iiinova — where your digital self keeps evolving

Reposted by Chenyang Wei

The International Biogeography Society @biogeography.bsky.social · 16/09/2026

To put out an ad in our October newsletter, submissions are due by September 30! Send us an email with details. We look forward to hearing from you! (We're always happy to share opportunities via email and social media as well! Reach out at any time.)

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

6/6 I'm deeply grateful to my coauthor and Ph.D. advisor, Adam M. Wilson, for his guidance and support throughout this project, and to the reviewers and editors for their constructive feedback. #OpenScience #MountainEcology #RemoteSensing

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

5/6 Everything is open: 📄 Paper: doi.org/10.1038/s415... 🗂️ Data: doi.org/10.5281/zeno... 💻 Code: doi.org/10.5281/zeno... 🌐 App: ee-chenyangwei.projects.earthengine.app/view/alpine-...

doi.org

A Global Dataset of Alpine Treeline Elevational Transects - Scientific Data

Scientific Data - A Global Dataset of Alpine Treeline Elevational Transects

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

4/6 We hope the dataset supports work on treeline dynamics, biodiversity, vegetation productivity, remote sensing, and climate-change impacts. It can also help researchers track how mountain environments shift with elevation and change over time.

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

3/6 Instead of forcing mountain terrain into a uniform grid, each transect follows a local forest-to-alpine transition along a single hillslope: standardized enough for global comparison, yet flexible enough to preserve the elevational gradient shaping each treeline ecotone.

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

2/6 In our new paper in Scientific Data, Adam M. Wilson and I introduce a global dataset of more than 2.3 million alpine treeline elevational transects: "A Global Dataset of Alpine Treeline Elevational Transects"

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Chenyang Wei @chenyangwei.bsky.social · 11/07/2026

1/6 Mountains are hard to measure. A fixed grid over steep, folded terrain blurs hillsides together and buries local detail in coarse pixels. Alpine treelines, where forest gives way to alpine vegetation, are exactly where that blurring matters. So we built something that follows the mountains 👇

Three-dimensional view of a forested hillside in Olympic National Park, Washington. Alpine treeline elevational transects appear as red polygon outlines running up the slope, each spanning the transition from lower-elevation forest to higher-elevation alpine terrain.

World map showing, for each HydroSHEDS watershed, the average difference in vegetation canopy height between the upper and lower segments of alpine treeline elevational transects. Darker blue marks watersheds with a larger canopy-height drop from lower to higher elevations, the expected forest-to-alpine decline.

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Chenyang Wei @chenyangwei.bsky.social · 01/07/2026

Thank you so much, Marta! I'm truly grateful for your support and guidance throughout this project, and I hope others find it as useful as we'd like.

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Chenyang Wei @chenyangwei.bsky.social · 01/07/2026

Thank you so much, Colin! That means a lot coming from you. I'm really grateful for your friendship, collaboration, and support throughout this project!

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Chenyang Wei @chenyangwei.bsky.social · 30/06/2026

I'm deeply grateful to my wonderful coauthors Colin Sweeney (@sciencecolin.bsky.social), Trevor Roberts, Hikaru Keebler, Daniel Fink, Benjamin Zuckerberg, Marta A. Jarzyna (@majarzyna.bsky.social), and Kaiguang Zhao for their collaboration and support.

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Chenyang Wei @chenyangwei.bsky.social · 30/06/2026

Everything's open: find all the analysis code on GitHub, and explore the interactive web app below. 💻Code: github.com/Chenyang-Wei... 🗺️Web app: lidar-birds.projects.earthengine.app/view/gifts

lidar-birds.projects.earthengine.app

GIFTS

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Chenyang Wei @chenyangwei.bsky.social · 30/06/2026

Excited to share our new open-access paper in Environmental Research Communications! We fused NASA GEDI LiDAR with optical, radar & environmental data to map 11 forest structural metrics at 30 m across 1.17M km² of eastern North America. 📄Paper: doi.org/10.1088/2515... #GEDI #LiDAR #RemoteSensing

Map panels showing predicted canopy height, canopy cover, plant area index, and foliage height diversity across temperate forests of eastern North America.

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