "32ead722fa2344ffb612568faad30d5d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_2c9b9254ad8346b68ac91d40a5689771" - ], - "layout": "IPY_MODEL_57535ef990e54863b1a335ce1e7e68d8" - } - }, - "418e6ba4da72499085f4ddec5bb6e648": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "description_width": "" - } - }, - "486f2d52e7e04b48ac6463bc940ae773": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_5af6eee4180747dba48d162d73ed31a9" - ], - "layout": "IPY_MODEL_6b59f85087fe48e09e8c6d5470a9a7f2" - } - }, - "49e06eccad144204ab52e6da140778ce": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "width": "100%" - } - }, - "535053aab00e427389d6b782cbfb4455": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "description_width": "" - } - }, - "536b1cbf437b4580a45327831333629c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "VBoxModel", - "state": { - "children": [ - "IPY_MODEL_114f867be2154c6880b3a30ab3cdff54", - "IPY_MODEL_32ead722fa2344ffb612568faad30d5d" - ], - "layout": "IPY_MODEL_aae1424a856a4883b6e5381b60c22b5b" - } - }, - "57535ef990e54863b1a335ce1e7e68d8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "5af6eee4180747dba48d162d73ed31a9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "IntProgressModel", - "state": { - "layout": "IPY_MODEL_316fec15a7744259b5c51bd2bd56d023", - "max": 15, - "style": "IPY_MODEL_7bdfbde680dd4182aa4cb41307c2dae1", - "value": 15 - } - }, - "63c1f6b7b32140afbb495f205f6775ac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "VBoxModel", - "state": { - "children": [ - "IPY_MODEL_7cd04cd189554807906531a1badec8e6", - "IPY_MODEL_486f2d52e7e04b48ac6463bc940ae773" - ], - "layout": "IPY_MODEL_6ff11d092bf7443480e7370a7d1e1b0f" - } - }, - "6b59f85087fe48e09e8c6d5470a9a7f2": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "6dce1520e4414d0782ac234676f72116": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "6fbbc30fdc45427f96f94ce6a831a1b9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "6ff11d092bf7443480e7370a7d1e1b0f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "width": "100%" - } - }, - "76f2ef2a73ef462d87b52ad0762545f4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "description_width": "" - } - }, - "7bdfbde680dd4182aa4cb41307c2dae1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "7cd04cd189554807906531a1badec8e6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "children": [ - "IPY_MODEL_9615163f3544487fb4ad4e2a6d7def4b", - "IPY_MODEL_a904fdf518f4462a894fdfb2cc7021a1" - ], - "layout": "IPY_MODEL_f4620693626f481f88f35ea5bcbcd00f" - } - }, - "7dda22eab93b4ae381bcfec237c66ef4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "description_width": "" - } - }, - "86d188ed5cca404e91ee63c8e2e3e70b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "justify_content": "space-between" - } - }, - "9615163f3544487fb4ad4e2a6d7def4b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "LabelModel", - "state": { - "layout": "IPY_MODEL_b8d96e302a4a4e079f9c7c94dd190257", - "style": "IPY_MODEL_535053aab00e427389d6b782cbfb4455", - "value": "FPS: 12.8 (0.0 s remaining)" - } - }, - "9c22aba1aa3f46ba8c8abe0f18882f3e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "LabelModel", - "state": { - "layout": "IPY_MODEL_6dce1520e4414d0782ac234676f72116", - "style": "IPY_MODEL_1a9c3a14aa664a5cb66b40b49fcd3730", - "value": "FPS: 4.5 (0.0 s remaining)" - } - }, - "a904fdf518f4462a894fdfb2cc7021a1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "LabelModel", - "state": { - "layout": "IPY_MODEL_6fbbc30fdc45427f96f94ce6a831a1b9", - "style": "IPY_MODEL_418e6ba4da72499085f4ddec5bb6e648", - "value": "15 of 15" - } - }, - "aae1424a856a4883b6e5381b60c22b5b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "width": "100%" - } - }, - "b80e2a2689a94b7ba0386c312d653a50": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "b8d96e302a4a4e079f9c7c94dd190257": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": {} - }, - "c49747ee9d0f467e911840023a8f0f9f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "LabelModel", - "state": { - "layout": "IPY_MODEL_b80e2a2689a94b7ba0386c312d653a50", - "style": "IPY_MODEL_76f2ef2a73ef462d87b52ad0762545f4", - "value": "81 of 81" - } - }, - "f4620693626f481f88f35ea5bcbcd00f": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "justify_content": "space-between" - } - } - }, + "state": {}, "version_major": 2, "version_minor": 0 } diff --git a/DEA_products/DEA_Water_Observations.ipynb b/DEA_products/DEA_Water_Observations.ipynb index e44b5b958..e93e36bf6 100644 --- a/DEA_products/DEA_Water_Observations.ipynb +++ b/DEA_products/DEA_Water_Observations.ipynb @@ -39,9 +39,7 @@ "As the WOs are separated from the derived statistics of the associated DEA Water Observations statistical products, the WOs are most useful for performing analyses requiring the investigation of surface water extent for particular times rather than over long term time series.