From 5984782a63bc536864ceab2143f2e506b25b2aae Mon Sep 17 00:00:00 2001 From: Samapriya Roy Date: Wed, 31 Jul 2024 23:59:36 -0500 Subject: [PATCH] beta merge - merging beta features to main --- docs/about_us.md | 27 + docs/blog/.authors.yml | 11 + docs/blog/.meta.yml | 3 + docs/blog/index.md | 1 + docs/{ => contributing}/bug.md | 0 docs/{ => contributing}/example.md | 0 docs/contributing/index.md | 116 +++ docs/{ => contributing}/submit.md | 0 docs/{ => contributing}/update.md | 0 docs/history.md | 14 + docs/insiders/index.md | 17 + .../insiders_program.md} | 3 - docs/involved.md | 82 ++ docs/{blogs.md => medium_blogs.md} | 19 +- docs/projects/index.md | 31 + docs/reference/index.md | 42 + docs/startup/catalog-assets.md | 85 +++ docs/startup/catalog-examples.md | 29 +- docs/startup/navigation.md | 2 +- docs/substack_blogs.md | 18 + docs/tutorials/examples/glc_fcs30d_lulc.md | 231 ++++++ docs/tutorials/examples/global_shorelines.md | 82 ++ docs/tutorials/examples/landscan_extracts.md | 99 +++ docs/tutorials/index.md | 39 + mkdocs.yml | 721 ++++++++++-------- 25 files changed, 1314 insertions(+), 358 deletions(-) create mode 100644 docs/about_us.md create mode 100644 docs/blog/.authors.yml create mode 100644 docs/blog/.meta.yml create mode 100644 docs/blog/index.md rename docs/{ => contributing}/bug.md (100%) rename docs/{ => contributing}/example.md (100%) create mode 100644 docs/contributing/index.md rename docs/{ => contributing}/submit.md (100%) rename docs/{ => contributing}/update.md (100%) create mode 100644 docs/history.md create mode 100644 docs/insiders/index.md rename docs/{insiders.md => insiders/insiders_program.md} (82%) create mode 100644 docs/involved.md rename docs/{blogs.md => medium_blogs.md} (51%) create mode 100644 docs/projects/index.md create mode 100644 docs/reference/index.md create mode 100644 docs/startup/catalog-assets.md create mode 100644 docs/substack_blogs.md create mode 100644 docs/tutorials/examples/glc_fcs30d_lulc.md create mode 100644 docs/tutorials/examples/global_shorelines.md create mode 100644 docs/tutorials/examples/landscan_extracts.md create mode 100644 docs/tutorials/index.md diff --git a/docs/about_us.md b/docs/about_us.md new file mode 100644 index 000000000..168f0a169 --- /dev/null +++ b/docs/about_us.md @@ -0,0 +1,27 @@ +# About Us + +[![Jetstream2](https://img.shields.io/badge/Supported%20by-Jetstream2-brightgreen)](https://jetstream-cloud.org/) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.12057445.svg)](https://doi.org/10.5281/zenodo.12057445) +[![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/samapriya) + +Welcome to the Awesome GEE Community Catalog, a comprehensive resource for discovering and contributing geospatial datasets designed for use with Google Earth Engine. The awesome-gee-community-catalog is an **unfunded open source grassroots project** with a mission to help collect **community sourced** and **community generated** geospatial datasets. Our goal is to make data **accessible** and tie it to an analysis platform **fostering accessibility** and **reducing digital divide**. + +The catalog was created by and maintained by [Dr. Samapriya Roy](https://www.linkedin.com/in/samapriya/) and this is currently a one person team. A [Google Developer Expert for Google Earth Engine](https://g.dev/samapriya) and Senior Product Manager at MAXAR,an [open source developer](https://github.com/samapriya) and a Geospatial Consultant & Speaker. Dr. Roy leads Developer Relations and champions open data access. Leveraging geospatial expertise as an affiliate faculty at the University of Hawaiʻi at Mānoa and a Designated Campus Colleague at the University of Arizona, Dr. Roy further drives the mission of the catalog. + +
+ +![profile_round](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/1c3b2459-f65d-407a-9b6e-0b204cb2e28e) + +
+ +The catalog is further a result of data requests and tutorial contributions from the **#awesome** community who use the community catalog and input, advise and feedback from community members. Our mission is to make geospatial data accessible and analysis-ready, fostering collaboration and reducing the digital divide. The Awesome GEE Community Catalog thrives on community participation and open-source principles. We aim to build on creating accessibility to high-quality geospatial data, enabling researchers, developers, and enthusiasts to leverage these resources for their projects. This year the [National Science Foundation (NSF) ACCESS program](https://access-ci.org/) granted us 1.5 million Service Units or CPU Core hours to continue the work on the catalog through [**Jestream2 a NSF project**](https://jetstream-cloud.org/) which allow us to preprocess the datasets as requests are made. + +## Community Contributions +Our catalog is powered by the contributions of the GEE user base. Community members submit datasets that are then reviewed, usually downloaded and preprocess and made Earth Engine ready and finally added to the catalog for everyone to use. This collaborative approach ensures a diverse and rich collection of data, covering a wide range of topics from waterbodies and population distribution to drought monitoring and more. Each contribution helps expand our repository, making it a go-to resource for geospatial data. 🔍 + +## Update Schedule +We understand the importance of keeping datasets current and reliable. While some datasets are regularly updated on a fixed cadence, others follow a more ad hoc schedule. Updates are made as requests come in or as additional information becomes available about a dataset. This flexible approach allows us to respond to the community's needs and maintain the relevance and accuracy of the data we provide. 🗓️ + +**We rely on users to spread the word and share the catalog with other users. Please cite and attribute the catalog [using our citation](http://gee-community-catalog.org/reference/) making this project more visible and relevant.** + +To get involved check out our [Get Involved Section](http://gee-community-catalog.org/contributing/) diff --git a/docs/blog/.authors.yml b/docs/blog/.authors.yml new file mode 100644 index 000000000..cd63d3097 --- /dev/null +++ b/docs/blog/.authors.yml @@ -0,0 +1,11 @@ +authors: + squidfunk: + name: Martin Donath + description: Creator + avatar: https://avatars.githubusercontent.com/u/932156 + url: https://github.com/squidfunk + alexvoss: + name: Alex Voss + description: Community support + avatar: https://avatars.githubusercontent.com/u/4134224 + url: https://github.com/alexvoss diff --git a/docs/blog/.meta.yml b/docs/blog/.meta.yml new file mode 100644 index 000000000..ea01dc9ab --- /dev/null +++ b/docs/blog/.meta.yml @@ -0,0 +1,3 @@ +comments: true +hide: + - feedback diff --git a/docs/blog/index.md b/docs/blog/index.md new file mode 100644 index 000000000..05761ac57 --- /dev/null +++ b/docs/blog/index.md @@ -0,0 +1 @@ +# Blog diff --git a/docs/bug.md b/docs/contributing/bug.md similarity index 100% rename from docs/bug.md rename to docs/contributing/bug.md diff --git a/docs/example.md b/docs/contributing/example.md similarity index 100% rename from docs/example.md rename to docs/contributing/example.md diff --git a/docs/contributing/index.md b/docs/contributing/index.md new file mode 100644 index 000000000..8e65a0ff5 --- /dev/null +++ b/docs/contributing/index.md @@ -0,0 +1,116 @@ +# Community Actions (Building the Catalog) + +The Awesome GEE Community Catalog is an actively maintained and evolving project that serves a diverse user base with versatile backgrounds and needs. To efficiently address the requirements of all our users, evaluate change requests, and fix bugs, update datasets, I put in a lot of work and your contributions are helpful.The catalog is a collaborative effort, and I welcome your contributions! This catalog aims to provide a comprehensive and up-to-date list of community-driven datasets readily accessible within Google Earth Engine (GEE). + +The Awesome GEE Community Catalog thrives on community contributions! Whether you've found a valuable dataset, spotted an error, or have a helpful tip to share, there are many ways to get involved. By contributing, you're not only helping us build a valuable resource for the GEE community, but you're also making it easier for others to find and utilize valuable Earth observation data. + + [discussion board]: https://github.com/samapriya/awesome-gee-community-datasets/discussions + [issue tracker]: https://github.com/samapriya/awesome-gee-community-datasets/issues + [documentation]: https://gee-community-catalog.org + +## **How you can contribute** + +I know your time is valuable. That's why I've streamlined contributing to the Awesome GEE Community Catalog! + +* Clear Guides and Templates: I offer clear instructions and templates for reporting bugs, requesting changes, and participating in discussions. This saves you time by making sure your contributions are well-organized and easy for me to understand. +* Focus on Finding, Not Formatting: I've designed the issue tracker and discussion board for easy navigation and search. This means you can spend less time formatting your contributions and more time focusing on the valuable information you're sharing. +* Faster Support: By following my guidelines, you help me process your contributions quickly and efficiently. This means you get the help or support you need faster. + +Ready to get started? Let's dive into the specific ways you can contribute! + + +## **Creating an issue** + +
+ +- :octicons-upload-16:   + __Bring or Add data to the Community Catalog__ + + --- + + Submit or bring your data request to community catalog + + --- + + [:octicons-arrow-right-24: Contribute data][contribute data] + +- :octicons-redo-16:   + __Notice an outdated dataset? Submit an update request__ + + --- + + Submit update request for dataset in community catalog + + --- + + [:octicons-arrow-right-24: Submit an update][submit update] + +- :octicons-bug-16:   + __Notice a Bug? Submit a Bug report for review__ + + --- + + Bug report for dataset in community catalog + + --- + + [:octicons-arrow-right-24: Submit a bug report][bug report] + +- :octicons-book-24:   + __Have a Tutorial you want to Contribute? Submit one__ + + --- + + Submit tutorials for datasets in community catalog + + --- + + [:octicons-arrow-right-24: Submit a tutorial][submit tutorial] + +- :material-account-question-outline:   + __Have a question or need help?__ + + --- + + Ask a question on our [discussion board] and get in touch with our + community + + --- + + [:octicons-arrow-right-24: Ask a question][discussion board] +
+ +## **Contributing** + +
+ +- :material-charity:   + __Support the Project & Donate__ + + --- + + We are an unfunded project so community donors and sponors make a world of difference to the project. + + --- + + [:octicons-arrow-right-24: Support & Donate][submit donate] + +- :material-source-pull:   + __Want to create a pull request?__ + + --- + + Learn how to create a comprehensive and useful pull request (PR) + + --- + + [:octicons-arrow-right-24: Create a pull request][create a pull request] + +
