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Oct 14 12 tweets 3 min read Read on X
Asia will bear 65% of global mangrove losses by 2100, whilst OECD countries face just 3%.

A new study reveals the massive inequality in climate impacts on coastal ecosystems.

Here's what you need to know: Image
A new study in Environmental Research: Climate presents the first global analysis of how warming seas threaten mangrove restoration efforts: Image
The researchers analysed mangrove cover data across 1,533 locations worldwide from 1996 to 2020.

They examined how climate variables and economic development influence mangrove area.

Their approach uses panel data analysis to isolate the causal effects of temperature and GDP. Image
They found two opposing forces at play:

1. Rising local GDP supports mangrove growth (likely through conservation efforts)

2. Warming sea surface temperatures damage mangroves above a certain threshold

However, the issue is these forces nearly cancel each other out.
Eeconomic growth alone could have achieved the global goal of restoring 50% of historical mangrove cover by 2100.

But warming ocean temperatures completely stall this progress. Image
The study reveals a non-linear relationship with temperature.

Mangroves in cooler areas initially benefit from warming, but once temperatures exceed a threshold, damage occurs.

The researchers identified sea surface temperature during the hottest month as the main factor.
By 2100, under a high emissions scenario (SSP5-RCP7.0), mangrove areas could be 150,000 hectares smaller than without climate change.

Countries like India, Myanmar, Brazil, Mozambique, and Australia face the largest reductions.
The economic losses are pretty nuts.

The researchers estimate annual welfare losses from reduced mangrove services could reach 28 billion USD by 2100.

This includes losses from:

• Cultural services
• Provisioning services (food, materials)
• Regulating services (storm protection, carbon storage)
The impacts are deeply unequal.

• Asia bears 65% of the losses (18.6 billion USD annually),
• Middle East and Africa bears 19% (5.4 billion USD),
• Latin America and the Caribbean bears 13% (3.6 billion USD), and
• OECD countries bear just 3% (0.8 billion USD). Image
The study also found a surprising pattern with economic development.

Lower-income regions initially deplete mangroves more, but as income rises, conservation eventually improves.

This creates a turning point where wealth becomes beneficial rather than destructive.
The authors reckon their estimates are likely conservative.

Many societal benefits of mangroves haven't been fully captured due to limited primary studies, especially regarding non-use values.

The actual impact could be significantly greater.
tl;dr:

Even with economic growth favouring mangrove restoration, ocean warming threatens to cancel out these gains entirely.

This means much greater conservation efforts will be needed to achieve substantial mangrove restoration by century's end.

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More from @yohaniddawela

Sep 9
Everyone is talking about Zarr.

ESA is adopting it and others are testing it.

Does this mean the end of Cloud Optimized GeoTIFFs?

Here is what you need to know: Image
ESA recently announced Zarr as the new format for Sentinel-1, 2 and 3.

USGS has benchmarked it for Landsat’s archive.

But many in the community are asking: does this mean the end of COG? Image
What are the basics?

• Zarr: best for large, n-dimensional data cubes (e.g. climate models, satellite time series, weather).

• COG: best for 2D rasters like imagery or elevation, especially when you need wide compatibility with existing tools.
Read 13 tweets
Sep 3
Turns out there are some pretty big issues with DHS data.

A new study finds massive subnational differences in data quality across 35 African countries.

Here's the breakdown: Image
A new study in Nature Communications, analyses geocoded DHS data at a 5km resolution.

It highlights serious concerns for health and development policymaking: Image
The researchers focus on three types of data errors:

• Incomplete age (missing birth month or year)
• Age heaping (ages ending in 0 or 5)
• Flagged HAZ (missing or implausible child height data)

These are widely used indicators of data quality.
Read 13 tweets
Aug 29
Air pollution is usually blamed for lung and heart disease.

But new clinical data shows it may also drive diabetes.

Here’s what you need to know: Image
The researchers combined:

• Outpatient clinical records from the Italian Association of Diabetologists (AMD)
• Municipality-level pollution exposure data from ISPRA, Italy’s environmental protection agency

This gave them a unique dataset of pollution and diabetes at the local level.Image
The AMD dataset is pretty powerful:

• Covers ~300 diabetes centres across all 20 Italian regions
• Half of all diabetes outpatients in Italy
• Based on clinical records, not self-reported cases

This makes it far more reliable than survey-based data.
Read 13 tweets
Aug 20
Changing your map’s resolution can change your conclusions.

It’s called the Support Effect.

And it distorts everything from poverty estimates to climate models.

Here’s how it works: Image
In spatial analysis, “support” refers to the unit of measurement in space.

It could be:
• a point (e.g., GPS location)
• an area (e.g., census tract)
• a pixel (e.g., satellite image cell)

The support determines how and where a variable is measured.
Here’s the issue:

If you change the size or shape of the support, the results change.

This is the Change of Support Problem (COSP).

It means that statistics like the mean, variance, or correlation can shift. Image
Read 15 tweets
Aug 12
We’ve been measuring HDI at the national level for decades.

But living standards can vary dramatically within a country.

A new dataset finally shows HDI at a much finer scale.

Here’s the breakdown: Image
The first sub-national HDI dataset was actually published in @ScientificData in 2019.

It was put together by @Globaldatalab. Image
@ScientificData @Globaldatalab They put together admin-1 level HDI estimates from 1990-2021.

You can access the data here: globaldatalab.org/shdi/table/Image
Read 10 tweets
Aug 2
Google DeepMind just released one of the most important tools in geospatial data science.

It’s called AlphaEarth Foundations.

I want to break it down for you in intuitive terms: Image
We have petabytes of satellite images.

But it’s still hard to answer questions like:

• What’s in this image?
• How has it changed?
• What kind of crop or forest is this?

AlphaEarth helps answer these questions, even in places with limited data.
AlphaEarth is a foundation model for Earth Observation.

It turns raw satellite data into compact numerical representations, called embeddings. Image
Read 22 tweets

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