\n", "\n", "### Publications\n", - "* Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). [Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia](https://doi.org/10.1016/j.rse.2015.11.003). Remote Sensing of Environment, 174, 341–352. \n", - "\n", - "> **Note:** For technical information about DEA Water Observations, visit the official [Geoscience Australia DEA Water Observations product description](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat)." + "* Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). [Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia](https://doi.org/10.1016/j.rse.2015.11.003). Remote Sensing of Environment, 174, 341–352. \n" ] }, { @@ -56,6 +54,12 @@ "3. Plotting WOs using the `plot_wo` function\n", "4. Inspecting the WOs bit flag information\n", "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Water Observations product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/) for detailed technical information including methods, quality, and data access.\n", + "\n", + "
\n", + "\n", "---" ] }, @@ -1388,7 +1392,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/DEA_products/DEA_Waterbodies.ipynb b/DEA_products/DEA_Waterbodies.ipynb index 34f177b06..5c98c9d72 100644 --- a/DEA_products/DEA_Waterbodies.ipynb +++ b/DEA_products/DEA_Waterbodies.ipynb @@ -5,13 +5,14 @@ "metadata": {}, "source": [ "# Introduction to DEA Waterbodies \n", - "

\n", - " \"Waterbodies\n", - "

\n", "\n", "* **[Sign up to the DEA Sandbox](https://app.sandbox.dea.ga.gov.au/)** to run this notebook interactively from a browser\n", "* **Compatibility:** Notebook currently compatible with both the `NCI` and `DEA Sandbox` environments\n", - "* **Products used:** [DEA Waterbodies](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat)\n" + "* **Products used:** [DEA Waterbodies](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat)\n", + "\n", + "

\n", + " \"Waterbodies\n", + "

\n" ] }, { @@ -21,11 +22,6 @@ "tags": [] }, "source": [ - "
\n", - "IMPORTANT: DEA Waterbody graphs show the wet surface area of waterbodies as estimated from satellites. They do not show depth, volume, purpose of the waterbody, nor the source of the water. Larger waterbodies are easier to detect and smaller or narrower waterbodies are harder to detect. Area estimates should be compared to additional data for verification.\n", - "\n", - "The update from version 2 to version 3.0 of the DEA Waterbodies product and service was created through a collaboration between Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI to make the product more useful in hazard applications. Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI advise that the information published by this service comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, FrontierSI, Geoscience Australia, the National Aerial Firefighting Centre and Natural Hazards Research Australia (including its employees and consultants) are excluded from all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.
\n", - "\n", "## Background\n", "It is important to have up-to-date information about the extent and location of surface water in Australia. \n", "It provides us with a common understanding of this valuable and increasingly scarce resource.\n", @@ -33,9 +29,7 @@ "### What this product offers\n", "[DEA Waterbodies](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat) uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where almost 300,000 waterbodies are in the Australian landscape and tells us the wet surface area within those waterbodies.\n", "\n", - "DEA Waterbodies uses a [water classification](https://www.dea.ga.gov.au/products/dea-water-observations) for every available Landsat satellite image for a pre-generated map of the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that were present more than 10% of the time between 1987-2020 and are larger than 2700 m$^2$ (3 Landsat pixels). These waterbodies have been derived from [DEA Water Observations](https://www.dea.ga.gov.au/products/dea-water-observations), a classifier which detects open water in Landsat pixels. \n", - "\n", - "- DEA Waterbodies can be viewed on the mapping portal [DEA Maps](https://maps.dea.ga.gov.au/#share=s-3ECq9avqVD8TopthD0gWnBnA3G9).\n", + "DEA Waterbodies uses a [water classification](https://www.dea.ga.gov.au/products/dea-water-observations) for every available Landsat satellite image for a pre-generated map of the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that were present more than 10% of the time between 1987-2020 and are larger than 2700 m$^2$ (3 Landsat pixels). These waterbodies have been derived from [DEA Water Observations](https://www.dea.ga.gov.au/products/dea-water-observations), a classifier which detects open water in Landsat pixels.\n", "\n", "The DEA Waterbodies graphs indicates changes in the wet surface area of waterbodies. This can be used to identify when waterbodies are increasing or decreasing in wet surface area.\n", "\n", @@ -46,9 +40,7 @@ "### Publications\n", "* *DEA Waterbodies v3.0*
