The BioMassters Hosted By MathWorks

7 weeks left
$10,000

An image of a forest in Finland

Overview

Do you ever fail to see the forest for the trees? How about the aboveground biomass for the forest? When it comes to understanding how forests change over time, scientists and conservationists across the world look to this exact metric. Aboveground biomass (ABGM) is a widespread measure in the study of carbon release and sequestration by forests. Forests can act as carbon sinks by removing carbon dioxide from the air through photosynthesis, but they can also release carbon dioxide through wildfires, respiration, and decomposition. In order to understand how much carbon a forest contains (its carbon stock) and how it changes (carbon flux), it is important to have an accurate measure of AGBM. In turn, such information allows landowners and policy makers to make better decisions for the conservation of forests.

There are a variety of methods that can be used to estimate ABGM, ranging from destructive sampling, which involves cutting down a representative sample of trees and measuring attributes such as the height and width of their crowns and trunks, to remote sensing methods. Remote sensing methods offer a much faster, less destructive, and more geographically expansive biomass estimate. In particular, LiDAR (Light Detection And Ranging) is one such method that is often used to generate precise three-dimensional information about the Earth's surface. However, airborne LiDAR measurements are time consuming and expensive sources of data to collect. To get accurate biomass, LiDAR must also be calibrated with on-the-ground sampling methods. It is challenging to cover and monitor large areas with continuous acquisitions.

Conversely, satellite imagery provided by satellites like Sentinel-1 and Sentinel-2 is more timely and has wider coverage. Sentinel-1 and Sentinel-2 are two of five missions developed by the European Commission and the European Space Agency as a part of the Copernicus program, which is an Earth-observation initiative. These satellites are built to monitor several phenomena such as sea ice, oil spills and ships, winds and waves, and land use changes. The data collected by Sentinel-1 and Sentinel-2 can be highly effective in measuring the most important metrics for forest management and conservation and climate change mitigation, if used correctly.

For this challenge, your job is to estimate the yearly biomass of different sections in Finland's forests using imagery from Sentinel-1 and Sentinel-2. The ground truth for this challenge is derived from airborne LiDAR surveys and in-situ measurements. Head over to the problem description page to learn more about how to see the forest!


Competition End Date:

Jan. 27, 2023, 11:59 p.m. UTC

Place Prize Amount
1st $5,000
2nd $2,000
3rd $1,000
Bonus $2,000

Bonus Award: Top MATLAB User

We're offering a bonus prize of $2,000 to the top contributor using MATLAB. Just make sure to fill out the Software Environment form to be considered for the prize. This is required to be eligible for the bonus prize, though we'd love to hear from you regardless of what environment you're using!

See the MathWorks competition page for more information about complimentary software licenses and learning resources for this challenge.

Note: Bonus recipient can also be a winner of the general prize pool. MathWorks employees are not eligible for prizes.


How to compete

  1. Click the "Join the competition" button in the sidebar to enroll in the competition.
  2. Get familiar with the problem through the about page and problem description. You might also want to reference some of the additional resources from the about page.
  3. Download the data from the data tab.
  4. Create and train your own model. The benchmark blog post, which is written in MATLAB, is a good place to start.
  5. Use your model to generate predictions that match the submission format.
  6. Click “Submit” in the sidebar, and “Make new submission”. You’re in!

Prize generously supplied by our friends at MathWorks.


Image courtesy of Miikka Luotio on Unsplash