Assessing above-ground biomass in reforested urban landscapes using machine learning and remotely sensed data

ABSTRACT

Urban reforestation mitigates climate change by sequestering carbon, but quantifying carbon gains requires accurate aboveground biomass estimation. This study estimated carbon sequestration in a reforested urban landscape using PlanetScope, Sentinel-1A, Sentinel-2A, SRTM data, and field measurements. Non-parametric machine learning algorithms (k-nearest neighbor, support vector machines, extreme gradient boosting, random forests) with 39 predictor features generated aboveground biomass density maps. The extreme gradient boosting model performed best, predicting 4.1–286.5t ha-1 aboveground biomass, demonstrating its effectiveness for modeling reforested biomass with multi-source data. Findings highlight extreme gradient boosting’s promise for urban biomass estimation, the importance of multi-source data, and machine learning’s potential in addressing environmental challenges like climate change.

KEYWORDS:

• Urbanization
• climate change
• urban forests
• nature-based solutions
• reforestation
• machine learning

Acknowledgments

The authors would like to acknowledge the DST-NRF SARChI Chair in LandUse Planning and Management at UKZN (Grant No. 84157) for their financial support. We extend a special acknowledgment to Durban municipality through the WoodRights Programme for granting permission to conduct this research on their protected reforestation site. Additionally, we express our gratitude to the reviewers for their constructive criticism, which greatly improved this research work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data availability is restricted for security or ethical reasons involving ongoing research projects.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the DST-NRF SARChI Chair in LandUse Planning and Management at UKZN [84157].