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GEDI and Sentinel data integration for quantifying agroforestry tree height and stocks.

Giovanni D'Amico1, Elia Vangi2, Martin Schwartz3

  • 1geoLAB - Laboratoy of Forest Geomatics, Department of Agricultural, Food, Environmental and Forestry Sciences and Technologies, University of Florence, via San Bonaventura 13, I-50144, Florence, Italy.

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Summary

Remote sensing and deep learning effectively monitor poplar plantations, estimating carbon stocks for climate change mitigation. This approach provides frequent updates crucial for managing these dynamic agricultural landscapes.

Keywords:
AgroforestryCarbon stockGEDIPoplar plantationRemote sensingSentinel

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Area of Science:

  • Forestry science
  • Remote sensing
  • Climate change mitigation

Background:

  • Agroforestry, particularly poplar plantations, enhances landscape resilience and sequesters carbon.
  • Conventional forest inventories are insufficient for monitoring fast-growing poplar plantations.
  • Remote sensing offers an effective solution for tracking plantation structural variables.

Purpose of the Study:

  • To estimate carbon stocks in poplar plantations in Italy's Padan Plain using remote sensing.
  • To develop a high-resolution canopy height model (CHM) for monitoring poplar plantations.
  • To quantify key forestry variables like growing stock volume and aboveground biomass.

Main Methods:

  • Developed a 10m resolution CHM using a deep learning U-Net model with Sentinel-1 and Sentinel-2 data.
  • Utilized GEDI waveforms for tree height estimation.
  • Integrated annual plantation data and a poplar-specific yield table for variable prediction.

Main Results:

  • Achieved a 2.6m mean absolute error for the U-Net CHM against NFI data.
  • Estimated average growing stock volume (GSV) at 70 m³ ha⁻¹ and carbon stock (CS) at 12 MgC ha⁻¹.
  • Quantified 370,000 m³ of harvested GSV (66,000 MgC) between 2021 and 2022.

Conclusions:

  • Integration of remote sensing and machine learning enables effective monitoring of dynamic poplar plantations.
  • This methodology accurately quantifies forest variables essential for climate change mitigation strategies.
  • The study demonstrates the value of remote sensing for precise and frequent updates in forestry management.