1. Assessing above-ground biomass in reforested urban landscapes using machine learning and remotely sensed data.
- Author
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Matiza, Collins, Mutanga, Onisimo, Peerbhay, Kabir, Odindi, John, and Lottering, Romano
- Subjects
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BIOMASS estimation , *MACHINE learning , *SUPPORT vector machines , *K-nearest neighbor classification , *RANDOM forest algorithms , *BOOSTING algorithms - 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 4.1โ286.5t 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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