Dunn, B., Krause, C., Newey, V., Lymburner, L., Alger, M.J., Adams, C., Yuan, F., Ma, S., Barzinpour, A., Ayers, D., McKenna, C., Schenk, L. 2024. Digital Earth Australia Waterbodies v3.0. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/148920 \n", "* *DEA Waterbodies*
Krause, C.E., Newey, V., Alger, M.J. and Lymburner, L., (2021). Mapping and monitoring the multi-decadal dynamics of Australia’s open waterbodies using Landsat. Remote Sensing, 13(8), p.1437. https://doi.org/10.3390/rs13081437\n", - "* *DEA Water Observations*
Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. *Remote Sensing of Environment*, 174, 341–352. https://doi.org/10.1016/j.rse.2015.11.003\n", - "\n", - "> **Note:** For more technical information about DEA Waterbodies, visit the official [Geoscience Australia DEA Waterbodies product description](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat)." + "* *DEA Water Observations*
Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., & Ip, A. (2016). Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. *Remote Sensing of Environment*, 174, 341–352. https://doi.org/10.1016/j.rse.2015.11.003\n" ] }, { @@ -63,6 +55,17 @@ "2. Accessing a polygon\n", "3. Plotting the surface area of the polygon over time\n", "\n", + "
\n", + "\n", + "**Important:** DEA Waterbody graphs show the wet surface area of waterbodies as estimated from satellites. They do not show depth, volume, purpose of the waterbody, nor the source of the water. Larger waterbodies are easier to detect and smaller or narrower waterbodies are harder to detect. Area estimates should be compared to additional data for verification.\n", + "\n", + "The update from version 2 to version 3.0 of the DEA Waterbodies product and service was created through a collaboration between Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI to make the product more useful in hazard applications. Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI advise that the information published by this service comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, FrontierSI, Geoscience Australia, the National Aerial Firefighting Centre and Natural Hazards Research Australia (including its employees and consultants) are excluded from all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.\n", + " \n", + "**Note:** Visit the [DEA Waterbodies product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-waterbodies-landsat/) for detailed technical information including methods, quality, and data access.\n", + "To explore DEA Waterbodies on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEAWaterbodies).\n", + "\n", + "
\n", + "\n", "***" ] }, diff --git a/DEA_products/DEA_Wetlands_Insight_Tool.ipynb b/DEA_products/DEA_Wetlands_Insight_Tool.ipynb index b6760bf4f..7b30fc63f 100755 --- a/DEA_products/DEA_Wetlands_Insight_Tool.ipynb +++ b/DEA_products/DEA_Wetlands_Insight_Tool.ipynb @@ -37,7 +37,7 @@ "id": "c0c98dd9-7f9b-48a8-8c2b-0877d21731a1", "metadata": {}, "source": [ - "## Expected output\n", + "### Expected output\n", "This notebook demonstrates the method for reproducing results from the Wetlands Insight Tool. \n", "At the end of the notebook you will display and save a WIT plot: \n", "\n", @@ -49,7 +49,7 @@ "id": "f7c6e424-9741-4d64-9882-8c1255bf6754", "metadata": {}, "source": [ - "## Applications\n", + "### Applications\n", "The product is designed to support wetland managers, catchment managers and environmental waterholders in understanding whether or not wetlands are changing over time. In instances where the wetlands are changing, the tool allows users to identify whether the changes are gradual, rapid, once-off or cyclical in nature. For example the response of wetlands to the following drivers can be assessed:\n", "\n", "* Changes in river flow volumes\n", @@ -61,18 +61,24 @@ "\n", "Care should be used when interpreting Wetlands Insight Tool results as increases/decreases in particular cover types can be associated with different processes. For example an increase in green cover could indicate canopy recovery of desirable wetland species or an increase in the amount of invasive weeds.