+ + [contribute data]: submit.md + [submit update]: update.md + [bug report]: bug.md + [submit donate]: https://github.com/sponsors/samapriya + [submit tutorial]: example.md + [create a pull request]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request diff --git a/docs/submit.md b/docs/contributing/submit.md similarity index 100% rename from docs/submit.md rename to docs/contributing/submit.md diff --git a/docs/update.md b/docs/contributing/update.md similarity index 100% rename from docs/update.md rename to docs/contributing/update.md diff --git a/docs/history.md b/docs/history.md new file mode 100644 index 000000000..12457b871 --- /dev/null +++ b/docs/history.md @@ -0,0 +1,14 @@ +# Building Data Commons + +I am a firm believer that **Communities are what communities build together**.The power of Google Earth Engine (GEE) lies not just in its processing capabilities, but also in its vibrant community. This community thrives on constant innovation and collaboration, evident in the ongoing iterations and shared code libraries. Inspired by this collaborative spirit, we embarked on a project to create a community-curated data repository – a space where users could contribute and access valuable geospatial datasets. + +The impetus for this project arose from a specific user query. Someone inquired about Facebook's high-resolution population density maps, a dataset absent from the official GEE catalog. This presented a perfect opportunity to experiment with a community-driven data commons. The dataset, hosted by Columbia University, offered detailed population data at an impressive 30-meter resolution.You can read the foundational [blog here](https://samapriyaroy.medium.com/community-datasets-in-google-earth-engine-an-experiment-b72daa474819) + +This Facebook dataset became the first and most frequently updated entry in the community catalog, now known as the #Awesome GEE Community Catalog. + +## Guiding Principle +The guiding principle behind this catalog draws inspiration from Elinor Ostrom's groundbreaking work on commons governance, a philosophy that has underpinned successful open-source projects like Linux and collaborative platforms like Wikipedia. Just as shared norms within a physical commons benefit everyone, fostering a similar collaborative environment within the digital realm was our goal.The idea was to use the inspiration from Digital Commons and create a **Community Data Commons** in the form of the **#Awesome GEE Community Catalog**. + +![83186_Awesome GEE Community Datasets_Flat_RD_New_092](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/a4605d83-7df0-4e10-b8d0-8a73d6990520) + +The #Awesome GEE Community Catalog aims to reduce barriers for users by providing easy access to a growing collection of public datasets. This democratizes access to valuable geospatial data, similar to how GEE itself has democratized access to processing capabilities. However, the challenge lies in effectively applying these principles to both large-scale datasets and smaller, user-contributed ones. The Earth Engine ecosystem itself thrives on a culture of community learning, adaptation, and iteration.This community data commons serves as a bridge, connecting users with the datasets they need and fostering further collaboration within the GEE community. The #Awesome GEE Community Catalog represents a collaborative effort, and its continued success relies on the active participation of its users. diff --git a/docs/insiders/index.md b/docs/insiders/index.md new file mode 100644 index 000000000..7ae1b0aba --- /dev/null +++ b/docs/insiders/index.md @@ -0,0 +1,17 @@ +# Why donate to the Community Catalog + +[![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/samapriya) + +The **GEE Community Catalog** is an Open Source and unfunded project that is developed and maintained by a one person team. You can read me and the work further in the [about me section](https:/gee-community-catalog.org/aboutme). While I started this as a personal side project in 2020, the realization was always present that this project has far reaching implications and applications in the larger geospatial community. I realized that this project could benefit not just the research community who are often producing valuable research products from their research but users who are interested in a share collection of community sources data sources. Behind the scenes most community catalog requests for adding a dataset to the community catalog is triaged by me, evaluated based on multiple factors such as license, data size and preprocessing complexity before I start the work on getting it ready. If we meet ask me for stickers to help spread the word 😊. + +
+ +![logo](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/0f4929a1-0176-4c2b-b182-ff7c8d173649) + +
+ +Over the last 4 years the project backend now includes over 100,000+ lines of code to often preprocess the dataset or make it ingest ready for Google Earth Engine and making it available for the geospatial community of GEE users. Currently the site serves over 500,000 requests from over 160+ countries. This work is built around creating a **Community Data Commons** and if you can and wish to support and donate to the project which goes towards simple things like cost of hosting, preprocessing feel free to do so using Github Sponsorship Tier setup for this project. + +[  Choose a sponsoring tier ][sponsoring-tiers]{ .md-button .md-button--primary .mdx-sponsorship-button } + + [sponsoring-tiers]: https://github.com/sponsors/samapriya diff --git a/docs/insiders.md b/docs/insiders/insiders_program.md similarity index 82% rename from docs/insiders.md rename to docs/insiders/insiders_program.md index ee2df62eb..86ba39033 100644 --- a/docs/insiders.md +++ b/docs/insiders/insiders_program.md @@ -1,6 +1,5 @@ # Insiders program -[![Donate](https://img.shields.io/badge/Donate-Buy%20me%20a%20Chai-teal)](https://www.buymeacoffee.com/samapriya) [![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/samapriya) The awesome GEE community catalog **insiders program** is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. As such this program is for sponsors and data contributors to the project you can sponsor the project by clicking on the sponsor button above :point_up: or submit a new dataset request [for example using this template](https://gee-community-catalog.org/submit/). If you fit under any of those categories [fill this form](https://forms.gle/VPmETGKyvMAd37MV6) to get insiders access. @@ -12,5 +11,3 @@ What do you get when you sign up for the Insiders program? * You will also recieve changelog and updates emails once in a while and you can post to the google group with questions, concerns and thoughts Any and all support is appreciated you can sponsor the project using the sponsorship links as well as contributing and helping data curation for the catalog.You can now find a list of insiders only datasets within the catalog for easily locating these. - -*All datasets that are part of the insiders’ program are released within a few months to the general catalog, along with the monthly release cycle for the catalog.* diff --git a/docs/involved.md b/docs/involved.md new file mode 100644 index 000000000..4624a6e27 --- /dev/null +++ b/docs/involved.md @@ -0,0 +1,82 @@ +# Stay updated & contribute + +The Awesome GEE Community Catalog is created and maintained by [Samapriya Roy](https://www.linkedin.com/in/samapriya/) with data, examples, tutorial contributions from our community. This is a community common meaning it needs involvement to survive as a grassroots open source project. Here are some ways in which you can get involved with this project and check out examples on how you can bring data, examples, bug reports and pull requests to the catalog here. Open up a Github discussion and create a pull request if you notice any issues so I can fix them. **Sign Up for Updates:** Never miss the latest catalog additions and in-depth explorations by subscribing to catalog updates [through out datacommons blog](https://datacommons.substack.com). + +### Choose your adventure + +
+ +- :material-star:   + __Browse & Star the Catalog__ + + --- + + Visit the [website](https://gee-community-catalog.org) and star the [Github Repo](https://github.com/samapriya/awesome-gee-community-datasets) so it's easily discovered & you get updates. + + --- + + [:octicons-arrow-right-24:Browse and Star][use star] + +- :material-office-building:   + __Integrate into Your Projects__ + + --- + + Build with the datasets in your GEE projects, use example code and [cite the project](https://gee-community-catalog.org/citation/) + + --- + + [:octicons-arrow-right-24: Build and Cite][build cite] + +- :material-web-plus:   + __Enrich the Community Catalog__ + + --- + + Bring datasets of value to the community catalog. Share it with the community by contributing new datasets + + --- + + [:octicons-arrow-right-24: Enrich the Catalog][byod] + +- :fontawesome-solid-note-sticky:   + __Submit a tutorial or Example__ + + --- + + Create and share examples demonstrating how you've leveraged the catalog's data in your projects. + + --- + + [:octicons-arrow-right-24: Submit a tutorial][submit tutorial] + +- :material-charity:   + __Support the Project & Donate__ + + --- + + We are an unfunded project so community donors and sponors make a world of difference to the project. + + --- + + [:octicons-arrow-right-24: Support & Donate][submit donate] + +- :material-source-pull:   + __Collaborate with Pull requests__ + + --- + + Creating a new pull request means you fixed something that I missed & I and the community apppreciate it. + + --- + + [:octicons-arrow-right-24: Create a pull request][create a pull request] +
+ + [use star]: https://github.com/samapriya/awesome-gee-community-datasets + [build cite]: https://beta.gee-community-catalog.org/reference/ + [byod]: https://beta.gee-community-catalog.org/contributing/submit/ + [submit tutorial]: https://beta.gee-community-catalog.org/contributing/example + [submit donate]: https://github.com/sponsors/samapriya + [create a pull request]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request + diff --git a/docs/blogs.md b/docs/medium_blogs.md similarity index 51% rename from docs/blogs.md rename to docs/medium_blogs.md index c89aff780..f8668334c 100644 --- a/docs/blogs.md +++ b/docs/medium_blogs.md @@ -1,21 +1,6 @@ -# Blog posts +# Medium Blog posts -Find a list of associated blog posts related to GEE and community catalog here. To get post subscribe to our [![Substack](https://img.shields.io/badge/Substack-FF6719.svg?style=flat&logo=Substack&logoColor=white)](https://datacommons.substack.com/) - - -* [Data Commons and Connectivity: Exploring Global Data and Tools That Measure Internet Access](https://datacommons.substack.com/p/data-commons-and-connectivity-exploring) -* [🌍 Community Catalog Summer Refresh: Land Cover, Forest Flux, and Field Boundaries Updates](https://datacommons.substack.com/p/community-catalog-summer-refresh) -* [GEE Community Catalog Celebrates 4 Years: May 2024 Data Refresh](https://datacommons.substack.com/p/gee-community-catalog-celebrates) -* [Mapping with the Flow: Rivers, Wells, and Obstruction Datasets in the Community Catalog](https://datacommons.substack.com/p/mapping-with-the-flow-rivers-wells) -* [Jupyter Lab & Linux on Windows: Level Up Your Data Science Setup](https://datacommons.substack.com/p/jupyter-lab-and-linux-on-windows) -* [Catalog Data Refresh: Updates to GEE Community Catalog Release 2.5.0](https://datacommons.substack.com/p/catalog-data-refresh-updates-to-gee) -* [A Weekly Pulse on Drought: Continuous Updates to US Drought Monitor in the Community Catalog](https://datacommons.substack.com/p/a-weekly-pulse-on-drought-bringing) -* [Paper Trails to Pixels: Historical USGS Topo Maps in Google Earth Engine Community Catalog](https://datacommons.substack.com/p/paper-trails-to-pixels-historical) -* [Beneath the Surface: Exploring GLOBGM High Resolution Global Groundwater Model](https://datacommons.substack.com/p/beneath-the-surface-exploring-globgm) -* [Get Your Open Data Fix: What's New in the GEE Community Catalog Release 2.4.0](https://datacommons.substack.com/p/get-your-open-data-fix-whats-new) -* [Our Dynamic World: Dense Land Cover Data & Stories of Shifting Landscapes](https://datacommons.substack.com/p/our-dynamic-world-dense-land-cover) -* [Exploring the Data Commons, One Dataset at a Time](https://datacommons.substack.com/p/exploring-the-data-commons-one-dataset) - +Find a list of associated blog posts related to GEE and community catalog here. * [How ClimateEngine.org and Awesome GEE Community Catalog are Expanding Open Geospatial Commons](https://medium.com/@samapriyaroy/how-climateengine-org-and-awesome-gee-community-catalog-are-expanding-open-geospatial-commons-30120b1bfbaf) * [Behind the Scenes: The Role of NSF’s Jetstream2 in Building the Awesome GEE Community Catalog](https://samapriyaroy.medium.com/behind-the-scenes-the-role-of-nsfs-jetstream2-in-building-the-awesome-gee-community-catalog-3f563b8cb9f0) diff --git a/docs/projects/index.md b/docs/projects/index.md new file mode 100644 index 000000000..2519e74d7 --- /dev/null +++ b/docs/projects/index.md @@ -0,0 +1,31 @@ +# Data Themes + +![GEE Community Datasets](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/samapriya/34bc0c1280d475d3a69e3b60a706226e/raw/community.json) + +The Awesome GEE (Google Earth Engine) Community Catalog is a valuable resource for researchers, developers, and environmental scientists. It organizes a diverse range of geospatial datasets into thematic groups, making them more accessible and findable. This structured approach allows users to efficiently locate datasets pertinent to their specific fields of study or interest. + +!!! tip "Insiders Program and Insiders only datasets" + + **Some datasets are part of the Insiders only datasets and they [can be found here](https://gee-community-catalog.org/insiders). The insiders program is designed for those who are helping keep open source projects sustainable and support the growth and curation of the catalog. The Insiders Program grants access to a few special selection of datasets. You can be part of the program click on the link :point_up: to find out more.** + +#### Thematic Groups +The datasets in the Awesome GEE Community Catalog are categorized into several thematic groups, for example: + +* Population and Socioeconomic Datasets: These datasets provide crucial information on demographics, economic activities, and social indicators, which are essential for urban planning, public health, and socio-economic research. + +* Hydrology Datasets: This category includes data on water bodies, hydrological cycles, and water quality, supporting research and decision-making in water resource management, flood risk assessment, and environmental conservation. + +* Global Land Use and Land Cover Datasets: These datasets offer insights into land use patterns and changes in land cover over time, aiding studies in agriculture, forestry, urbanization, and climate change. + +* Climate and Weather Datasets: Essential for climate science, these datasets include historical and real-time data on weather patterns, temperature, precipitation, and other climatic factors. + +
