Using Sentinel 2A and Landsat 8 imagery to assess changes in forest carbon storage

TL;DR

This study uses satellite imagery to estimate forest carbon storage and finds that higher-resolution Sentinel 2A data improves accuracy compared to lower-resolution Landsat 8.

Contribution

A novel approach using Sentinel 2A imagery and machine learning improves carbon storage estimation accuracy for dominant forest species and types.

Findings

Approach 2 using Sentinel 2A data provided more accurate carbon storage estimates for species like Populus, Salix, and Pinus tabuliformis.

Machine learning models effectively estimated carbon storage using Sentinel 2A imagery and dominant species classification.

The Ordos Forest carbon storage increased by 27 Mt (89%) from 2013 to 2023, showing the feasibility of long-term monitoring with lower-resolution imagery.

Abstract

Estimating carbon storage using high-resolution imagery of dominant species and types is often constrained by the availability of data. Herein, we developed a carbon storage estimation model for dominant species and types using high-resolution Sentinel 2A imagery and compared the two approaches using lower-resolution Landsat 8 imagery for whole-forest estimation. Approach 1 employs a traditional method using in-situ carbon storage measurements with Landsat 8 vegetation indices, whereas Approach 2 uses Sentinel 2A carbon storage estimates as a reference. Using Random Forest, Decision Tree, and Multiple Linear Regression models, we compared both approaches and found that Approach 2 estimates matched the Sentinel 2A results for different species and types more accurately, including Populus, Salix, Pinus tabuliformis, and shrub types. At the same time, our research results show that machine learning models effectively estimated carbon storage using Sentinel 2A imagery and dominant species classification. For the whole forest assessment with Landsat 8 imagery, Approach 2 yielded superior accuracy over Approach 1. This method enabled the calculation of historical carbon storage, showing that the Ordos Forest carbon storage increased by 27 Mt (89%) from 2013 to 2023, demonstrating the feasibility of long-term carbon monitoring using lower-resolution imagery.

The online version contains supplementary material available at 10.1038/s41598-025-21607-0.

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