\n", "\n", - "## Publications\n", + "### Publications\n", "* Dunn, B., Ai, E., Alger, M.J., Fanson, B., Fickas, K.C., Krause, C.E., Lymburner, L., Nanson, R., Papas, P., Ronan, M., Thomas, R.F., 2023. Wetlands Insight Tool: Characterising the Surface Water and Vegetation Cover Dynamics of Individual Wetlands Using Multidecadal Landsat Satellite Data. Wetlands 43, 37. Available: https://doi.org/10.1007/s13157-023-01682-7\n", "* Dunn, B., Lymburner, L., Newey, V., Hicks, A. and Carey, H., 2019. Developing a Tool for Wetland Characterization Using Fractional Cover, Tasseled Cap Wetness And Water Observations From Space. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 6095-6097. Available: https://doi.org/10.1109/IGARSS.2019.8897806.\n", "* Krause, C., Dunn, B., Bishop-Taylor, R., Adams, C., Burton, C., Alger, M., Chua, S., Phillips, C., Newey, V., Kouzoubov, K., Leith, A., Ayers, D., Hicks, A., DEA Notebooks contributors 2021. Digital Earth Australia notebooks and tools repository. Geoscience Australia, Canberra. https://doi.org/10.26186/145234\n", "\n", - "**Please cite Dunn et. al 2023 and Krause et al 2021 when using the results of this notebook, and contact us as we'd love to hear about your use case! \n", - "Licencing information is provided at the bottom of this notebook, and requires attribution.** \n", - "\n", - "## Related products\n", + "### Related products\n", "* [DEA Fractional Cover](https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-landsat/)\n", "* [DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-tasseled-cap-percentiles-landsat/)\n", - "* [DEA Water Observations (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/)" + "* [DEA Water Observations (Landsat)](https://knowledge.dea.ga.gov.au/data/product/dea-water-observations-landsat/)\n", + "\n", + "
\n", + " \n", + "**Note:** Visit the [DEA Wetlands Insight Tool product description](https://knowledge.dea.ga.gov.au/data/category/dea-wetlands-insight-tool/) for detailed technical information including methods, quality, and data access.\n", + " \n", + "**Citing:** Please cite [Dunn et. al 2023](#Publications) and [Krause et al 2021](#Publications) above when using the results of this notebook, and contact us as we'd love to hear about your use case. \n", + "Licencing information is provided at the bottom of this notebook, and requires attribution.\n", + "\n", + "
" ] }, { diff --git a/Supplementary_data/02_DEA/dea_products.jpg b/Supplementary_data/02_DEA/dea_products.jpg index 17d04958e..7ae24ebff 100644 Binary files a/Supplementary_data/02_DEA/dea_products.jpg and b/Supplementary_data/02_DEA/dea_products.jpg differ diff --git a/Tests/test_notebooks.sh b/Tests/test_notebooks.sh index fcbe900c4..7d5552fda 100755 --- a/Tests/test_notebooks.sh +++ b/Tests/test_notebooks.sh @@ -11,6 +11,6 @@ pip3 install ./Tools pytest Tests/dea_tools # Test Juputer Notebooks -pytest --durations=10 --nbval-lax Beginners_guide DEA_products --ignore DEA_products/DEA_Wetlands_Insight_Tool.ipynb How_to_guides/Animated_timeseries.ipynb How_to_guides/Contour_extraction.ipynb How_to_guides/Calculating_band_indices.ipynb How_to_guides/Downloading_data_with_STAC.ipynb How_to_guides/Exporting_GeoTIFFs.ipynb How_to_guides/Generating_composites.ipynb How_to_guides/Image_segmentation.ipynb How_to_guides/Interpolation.ipynb How_to_guides/Opening_GeoTIFFs_NetCDFs.ipynb How_to_guides/Pansharpening.ipynb How_to_guides/Planetary_computer.ipynb How_to_guides/Polygon_drill.ipynb How_to_guides/Principal_component_analysis.ipynb How_to_guides/Rasterize_vectorize.ipynb How_to_guides/Tidal_modelling.ipynb How_to_guides/Using_load_ard.ipynb How_to_guides/Virtual_products.ipynb Real_world_examples/Coastal_erosion.ipynb Real_world_examples/Intertidal_elevation.ipynb +pytest --durations=10 --nbval-lax Beginners_guide DEA_products --ignore DEA_products/DEA_Wetlands_Insight_Tool.ipynb How_to_guides/Animated_timeseries.ipynb How_to_guides/Contour_extraction.ipynb How_to_guides/Calculating_band_indices.ipynb How_to_guides/Exporting_GeoTIFFs.ipynb How_to_guides/Generating_composites.ipynb How_to_guides/Image_segmentation.ipynb How_to_guides/Interpolation.ipynb How_to_guides/Opening_GeoTIFFs_NetCDFs.ipynb How_to_guides/Pansharpening.ipynb How_to_guides/Planetary_computer.ipynb How_to_guides/Polygon_drill.ipynb How_to_guides/Principal_component_analysis.ipynb How_to_guides/Rasterize_vectorize.ipynb How_to_guides/Tidal_modelling.ipynb How_to_guides/Using_load_ard.ipynb How_to_guides/Virtual_products.ipynb Real_world_examples/Coastal_erosion.ipynb Real_world_examples/Intertidal_elevation.ipynb