+ +???+ note + + **While every effort has been made to place datasets in the most suitable thematic groups, it is acknowledged that some datasets may rightfully belong to more than one category. Users are encouraged to explore multiple themes if their research spans across different areas.** + +
+ +#### Accessibility and Findability +The thematic grouping of datasets in the Awesome GEE Community Catalog enhances their accessibility and findability. By organizing datasets into clearly defined categories, the catalog simplifies the process of searching and identifying relevant data. This organization not only saves time but also ensures that users can easily locate the most appropriate datasets for their specific needs with the changelog recording periodic updates. diff --git a/docs/reference/index.md b/docs/reference/index.md new file mode 100644 index 000000000..24296f071 --- /dev/null +++ b/docs/reference/index.md @@ -0,0 +1,42 @@ +# Reference & Citation + +While most datasets in our community catalog are citable themselves, it's important to note that the catalog as a whole should also be cited by users. Our citation information and release details are sourced from [**Zenodo**](https://zenodo.org) and you can always find the latest [DOI & Citation here](https://zenodo.org/doi/10.5281/zenodo.7144933). + +We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release. + +## Citation + +``` +Samapriya Roy, Muhammed Abdelaal, Ujaval Gandhi, & Swetnam, T. (2024). samapriya/awesome-gee-community-datasets: Community Catalog (2.7.0). +Zenodo. https://doi.org/10.5281/zenodo.12057445 +``` + +## Earn your place in the citation + +
+ +- :fontawesome-solid-note-sticky:   + __Submit a tutorial or Example__ + + --- + + Create and share examples demonstrating how you've leveraged the catalog's data in your projects. + + --- + + [:octicons-arrow-right-24: Submit a tutorial][submit tutorial] + +- :material-source-pull:   + __Collaborate with Pull requests__ + + --- + + Creating a new pull request means you fixed something that I missed & I and the community apppreciate it. + + --- + + [:octicons-arrow-right-24: Create a pull request][create a pull request] +
+ + [submit tutorial]: https://gee-community-catalog.org/contributing/example + [create a pull request]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request diff --git a/docs/startup/catalog-assets.md b/docs/startup/catalog-assets.md new file mode 100644 index 000000000..9f06e99ae --- /dev/null +++ b/docs/startup/catalog-assets.md @@ -0,0 +1,85 @@ +# Catalog assets + +![GEE Community Datasets](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/samapriya/34bc0c1280d475d3a69e3b60a706226e/raw/community.json) + +It is feasible to sometimes use a machine readable list of catalog assets. While we are going to introduce a STAC catalog again at some point the assets are also available in two specific formats with the total running count above. + +
+ +
+ +[  Download latest JSON version here:fontawesome-solid-download:][download-json]{ .md-button .md-button--primary} + +
+ +
+ +[download-json]: https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/master/community_datasets.json + +## JSON format +This holds information about the datasets in this structure as a JSON list. If the license is custom for a dataset license text is included to clarify the details. The structure is the following + +| **Field** | **Description** | +|-------------------|-------------------------------------------------------------------------------------------------------------| +| **Title** | The name of the dataset. | +| **Sample Code** | A link to a sample script demonstrating how to use the dataset in Google Earth Engine. | +| **Type** | The type of data (e.g., table). | +| **ID** | The unique identifier for the dataset in the Earth Engine catalog. | +| **Provider** | The organization or entity that provides the dataset. | +| **Tags** | Keywords associated with the dataset to help with search and categorization. | +| **License** | The licensing terms under which the dataset is provided. | +| **License Text** | Additional text explaining the license (if applicable). | +| **Docs** | A link to documentation or more information about the dataset. | +| **Thematic Group**| The category or group under which the dataset falls (e.g., Oceans and Shorelines, Hydrology). | + + +```json +[ + { + "title": "Global Shoreline Dataset", + "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES", + "type": "table", + "id": "projects/sat-io/open-datasets/shoreline/mainlands", + "provider": "United States Geological Survey, USGS", + "tags": "Global Shoreline, Shoreline, mainlands, Oceans", + "license": "Creative Commons Attribution Share Alike 4.0 International", + "docs": "https://gee-community-catalog.org/projects/shoreline/", + "thematic_group": "Oceans and Shorelines" + }, + { + "title": "NWI_CO_Riparian_Project_Metadata", + "sample_code": "https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY", + "type": "table", + "id": "projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata", + "provider": "U.S. Fish and Wildlife Service", + "tags": "wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata", + "license": "custom", + "license_text": "The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US", + "docs": "https://gee-community-catalog.org/projects/nwi/", + "thematic_group": "Hydrology" + } +] +``` + +## CSV Format +The CSV file is created using a Github action within the repository and contains all fields in the JSON representation. Fields like license_text if empty for a specific license are left empty. + +
+ +
+ +[  Download latest CSV version here:fontawesome-solid-download:][download-csv]{ .md-button .md-button--primary} + +
+ +
+ +[download-csv]: https://raw.githubusercontent.com/samapriya/awesome-gee-community-datasets/master/community_datasets.csv + + +|id |provider |title |type |tags |sample_code |license |license_text|docs_page |thematic_group | +|-------------------------------------------------|-------------------------------------|------------------------|-----|----------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------|------------|-----------------------------------------------------|---------------------| +|projects/sat-io/open-datasets/shoreline/mainlands|United States Geological Survey, USGS|Global Shoreline Dataset|table|Global Shoreline, Shoreline, mainlands, Oceans|https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:oceans-shorelines/GLOBAL_SHORELINES|Creative Commons Attribution Share Alike 4.0 International|NA |https://gee-community-catalog.org/projects/shoreline/|Oceans and Shorelines| +|projects/sat-io/open-datasets/NWI/rpm/CO_Riparian_Project_Metadata|U.S. Fish and Wildlife Service |NWI_CO_Riparian_Project_Metadata|table|wetlands, conservation areas, habitats, fish, wildlife, drinking water, recreation, U.S. Fish and Wildlife Service,CO_Riparian_Project_Metadata|https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:hydrology/NATIONAL-WETLANDS-INVENTORY|custom |The US FWS National Wetlands Inventory (NWI) is a publicly available resource that provides detailed information on the abundance, characteristics, and distribution of US|https://gee-community-catalog.org/projects/nwi/ |Hydrology | + + diff --git a/docs/startup/catalog-examples.md b/docs/startup/catalog-examples.md index 5e933525b..5c7423ebe 100644 --- a/docs/startup/catalog-examples.md +++ b/docs/startup/catalog-examples.md @@ -5,4 +5,31 @@ The awesome GEE catalog dataset examples are now part of a repo. Add this to you ![repository_add](https://i.imgur.com/hcbHHM2.gif) -Use this [link to add examples repo to your reader repository list](https://code.earthengine.google.com/?accept_repo=users/sat-io/awesome-gee-catalog-examples) +
+ +
+ +[  Add examples repo to your GEE reader repository list :fontawesome-solid-circle-plus:][accept-repo]{ .md-button .md-button--primary} + +
+ +**OR** + +
+ + +[accept-repo]: https://code.earthengine.google.com/?accept_repo=users/sat-io/awesome-gee-catalog-examples + + + +
+ +
+ +[  Download GEE Community Catalog Examples Folder :fontawesome-solid-download:][download-examples]{ .md-button .md-button--primary} + +
+ +
+ +[download-examples]: https://github.com/samapriya/awesome-gee-community-datasets/raw/master/awesome-gee-catalog-examples.zip diff --git a/docs/startup/navigation.md b/docs/startup/navigation.md index 9f2a4b580..bee1ad4c5 100644 --- a/docs/startup/navigation.md +++ b/docs/startup/navigation.md @@ -2,7 +2,7 @@ The awesome-gee-community catalog is designed to be completely compatible with the existing GEE master catalog. It is also designed to be as open and exploratory as possible. The catalog is grouped by domains for example datasets that belong to themes about Population and Socioeconomics will be within that group and you should be able to expand and look at the datasets. -![expand_domain](https://user-images.githubusercontent.com/6677629/193892821-31355044-5fa7-4e82-bf53-f79d57f86c2f.gif) +![navigation](https://github.com/samapriya/awesome-gee-community-datasets/assets/6677629/20ad544f-c143-4354-a9af-0dd956f5d50f) You can also search by dataset name or keywords or tags in the search bar. diff --git a/docs/substack_blogs.md b/docs/substack_blogs.md new file mode 100644 index 000000000..20221799b --- /dev/null +++ b/docs/substack_blogs.md @@ -0,0 +1,18 @@ +# Substack Blogs + +Find a list of associated blog posts related to GEE and community catalog here. To get post subscribe to our [![Substack](https://img.shields.io/badge/Substack-FF6719.svg?style=flat&logo=Substack&logoColor=white)](https://datacommons.substack.com/) + + +* [🌍 Community Catalog Summer Refresh: Land Cover, Forest Flux, and Field Boundaries Updates](https://datacommons.substack.com/p/community-catalog-summer-refresh) +* [GEE Community Catalog Celebrates 4 Years: May 2024 Data Refresh](https://datacommons.substack.com/p/gee-community-catalog-celebrates) +* [Mapping with the Flow: Rivers, Wells, and Obstruction Datasets in the Community Catalog](https://datacommons.substack.com/p/mapping-with-the-flow-rivers-wells) +* [Jupyter Lab & Linux on Windows: Level Up Your Data Science Setup](https://datacommons.substack.com/p/jupyter-lab-and-linux-on-windows) +* [Catalog Data Refresh: Updates to GEE Community Catalog Release 2.5.0](https://datacommons.substack.com/p/catalog-data-refresh-updates-to-gee) +* [A Weekly Pulse on Drought: Continuous Updates to US Drought Monitor in the Community Catalog](https://datacommons.substack.com/p/a-weekly-pulse-on-drought-bringing) +* [Paper Trails to Pixels: Historical USGS Topo Maps in Google Earth Engine Community Catalog](https://datacommons.substack.com/p/paper-trails-to-pixels-historical) +* [Beneath the Surface: Exploring GLOBGM High Resolution Global Groundwater Model](https://datacommons.substack.com/p/beneath-the-surface-exploring-globgm) +* [Get Your Open Data Fix: What's New in the GEE Community Catalog Release 2.4.0](https://datacommons.substack.com/p/get-your-open-data-fix-whats-new) +* [Our Dynamic World: Dense Land Cover Data & Stories of Shifting Landscapes](https://datacommons.substack.com/p/our-dynamic-world-dense-land-cover) +* [Exploring the Data Commons, One Dataset at a Time](https://datacommons.substack.com/p/exploring-the-data-commons-one-dataset) + + diff --git a/docs/tutorials/examples/glc_fcs30d_lulc.md b/docs/tutorials/examples/glc_fcs30d_lulc.md new file mode 100644 index 000000000..def17c629 --- /dev/null +++ b/docs/tutorials/examples/glc_fcs30d_lulc.md @@ -0,0 +1,231 @@ +# Exploring the Global 30m Land Cover Change Dataset (1985-2022) GLC_FCS30D + +

+by Ujaval Gandhi from Spatial Thoughts +

