<p>You could try building one using a global Digital Elevation Model. There are several freely available like <a href="https://geoservice.dlr.de/web/dataguide/tdm90/" rel="nofollow noreferrer">TanDEM-X</a>, <a href="https://www2.jpl.nasa.gov/srtm/" rel="nofollow noreferrer">SRTM</a>, or <a href="https://lpdaac.usgs.gov/news/nasa-and-meti-release-aster-global-dem-version-3/" rel="nofollow noreferrer">ASTER GDEM</a>. You'd have to look for all the pixels containing a negative value. Adjacent negative pixels would give you connected regions below sea level, and you could easily estimate their area just by multiplying the number of pixels of a region by the area of one pixel.</p>
<p>I'm not saying it would be easy. The main challenge would probably be that some regions would be split across several tiles.</p>
<hr />
<p>An update on this. I spent a couple hours trying this as an exercise. I downloaded three tiles from the ASTER global DEM centered around Badwater Basin (Death Valley, USA), one of the regions in the Wikipedia list. I wrote a small (~20 lines) Matlab script that does the following:</p>
<ul>
<li>Binarization of the images, so that every pixel above sea level is set to 0 (black) and every pixel below sea level is set to 1 (white).</li>
<li>Concatenation of the three arrays in order to have only one image for the next step (I manually added a fourth, all black array for the sake of dimension consistency).</li>
<li>With a binary image, Matlab has a useful function called <code>regionprops</code> that finds all connected regions and returns their properties, such as their area (as a number of pixel).</li>
</ul>
<p>Here is the result:
<a href="https://i.sstatic.net/Xbo04.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Xbo04.png" alt="Badland_Basin_black_and_white" /></a>
I'm pretty happy with it, as it looks very similar to the yellow area labelled &quot;below sea level&quot; on <a href="https://web.archive.org/web/20071224063142/http://geology.wr.usgs.gov/parks/deva/ftbad2.html" rel="nofollow noreferrer">this USGS map</a>.</p>
<p><a href="https://i.sstatic.net/G9iwo.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/G9iwo.gif" alt="enter image description here" /></a></p>
<p>Now, how does it translate in terms of area? According to Matlab, the white region has an area of 1 236 989 pixels. Each pixel being approximately 30x30 meters, it yields a below sea level area of 1 113 km<span class="math-container">$^2$</span>. The only figure I could find online is <a href="https://www.britannica.com/place/Death-Valley" rel="nofollow noreferrer">1 425 km<span class="math-container">$^2$</span></a> from Encyclopedia Britannica, with no source. But for me that's close enough to have confidence in my estimate!</p>
<p>Some shortcomings:</p>
<ul>
<li>I knew exactly which tiles contained this particular below sea level region. If you were to go through all tiles (almost 23 000!), you'd have to figure a way to detect when a region is split across multiple tiles in order to reconstruct the region by concatenation. Doesn't seem that hard, but not trivial.</li>
<li>I did not take into account the fact that there is a one pixel overlap between adjacent tiles (it should not change change the result much).</li>
<li>I did not take into account the fact that pixel size actually vary (the resolution is one arc second, which is ~30 meters at the equator but changes with latitude).</li>
</ul>

CC BY-SA 4.0
2020-01-17T23:32:52.773Z
Yeah, this seems fairly straight forward, and in combination with the wiki list above, it's probably doable globally. I figured I might have to end up doing that, but I though it'd be worth asking if something already existed first.
naught101
CC BY-SA 4.0
2020-01-18T09:57:49.503Z
If you end up doing it, please share it here as a self answer! :)
Jean-Marie Prival
CC BY-SA 4.0
2020-01-16T08:45:02.490Z
2021-01-08T15:44:06.667Z
2021-01-08T15:44:06.667Z
Jean-Marie Prival
<p>Anyone know of an existing dataset of areas below sea level? Vector extents would be ideal. Doesn't need to be super detailed for my purposes, but it would be good if it included all areas (e.g. over-estimate is better than under-estimate).</p>

<p>There is a <a href="https://en.wikipedia.org/wiki/List_of_places_on_land_with_elevations_below_sea_level" rel="noreferrer">List of places on land with elevations below sea level on Wikipedia</a>, which looks like it's fairly complete, but finding the extents of the below-sea-level area for each of those locations is not likely to be simple.</p>

CC BY-SA 4.0
2020-01-16T06:54:30.273Z
on the page in your link you can find the sources to all of the places in the article and more.i do not think anybody here can give a better or more detailed answer about this.
trond hansen
<sea-level><open-data><land><landscape>
Global areas below sea level dataset?
naught101
# Global areas below sea level dataset?

