Trees Cooling Urban Jungles

Brooklyn Bridge Park.

Thanks to Cool Green Science:

Using Cloud Computing to Untangle How Trees Can Cool Cities


We’ve all used Google Earth — to explore remote destinations around the world or to check out our house from above. But Google Earth Engine is a valuable tool for conservationists and geographers like myself that allows us to tackle some tricky remote-sensing analysis.

After having completed a few smaller spatial science projects in the cloud (mostly on the Google Earth Engine, or GEE, platform), I decided to give it a real workout — by analyzing more than 300 gigabytes of data across 28 United States and seven Chinese cities.

This project was part of a larger study looking at trees in cities. Why trees? Trees provide numerous valuable ecosystem services to communities: benefits associated with air and water quality, energy conservation, cooler air temperatures, and many other environmental and social benefits.

It’s easy to understand the benefits of trees: stand outside on a hot sunny day and you immediately feel cooler in the shade of a tree. But what’s not as obvious is the cooling effect that comes from tree’s ability to remove particulate matter (PM2.5) floating around in the air we breath. And this important, as this type of air pollution is implicated in the deaths of ~3 million people per year.

The Conservancy researched the relationship between city air quality and the cooling effects of trees. Results of this study will inform the Global Cities Program initiative on Planting Healthy Air for cities ­­— the objective being to show how much trees can clean and cool, how much it will cost, and so forth.

The first step to understanding the cooling effect of trees is knowing the number and exact position of every tree in each of our 28 study cities — which is about as difficult as it sounds. Knowing exactly where individual trees are located will also enable us to target where trees should be planted for the maximum cooling and cleaning effect.

Getting this amount of detailed information is no small task, as it requires a lot of very high-resolution spatial data; imagery of 1 to 2 meter resolution covering more than 18 million acres. This works out to around 300 GB of data. Analyzing that amount of data could take a long time and potentially be both complicated and expensive.

We needed a solution, and cloud computing was the answer…

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