+ +A temporally consistent global multi-class time-series classification dataset is critical to understand and quantify long-term changes. Previously, options were limited to lower resolution datasets such as MODIS Landcover (2000-present) at 500m resolution or ESA CCI (1992-present) at 300m resolution. The new GLC_FCS30D dataset provides a high-resolution landcover time-series derived from the Landsat archive (1984-2022) at 30m resolution with 35 classes. This dataset is valuable for studying landscape dynamics at high resolution and is available in the public domain. It can be downloaded from Zenodo as GeoTIFF files or accessed directly in the Google Earth Engine (GEE) Community catalog. In this tutorial, we will: + +- Access and preprocess the GLC_FCS30D dataset. +- Visualize and compare landcover changes between 1985-2022. +- Calculate landcover statistics and export a CSV with areas of each class for the entire time series over multiple regions. + +### 1. Preprocessing the Data + +The original dataset was produced in 5° x 5° tiles with each image having bands for each year of classification. This structure was uploaded to the Earth Engine Community Catalog, split into datasets for five-yearly classifications (1985-90, 1990-95, and 1995-2000) and yearly classifications (2000-2022). GEE workflows are structured around ImageCollections rather than multiband images, so we need to transform the original data into an ImageCollection. + +#### Steps: + +1. Merge the tiles into a global mosaic. +2. Convert the multi-band images into ImageCollections. +3. Merge the five-yearly and yearly images into a single ImageCollection. +4. Reclassify the pixels to sequential class values. + +Here's the Earth Engine code for the preprocessing step and a [link to the code](https://code.earthengine.google.co.in/fa41d55186e6d708a4be367237353ff0) + +```javascript +// Example script showing how to pre-process the GLC_FCS30D landcover dataset + +// Yearly data from 2000-2022 +var annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual'); +// Five-Yearly data for 1985-90, 1990-95 and 1995-2000 +var fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map'); + +// Classification scheme has 36 classes (35 landcover classes and 1 fill value) +var classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81, 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0]; +var classNames = ['Rainfed_cropland', 'Herbaceous_cover_cropland', 'Tree_or_shrub_cover_cropland', 'Irrigated_cropland', 'Open_evergreen_broadleaved_forest', 'Closed_evergreen_broadleaved_forest', 'Open_deciduous_broadleaved_forest', 'Closed_deciduous_broadleaved_forest', 'Open_evergreen_needle_leaved_forest', 'Closed_evergreen_needle_leaved_forest', 'Open_deciduous_needle_leaved_forest', 'Closed_deciduous_needle_leaved_forest', 'Open_mixed_leaf_forest', 'Closed_mixed_leaf_forest', 'Shrubland', 'Evergreen_shrubland', 'Deciduous_shrubland', 'Grassland', 'Lichens_and_mosses', 'Sparse_vegetation', 'Sparse_shrubland', 'Sparse_herbaceous', 'Swamp', 'Marsh', 'Flooded_flat', 'Saline', 'Mangrove', 'Salt_marsh', 'Tidal_flat', 'Impervious_surfaces', 'Bare_areas', 'Consolidated_bare_areas', 'Unconsolidated_bare_areas', 'Water_body', 'Permanent_ice_and_snow', 'Filled_value']; +var classColors = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff']; + +// Mosaic the data into a single image +var annualMosaic = annual.mosaic(); +var fiveYearMosaic = fiveyear.mosaic(); + +// Rename bands from b1, b2, etc. to 2000, 2001, etc. +var fiveYearsList = ee.List.sequence(1985, 1995, 5).map(function(year) { return ee.Number(year).format('%04d'); }); +var fiveyearMosaicRenamed = fiveYearMosaic.rename(fiveYearsList); +var yearsList = ee.List.sequence(2000, 2022).map(function(year) { return ee.Number(year).format('%04d'); }); +var annualMosaicRenamed = annualMosaic.rename(yearsList); +var years = fiveYearsList.cat(yearsList); + +// Convert the multiband image to an ImageCollection +var fiveYearlyMosaics = fiveYearsList.map(function(year) { + var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1); + return fiveyearMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)}); +}); +var yearlyMosaics = yearsList.map(function(year) { + var date = ee.Date.fromYMD(ee.Number.parse(year), 1, 1); + return annualMosaicRenamed.select([year]).set({'system:time_start': date.millis(), 'system:index': year, 'year': ee.Number.parse(year)}); +}); +var allMosaics = fiveYearlyMosaics.cat(yearlyMosaics); +var mosaicsCol = ee.ImageCollection.fromImages(allMosaics); + +// Recode the class values into sequential values +var newClassValues = ee.List.sequence(1, ee.List(classValues).length()); +var renameClasses = function(image) { + var reclassified = image.remap(classValues, newClassValues).rename('classification'); + return reclassified; +}; +var landcoverCol = mosaicsCol.map(renameClasses); + +print('Pre-processed Collection', landcoverCol); + +// Visualize the data +var year = 2022; +var selectedLandcover = landcoverCol.filter(ee.Filter.eq('year', year)).first(); +var palette = ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff']; +var classVisParams = {min:1, max:36, palette: palette}; +Map.addLayer(selectedLandcover, classVisParams, 'Landcover ' + year); +``` + +### 2. Visualizing Changes Using a Split-panel App + +A useful way to visualize a landcover time-series is through a user interface that allows us to compare and contrast data for multiple years. Using a split-panel, we can load classifications for two different years and swipe to see changes between them. We will create a split panel interface with a dropdown selector allowing you to change the year and visualize the changes. To make the map interpretation easier, we will also construct a legend. + +You can explore the app at [Global Landcover Change Explorer](https://spatialthoughts.projects.earthengine.app/view/global-landcover-change-explorer). + +Here's the source code for the app: + +```javascript +// Example script for an App to explore GLC_FCS30D landcover dataset using a split-panel + +// Pre-process the Collection +var annual = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/annual'); +var fiveyear = ee.ImageCollection('projects/sat-io/open-datasets/GLC-FCS30D/five-years-map'); +var classValues = [10, 11, 12, 20, 51, 52, 61, 62, 71, 72, 81, + + 82, 91, 92, 120, 121, 122, 130, 140, 150, 152, 153, 181, 182, 183, 184, 185, 186, 187, 190, 200, 201, 202, 210, 220, 0]; +var newClassValues = ee.List.sequence(1, ee.List(classValues).length()); +var allImages = annual.merge(fiveyear); +var renameClasses = function(image) { + var reclassified = image.remap(classValues, newClassValues).rename('classification'); + return reclassified; +}; +var landcoverCol = allImages.map(renameClasses); +var years = ee.List.sequence(1985, 2022).map(function(year) { return ee.Number(year).format('%04d'); }); + +var visParams = {min:1, max:36, palette: ['#ffff64', '#ffff64', '#ffff00', '#aaf0f0', '#4c7300', '#006400', '#a8c800', '#00a000', '#005000', '#003c00', '#286400', '#285000', '#a0b432', '#788200', '#966400', '#964b00', '#966400', '#ffb432', '#ffdcd2', '#ffebaf', '#ffd278', '#ffebaf', '#00a884', '#73ffdf', '#9ebb3b', '#828282', '#f57ab6', '#66cdab', '#444f89', '#c31400', '#fff5d7', '#dcdcdc', '#fff5d7', '#0046c8', '#ffffff', '#ffffff']}; + +// Create a Split-panel Map +var leftMap = ui.Map(); +var rightMap = ui.Map(); +var splitPanel = ui.SplitPanel({ + firstPanel: leftMap, + secondPanel: rightMap, + wipe: true, + style: {stretch: 'both'} +}); +ui.root.clear(); +ui.root.add(splitPanel); + +var createLegend = function() { + var legend = ui.Panel({ + style: { + position: 'bottom-left', + padding: '8px 15px' + } + }); + var legendTitle = ui.Label({ + value: 'Landcover Legend', + style: {fontWeight: 'bold', fontSize: '14px', margin: '0 0 4px 0', padding: '0'} + }); + legend.add(legendTitle); + + var makeRow = function(color, name) { + var colorBox = ui.Label({ + style: { + backgroundColor: color, + padding: '8px', + margin: '0 0 4px 0' + } + }); + var description = ui.Label({ + value: name, + style: {margin: '0 0 4px 6px'} + }); + return ui.Panel({ + widgets: [colorBox, description], + layout: ui.Panel.Layout.Flow('horizontal') + }); + }; + + for (var i = 0; i < classNames.length; i++) { + legend.add(makeRow(classColors[i], classNames[i])); + } + return legend; +}; + +var legend = createLegend(); +leftMap.add(legend); +var intro = ui.Panel([ + ui.Label('Global Landcover Change Explorer', {fontWeight: 'bold', fontSize: '20px'}), + ui.Label('Explore landcover change over time by selecting different years and comparing side-by-side. Zoom in and out to explore regions of interest.') +]); +leftMap.add(intro); + +var createDropdown = function(map, labelText, defaultValue) { + var yearLabel = ui.Label(labelText); + var yearSelect = ui.Select({ + items: years.getInfo(), + value: defaultValue, + onChange: function(year) { + var selectedImage = landcoverCol.filter(ee.Filter.eq('year', parseInt(year))).first(); + map.layers().set(0, ui.Map.Layer(selectedImage, visParams, 'Landcover ' + year)); + } + }); + var panel = ui.Panel([yearLabel, yearSelect]); + map.add(panel); + return yearSelect; +}; + +var leftYearSelect = createDropdown(leftMap, 'Select Left Year:', '1985'); +var rightYearSelect = createDropdown(rightMap, 'Select Right Year:', '2022'); + +leftMap.centerObject(landcoverCol.first().geometry(), 3); +``` + +### 3. Calculating and Exporting Landcover Statistics + +A crucial step for any analysis is to calculate the area covered by each landcover class over time and export the data for further analysis. Here, we will use the zonal statistics approach to compute the area of each class for the entire time series. We will create a table with landcover class and year-wise area statistics and export it as a CSV file. + +Here's the Earth Engine code to achieve this. You can find the [full code here](https://code.earthengine.google.co.in/0cf53210dbad828ca49dc837d2c1d47e) + +```javascript +// Function to calculate area of each class +var calculateArea = function(image) { + var areaImage = ee.Image.pixelArea().divide(10000).addBands(image); + var areas = areaImage.reduceRegion({ + reducer: ee.Reducer.sum().group({ + groupField: 1, + groupName: 'class' + }), + geometry: geometry, + scale: 30, + maxPixels: 1e10 + }); + return ee.Feature(null, areas); +}; + +// Apply the function on the ImageCollection +var areasCol = landcoverCol.map(calculateArea); + +// Flatten the collection to create a single FeatureCollection +var features = areasCol.map(function(feature) { + var dict = ee.Dictionary(feature.get('groups')).map(function(key, value) { + return ee.Number(value).get(0); + }); + return ee.Feature(null, dict); +}); + +// Export the data +Export.table.toDrive({ + collection: features, + description: 'LandcoverAreaStatistics', + fileFormat: 'CSV' +}); +``` + +### Conclusion + +The GLC_FCS30D dataset opens up new avenues for detailed and high-resolution landcover analysis. This tutorial demonstrated how to preprocess, visualize, and analyze the dataset within Google Earth Engine, providing a robust starting point for further exploration and research. + +Keywords: GLC_FCS30D, Preprocessing, Mosaicking, Reclassification, Visualization, Split-panel App, Interactive Map, Legend, Land Cover Statistics, Zonal Statistics, Area Calculation, CSV Export diff --git a/docs/tutorials/examples/global_shorelines.md b/docs/tutorials/examples/global_shorelines.md new file mode 100644 index 000000000..4dc76af4f --- /dev/null +++ b/docs/tutorials/examples/global_shorelines.md @@ -0,0 +1,82 @@ +# Using the Global Shoreline Dataset to Create Land and Ocean Masks with Google Earth Engine (GEE) + +

+by Ujaval Gandhi from Spatial Thoughts +