(Asked by: username_0 on 2020-01-16)

Anyone know of an existing dataset of areas below sea level? Vector extents would be ideal. Doesn't need to be super detailed for my purposes, but it would be good if it included all areas (e.g. over-estimate is better than under-estimate).

There is a [List of places on land with elevations below sea level on Wikipedia](https://en.wikipedia.org/wiki/List_of_places_on_land_with_elevations_below_sea_level), which looks like it's fairly complete, but finding the extents of the below-sea-level area for each of those locations is not likely to be simple.

**Tags:** sea-level, open-data, land, landscape

> **Comments:**
> **username_1**: on the page in your link you can find the sources to all of the places in the article and more.i do not think anybody here can give a better or more detailed answer about this.  

---

## 1 answers

### Answer by username_2 on 2020-01-16. Score: 8

You could try building one using a global Digital Elevation Model. There are several freely available like [TanDEM-X](https://geoservice.dlr.de/web/dataguide/tdm90/), [SRTM](https://www2.jpl.nasa.gov/srtm/), or [ASTER GDEM](https://lpdaac.usgs.gov/news/nasa-and-meti-release-aster-global-dem-version-3/). You'd have to look for all the pixels containing a negative value. Adjacent negative pixels would give you connected regions below sea level, and you could easily estimate their area just by multiplying the number of pixels of a region by the area of one pixel.

I'm not saying it would be easy. The main challenge would probably be that some regions would be split across several tiles.

---

An update on this. I spent a couple hours trying this as an exercise. I downloaded three tiles from the ASTER global DEM centered around Badwater Basin (Death Valley, USA), one of the regions in the Wikipedia list. I wrote a small (~20 lines) Matlab script that does the following:

* Binarization of the images, so that every pixel above sea level is set to 0 (black) and every pixel below sea level is set to 1 (white).
* Concatenation of the three arrays in order to have only one image for the next step (I manually added a fourth, all black array for the sake of dimension consistency).
* With a binary image, Matlab has a useful function called `regionprops` that finds all connected regions and returns their properties, such as their area (as a number of pixel).

Here is the result:
[![Badland_Basin_black_and_white](https://i.sstatic.net/Xbo04.png)](https://i.sstatic.net/Xbo04.png)
I'm pretty happy with it, as it looks very similar to the yellow area labelled "below sea level" on [this USGS map](https://web.archive.org/web/20071224063142/http://geology.wr.usgs.gov/parks/deva/ftbad2.html).

[![enter image description here](https://i.sstatic.net/G9iwo.gif)](https://i.sstatic.net/G9iwo.gif)

Now, how does it translate in terms of area? According to Matlab, the white region has an area of 1 236 989 pixels. Each pixel being approximately 30x30 meters, it yields a below sea level area of 1 113 km$^2$. The only figure I could find online is [1 425 km$^2$](https://www.britannica.com/place/Death-Valley) from Encyclopedia Britannica, with no source. But for me that's close enough to have confidence in my estimate!

Some shortcomings:

* I knew exactly which tiles contained this particular below sea level region. If you were to go through all tiles (almost 23 000!), you'd have to figure a way to detect when a region is split across multiple tiles in order to reconstruct the region by concatenation. Doesn't seem that hard, but not trivial.
* I did not take into account the fact that there is a one pixel overlap between adjacent tiles (it should not change change the result much).
* I did not take into account the fact that pixel size actually vary (the resolution is one arc second, which is ~30 meters at the equator but changes with latitude).

> **Comments:**
> **username_0**: Yeah, this seems fairly straight forward, and in combination with the wiki list above, it's probably doable globally. I figured I might have to end up doing that, but I though it'd be worth asking if something already existed first.  
> **username_2**: If you end up doing it, please share it here as a self answer! :)  

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