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ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation.

Antonio Elia Pascarella1, Giovanni Giacco1,2, Mattia Rigiroli2

  • 1Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.

Journal of Imaging
|March 28, 2023
PubMed
Summary

A new deep learning method, ReUse, estimates forest carbon absorption using satellite data. It shows superior accuracy in predicting above-ground biomass changes, aiding climate change mitigation efforts.

Keywords:
CNNESA CCI Biomass projectSentinel-2U-Netabove-ground biomasscarbon storagedeep learningremote sensing

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Machine Learning

Background:

  • Accurate forest carbon stock estimation is crucial for the UNFCCC's REDD+ program.
  • National greenhouse gas inventories require reliable data on carbon emissions and sinks.
  • In situ measurements for carbon sequestration are labor-intensive and limited in scope.

Purpose of the Study:

  • To develop an automated system for estimating forest carbon absorption using remote sensing data.
  • To introduce the ReUse deep learning approach for above-ground biomass (AGB) estimation.
  • To assess the generalization capability of ReUse compared to existing methods.

Main Methods:

  • Utilized Sentinel-2 satellite imagery and ESA's Climate Change Initiative Biomass data.
  • Developed a pixel-wise regressive UNet model (ReUse) for AGB estimation.
  • Compared ReUse against two literature methods using private datasets and human-engineered features.

Main Results:

  • ReUse demonstrated superior generalization ability across Vietnam, Myanmar, and Central Europe.
  • Significant reductions in Mean Absolute Error and Root Mean Square Error were observed compared to the runner-up method.
  • Case study in Astroni reserve showed predictions consistent with expert in situ findings.

Conclusions:

  • The ReUse approach offers a scalable and effective solution for estimating forest carbon sequestration.
  • The method supports early detection of above-ground biomass variations in diverse landscapes.
  • Automated AGB estimation using deep learning and remote sensing is vital for climate policy and monitoring.