+ +### Introduction + +The Global Shoreline dataset, hosted on the [Gee-Community Catalog](https://gee-community-catalog.org/projects/shoreline/), is a valuable resource for creating land and ocean masks in Google Earth Engine (GEE). This tutorial provides an overview of how to use this dataset to generate these masks, which can be useful for various geospatial analyses and applications. The complete code [can be found here](https://code.earthengine.google.co.in/a5cbf2f9d14a545efef050b80e5182d9) + +### Dataset Description + +The Global Shoreline dataset comprises three sets of polygon features: + +1. Mainland coastlines from continents around the world. +2. Large island chains (with an area greater than n5,000 square kilometers). +3. Smaller islands not included in either set. + +### Getting Started with GEE and Shoreline Dataset + + +To begin working with this dataset on Google Earth Engine, first import the necessary collections: + +```javascript +// Importing Global Shoreline Dataset Collections +var mainlands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/mainlands'); +var big_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/big_islands'); +var small_islands ee.FeatureCollection('projects/sat-io/open-datasets/shoreline/small_islands'); +``` + +### Merging Collections and Rasterizing Polygons + + +The next step is to merge the individual collections into a single collection, which can then be rasterized: + +```javascript +// Merge all collections +var merged mainlands.merge(big_islands).merge(small_islands); + +// Rasterize polygons using 'ee.Reducer.count()' to get land pixel counts +var mask merged.reduceToImage({ + reducer: ee.Reducer.count(), +}); +``` + +### Visualizing Land and Ocean Areas + + +The rasterized image can now be visualized on the map using different color palettes - brown for land (with higher pixel counts) and blue for ocean: + +```javascript +// Add mask as a layer to display land areas in brown +Map.addLayer(mask, {min: 0, max: 1}, 'Land Mask'); + +// Create an inverted version of the mask to visualize ocean areas in blue +var invertMask mask.multiply(-1); +Map.addLayer(invertMask, {min: -1, max: 0}, 'Ocean Mask'); +``` + +### Creating Land and Ocean Masks for Further Processing + + +The rasterized image can be used to create separate land and ocean masks for further processing within GEE: + +```javascript +// Create an ocean mask using '.selfMask()' on the inverted version of 'mask' +var oceanMask invertMask.updateMask(invertMask); + +// Use 'image.updateMask(oceanMask)' to remove ocean pixels from another image + +// Create a land mask by inverting 'oceanMask' and using '.selfMask()' +var landMask oceanMask.not().updateMask(oceanMask); + +// Use 'image.updateMask(landMask)' to remove land pixels from another image +``` + +### Conclusion + +This tutorial demonstrates how to use the Global Shoreline dataset in Google Earth Engine to create rasterized masks representing land and ocean areas, which can be visualized +on a map or used for further processing. This approach provides a valuable resource for various geospatial analyses and applications. + +Keywords: GEE, Global Shoreline Dataset, Land Mask, Ocean Mask, Rasterization, Shoreline, Landcover, Image Processing diff --git a/docs/tutorials/examples/landscan_extracts.md b/docs/tutorials/examples/landscan_extracts.md new file mode 100644 index 000000000..88a80b1c0 --- /dev/null +++ b/docs/tutorials/examples/landscan_extracts.md @@ -0,0 +1,99 @@ +# Comparing Global Population Trends with GeoBoundaries and Landscan + +

+by Ujaval Gandhi from Spatial Thoughts +

+ +### Introduction + +This tutorial demonstrates how to use GeoBoundaries and the Landscan Population Dataset to compare population data for different Admin1 regions using Earth Engine. You will learn how to load admin boundaries, filter a population dataset by date range, extract resolution information, and create a time-series chart comparing population data. + +To view the complete code for this tutorial, click [here](https://code.earthengine.google.com/580fe304e023625748b4f3e0ef34b2cf). + +### Section 1: Load Admin Boundaries (GeoBoundaries) and Select Regions + +Use the `ee.FeatureCollection` method to load the admin boundaries dataset from GeoBoundaries. + +```javascript +var admin0 = ee.FeatureCollection("projects/sat-io/open-datasets/geoboundaries/CGAZ_ADM0"); +``` + +Select two Admin1 regions to compare: Japan and Mexico. + +```javascript +var region1 = 'Japan'; +var region2 = 'Mexico'; +``` + +Use the `filter` method to select the desired regions from the admin boundaries dataset. + +```javascript +var selectedRegions = admin0.filter(ee.Filter.inList('shapeName', [region1, region2])); +print('Filtered Admin1 collection', selectedRegions); +``` + +### Section 2: Load Landscan Population Dataset + +Use the `ee.ImageCollection` method to load the Landscan population dataset. + +```javascript +var landscan = ee.ImageCollection("projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL"); +var band = 'b1'; +``` + +Set the date range for the population data using `ee.Date.fromYMD`. + +```javascript +var startYear = 2000; +var endYear = 2020; + +var startDate = ee.Date.fromYMD(startYear, 1, 1); +var endDate = ee.Date.fromYMD(endYear + 1, 1, 1); +``` + +Use the `filter` method to filter the population dataset by date range. + +```javascript +var populationFiltered = landscan.filter(ee.Filter.date(startDate, endDate)).select(band); +print('Filtered Population Collection', populationFiltered); +``` +### Section 3: Extract Resolution of Landscan Dataset + +Get the resolution of the population dataset using `projection.nominalScale`. + +```javascript +var projection = populationFiltered.first().projection(); +var resolution = projection.nominalScale(); +print('Landscan Global Resolution', resolution); +``` + +### Section 4: Create Time-Series Chart Comparing Population + +Create a time-series chart comparing the population data for the selected regions. + +```javascript +var chartOptions = { + title: 'Population Time Series', + vAxis: { + title: 'Population', + }, + hAxis: { + title: '', + format: 'YYYY', + gridlines: {color: 'transparent'} + + }, + } + +var chart = ui.Chart.image.seriesByRegion({ + imageCollection: populationFiltered, + regions: selectedRegions, + reducer: ee.Reducer.sum(), + scale: resolution, + seriesProperty: 'shapeName' +}).setChartType('ColumnChart') + .setOptions(chartOptions); +print(chart); +``` + +Keywords: GeoBoundaries, Landscan Population Dataset, Earth Engine, Admin1 regions, Population data diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md new file mode 100644 index 000000000..4a894fff4 --- /dev/null +++ b/docs/tutorials/index.md @@ -0,0 +1,39 @@ +# Community Catalog Tutorials + +In this section of the community catalog, we'll explore user contributed Tutorials featuring catalog datasets or a mix of community catalog and the main catalog. + +#### The Importance of Tutorials + +Tutorials are an essential component of any learning platform, as they provide a clear and concise guide on how to achieve specific goals or solve particular problems. In the context of Earth Engine, tutorials are particularly valuable because they help users: + +* Understand the capabilities and limitations of EE's tools and datasets +* Learn how to manipulate and analyze data using EE's API and visualizations +* Apply EE's resources to real-world problems and scenarios + +#### Community Catalog Tutorials: A Valuable Resource + +The Community Catalog tutorials are designed to be a comprehensive resource for learning about Earth Engine. These tutorials cover a range of topics, including: + +* Data discovery and manipulation +* Visualization and analysis techniques +* Integration with other tools and platforms +* Best practices for working with EE's datasets + +These tutorials are not only helpful for beginners but also serve as a valuable reference for experienced users looking to expand their skills or explore new applications. + +#### Encouraging Participation: Contributing Code Examples, Tutorials, Edits, and More! + +We encourage participation in our releases by contributing code examples, tutorials, edits through pull requests, documentation, or by being involved in planning and developing the community catalog further. Your contributions ensure that you are recognized as part of the citation for each release. + +#### Benefits of Contributing + +By participating in the development of the Community Catalog, you can: + +* Gain recognition for your contributions and expertise +* Enhance your skills and knowledge by working with other experts in the field +* Help shape the direction of EE's tutorials and resources to meet the needs of the community +* Contribute to the growth and development of the Earth Engine ecosystem + +#### Conclusion + +The Community Catalog is a vital resource for learning about Earth Engine, and its tutorials are an essential component. By contributing code examples, tutorials, edits, or other forms of participation, you can help shape the catalog and benefit from the collective knowledge and expertise of the community. diff --git a/mkdocs.yml b/mkdocs.yml index 95544217f..e82fb4a9a 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -11,7 +11,7 @@ copyright: "Copyright © 2020 - 2024 Samapriya Roy" # Configuration theme: - name: "material" + name: material custom_dir: overrides features: - announce.dismiss @@ -19,77 +19,93 @@ theme: - content.action.view - content.code.annotate - content.code.copy + # - content.code.select + # - content.footnote.tooltips + # - content.tabs.link - content.tooltips + # - header.autohide + # - navigation.expand + - navigation.footer + - navigation.indexes + # - navigation.instant + # - navigation.instant.prefetch + # - navigation.instant.progress + # - navigation.prune + - navigation.sections + - navigation.tabs + #- navigation.tabs.sticky + - navigation.top + - navigation.tracking - search.highlight - 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git-authors + - git-committers: + repository: "samapriya/agcc" + branch: main + token: !ENV GH_TOKEN + - git-revision-date-localized: + enable_creation_date: true + type: timeago + - search: + separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])' + - minify: + minify_html: true + +# Hooks +# hooks: +# - material/overrides/hooks/shortcodes.py +# - material/overrides/hooks/translations.py + +# Additional configuration extra: social: - icon: fontawesome/brands/github link: https://github.com/samapriya + - icon: fontawesome/brands/linkedin + link: https://www.linkedin.com/in/samapriya - icon: fontawesome/brands/medium link: https://medium.com/@samapriyaroy - icon: fontawesome/brands/mastodon link: https://mapstodon.space/@samapriya - icon: fontawesome/brands/twitter link: https://twitter.com/samapriyaroy - - icon: fontawesome/brands/linkedin - link: https://www.linkedin.com/in/samapriya analytics: provider: google property: G-G7SXDJEWXV -extra_css: - - stylesheets/extra.css - -plugins: - - search - - git-revision-date-localized: - enable_creation_date: true - type: timeago - - minify: - minify_html: true - # Extensions markdown_extensions: - - admonition - abbr + - admonition - attr_list - def_list - footnotes - - meta - md_in_html - toc: permalink: true @@ -98,330 +114,363 @@ markdown_extensions: - pymdownx.betterem: smart_enable: all - pymdownx.caret - - pymdownx.critic - pymdownx.details - pymdownx.emoji: emoji_generator: !!python/name:material.extensions.emoji.to_svg emoji_index: !!python/name:material.extensions.emoji.twemoji - options: - custom_icons: - - overrides/.icons - - pymdownx.highlight + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true - pymdownx.inlinehilite - pymdownx.keys - pymdownx.magiclink: + normalize_issue_symbols: true repo_url_shorthand: true user: squidfunk repo: mkdocs-material - pymdownx.mark - pymdownx.smartsymbols + - pymdownx.snippets: + auto_append: + - includes/mkdocs.md - pymdownx.superfences: custom_fences: - name: mermaid class: mermaid format: !!python/name:pymdownx.superfences.fence_code_format - - pymdownx.tabbed + - pymdownx.tabbed: + alternate_style: true + combine_header_slug: true + slugify: !!python/object/apply:pymdownx.slugs.slugify + kwds: + case: lower - pymdownx.tasklist: custom_checkbox: true - pymdownx.tilde # Page tree nav: - - Introduction: index.md - - License: license.md - - Code of Conduct: code_of_conduct.md - - Insiders program: insiders.md + - Home: + - index.md + - History of the Community Catalog: history.md + - Stay updated & contribute: involved.md + - About Us: about_us.md - Getting Started: - Navigating the Catalog: startup/navigation.md - - Access awesome-gee-catalog-examples repo: startup/catalog-examples.md - - Blog posts: blogs.md - - Catalog Stats: stats.md - - Changelog: changelog.md - - Submit or bring your data request to community catalog: submit.md - - Submit update request for dataset in community catalog: update.md - - Bug report for dataset in community catalog: bug.md - - Submit example for dataset in community catalog: example.md - - Insiders only datasets: - - Microsoft Bing Global Mined Roads: projects/msroads.md - - EOG Annual VIIRS Night Time Light (2013-2021): projects/eog_viirs_ntl.md - - Canada High Resolution Digital Elevation Model (HRDEM): projects/hrdem.md - - swissSURFACE3D Raster Digital Surface Model (DSM): projects/swiss3d.md - - Carbon Mapper Data Portal Methane Emissions: projects/cmapper.md - - Population & Socioeconomic: - - High Resolution Settlement Layers: projects/hrsl.md - - LandScan Population Data: projects/landscan.md - - POMELO Model Population Density Maps: projects/pomelo.md - - GPW Version 4 Admin Units: projects/GPWv4.md - - geoBoundaries Global Database of Political Administrative Boundaries: projects/geoboundary.md - - Edge-matched Global, Subnational and operational Boundaries: projects/edge_matched.md - - West Africa Coastal Vulnerability Mapping: projects/wacvm.md - - Relative Wealth Index (RWI): projects/rwi.md - - Rural Access Index (RAI): projects/rai.md - - Social Connectedness Index (SCI): projects/sci.md - - Gridded Global GDP and HDI (1990-2015): projects/gridded_gdp_hdi.md - - Global human modification v1.5: projects/ghm.md - - Global Human Settlement Layer 2023: projects/ghsl.md - - Harmonized Global Critical infrastructure & Index (CISI): projects/cisi.md - - Native Land (Indigenous Land Maps): projects/native.md - - Gridded Sex-Disaggregated School-Age Population (2020): projects/wpschool.md - - Geophysical, Biological & Biogeochemical: - - Geomorpho90m Geomorphometric Layers: projects/geomorpho90.md - - Bare Earth’s Surface Spectra 1980-2019: projects/bss.md - - Normalized Sentinel-1 Global Backscatter Model Land Surface: projects/s1gbm.md - - Soil Organic Carbon Stocks & Trends South Africa: projects/soc.md - - Soil nematode abundance & functional group composition: projects/soil_nematode.md - - Global maps of habitat types: projects/habitat.md - - Soil carbon storage in terrestrial ecosystems of Canada: projects/scs.md - - Irrecoverable carbon in Earth’s ecosystems: projects/irc.md - - Global Land subsidence mapping: projects/land_subsidence.md - - Global Surface water and groundwater salinity measurements (1980-2019): projects/salinity.md - - Elevation and Bathymetry: - - Copernicus Digital Elevation Model (GLO-30 DEM): projects/glo30.md - - FABDEM (Forest And Buildings removed Copernicus 30m DEM): projects/fabdem.md - - DeltaDTM Global coastal digital terrain model: projects/delta_dtm.md - - Global Glacier Elevation change products: projects/glacier.md - - ASTER Global Digital Elevation Model (GDEM) v3: projects/aster.md - - ASTER Global Water Bodies Database (ASTWBD) Version 1: projects/astwbd.md - - General Bathymetric Chart of the Oceans (GEBCO): projects/gebco.md - - Coastal National Elevation Database (CoNED) Project -Topobathymetric digital elevation models (TBDEMs): projects/tbdem.md - - NOAA Sea-Level Rise Digital Elevation Models (DEMs): projects/slrdem.md - - ÍslandsDEM v1.0 10m: projects/iceland_dem.md - - DEM France (Continental) 5m IGN RGE Alti: projects/france5m.md - - Soil Properties: - - Soil Grids 250m v2.0: projects/isric.md - - Soil Properties 800m: projects/soilprop.md - - Polaris 30m Probabilistic Soil Properties US: projects/polaris.md - - HiHydroSoil v2.0 layers: projects/hihydro_soil.md - - Global Soil Salinity Maps (1986-2016): projects/global_salinity.md - - Global Soil bioclimatic variables: projects/soil_bioclim.md - - Harmonized World Soil Database (HWSD) version 2.0: projects/hwsd.md - - National-Scale Soil Erosion Dataset for Pakistan (2005 and 2015): projects/pk_nssed.md - - Global Land Use and Land Cover: - - Global Mangrove Project: projects/mangrove.md - - Global Mangrove Distribution, Aboveground Biomass, and Canopy Height: projects/gmd.md - - Global Mangrove Canopy Height Maps Derived from TanDEM-X: projects/mangrove_ht_tandemx.md - - Randolph Glacial Inventory: projects/rgi.md - - ESRI 2020 Global Land Use Land Cover from Sentinel-2: projects/esrilc2020.md - - ESRI 10m Annual Land Cover (2017-2023): projects/S2TSLULC.md - - ESA WorldCover 10 m 2020 V100 InputQuality: projects/esa_iq.md - - GlobCover Global Land Cover: projects/globcover_esa.md - - GLC_FCS30D Global 30-meter Land Cover Change Dataset (1985-2022): projects/glc_fcs.md - - Daylight Map Distribution map data: projects/daylight_maps.md - - Finer Resolution Observation and Monitoring of Global Land Cover 10m (FROM-GLC10): projects/glc10.md - - GLANCE Global Landcover Training dataset: projects/glance_training.md - - Global Impervious Surface Area (1972-2019): projects/gisa.md - - Global 30m Impervious-Surface Dynamic Dataset (GISD30): projects/gisd30.md - - Global urban extents from 1870 to 2100: projects/gue.md - - Global urban projections under SSPs (2020-2100): projects/urban_projection.md - - Global Intra-Urban Land Use: projects/giulu.md - - Global 30 m Wetland Map with a Fine Classification System: projects/gwl_fcs.md - - World Settlement Footprint & Evolution: projects/wsf.md - - LandCoverNet Training Labels v1.0: projects/lcnet.md - - Global Oil Palm Dataset 1990-2021: projects/global_palm_oil.md - - CloudSEN12 Global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2: projects/cloudsen12.md - - Regional Land Use and Land Cover: - - Mapbiomas Annual land cover and use maps: projects/mapbiomas.md - - Land Change Monitoring, Assessment, and Projection (LCMAP) v1.3: projects/lcmap.md - - West Africa Land Use Land Cover: projects/wa_lulc.md - - CCI LAND COVER S2 PROTOTYPE LAND COVER 20M MAP OF AFRICA 2016: projects/cci_lc.md - - South African National Land Cover (SANLC): projects/sa_nlc.md - - Mississippi River Basin Floodplain Land Use Change (1941-2000): projects/floodplain_lc.md - - Continental-scale land cover mapping at 10 m resolution over Europe: projects/elc.md - - Digital Earth Australia(DEA) Landsat Land Cover 25m v1.0.0: projects/dea_lc.md - - UrbanWatch 1m Land Cover & Land Use: projects/urban-watch.md - - Vermont High Resolution Land Cover 2016: projects/vt_lc.md - - Chesapeake Bay High Resolution Land Cover Dataset (2013-2014): projects/cc.md - - C-CAP High-Resolution Land Cover: projects/ccap_lc.md - - C-CAP Medium-Resolution Land Cover Beta: projects/ccap_mlc.md - - C-CAP Wetland Potential 30m: projects/ccap_wpotential.md - - Oil Palm Plantation Layers: projects/oil-palm.md - - Rasterized building footprint dataset for the US: projects/usbuild_raster.md - - Hydrology: - - OSM Water Layer Surface Waters in OpenStreetMap: projects/osm_water.md - - Global 30m Height Above the Nearest Drainage: projects/hand.md - - Hydrography 90m Layers: projects/hydro90.md - - HydroLAKES v1.0: projects/hydrolakes.md - - HydroATLAS v1.0: projects/hydroatlas.md - - HydroWaste v1.0: projects/hydrowaste.md - - SWOT River Database (SWORD): projects/sword.md - - Surface Area of Rivers and Lakes (SARL): projects/sarl.md - - Global River Obstruction Database (GROD): projects/grod.md - - United States Groundwater Well Database (USGWD): projects/usgwd.md - - Global River Classification (GloRiC): projects/gloric.md - - GLOBathy (Global lakes bathymetry dataset): projects/globathy.md - - High-Res water body dataset for tundra and boreal forests North America: projects/nawbd.md - - Global River Width from Landsat (GRWL): projects/grwl.md - - GLOBGM v1.0 global-scale groundwater model: projects/globgm.md - - Global Channel Belt (GCB): projects/gcb.md - - DynQual Global Surface Water Quality Dataset: projects/dynqual.md - - Global coastal rivers and environmental variables: projects/rivermouth.md - - Global River Deltas and vulnerability: projects/river_deltas.md - - Streamflow reconstruction for Indian sub-continental river basins 1951–2021: projects/streamflow_india.md - - Global georeferenced Database of Dams(GOODD): projects/goodd.md - - RealSAT Global Dataset of Reservoir and Lake Surface Area: projects/realsat.md - - Global Hydrologic Curve Number(GCN250): projects/gcn250.md - - Global high-resolution floodplains (GFPLAIN250m): projects/gfplain250.md - - Global river networks & Corresponding Water resources zones: projects/grn_wrz.md - - National Wetland Inventory (Surface Water and Wetlands): projects/nwi.md - - National Hydrography Dataset (NHD): projects/nhd.md - - Temporal trends of Surface water across Indian Rivers & Basins: projects/india_river_trends.md - - Tensor Flow Hydra Flood Models: projects/hydra_water.md - - High-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds (1981-2015): projects/gridded_ppt.md - - Oceans and Shorelines: - - Global Shoreline Dataset: projects/shoreline.md - - Digital Earth Australia Coastlines: projects/dea_shorlines.md - - Digital Earth Africa Coastlines: projects/deaf_shorlines.md - - Argo Float Data(Subset): projects/argo.md - - Global gridded sea surface temperature (SSTG): projects/sstg.md - - Global Storm Surge Reconstruction (GSSR) database: projects/gssr.md - - Aqualink ocean surface and subsurface temperature subset: projects/aqualink.md - - Plastic Inputs from Rivers into Oceans: projects/plastic.md - - Mismanaged Plastic Waste Dataset in Rivers: projects/mpw.md - - Global Ocean Data Analysis Project (GLODAP) v2.2023: projects/glodap.md - - Agriculture, Vegetation and Forestry: - - Sensor-Independent MODIS & VIIRS LAI/FPAR CDR 2000 to 2022: projects/fpar.md - - Landfire Mosaics LF: projects/landfire.md - - Vegetation dryness for western USA: projects/veg_dry.md - - USGS VIIRS Evapotranspiration: projects/usgs_viirs.md - - USGS MODIS Evapotranspiration: projects/usgs_modis_et.md - - NOAA Evaporative Stress Index (ESI): projects/global_esi.md - - Forecast Reference Crop Evapotranspiration (FRET): projects/fret.md - - High Resolution 1m Global Canopy Height Maps: projects/meta_trees.md - - ETH Global Sentinel-2 10m Canopy Height (2020): projects/canopy.md - - Global Forest Carbon Fluxes (2001-2022): projects/cflux.md - - Field Boundaries of Agriculture (FIBOA) UK Fields: projects/fiboa_uk.md - - GIMMS Normalized Difference Vegetation Index 1982-2022: projects/gimms_ndvi.md - - High resolution map of African tree cover: projects/af_trees.md - - High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2019): projects/ca_lc.md - - High Resolution Tree Species Information for Canada: projects/ca_species.md - - Canopy height forested ecosystems of Canada: projects/ca_canopy_ht.md - - Canada Landsat derived FAO forest identification (2019): projects/ca_fao.md - - Canada Landsat Derived Forest harvest disturbance 1985-2020: projects/ca_forest_harvest.md - - Canadian Satellite-Based Forest Inventory (SBFI): projects/ca_sbfi.md - - Landsat-derived forest age for Canada's forested ecosystems: projects/ca_fa.md - - US National Forest Type and Groups: projects/us_ftype_fgroup.md - - USDA Crop Sequence Boundaries 2015-2022: projects/csb.md - - ESA CCI Global Forest Above Ground Biomass: projects/cci_agb.md - - geeSEBAL-MODIS Continental scale ET for South America: projects/gee_sebal.md - - Global Fungi Database: projects/global_fungi.md - - Global Forest Canopy Height from GEDI & Landsat: projects/gfch.md - - Global Forest Management dataset 2015: projects/gfm_100.md - - Global 30m Landsat Tree Canopy Cover v4: projects/global_tcc.md - - Global tree allometry and crown architecture (Tallo) database: projects/tallo.md - - Global Leaf trait estimates for land modelling: projects/ltrait.md - - Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA): projects/vodca.md - - Global Sunlit and Shaded GPP for vegetation canopies (1992-2020): projects/shd_sun_gpp.md - - Aboveground carbon accumulation in global monoculture plantation forests: projects/monoculture.md - - Rangeland Analysis Platform layers: projects/rap.md - - NASA Harvest Layers: projects/harvest.md - - ACES-Enhanced Rice Crop Maps for Bhutan (2016-2022): projects/aces_bhutan.md - - Benchmark maps Secondary Forest Brazil: projects/secondary_forest.md - - NAFD Forest Disturbance History 1986-2010: projects/nafd.md - - Digital Earth Africa's cropland extent map Africa 2019: projects/dea_croplands.md - - GFSAD Global Cropland Extent Product (GCEP): projects/gcep30.md - - Ensemble Source Africa Cropland Mask 2016: projects/af_cmask.md - - GFSAD Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP): projects/lgrip30.md - - Global irrigation areas (2001 to 2015): projects/global_irrigation.md - - Tile Drained Croplands (30m): projects/tile.md - - Global crop production tillage practices: projects/tillage.md - - Global Fertilizer usage by crop & country: projects/global_fertilizer.md - - Analysis Ready Data: - - Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database: projects/histarfm.md - - Open Aerial Map Subset: projects/oam.md - - HySpecNet-11K Hyperspectral Benchmark dataset: projects/hyspecnet.md - - Santa Rita Experimental Range Drone Imagery: projects/srer_drone.md - - USGS Historical Topo Maps: projects/usgs_topo.md - - USGS Historical Imagery Western US: projects/historical_us.md - - Global Utilities, Assets and Amenities Layers: - - Global Power: projects/global_power.md - - Facebook Electrical Distribution Grid Maps: projects/electric_grid.md - - Harmonized Global Night Time Lights (1992-2021): projects/hntl.md - - Climate Trace Global Emissions Data: projects/climate_trace.md - - Oil and Gas Infrastructure Mapping (OGIM) database: projects/ogim.md - - Global NPP-VIIRS-like nighttime light (2000-2022): projects/npp_viirs_ntl.md - - GAN based Synthetic VIIRS (NTL) India: projects/syn_ntl.md - - Global Roads Inventory Project: projects/grip.md - - Global Highres Mining Footprints: projects/global-mining.md - - Global ML Building Footprints: projects/msbuildings.md - - Global Google-Microsoft Open Buildings Dataset: projects/global_buildings.md - - USA Structures: projects/usa_structures.md - - Global Electric Consumption revised GDP: projects/elc_gdp.md - - Global Mining Areas and Validation Datasets: projects/global_mining.md - - Global Healthsites Mapping Project: projects/health_sites.md - - Global fixed broadband and mobile (cellular) network performance: projects/speedtest.md - - Ookla 5G Map: projects/ookla_5g.md - - Measurement Lab Network Extracts (M-Lab): projects/mlab_extracts.md - - Global Power Plant Database: projects/pwplants.md - - Global offshore wind turbine dataset: projects/gowt.md - - Harmonised global datasets of wind and solar farm locations and power: projects/energy_farms.md - - TransitionZero Solar Asset Mapper: projects/tzero.md - - Global Database of Cement Production Assets: projects/gcd.md - - Global database of cement production assets and upstream suppliers: projects/gcd_assets.md - - Global Database of Iron and Steel Production Assets: projects/gid.md - - Global Photovoltaics Inventory (2016-2018): projects/global_pv.md - - Biodiversity, Ecosystems & Habitat Layers: - - Biodiversity Intactness Index(BII): projects/bii.md - - Global Consensus Landcover: projects/gcl.md - - Global Freshwater Variables: projects/gfv.md - - Global Habitat Heterogeneity: projects/ghh.md - - Global 1-km Cloud Cover: projects/gcc.md - - Areas of global conservation value: projects/gci.md - - Weather and Climate Layers: - - Global Reference Evapotranspiration Layers: projects/et0.md - - Global Aridity Index: projects/ai0.md - - Global Wind Atlas Datasets: projects/gwa.md - - Global Solar Atlas Datasets: projects/gsa.md - - Global Extreme Heat Hazard: projects/heat-hazard.md - - Brazilian Daily Weather Gridded Data(BR-DWGD) 1961-2020: projects/br_dwgd.md - - International Satellite Cloud Climatology Project HXG Cloud Cover: projects/isccp_hxg.md - - Current and projected climate data for North America (CMIP6 scenarios): projects/aogcm_cmip6.md - - Terraclimate Individual years for +2C and +4C climate futures: projects/terraclim.md - - Global MODIS-based snow cover monthly values (2000-2020): projects/snow_cover.md - - MOD10A2061 Snow Cover 8-Day L3 Global 500m: projects/modis_8day_snow.md - - MODIS Gap filled Long-term Land Surface Temperature Daily (2003-2020): projects/daily_lst.md - - Global Seamless High-resolution Temperature Dataset (GSHTD): projects/gshtd.md - - Global Daily near-surface air temperature (2003-2020): projects/airtemp.md - - Snow Data Assimilation System (SNODAS): projects/snodas.md - - United States Drought Monitor Layers: projects/usdm.md - - North American Drought Monitor (NADM): projects/nadm.md - - Canadian Drought Outlook: projects/can_drought_outlook.md - - United States Seasonal Drought Outlook: projects/ussdo.md - - Global Precipitation Measurement (GPM): projects/gpm.md - - ANUSPLIN Gridded Climate Dataset: projects/anusplin.md - - AgERA5 (ECMWF) dataset: projects/agera5_datasets.md - - Vegetation Drought Response Index (VegDRI): projects/veg_dri.md - - ERA5-HEAT Dataset: projects/era5_heat.md - - High Resolution Deterministic Precipitation Analysis (HRDPA): projects/hrdpa.md - - High Resolution Deterministic Prediction System (HRDPS): projects/hrdps.md - - Regional Deterministic Precipitation Analysis (RDPA): projects/rdpa.md - - Regional Deterministic Prediction System (RDPS): projects/rdps.md - - Climate Prediction Center (CPC) Morphing Technique (MORPH): projects/cpc_morph.md - - Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2): projects/merrav2.md - - Applied Climate Information System (ACIS) NRCC NN: projects/noaa_acis.md - - Climate Hazards Group InfraRed Precipitation with Station Data-Prelim (CHIRPS-Prelim): projects/chirps_prelim.md - - NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid): projects/nclim_grid.md - - High-spatial-resolution Thermal-stress Indices over South and East Asia (HiTiSAE): projects/hitisae.md - - Long-term Gap-free High-resolution Air Pollutants (LGHAP): projects/lghap.md - - Global High Air Pollutants(GHAP) PM2.5 Concentrations (2017-2022): projects/ghap.md - - Global Monthly Satellite-derived PM2.5: projects/global_pm25.md - - Reference ET gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm): projects/piscoeo.md - - Global Events Layers: - - Global large flood events (1985-2016): projects/flood.md - - Global Landslide Catalog (1970-2019): projects/landslide.md - - MAXAR Open Data Events: projects/maxar_opendata.md - - Umbra SAR Open Data: projects/umbra_opendata.md - - RADD Forest Disturbance Alert: projects/radd.md - - Geocoded Disasters (GDIS) Dataset (1960 – 2018): projects/gdis.md - - USGS Global Earthquake dataset: projects/global_earthquakes.md - - Emergency Observation Data for the 2024 Sea of Japan Earthquake: projects/japan_eq2024.md - - Fire Monitoring and Analysis: - - CEMS Fire Danger Indices: projects/cems_fire.md - - Canada National Burned Area Composite (NBAC): projects/nbac.md - - Wildfire Risk to Communities (WRC): projects/wrc.md - - Global Fire WEather Database (GFWED): projects/gfwed.md - - Global Fire Atlas (2003-2016): projects/gfa.md - - Archival NRT FIRMS Global VIIRS and MODIS vector data: projects/firms_vector.md - - Monitoring Trends in Burn Severity (MTBS) 1984-2019: projects/mtbs.md - - 30m Global Annual Burned Area Maps (GABAM): projects/gabam.md - - Canada Landsat Derived Wildfire disturbance & Magnitude 1985-2020: projects/ca_forest_fire.md - - ESA Fire Disturbance Climate Change Initiative (CCI): projects/avhrr-ltdr.md + - Access examples repo: startup/catalog-examples.md + - Catalog assets lists: startup/catalog-assets.md + - Catalog Stats: stats.md + - Code of Conduct: code_of_conduct.md + - License: license.md + - Data Catalog: + - Data Themes: + - projects/index.md + - "Population & Socioeconomic": + - High Resolution Settlement Layers: projects/hrsl.md + - LandScan Population Data: projects/landscan.md + - POMELO Model Population Density Maps: projects/pomelo.md + - GPW Version 4 Admin Units: projects/GPWv4.md + - geoBoundaries Global Database of Political Administrative Boundaries: projects/geoboundary.md + - Edge-matched Global, Subnational and operational Boundaries: projects/edge_matched.md + - West Africa Coastal Vulnerability Mapping: projects/wacvm.md + - Relative Wealth Index (RWI): projects/rwi.md + - Rural Access Index (RAI): projects/rai.md + - Social Connectedness Index (SCI): projects/sci.md + - Gridded Global GDP and HDI (1990-2015): projects/gridded_gdp_hdi.md + - Global human modification v1.5: projects/ghm.md + - Global Human Settlement Layer 2023: projects/ghsl.md + - Harmonized Global Critical infrastructure & Index (CISI): projects/cisi.md + - Native Land (Indigenous Land Maps): projects/native.md + - Gridded Sex-Disaggregated School-Age Population (2020): projects/wpschool.md + - "Geophysical, Biological & Biogeochemical": + - Geomorpho90m Geomorphometric Layers: projects/geomorpho90.md + - Bare Earth’s Surface Spectra 1980-2019: projects/bss.md + - Normalized Sentinel-1 Global Backscatter Model Land Surface: projects/s1gbm.md + - Soil Organic Carbon Stocks & Trends South Africa: projects/soc.md + - Soil nematode abundance & functional group composition: projects/soil_nematode.md + - Global maps of habitat types: projects/habitat.md + - Soil carbon storage in terrestrial ecosystems of Canada: projects/scs.md + - Irrecoverable carbon in Earth’s ecosystems: projects/irc.md + - Global Land subsidence mapping: projects/land_subsidence.md + - Global Surface water and groundwater salinity measurements (1980-2019): projects/salinity.md + - "Elevation and Bathymetry": + - Copernicus Digital Elevation Model (GLO-30 DEM): projects/glo30.md + - FABDEM (Forest And Buildings removed Copernicus 30m DEM): projects/fabdem.md + - DeltaDTM Global coastal digital terrain model: projects/delta_dtm.md + - Global Glacier Elevation change products: projects/glacier.md + - ASTER Global Digital Elevation Model (GDEM) v3: projects/aster.md + - ASTER Global Water Bodies Database (ASTWBD) Version 1: projects/astwbd.md + - General Bathymetric Chart of the Oceans (GEBCO): projects/gebco.md + - Coastal National Elevation Database (CoNED) Project -Topobathymetric digital elevation models (TBDEMs): projects/tbdem.md + - NOAA Sea-Level Rise Digital Elevation Models (DEMs): projects/slrdem.md + - ÍslandsDEM v1.0 10m: projects/iceland_dem.md + - DEM France (Continental) 5m IGN RGE Alti: projects/france5m.md + - "Soil Properties": + - Soil Grids 250m v2.0: projects/isric.md + - Soil Properties 800m: projects/soilprop.md + - Polaris 30m Probabilistic Soil Properties US: projects/polaris.md + - HiHydroSoil v2.0 layers: projects/hihydro_soil.md + - Global Soil Salinity Maps (1986-2016): projects/global_salinity.md + - Global Soil bioclimatic variables: projects/soil_bioclim.md + - Harmonized World Soil Database (HWSD) version 2.0: projects/hwsd.md + - National-Scale Soil Erosion Dataset for Pakistan (2005 and 2015): projects/pk_nssed.md + - "Global Land Use and Land Cover": + - Global Mangrove Project: projects/mangrove.md + - Global Mangrove Distribution, Aboveground Biomass, and Canopy Height: projects/gmd.md + - Global Mangrove Canopy Height Maps Derived from TanDEM-X: projects/mangrove_ht_tandemx.md + - Randolph Glacial Inventory: projects/rgi.md + - ESRI 2020 Global Land Use Land Cover from Sentinel-2: projects/esrilc2020.md + - ESRI 10m Annual Land Cover (2017-2023): projects/S2TSLULC.md + - ESA WorldCover 10 m 2020 V100 InputQuality: projects/esa_iq.md + - GlobCover Global Land Cover: projects/globcover_esa.md + - GLC_FCS30D Global 30-meter Land Cover Change Dataset (1985-2022): projects/glc_fcs.md + - Daylight Map Distribution map data: projects/daylight_maps.md + - Finer Resolution Observation and Monitoring of Global Land Cover 10m (FROM-GLC10): projects/glc10.md + - GLANCE Global Landcover Training dataset: projects/glance_training.md + - Global Impervious Surface Area (1972-2019): projects/gisa.md + - Global 30m Impervious-Surface Dynamic Dataset (GISD30): projects/gisd30.md + - Global urban extents from 1870 to 2100: projects/gue.md + - Global urban projections under SSPs (2020-2100): projects/urban_projection.md + - Global Intra-Urban Land Use: projects/giulu.md + - Global 30 m Wetland Map with a Fine Classification System: projects/gwl_fcs.md + - World Settlement Footprint & Evolution: projects/wsf.md + - LandCoverNet Training Labels v1.0: projects/lcnet.md + - Global Oil Palm Dataset 1990-2021: projects/global_palm_oil.md + - CloudSEN12 Global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2: projects/cloudsen12.md + - "Regional Land Use and Land Cover": + - Mapbiomas Annual land cover and use maps: projects/mapbiomas.md + - Land Change Monitoring, Assessment, and Projection (LCMAP) v1.3: projects/lcmap.md + - West Africa Land Use Land Cover: projects/wa_lulc.md + - CCI LAND COVER S2 PROTOTYPE LAND COVER 20M MAP OF AFRICA 2016: projects/cci_lc.md + - South African National Land Cover (SANLC): projects/sa_nlc.md + - Mississippi River Basin Floodplain Land Use Change (1941-2000): projects/floodplain_lc.md + - Continental-scale land cover mapping at 10 m resolution over Europe: projects/elc.md + - Digital Earth Australia(DEA) Landsat Land Cover 25m v1.0.0: projects/dea_lc.md + - UrbanWatch 1m Land Cover & Land Use: projects/urban-watch.md + - Vermont High Resolution Land Cover 2016: projects/vt_lc.md + - Chesapeake Bay High Resolution Land Cover Dataset (2013-2014): projects/cc.md + - C-CAP High-Resolution Land Cover: projects/ccap_lc.md + - C-CAP Medium-Resolution Land Cover Beta: projects/ccap_mlc.md + - C-CAP Wetland Potential 30m: projects/ccap_wpotential.md + - Oil Palm Plantation Layers: projects/oil-palm.md + - Rasterized building footprint dataset for the US: projects/usbuild_raster.md + - "Hydrology": + - OSM Water Layer Surface Waters in OpenStreetMap: projects/osm_water.md + - Global 30m Height Above the Nearest Drainage: projects/hand.md + - Hydrography 90m Layers: projects/hydro90.md + - HydroLAKES v1.0: projects/hydrolakes.md + - HydroATLAS v1.0: projects/hydroatlas.md + - HydroWaste v1.0: projects/hydrowaste.md + - SWOT River Database (SWORD): projects/sword.md + - Surface Area of Rivers and Lakes (SARL): projects/sarl.md + - Global River Obstruction Database (GROD): projects/grod.md + - United States Groundwater Well Database (USGWD): projects/usgwd.md + - Global River Classification (GloRiC): projects/gloric.md + - GLOBathy (Global lakes bathymetry dataset): projects/globathy.md + - High-Res water body dataset for tundra and boreal forests North America: projects/nawbd.md + - Global River Width from Landsat (GRWL): projects/grwl.md + - GLOBGM v1.0 global-scale groundwater model: projects/globgm.md + - Global Channel Belt (GCB): projects/gcb.md + - DynQual Global Surface Water Quality Dataset: projects/dynqual.md + - Global coastal rivers and environmental variables: projects/rivermouth.md + - Global River Deltas and vulnerability: projects/river_deltas.md + - Streamflow reconstruction for Indian sub-continental river basins 1951–2021: projects/streamflow_india.md + - Global georeferenced Database of Dams(GOODD): projects/goodd.md + - RealSAT Global Dataset of Reservoir and Lake Surface Area: projects/realsat.md + - Global Hydrologic Curve Number(GCN250): projects/gcn250.md + - Global high-resolution floodplains (GFPLAIN250m): projects/gfplain250.md + - Global river networks & Corresponding Water resources zones: projects/grn_wrz.md + - National Wetland Inventory (Surface Water and Wetlands): projects/nwi.md + - National Hydrography Dataset (NHD): projects/nhd.md + - Temporal trends of Surface water across Indian Rivers & Basins: projects/india_river_trends.md + - Tensor Flow Hydra Flood Models: projects/hydra_water.md + - High-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds (1981-2015): projects/gridded_ppt.md + - "Oceans and Shorelines": + - Global Shoreline Dataset: projects/shoreline.md + - Digital Earth Australia Coastlines: projects/dea_shorlines.md + - Digital Earth Africa Coastlines: projects/deaf_shorlines.md + - Argo Float Data(Subset): projects/argo.md + - Global gridded sea surface temperature (SSTG): projects/sstg.md + - Global Storm Surge Reconstruction (GSSR) database: projects/gssr.md + - Aqualink ocean surface and subsurface temperature subset: projects/aqualink.md + - Plastic Inputs from Rivers into Oceans: projects/plastic.md + - Mismanaged Plastic Waste Dataset in Rivers: projects/mpw.md + - Global Ocean Data Analysis Project (GLODAP) v2.2023: projects/glodap.md + - "Agriculture, Vegetation and Forestry": + - Sensor-Independent MODIS & VIIRS LAI/FPAR CDR 2000 to 2022: projects/fpar.md + - Landfire Mosaics LF: projects/landfire.md + - Vegetation dryness for western USA: projects/veg_dry.md + - USGS VIIRS Evapotranspiration: projects/usgs_viirs.md + - USGS MODIS Evapotranspiration: projects/usgs_modis_et.md + - NOAA Evaporative Stress Index (ESI): projects/global_esi.md + - Forecast Reference Crop Evapotranspiration (FRET): projects/fret.md + - High Resolution 1m Global Canopy Height Maps: projects/meta_trees.md + - ETH Global Sentinel-2 10m Canopy Height (2020): projects/canopy.md + - Global Forest Carbon Fluxes (2001-2022): projects/cflux.md + - Field Boundaries of Agriculture (FIBOA) UK Fields: projects/fiboa_uk.md + - GIMMS Normalized Difference Vegetation Index 1982-2022: projects/gimms_ndvi.md + - High resolution map of African tree cover: projects/af_trees.md + - High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2019): projects/ca_lc.md + - High Resolution Tree Species Information for Canada: projects/ca_species.md + - Canopy height forested ecosystems of Canada: projects/ca_canopy_ht.md + - Canada Landsat derived FAO forest identification (2019): projects/ca_fao.md + - Canada Landsat Derived Forest harvest disturbance 1985-2020: projects/ca_forest_harvest.md + - Canadian Satellite-Based Forest Inventory (SBFI): projects/ca_sbfi.md + - Landsat-derived forest age for Canada's forested ecosystems: projects/ca_fa.md + - US National Forest Type and Groups: projects/us_ftype_fgroup.md + - USDA Crop Sequence Boundaries 2015-2022: projects/csb.md + - ESA CCI Global Forest Above Ground Biomass: projects/cci_agb.md + - geeSEBAL-MODIS Continental scale ET for South America: projects/gee_sebal.md + - Global Fungi Database: projects/global_fungi.md + - Global Forest Canopy Height from GEDI & Landsat: projects/gfch.md + - Global Forest Management dataset 2015: projects/gfm_100.md + - Global 30m Landsat Tree Canopy Cover v4: projects/global_tcc.md + - Global tree allometry and crown architecture (Tallo) database: projects/tallo.md + - Global Leaf trait estimates for land modelling: projects/ltrait.md + - Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA): projects/vodca.md + - Global Sunlit and Shaded GPP for vegetation canopies (1992-2020): projects/shd_sun_gpp.md + - Aboveground carbon accumulation in global monoculture plantation forests: projects/monoculture.md + - Rangeland Analysis Platform layers: projects/rap.md + - NASA Harvest Layers: projects/harvest.md + - ACES-Enhanced Rice Crop Maps for Bhutan (2016-2022): projects/aces_bhutan.md + - Benchmark maps Secondary Forest Brazil: projects/secondary_forest.md + - NAFD Forest Disturbance History 1986-2010: projects/nafd.md + - Digital Earth Africa's cropland extent map Africa 2019: projects/dea_croplands.md + - GFSAD Global Cropland Extent Product (GCEP): projects/gcep30.md + - Ensemble Source Africa Cropland Mask 2016: projects/af_cmask.md + - GFSAD Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP): projects/lgrip30.md + - Global irrigation areas (2001 to 2015): projects/global_irrigation.md + - Tile Drained Croplands (30m): projects/tile.md + - Global crop production tillage practices: projects/tillage.md + - Global Fertilizer usage by crop & country: projects/global_fertilizer.md + - "Analysis Ready Data": + - Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) database: projects/histarfm.md + - Open Aerial Map Subset: projects/oam.md + - HySpecNet-11K Hyperspectral Benchmark dataset: projects/hyspecnet.md + - Santa Rita Experimental Range Drone Imagery: projects/srer_drone.md + - USGS Historical Topo Maps: projects/usgs_topo.md + - USGS Historical Imagery Western US: projects/historical_us.md + - "Global Utilities, Assets and Amenities Layers": + - Global Power: projects/global_power.md + - Facebook Electrical Distribution Grid Maps: projects/electric_grid.md + - Harmonized Global Night Time Lights (1992-2021): projects/hntl.md + - Climate Trace Global Emissions Data: projects/climate_trace.md + - Oil and Gas Infrastructure Mapping (OGIM) database: projects/ogim.md + - Global NPP-VIIRS-like nighttime light (2000-2022): projects/npp_viirs_ntl.md + - GAN based Synthetic VIIRS (NTL) India: projects/syn_ntl.md + - Global Roads Inventory Project: projects/grip.md + - Global Highres Mining Footprints: projects/global-mining.md + - Global ML Building Footprints: projects/msbuildings.md + - Global Google-Microsoft Open Buildings Dataset: projects/global_buildings.md + - USA Structures: projects/usa_structures.md + - Global Electric Consumption revised GDP: projects/elc_gdp.md + - Global Mining Areas and Validation Datasets: projects/global_mining.md + - Global Healthsites Mapping Project: projects/health_sites.md + - Global fixed broadband and mobile (cellular) network performance: projects/speedtest.md + - Ookla 5G Map: projects/ookla_5g.md + - Measurement Lab Network Extracts (M-Lab): projects/mlab_extracts.md + - Global Power Plant Database: projects/pwplants.md + - Global offshore wind turbine dataset: projects/gowt.md + - Harmonised global datasets of wind and solar farm locations and power: projects/energy_farms.md + - TransitionZero Solar Asset Mapper: projects/tzero.md + - Global Database of Cement Production Assets: projects/gcd.md + - Global database of cement production assets and upstream suppliers: projects/gcd_assets.md + - Global Database of Iron and Steel Production Assets: projects/gid.md + - Global Photovoltaics Inventory (2016-2018): projects/global_pv.md + - "Biodiversity, Ecosystems & Habitat Layers": + - Biodiversity Intactness Index(BII): projects/bii.md + - Global Consensus Landcover: projects/gcl.md + - Global Freshwater Variables: projects/gfv.md + - Global Habitat Heterogeneity: projects/ghh.md + - Global 1-km Cloud Cover: projects/gcc.md + - Areas of global conservation value: projects/gci.md + - "Weather and Climate Layers": + - Global Reference Evapotranspiration Layers: projects/et0.md + - Global Aridity Index: projects/ai0.md + - Global Wind Atlas Datasets: projects/gwa.md + - Global Solar Atlas Datasets: projects/gsa.md + - Global Extreme Heat Hazard: projects/heat-hazard.md + - Brazilian Daily Weather Gridded Data(BR-DWGD) 1961-2020: projects/br_dwgd.md + - International Satellite Cloud Climatology Project HXG Cloud Cover: projects/isccp_hxg.md + - Current and projected climate data for North America (CMIP6 scenarios): projects/aogcm_cmip6.md + - Terraclimate Individual years for +2C and +4C climate futures: projects/terraclim.md + - Global MODIS-based snow cover monthly values (2000-2020): projects/snow_cover.md + - MOD10A2061 Snow Cover 8-Day L3 Global 500m: projects/modis_8day_snow.md + - MODIS Gap filled Long-term Land Surface Temperature Daily (2003-2020): projects/daily_lst.md + - Global Seamless High-resolution Temperature Dataset (GSHTD): projects/gshtd.md + - Global Daily near-surface air temperature (2003-2020): projects/airtemp.md + - Snow Data Assimilation System (SNODAS): projects/snodas.md + - United States Drought Monitor Layers: projects/usdm.md + - North American Drought Monitor (NADM): projects/nadm.md + - Canadian Drought Outlook: projects/can_drought_outlook.md + - United States Seasonal Drought Outlook: projects/ussdo.md + - Global Precipitation Measurement (GPM): projects/gpm.md + - ANUSPLIN Gridded Climate Dataset: projects/anusplin.md + - AgERA5 (ECMWF) dataset: projects/agera5_datasets.md + - Vegetation Drought Response Index (VegDRI): projects/veg_dri.md + - ERA5-HEAT Dataset: projects/era5_heat.md + - High Resolution Deterministic Precipitation Analysis (HRDPA): projects/hrdpa.md + - High Resolution Deterministic Prediction System (HRDPS): projects/hrdps.md + - Regional Deterministic Precipitation Analysis (RDPA): projects/rdpa.md + - Regional Deterministic Prediction System (RDPS): projects/rdps.md + - Climate Prediction Center (CPC) Morphing Technique (MORPH): projects/cpc_morph.md + - Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2): projects/merrav2.md + - Applied Climate Information System (ACIS) NRCC NN: projects/noaa_acis.md + - Climate Hazards Group InfraRed Precipitation with Station Data-Prelim (CHIRPS-Prelim): projects/chirps_prelim.md + - NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid): projects/nclim_grid.md + - High-spatial-resolution Thermal-stress Indices over South and East Asia (HiTiSAE): projects/hitisae.md + - Long-term Gap-free High-resolution Air Pollutants (LGHAP): projects/lghap.md + - Global High Air Pollutants(GHAP) PM2.5 Concentrations (2017-2022): projects/ghap.md + - Global Monthly Satellite-derived PM2.5: projects/global_pm25.md + - Reference ET gridded database based on FAO Penman-Monteith for Peru (PISCOeo_pm): projects/piscoeo.md + - "Global Events Layers": + - Global large flood events (1985-2016): projects/flood.md + - Global Landslide Catalog (1970-2019): projects/landslide.md + - MAXAR Open Data Events: projects/maxar_opendata.md + - Umbra SAR Open Data: projects/umbra_opendata.md + - RADD Forest Disturbance Alert: projects/radd.md + - Geocoded Disasters (GDIS) Dataset (1960 – 2018): projects/gdis.md + - USGS Global Earthquake dataset: projects/global_earthquakes.md + - Emergency Observation Data for the 2024 Sea of Japan Earthquake: projects/japan_eq2024.md + - "Fire Monitoring and Analysis": + - CEMS Fire Danger Indices: projects/cems_fire.md + - Canada National Burned Area Composite (NBAC): projects/nbac.md + - Wildfire Risk to Communities (WRC): projects/wrc.md + - Global Fire WEather Database (GFWED): projects/gfwed.md + - Global Fire Atlas (2003-2016): projects/gfa.md + - Archival NRT FIRMS Global VIIRS and MODIS vector data: projects/firms_vector.md + - Monitoring Trends in Burn Severity (MTBS) 1984-2019: projects/mtbs.md + - 30m Global Annual Burned Area Maps (GABAM): projects/gabam.md + - Canada Landsat Derived Wildfire disturbance & Magnitude 1985-2020: projects/ca_forest_fire.md + - ESA Fire Disturbance Climate Change Initiative (CCI): projects/avhrr-ltdr.md + - Tutorials: + - tutorials/index.md + - Global Land & Shorelines Masks: tutorials/examples/global_shorelines.md + - Exploring Global 30m Land Cover Change: tutorials/examples/glc_fcs30d_lulc.md + - Population Trends with Landscan: tutorials/examples/landscan_extracts.md + # - Assessing Brazil's Biodiversity and Conservation Needs: tutorials/examples/conservation_priorities.md + # - Forest Loss and Cost Surface Analysis for Brazil's Tall Forests: tutorials/examples/tree_canopy.md + - Changelog: + - Data Changelog: changelog.md + - How to Cite: + - reference/index.md + - Insiders: + - insiders/index.md + - Insiders Program: insiders/insiders_program.md + - "Insiders only datasets": + - Microsoft Bing Global Mined Roads: projects/msroads.md + - EOG Annual VIIRS Night Time Light (2013-2021): projects/eog_viirs_ntl.md + - Canada High Resolution Digital Elevation Model (HRDEM): projects/hrdem.md + - swissSURFACE3D Raster Digital Surface Model (DSM): projects/swiss3d.md + - Carbon Mapper Data Portal Methane Emissions: projects/cmapper.md + - Get Involved: + - Community Actions (What you can do): + - contributing/index.md + - Submit or bring your data request to community catalog: contributing/submit.md + - Submit update request for dataset in community catalog: contributing/update.md + - Bug report for dataset in community catalog: contributing/bug.md + - Submit example for dataset in community catalog: contributing/example.md + - Blogs: + - Substack Blogs: substack_blogs.md + - Medium Blogs: medium_blogs.md